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Rapidly developing remote sensing techniques are shedding new light on large-scale crop growth status monitoring, especially in recent applications of unmanned aerial vehicles (UAVs). Many inversion models have been built to estimate crop growth variables. However, the present methods focused on building models for each single crop stage, and the features generally used in the models are vegetation indices (VI) or joint VI with data derived from UAV-based sensors (e.g., texture, RGB color information, or canopy height). It is obvious these models are either limited to a single stage or have an unstable performance across stages. To address these issues, this study selected four key wheat growth parameters for inversion: above-ground biomass (AGB), plant nitrogen accumulation (PNA) and concentration (PNC), and the nitrogen nutrition index (NNI). Crop data and multispectral data were acquired in five wheat growth stages. Then, the band reflectance and VI were obtained from multispectral data, along with the five stages that were recorded as phenology indicators (PIs) according to the stage of Zadok's scale. These three types of data formed six combinations (C1–C6): C1 used all of the band reflectances, C2 used all VIs, C3 used bands and VIs, C4 used bands and PIs, C5 used VIs and PIs, and C6 used bands, Vis, and PIs. Some of the combinations were integrated with PIs to verify if PIs can improve the model accuracy. Random forest (RF) was used to build models with combinations of different parameters and evaluate the feature importance. The results showed that all models of different combinations have good performance in the modeling of crop parameters, such as R2 from 0.6 to 0.79 and NRMSE from 10.51 to 15.83%. Then, the model was optimized to understand the importance of PIs. The results showed that the combinations that integrated PIs showed better estimations and the potential of using PIs to minimize features while still achieving good predictions. Finally, the varied model results were evaluated to analyze their performances in different stages or fertilizer treatments. The results showed the models have good performances at different stages or treatments (R2 > 0.6). This paper provides a reference for monitoring and estimating wheat growth parameters based on UAV multispectral imagery and phenology information. [ABSTRACT FROM AUTHOR]

Precise fertilization of rice depends on the timely and effective acquisition of fertilizer application recommended by prescription maps in large-scale cropland, which can provide fertilization spatial information reference. In this paper, the prescription map was discussed based on the improved nitrogen fertilizer optimization algorithm (NFOA), using satellite and unmanned aerial vehicle (UAV) imagery, and supplemented by meteorological data. Based on the principles of NFOA, firstly, remote sensing data and meteorological data were collected from 2019 to 2021 to construct a prediction model for the potential yield of rice based on the in-season estimated yield index (INSEY). Secondly, based on remote sensing vegetation indices (VIs) and spectral features of bands, the grain nitrogen content (GNC) prediction model constructed using the Random Forest (RF) algorithm was used to improve the values of GNC taken in the NFOA. The nitrogen demand for rice was calculated according to the improved NFOA. Finally, the nitrogen fertilizer application recommended prescription map of rice in large-scale cropland was generated based on UAV multispectral images, and the economic cost-effectiveness of the prescription map was analyzed. The analysis results showed that the potential yield prediction model of rice based on the improved INSEY had a high fitting accuracy (R2 = 0.62). The accuracy of GNC estimated with the RF algorithm reached 96.3% (RMSE = 0.07). The study shows that, compared with the non-directional and non-quantitative conventional tracking of N fertilizer, the recommended prescription map based on the improved NFOA algorithm in large-scale cropland can provide accurate information for crop N fertilizer variable tracking and provide effective positive references for the economic benefits of rice and ecological benefits of the field environment. [ABSTRACT FROM AUTHOR]

The fundamental basis for applying remote sensing methods to crop assessments is the interrelationships between crop features and canopy spectral characteristics. Thus, a thorough understanding of the differences in the distributions of crop variables among different vertical layers and modules, as well as the effects of these layers and modules on canopy spectral characteristics and derived vegetation indices, is vital to improving the performance of crop monitoring by remote sensing. The main goal of this work was to provide insight into the above issues based on field measurements of winter wheat under different treatments during anthesis. The results demonstrated that the leaf area index (LAI), leaf chlorophyll a and b content (Chl a + b ), nitrogen (N) content per unit leaf area (N L ), N concentration on a mass basis (N M ), dry matter accumulation (DM), N accumulation (NA), and water content (W c ) in leaves and stems generally exhibited great vertical differences within wheat canopies during anthesis. There were also significant differences in N M , DM, NA, and W c among the different plant modules (i.e., leaves, stems, and spikes) and the entire canopy. Moreover, the removal of wheat spikes and different vertical leaf layers had evident effects on the canopy spectral reflectance, and there were differences in the types and degrees of these effects at different wavelengths and treatments. In most cases, the vegetation indices decreased after removing wheat spikes and different leaf layers, although a few indices increased. There were also some vegetation indices that responded weakly to removal events. In comparison, the vegetation indices mostly showed stronger responses to the removal of the top 1st leaves than the spikes. To a certain extent, the responses to the removal of the top 2nd leaves were comparable to those of the top 1st leaves. The responses of most vegetation indices to the top 3rd leaves' removal were less than the responses to the top 2nd leaves' removal. In the case of the removal of the top 4th leaves and below, many of the R V (i.e., relative variation rate) absolute values of vegetation indices were greater than those of the top 3rd leaves' removal but smaller than or close to those of the top 2nd leaves' removal; this may partly be because all the withered and dead leaves covering the soil surface were also removed in this case. The above findings indicated that crop features may not be well characterized when they are based solely on leaves, especially only the upper leaves, as the lower leaves, stems, and spikes also have important effects. Moreover, wheat spikes, not just leaves, and even bottom-layer leaves, could have evident effects on canopy reflectance characteristics and derived vegetation indices. More effort is needed in future research to obtain a thorough understanding of the effects of canopy heterogeneity on canopy spectral characteristics and the relationships between crop variables and spectral vegetation indices. [ABSTRACT FROM AUTHOR]

• Meteorological data was introduced to build the HLM. • An AGB model for different regions was proposed and validated. • HLM models provided better AGB estimation at different regions. Above-ground biomass (AGB) is an important agronomic indicator that reflects crop growth and estimates yield. The AGB estimation using remote sensing becomes a non-destructive, rapid, and alternative method to post-harvest laboratory measurements. However, most of the AGB estimation models constructed based on remote sensing data are difficult to expand regionally, which limits the applicability of the models. This study combined ground-based hyperspectral and meteorological data by using a hierarchical linear modeling (HLM) to construct an AGB estimation model that was generalized across different regions. Experimental data from both regions were acquired and validated, namely from Xiaotangshan Experimental Base, Beijing, 2019 (North China) and Keshan Farm, Qiqihar Branch, Heilongjiang General Bureau of Reclamation, 2022 (Northeast China). Compared to OLS, RFR and GRU, the HLM method was better for estimating potato AGB in different regions with R2 = 0.54, RMSE = 429.62 kg/hm2, NRMSE = 28.20 %. The results of this study demonstrated that HLM could be used as a powerful method to improve the transferability of AGB estimation at different regions. [ABSTRACT FROM AUTHOR]

• The optimized vegetation indices (Opt-VIs) could resist spectral saturation. • Sensitive wavelengths in response to potato AGB were 734 and 742 nm. • Opt-VIs combined with texture features improved potato AGB estimation. Above-ground biomass (AGB) estimation is critical for monitoring crop growth and assessing yields. Unmanned aerial vehicle (UAV) optical remote sensing technology offers robust support for crop AGB estimation through vegetation indices (VIs). However, under conditions of high nitrogen or high AGB, most VIs lose their response to the presence of a dense plant canopy. To address the inaccuracy of estimating crop multistage AGB based on VIs formed in two bands, a UAV imaging hyperspectral experiment was conducted on potato test plots with different varieties, planting densities, and fertilizer application gradients. The ratio vegetation index (RVI) and normalized difference vegetation index (NDVI) were initially modified using a lambda-by-lambda optimization algorithm, which yielded the optimized RVI (Opt-RVI) and NDVI (Opt-NDVI). Their performances were compared with published VIs. Then, the corresponding bands from optimized VIs were used to extract textural features and their estimation performances were evaluated. Finally, based on published VIs, Opt-VIs, textures of sensitive bands, and Opt-VIs combined with the textures, the potato multistage AGB was estimated by optimized random forest (RF) regression. The RF estimation model was simplified according to variable importance scores. Results showed the spectral regions sensitive to AGB were mainly located in the red-edge range. The wavelengths of Opt-RVI and Opt-NDVI that exhibited the strongest correlation with AGB were 734 and 742 nm. Except for the normalized red-edge index formed by two red-edge bands, the published VIs (R2 = 0.07–0.28) demonstrated saturation in the context of high AGB. The optimized Opt-RVI and Opt-NDVI suppressed this phenomenon, such that R2 values for both reached 0.44 for estimating AGB. The R2 values of textural features relative to AGB for both bands ranged from 0.06 and 0.31, and CON 734 , COR 734 , DIS 742 , ENT 742 , SEC 742 , and COR 742 exhibited the strongest correlations with AGB (R2 > 0.2). The performance of estimating potato AGB by a single indicator was sensitive texture features, Opt-VIs and published VIs from high to low and the estimation capacities of these models were limited. The combination of Opt-VIs and textural features of sensitive bands demonstrated the greatest estimation accuracy (R2 = 0.62 and RMSE = 293.08 kg/hm2). Based on variable importance scores, the accuracy of the simplified RF estimation model (four variables: Opt-RVI, CON 734 , ENT 742 , COR 742) slightly decreased (R2 = 0.59 and RMSE = 301.01 kg/hm2), but model complexity was reduced and computational efficiency was improved. The simplified model accuracy was mainly affected by potato variety, fertilizer gradient, and growth stage, but not by planting density. The results of this study can be used as a reference for potato growth monitoring in the field. [ABSTRACT FROM AUTHOR]

Crop residue is an important component of farmland ecosystems, which is of great significance for increasing soil organic carbon, mitigating wind erosion and water erosion and conserving soil and water. Crop residue coverage (CRC) is an important parameter to characterize the number and distribution of crop residues, and also a key indicator of conservation tillage. In this study, the CRC of wheat was taken as the research object. Based on the high-resolution GF-1 satellite remote sensing imagery from China, decision tree (DT), gradient boosting decision tree (GBDT), random forest (RF), least absolute shrinkage and selection operator (LASSO), extreme gradient boosting regression (XGBR) and other machine learning algorithms were used to carry out the estimation of wheat CRC by remote sensing. In addition, the comparisons with sentinel-2 imagery data were also utilized to assess the potential of GF satellite data for CRC estimates. The results show the following: (1) Among the spectral indexes using shortwave infrared characteristic bands from sentinel-2 imagery, the dead fuel index (DFI) was the best for estimating wheat CRC, with an R2 of 0.54 and an RMSE of 10.26%. The ratio vegetation index (RVI) extracted from visible and near-infrared characteristic bands from GF-1 data performed the best, with an R2 of 0.46 and an RMSE of 11.39%. The spectral index extracted from GF-1 and sentinel-2 images had a significant response relationship with wheat residue coverage. (2) When only the characteristic bands from the visible and near-infrared spectral ranges were applied, the effects of the spatial resolution differences of different images on wheat CRC had to be taken into account. The estimations of wheat CRC with the high-resolution GF-1 data were significantly better than those with the Sentinel-2 data, and among multiple machine learning algorithms adopted to estimate wheat CRC, LASSO had the most stable capability, with an R2 of 0.46 and an RMSE of 11.4%. This indicates that GF-1 high-resolution satellite imagery without shortwave infrared bands has a good potential in applications of monitoring crop residue coverage for wheat, and the relevant technology and method can also provide a useful reference for CRC estimates of other crops. [ABSTRACT FROM AUTHOR]

The accurate large-scale measurement of peach crowns is vital in horticultural science and the optimization of orchard management. Nowadays, numerous crown parameters (e.g., crown area, height, and volume) can be obtained via the analysis of point clouds or photographs. Current laser-based sensors provide the required reliable and accurate information; however, they are costly and time-consuming. Therefore, a simpler approach for crown measurement is required. For this purpose, this study presents a pipeline for the monitoring and clustering of 259 peach tree crowns based on unmanned aerial vehicle (UAV) images of a peach orchard in Southeast China. Considering the limitation that the original aerial image dataset contains little information, a data augmentation process is adopted, and an efficient deep learning architecture based on conditional generative adversarial networks (cGANs) was designed to extract the crown area. Then, the shape of the crown area was clustered using an edge detection process and a $k$ -means algorithm. Finally, an ellipsoid volume method (EVM) was applied to estimate the crown volume. Five indicators—namely, $Q_{\mathrm {seg}}$ , $S_{\mathrm {r}}$ , Precision, Recall, and F-measure—were employed to evaluate the crown extraction effects, and the average results for testing samples were 0.832, 0.847, 0.851, 0.828, and 0.846, respectively. Compared with other approaches—namely, fully convolutional network (FCN), U-Net, SegNet21, the excess green index (ExG), and the color index of vegetation extraction (CIVE)—the proposed cGAN model performs better, achieving an accuracy improvement of 5%–25%. For the estimation of crown volume, using measurements from a light detection and ranging (LIDAR) scanner as a reference, the correlation coefficient and relative-root-mean-square error (R-RMSE) were found to be 0.836% and 14.93%, respectively. Overall, the results demonstrate that the proposed method is feasible for measuring peach tree crowns. The wide application of such technology would facilitate applied research in plant phenotyping and precision horticulture. [ABSTRACT FROM AUTHOR]

In this study, a simple one‐step hydrothermal synthesis of three‐element complex system photocatalyst technology is systematically studied. The results show that due to the complex heterojunction formed by titanium dioxide (TiO2), tungsten trioxide (WO3) and activated carbon (AC) and the strong adsorption performance of AC, the catalytic efficiency and cycle stability of the composite have been significantly improved. The excellent electron transport ability of AC is conducive to the separation of carriers, so that the electron transfer between AC/TiO2 and AC/WO3 can make it achieve a heterojunction effect similar to TiO2/WO3. The rich specific surface area of AC and the pores of the "labyrinth" structure can provide a higher concentration of degradable substances inside the catalyst, and the degraded substances are quickly transferred to the active site for degradation. Therefore, by controlling the mass ratio of AC/TiO2/WO3, a photocatalyst with high catalytic efficiency and strong cycle stability was successfully prepared (the degradation efficiency is above 95 %, and the degradation efficiency after 5 cycles is above 80 %). [ABSTRACT FROM AUTHOR]

• Findings indicate a direct binding between PPARγ and miR-130b-3p. • exogenous overexpression of miR-130 b-3 p resulted in the decrease of ATG5, Becline and LC3B II/I. • MiR-130b-3p/PPARγ contributed to the premature senescence of these H 2 O 2 -induced HEI-OC1 cells. The study focuses on the underlying regulatory mechanism of age-related hearing loss (ARHL), which results from autophagy dysregulation mediated by miR-130b-3p targeting PPARγ. We constructed miR-130b-3p knockout (antagomir) and PPARγ over-expression (OE-PPARγ) mice model by injecting mmu-miR-130b-3p antagomir and HBAAV2/Anc80-m-Pparg-T2A-mCHerry into the right ear' round window of each mouse, respectively. In vitro, we introduced oxidative stress within HEI-OC1 cells by H 2 O 2 and exogenously changed the miR-130b-3p and PPARγ levels. MiRNA level was detected by RT-qPCR, proteins by western blotting and immunohistochemistry. Morphology of autophagosomes was observed by electron microscopy. In vivo, the cochlea of aged mice showed higher miR-130b-3p expression and lower PPARγ expression, while exogenous inhibition of miR-130b-3p up-regulated PPARγ expression. Autophagy-related biomarkers expression (ATG5, Beclin-1 and LC3B II/I) decreased in aged mice, which reversely increased after the inhibition of miR-130b-3p. The elevation of PPARγ demonstrated similar effects. Contrarily, exogenous overexpression of miR-130b-3p resulted in the decrease of ATG5, Beclin-1 and LC3B II/I. We created oxidative stress within HEI-OC1 by H 2 O 2 , subsequently observed the formation of autophagosomes under electron microscope, so as the elevated cell apoptosis rate and weakened cell viability. MiR-130b-3p/PPARγ contributed to the premature senescence of these H 2 O 2 -induced HEI-OC1 cells. MiR-130b-3p regulated HEI-OC1 cell growth by targeting PPARγ, thus leading to ARHL. [ABSTRACT FROM AUTHOR]

• Elucidated the possibility of using above-ground parameter to monitor yield of potato. • HLM was used to establish connections among VIs, EVs, and potato yield. • EVs successfully corrected inter-annual and regional variability in VIs-yield relationship. Timely monitoring of potato yield status is crucial for guiding field management and ensuring the sustainability of food trade. In recent years, non-destructive remote sensing techniques have emerged as one of the most promising methods for crop yield monitoring. However, most existing potato yield estimation models have primarily been tested at specific growth stages within a single ecosystem, highlighting the urgent need for a model that can be applied across different ecological environments. In this study, we developed a semi-physical model based on hierarchical linear modeling (HLM) to automatically correct yield estimation based on remotely sensed vegetation indices using environmentally relevant variables such as net solar radiation at the surface (ssr), air temperature at 2 m above the surface (t2m), and soil water content in the root zone (swvl). The accuracy and transferability of the model were further evaluated by comparing it with aboveground destructive sampling methods, conventional empirical models, and machine learning approaches. The results demonstrated that site-specific relationships exist between aboveground agronomic traits parameters like biomass and leaf area index (LAI) with respect to yield estimation. Conventional linear statistical and machine learning methods faced challenges in transferring their constructed yield estimation models across regions and years. However, the HLM approach exhibited excellent generalization ability across all trial samples. In the middle and later stages of potato growth, the standardized LAI determining index proved to be the most effective vegetation for predicting yield, while ssr, skt max , and shallow swvl were identified as key environmental variables affecting yield. By integrating these variables using HLM modeling techniques, we achieved optimal generalization performance (R2 = 0.57, RMSE = 7.65 t/ha, NRMSE = 25.02 % during tuber growth stage; R2 = 0.60, RMSE = 7.37 t/ha, NRMSE = 24.13 % during starch accumulation stage). This study underscores the importance of considering environmental factors when constructing a reliable model for estimating potato yield at different spatial and temporal scales using remote sensing technology. [ABSTRACT FROM AUTHOR]

Abstract: Leaf area index (LAI) is a major indicator for crop growth monitoring and yield estimation. Data assimilation as an effective tool for crop LAI estimation fully considers the properties of actual observations and physical model simulations. In this work, we present a new data assimilation scheme, introducing a very fast simulated annealing (VFSA) optimization algorithm into the process of crop LAI assimilation with a four dimensional variational data assimilation (4DVAR) algorithm. Firstly, calibrating the input parameters of a crop growth simulation model based on history observations. Secondly, quantitatively describing the relationship between fields observed data and model simulated data by the cost function of 4DVAR algorithm. Finally, the optimization process of the cost function is accomplished by the VFSA optimization algorithm, and further the optimal solution is taken as the best combination of input parameters of physical model for LAI estimation. Winter wheat in Beijing is taken as an experimental object. The numerical results show not only the improved time efficiency of this proposed assimilation scheme, but also enhanced assimilation accuracy of all LAI assimilations, especially for LAI 3.00. Theoretical analysis and practical experiments confirm the application prospect of VFSA optimization algorithm in LAI variational assimilation. [Copyright &y& Elsevier]

• A hyperspectral data-processing framework was developed using Boruta and DNN. • Hyperspectral bands that can estimate the LNC of apple trees were explicitly identified. • Investigated the potential of phenological data to enhance apple LNC estimation. Rapid and accurate assessment of apple-tree leaf nitrogen content (LNC) plays an important role in smart orchards for accurate fertilization. Hyperspectral remote sensing technology provides a reliable, economical, and fast method to estimate LNC. However, high-dimensional spectral data contain redundant information that is irrelevant to the target parameters, necessitating the development of robust hyperspectral analysis techniques to estimate apple-tree LNC. Additionally, the role of phenological information in improving LNC estimates across multiple growth stages remains uncertain. The present study thus proposes a novel hyperspectral data-processing framework (Boruta-Iteration-DNN) to optimize estimates of apple-tree LNC and investigates the potential of phenological information to improve LNC estimates of multi-fertility apple trees. The proposed framework incorporates the Boruta feature-selection method for identifying important spectral bands and assessing how they affect model performance through iterative feature analysis. A deep neural network (DNN) regression model is then developed for LNC estimation. In addition, a widely used Pearson-DNN method is used for comparison to demonstrate the superiority of the proposed approach. Furthermore, the potential of incorporating phenological information (Day After Anthesis, DAA) into the estimation models is examined to evaluate its ability to better estimate LNC. The results demonstrate that the proposed framework not only enhances the accuracy of apple-tree LNC estimates with respect to the full-spectrum model (without DAA: validation R2 improved from 0.69 to 0.75, NRMSE reduced from 11.92 % to 9.97 %; with DAA: validation R2 improved from 0.72 to 0.79, NRMSE reduced from 11.09 % to 9.06 %), but also reduces the complexity and dimensionality of the models by eliminating 96 % of the redundant spectral bands (2015 bands). Moreover, the inferior results of the Pearson-Iteration-DNN framework further underscore the effectiveness of the Boruta method in selecting relevant spectral bands. Additionally, including the DAA phenological information enhances the accuracy of all models (the total R2 of training and validation increased by 0.05 ∼ 0.12, and the total NRMSE reduced by 1.49 % ∼ 1.93 %). These findings highlight the ability of DNN models integrated with hyperspectral data and phenological information to accurately assess LNC in apple trees, thereby offering valuable guidance to orchard management for developing precise fertilization strategies. [ABSTRACT FROM AUTHOR]

• Deep features were extracted by combining SPA and LSTM. • The AGB estimation performance of spectral and deep features was compared. • The accuracy of the SPA-LSTM model across years and growth stages was evaluated. The accurate estimation of above ground biomass (AGB) is valuable in grasping the status of potato growth and assessing yield. Spectral analysis techniques play an important role in AGB estimation by virtue of its non-destructive and rapid advantages. However, the models constructed based on spectral features at the entire growth stages due to differences in phenological stages, such as vegetation indices (VIs) and locations at fixed wavelengths, can be not applicable to single growth stages. Therefore, the main objective of this study is to improve the applicability of the AGB estimation model to mitigate the impacts of phenological stages by mining depth features from hyperspectral data. The canopy hyperspectral and AGB data of potato tuber formation, tuber growth and starch accumulation stages were measured in 2017–2019. The acquired canopy hyperspectral reflectance was smoothed by Savitzky-Golay (SG) filter, and the green edge (GE), the red edge (RE), red edge chlorophyll index (CIred-edge), optimized soil adjusted vegetation index (OSAVI), green normalized difference vegetation index (GNDVI), and normalized difference vegetation index (NDVI) were extracted. The spectral changes were analyzed at different growth stages of each year. To obtain features in response to AGB changes, a new approach is proposed to analyze the hidden depth features between sensitive wavelengths by combining a successive projections algorithm (SPA) and a long-short-term memory network (LSTM). The AGB estimation models were constructed by partial least squares regression (PLSR) based on traditional VIs + GE + RE, full spectrum, sensitive wavelengths obtained by SPA, and depth features obtained by SPA-LSTM. The results showed that GE, RE and VIs alone explained only 24 %-36 % of the variation in AGB. The accuracy of estimating AGB at sensitive wavelengths obtained after SPA was higher (R2 = 0.70, RMSE = 406 kg/hm2, and NRMSE = 27.14 %) than that of conventional VIs + GE + RE (R2 = 0.51, RMSE = 536 kg/hm2, and NRMSE = 35.81 %) and full spectrum (R2 = 0.63, RMSE = 452 kg/hm2, and NRMSE = 30.19 %) in the three-year dataset. The depth features extracted by the SPA-LSTM method proposed in this study achieved the best AGB estimation (R2 = 0.82, RMSE = 318 kg/hm2, and NRMSE = 21.25 %). The applicability of the model was validated in different years and growth stages. The R2 of each year was in the range of 0.48–0.77, the RMSE was in the range of 290–367 kg/hm2, and the NRMSE was in the range of 16.69 %-25.81 %. The R2 of each growth stage within three year was in the range of 0.48–0.78, the RMSE was in the range of 184–386 kg/hm2, and the NRMSE was in the range of 14.91 %-29.45 %. The method proposed in this study mitigates the impacts of phenological stage and improves the robustness and accuracy of the AGB estimation model, which provides remote sensing technical support for realizing potato growth monitoring and yield assessment in the field. [ABSTRACT FROM AUTHOR]

• Coupling HLM method with remote sensing to estimate rice yield and quality. • Propose a modeling methed combining remote sensing with meteorological data. • A model was proposed with a better scalability at interannual and regional scales. Timely and accurate estimation of crop yield and quality can provide a practical and effective basis for the formulation of national food policies. Based on remote sensing data of rice multi-growth stages from 2021 to 2022 in Ninghe District of Tianjin, the methods coupling hierarchical linear model (HLM) with typical vegetation indices and meteorological data were used to estimate yield and quality in rice. The results showed that: (1) Compared with the multiple linear regression model, the methods of estimating rice yield and quality based on HLM had higher stability in interannual expansion; (2) For the three growth stages of rice, namely, jointing, booting and filling stage, the hierarchical linear model of yield and quality estimation at booting stage showed the highest performance. The R^2 of the GRVI rice yield estimation model, MSR rice amylose content estimation model, and RDVI rice protein content estimation model were 0.52, 0.70, and 0.88, and RMSE were 1.17 t/ha, 0.85 %, and 0.33 %, respectively. The nRMSE was 11.06 %, 4.46 %, and 3.37 %, respectively. The HLM rice yield and quality estimation models based on multi-growth period data were further established. The GCI rice yield estimation model, MSR amylose content estimation model, and DVI protein content estimation model R^2 were 0.67, 0.75, and 0.91, and RMSE were 1.01 t/ha, 0.78 %, and 0.30 %. The nRMSE was 9.48 %, 4.07 %, and 3.05 %. It indicates that the method using HLM has a good potential for accessing yield and quality in rice and shows better scalability at interannual and regional scales. [ABSTRACT FROM AUTHOR]

• The AGB estimation performance of ASD and UHD185 was compared. • HD was employed to improve spectral sensitivity to AGB. • The AGB spatial distribution map was created. Accurately estimating potato above-ground biomass (AGB), which is closely associated with the growth and yield of crops, carries significant importance for guiding field management practices. Hyperspectral techniques have emerged as a powerful and efficient tool for quickly and non-invasively acquiring information about AGB due to its capability to provide rich spectral data closely related to crop physiology and biochemistry. However, using spectral features obtained from hyperspectral data, such as spectral reflectance and vegetation indices (VIs), often leads to inaccurate estimations of crop AGB at multiple growth stages due to spectral saturation effects and dynamic changes in spectral responses. To enhance the robustness of AGB estimation models, this study proposed a harmonic decomposition (HD) method derived from Fourier series to extract energy features. The ground (referred to as ASD) and unmanned aerial vehicle hyperspectral (referred to as UHD185) remote sensing data from three growth stages of potatoes in 2018 (validation set) and 2019 (calibration set) were utilized in the study. Firstly, a comparison was made between the spectral reflectance of the potato canopy measured by the ASD and UHD185 sensors. Subsequently, the correlation between spectral reflectance, VIs, and harmonic components obtained from ASD and UHD185 sensors was analyzed in relation to AGB at both the individual and whole growth stage. Then, sensitive bands selected through CARS (competitive adaptive reweighted sampling), the entire spectral reflectance, VIs, and harmonic components, were utilized to construct AGB estimation models by partial least squares regression (PLSR). Finally, the optimal model performance was validated across different years, growth stages, and treatment conditions. The results showed there were differences in spectral reflectance acquired by ASD and UHD185 sensors across various wavelengths, but overall, there was a high level of consistency between the two. The correlation of spectral reflectance and VIs with potato AGB at individual growth stage was notably higher than that observed for entire growth stages. The accuracy of AGB estimation using VIs obtained from ASD (the R2, RMSE and NRMSE of validation sets were 0.52, 592 kg/hm2 and 26.91 %, respectively) and UHD185 (the R2, RMSE and NRMSE of validation sets were 0.46, 612 kg/hm2 and 27.82 %, respectively) sensors were low. Utilizing sensitive bands and full spectral reflectance separately improved the precision of models, although the enhancement was somewhat limited. The HD-PLSR models from ASD (the R2, RMSE and NRMSE of validation sets were 0.69, 477 kg/hm2 and 21.69 %, respectively) and UHD185 (the R2, RMSE and NRMSE of validation sets were 0.66, 481 kg/hm2 and 21.86 %, respectively) achieved the best AGB estimation results. Using the HD-PLSR model to estimate AGB for two years, the R2 values were 0.79 and 0.76 for ASD and UHD185, with RMSE values of 381 kg/hm2 and 386 kg/hm2 and NRMSE values of 22.35 % and 22.70 %, respectively. The capability of the HD-PLSR model was confirmed at various growth stages and treatments. This work offers valuable remote sensing technical support for implementing potato growth monitoring and yield assessment in the field. [ABSTRACT FROM AUTHOR]

To obtain accurate spatially continuous reflectance from Unmanned Aerial Vehicle (UAV) remote sensing, UAV data needs to be integrated with the data on the ground. Here, we tested accuracy of two methods to inverse reflectance, Ground-UAV-Linear Spectral Mixture Model (G-UAV-LSMM) and Minimum Noise Fraction-Pixel Purity Index-Linear Spectral Mixture Model (MNF-PPI-LSMM). At wavelengths of 550, 660, 735 and 790 nm, which were obtained by UAV multispectral observations, we calculated the canopy abundance based on the two methods to acquire the inversion reflectance. The correlation of the inversion and measured reflectance values was stronger in G-UAV-LSMM than MNF-PPI-LSMM. We conclude that G-UAV-LSMM is the better model to obtain the canopy inversion reflectance. [ABSTRACT FROM AUTHOR]

• Late Blight in Potato, a prevalent potato disease, significantly impacts potato yield and stands as one of the principal afflictions affecting the potato crop. Timely and effective monitoring of late blight severity in potato and its spatial distribution has become an immediate priority. In the studies we know of, the research focus on late blight in potato monitoring still predominantly revolves around single-sensor approaches, such as multispectral UAV monitoring and hyperspectral UAV monitoring. However, as of now, there remains a lack of research utilizing the collaborative monitoring of potato late blight using multiple sensors on drones. By integrating the vegetation index derived from the multispectral UAV, the texture index from the RGB UAV, and the estimated crown coverage feature, we combined the Relief-mRmR technique with machine learning modeling algorithms to monitor late blight in potato. We compared and constructed severity monitoring models for late blight in potato using Support Vector Machine (SVM), Random Forest (RF), and K-Nearest Neighbors (KNN) classification algorithms. The results indicate that the RF model exhibited the highest accuracy. For the training dataset, the overall accuracy (OA) and kappa coefficient were 92.50% and 0.90, respectively, while for the independent validation dataset, the OA and kappa coefficient reached 97.50% and 0.96, respectively. Late Blight in Potato, a prevalent potato disease, significantly impacts potato yield and stands as one of the principal afflictions affecting the potato crop. Timely and effective monitoring of late blight severity in potato and its spatial distribution has become an immediate priority. In this study, we employed a dual-drone collaborative approach, utilizing the multispectral drone from DJI as well as an ultra-high-resolution RGB drone. By integrating the vegetation index derived from the multispectral UAV, the texture index from the RGB drone, and the estimated crown coverage feature, we combined the relief-mRmR technique with machine learning modeling algorithms to monitor late blight in potato. We compared and constructed severity monitoring models for late blight in potato using Support Vector Machine (SVM), Random Forest (RF), and K-Nearest Neighbors (KNN) classification algorithms. The results indicate that the RF model exhibited the highest accuracy. For the training dataset, the overall accuracy (OA) and kappa coefficient were 92.50% and 0.90, respectively, while for the independent validation dataset, the OA and kappa coefficient reached 97.50% and 0.96, respectively. The findings also demonstrate that augmenting the vegetation index and texture index with the estimated high-resolution plant crown coverage information significantly enhances the accuracy of the late blight in potato monitoring model. [ABSTRACT FROM AUTHOR]

• We developed an RRCNet that fuses the deep and shallow spectral features to map RRC. • RRCNet was trained based on an RTM-based "soil-crop-crop residue" spectra dataset. • Use transfer learning (TL) to recalibrate the fully connected layer of RRCNet. • RRCNet + TL can provide more accurate RRC estimates than SI + statistical regression. Accurate determination of rice residue cover (RRC) can improve the monitoring of tillage information. Currently, the accurate determination of RRC using optical remote sensing is hindered by variations in cropland moisture and cover of following crops. The fractional cover (FC) of the soil (f S), crop (f C), and crop residue (f CR) changes (f S + f C + f CR = 1) after the following crop is planted, which increases the difficulty of remote-sensing RRC estimation. Cropland soil moisture and crop residue moisture affect the values of cropland and crop residue spectral indices (CRSIs), thereby reducing the accuracy of remote-sensing RRC estimation. Deep learning techniques (e.g., convolutional neural networks [CNN] and transfer learning [TL]) have been proven to extract the deep features of input images with distortion invariance, such as displacement and scaling, which are similar to moisture and the following crop effects on remote-sensing CRSIs. This study aimed to evaluate the combined use of deep features of cropland spectra extracted by deep learning techniques to estimate the cropland RRC under the effects of variations in cropland moisture and cover of the following crops. This study proposes an RRCNet CNN that fuses deep and shallow features to improve RRC estimation. A PROSAIL radiative transfer model was employed to simulate a cropland "soil–crop–crop residue" mixed spectra dataset (n = 103,068), considering the variations in cropland moisture and the cover of the following crop. The RRCNet was first pre-trained using the simulated dataset, and then the knowledge from the pre-trained RRCNet was updated based on field experimental FCs, RRCs, and Sentinel-2 image spectra using the TL technique. Our study indicates that RRCNet can incorporate shallow and deep spectral features of cropland "soil–crop–crop residue" mixed spectra, providing high-performance FCs and RRC estimation. The FCs and RRC estimates from RRCNet + TL (FCs: R 2 = 0.939, root mean squared error (RMSE) = 0.071; RRC: R 2 = 0.891, RMSE = 0.083) were more accurate than those from CRSI + multiple linear regression, CRSI + random forest, and CRSI + support vector machine (FCs: R 2 = 0.877–907, RMSE = 0.086–0.101; RRC: R 2 = 0.378–0.714, RMSE = 0.133–0.229). We mapped the multistage RRC based on Sentinel-2 multispectral instrument (MSI) images and RRCNet. Tillage information can be inferred from RRC and RRC difference maps changes. [ABSTRACT FROM AUTHOR]

• AGB was estimated by combining UAV RGB and hyperspectral images. • The ability of different wavelet basis functions to extract features was compared. • The high-frequency information and wavelet coefficients were used to estimate AGB. • The problem of inaccurate estimation of potato AGB using VIs was solved. Crops' above-ground biomass (AGB) is a crucial indicator that reflects crop health and predicts crop yield. However, using only optical vegetation indices (VIs) can produce inaccurate AGB estimates due to differences in crop varieties, growth stages, and measurement environments. Given the advantages of unmanned aerial vehicle (UAV) RGB and hyperspectral image fusion, this study evaluated the performance of multi-source remote sensing data for estimating potato AGB at multiple growth stages. In 2019, this study conducted potato trials with different varieties, fertilization levels, and planting densities at the Xiaotangshan Experiment Base (Beijing). UAV image and AGB data of potato three main stages were obtained from ground survey work. High-frequency information of the potato canopy was extracted from RGB images using discrete wavelet transform (DWT). VIs and wavelet energy coefficients were extracted from hyperspectral images using continuous wavelet transform (CWT). The linear relationships between potato AGB with VIs, high-frequency information, and wavelet coefficients were analyzed. Potato AGB estimation models were constructed based on single and multiple types of variables using multiple stepwise regression (MSR) and random forest (RF) models, respectively. This work showed the following results: (i) High-frequency information and wavelet coefficients were more sensitive to potato multi-growth stage AGB than VIs, and the latter were the most sensitive. (ii) Using VIs, high-frequency information, or wavelet coefficients separately to estimate the potato multi-growth stage AGB resulted in higher error and lower model accuracy. (iii) Combining VIs with either high-frequency information or wavelet coefficients improved the accuracy of AGB estimation, which was further improved by combining high-frequency information with wavelet coefficients. (iv) Combining VIs with both high-frequency information and wavelet coefficients provided the highest estimation accuracy using the MSR method. This combined AGB estimation model reduced the RMSE by 27%, 21%, and 16%, respectively, relative to VIs, high-frequency information, or wavelet coefficients alone. This result shows that the complementary advantages of multi-source UAV data can solve the challenge of insufficient AGB estimation by optical remote sensing. The work in this study provides remote sensing technology support to achieve potato crop growth monitoring and improve yield predictions. [ABSTRACT FROM AUTHOR]

Vertical heterogeneity of the biochemical characteristics of crop canopy is important in diagnosing and monitoring nutrition, disease, and crop yield via remote sensing. However, the research on vertical isomerism was not comprehensive. Experiments were carried out from the two levels of simulation and verification to analyze the applicability of this recently development model. Effects of winter wheat on spectrum were studied when input different structure parameters (e.g., leaf area index (LAI)) and physicochemical parameters (e.g., chlorophyll content ( C h l a + b ) and water content ( C w )) to the mSCOPE (Soil Canopy Observation, Photochemistry, and Energy fluxes) model. The maximum operating efficiency was 127.43, when the winter wheat was stratified into three layers. Meanwhile, the simulation results also proved that: the vertical profile of LAI had an influence on canopy reflectance in almost all bands; the vertical profile of C h l a + b mainly affected the reflectivity of visible region; the vertical profile of C w only affected the near-infrared reflectance. The verification results showed that the vegetation indexes (VIs) selected of different bands were strongly correlated with the parameters of the canopy. LAI, C h l a + b and C w affected VIs estimation related to LAI, C h l a + b and C w respectively. The Root Mean Square Error (RMSE) of the new-proposed N D V I g r e e n was the smallest, which was 0.05. Sensitivity analysis showed that the spectrum was more sensitive to changes in upper layer parameters, which verified the rationality of mSCOPE model in explaining the law that light penetration in vertical nonuniform canopy gradually decreases with the increase of layers. [ABSTRACT FROM AUTHOR]

The avian EB66® cell line, derived from duck embryonic stem cells, has been widely used for producing human and animal therapeutic proteins and vaccines. In current study we evaluated the potential use of EB66® cell line in a cell culture-derived duck Tembusu virus (DTMUV) vaccine development. After optimizing the growth conditions of DTMUV HB strain in EB66® cells, we successfully generated three batches of viruses with ELD50 titres of 105.9/0.1 ml, 105.3/0.1 ml and 105.5/0.1 ml, respectively, for using in the preparation of inactivated vaccines. The immunogenicity and protective efficacy of these EB66® cells-derived inactivated vaccines were examined in ducks. Results indicated that all three batches of vaccines induced haemagglutination-inhibition (HI) antibody response in immunized birds at 2 weeks after a single immunization. Immunized ducks and ducklings were protected against a virulent challenge at 4 weeks after a booster immunization. The duration of immunity was for 3–4 months after a booster immunization. These results demonstrated the feasibility of using EB66® cell line to grow up DTMUV for vaccine preparation. RESEARCH HIGHLIGHTS Duck Tembusu virus can be propagated in EB66® cells. EB66® cell-derived inactivated DTMUV vaccines are immunogenic and can provide protection against a virulent challenge. A long-lasting immunity is induced after a booster immunization. [ABSTRACT FROM AUTHOR]

Activated carbon (AC) was synthesized with various weight ratios of manganese dioxide (MO) through a simple hydrothermal approach. The electrochemical performance of the synthesized activated carbon/MnO2 composites was investigated. The effect of the activated carbon/MnO2 (AM) ratio on the electrochemical properties of the activated carbon/MnO2 composites and the pore structure was also examined. The results show that the specific capacitance of the activated carbon material has been improved after the addition of MO. The as-synthesized composite material exhibits specific capacitance of 60.3 F g−1 at 1 A g−1, as well as stable cycle performance and 99.6% capacitance retention over 5000 cycles. [ABSTRACT FROM AUTHOR]

Yield evaluation of breeding lines is the key to successful release of cultivars, which is becoming a serious issue due to soil heterogeneity in enlarged field tests. This study aimed at establishing plot-yield prediction models using unmanned aerial vehicle (UAV)-based hyperspectral remote sensing for yield-selection in large-scale soybean breeding programs. Three sets of soybean breeding lines (1103 in total) were tested in blocks-in-replication experiments for plot yield and canopy spectral reflectance on 454~950 nm bands at different growth stages using a UAV-based hyperspectral spectrometer (Cubert UHD185 Firefly). The four elements for plot-yield prediction model construction were studied respectively and concluded as: the suitable reflectance-sampling unit-size in a plot was its 20%–80% central part; normalized difference vegetation index (NDVI) and ration vegetation index (RVI) were the best combination of vegetation indices; the initial seed-filling stage (R5) was the best for a single stage prediction, while another was the best combination for a two growth-stage prediction; and multi-variate linear regression was suitable for plot-yield prediction. In model establishment for each material-set, a random half was used for modelling and another half for verification. Twenty-one two growth-stage two vegetation-index prediction models were established and compared for their modelling coefficient of determination (RM2) and root mean square error of the model (RMSEM), verification RV2 and RMSEV, and their sum RS2 and RMSES. Integrated with the coincidence rate between the model predicted and the practical yield-selection results, the models, MA1-2, MA4-2 and MA6-2, with coincidence rates of 56.8%, 58.5% and 52.4%, respectively, were chosen for yield-prediction in yield-test nurseries. The established model construction elements and methods can be used as local models for pre-harvest yield-selection and post-harvest integrated yield-selection in advanced breeding nurseries as well as yield potential prediction in plant-derived-line nurseries. Furthermore, multiple models can be used jointly for plot-yield prediction in soybean breeding programs. [ABSTRACT FROM AUTHOR]

Timely and effective prediction of nitrogen content in summer maize could provide support data for precise fertilization. In this study, the feasibility and expansibility of predicting the nitrogen mechanism model of summer maize leaves through its entire growth period were investigated on the basis of the theory of leaf radiation transmission mechanism. A complete random test of data from two maize varieties and two nitrogen fertilizer applications in 2017 was conducted. Three versions of the leaf optical PROperties SPECTra (PROSPECT) model, namely, PROSPECT-4, PROSPECT-5, and PROSPECT-D were used to link the established leaf nitrogen density (LND) and chlorophyll-a + b (chl-a + b) models, that is, chl-a + b-LND model. A nitrogen response transfer model (N-RTM) was established by linking the optimal PROSPECT and chl-a + b-LND models. Results were as follows. (1) chl-a + b estimation using the PROSPECT-D model yielded the highest accuracy (the coefficient of determination (R2) = 0.774, the normalized root mean squared error (nRMSE) = 13.19%) among the three PROSPECT models, it shows that the model considering more factors can better reflect the internal law of blade, and could be used as the basic model of N-RTM; (2) Established chl-a + b-LND models based on the dataset from each growth stage showed differences using the confidence interval method, and the R2 values of the optimal regression model at V12, VT, and R3 were 0.794, 0.781, and 0.821, respectively. Based on the changes of chl-a + b and LND during the growth period, a piecewise model was constructed; (3) The R2 and nRMSE values between the measured and estimated LNDs were 0.656% and 22.86%, respectively. The validation results are better than the traditional empirical model. The results showed that the segmented model, which considered the interaction of various factors within the leaves and the change of chl-a + b-LND during the growth period, had better performance in nitrogen monitoring. The constructed nitrogen model in this study preliminarily realized the remote sensing prediction of the nitrogen mechanism model and had a certain mechanism. [ABSTRACT FROM AUTHOR]

The nitrogen nutrition index (NNI) is a quantitative and reliable indicator of the nitrogen nutrition distribution or status of crops. The timely and accurate estimation of the NNI is crucial in agriculture management. In this study, the quantitative analysis and hyperspectral remote sensing modelling of the NNI were conducted, in which the hyperspectral remote sensing data and NNI data at different growth stages of winter wheat were measured using ground and unmanned aerial vehicle (UAV) carrying high spectrometer equipment. First, the NNIs of the four growth stages of winter wheat were calculated and statistically analyzed. Then, the hyperspectral characteristics at different growth stages and various NNIs were examined. Second, the representation wavebands of the hyperspectral data, which were sensitive to the NNI of winter wheat, were acquired and evaluated. In addition, hyperspectral models were established and comparatively assessed for the NNI estimation. Finally, the hyperspectral characteristics and the remote sensing estimation of the NNIs were determined on the basis of UAV-based hyperspectral data. The results are as follows. (1) As the NNIs of winter wheat changed, the characteristic of the red shift, the variations in the red edge position, and the near-infrared waveband range of the hyperspectral data became apparent. (2) The green band, red edge, and near-infrared were sensitive to the NNIs of winter wheat, and they could be effectively used for estimating the NNI. Moreover, the multiple statistical regression models, which were based on representative wavebands, performed well in estimating the NNI results for the different growth stages of winter wheat. [ABSTRACT FROM AUTHOR]

Lodging is a common phenomenon in maize production, which seriously affects its yield, quality, and mechanical harvesting capacity. With good penetrating power, satellite radar can monitor crop growth even under cloudy weather conditions. In this study, a method based on the change in plant height before and after lodging in maize is proposed to calculate the lodging angle and monitor the lodging degree by using dual-polarization Sentinel-1A data. The results show that the optimal sensitive polarization combinations of maize plant height before and after lodging are VH/VV and VV, respectively. The lodging angle is calculated using the plant height inversion results before and after lodging. The overall accuracy of classifying lodging grade of maize is 67%. The proposed model based on lodging angle could effectively mapped the maize lodging range on a regional scale and classify the lodging grades. [ABSTRACT FROM AUTHOR]

Fusarium damage in wheat reduces the quality and safety of associated food and feed products. In this study, a specific Fusarium head blight (FHB) classification index (FCI) for detection of this disease in wheat is proposed. Hyperspectral microscopy images of wheat spikelets are used as the data source. An algorithm combining the instability index (ISI) and spectral angle mapper (SAM) classifier (ISI-SAM) is used to extract four sensitive single wavelengths. Then, partial least-squares regression of the disease area ratio with simple spectral vegetation indices (SVIs) for each image is used to determine the most relevant spectral index (i.e., difference spectral index, DSI (668, 417)). On this basis, in order to develop a hyperspectral index for FHB detection, an exhaustive search for the best weighted combination of a single wavelength and DSI (668, 417) is conducted, with all possible combinations being tested. The final FCI is F C I = 0.25 × [ 2 (R 668 − R 417) − R 539 ] , with an overall classification accuracy of 89.80%. The FCI is tested for its ability to detect and classify the healthy and diseased areas of wheat spikelets through comparison with six commonly used SVIs, and its disease identification accuracy is almost 30% higher than that of the best-performing SVI. The FCI is also successfully applied to classification of hyperspectral image data from wheat spikes. The FCI developed in this study constitutes a stable and feasible method of early FHB detection through spectral and low-altitude remote sensing, and will improve FHB detection, identification, and monitoring performance in precision agriculture applications. Image 1 • Hyperspectral microscopic images used to develop Fusarium classification index FCI. • A joint algorithm, ISI-SAM, is proposed to select the optimum sensitive wavebands. • A diseased area ratio of each HMI image is provided to assess damage level. • FCI based on combination of optimal single waveband and difference spectral index. • The proposed FCI is effective for recognition of FHB infected wheat ears. [ABSTRACT FROM AUTHOR]

• The invariant shape spectral preprocessing method is effective for WYMV detection. • Machine learning has great potential for narrow to wide band migration applications. • The 3SV-SIW-SVM mode is most effective for WYMV detection. Wheat yellow mosaic disease (WYMD) is a low-temperature soil-borne disease that causes serious yield and economic losses. Reflectance spectroscopy has great potential for detecting crop diseases, but it has not been applied for detection of WYMD. Herein, we collected 2 years of wheat physiological and reflectance spectroscopy data at green-up and jointing stages, and conducted disease detection studies. We evaluated standard normal variate (SNV), multiplicative scatter correction (MSC), and spectral separation of soil and vegetation (3SV) as preprocessing methods for invariant spectral shapes, and 96 spectral indices. The spectral index wavelength (SIW), threshold method, and variance inflation factor (TVIF) were used as feature selection methods combined with machine learning to construct a systematic disease detection scheme. The results showed that the visible region centred at 750 nm was the most sensitive wavelength. Additionally, 900–––1000 nm was the sensitive wavelength in the middle and latter stages, during disease aggravation and fertility development. The preprocessing method was best with 3SV, which was highly consistent with the characteristic regions of the original spectra, followed by SNV and MSC. spectral indices OR-BG1 I, SNV-RPMI, MSC-RPMI and 3SV-CTRI 1 could be applied to detect WYMD, and the best index was 3SV-CTRI 1, with OA and Kappa values of 75.88% and 0.516, respectively. The effectiveness of the SIW feature selection method and coupling with machine learning was superior to TVIF. The best results among different machine learning models were obtained with SVM, followed by RF and KNN. The optimal 3SV-SIW-SVM disease detection mode achieved OA and Kappa values of 94.71% and 0.893, respectively, for field data validation, and 68.06% and 0.381, respectively, for simulated satellite data validation. The findings will facilitate migration of ground-based research to satellite platforms, and provide new ideas for quantitative identification of crop diseases. [ABSTRACT FROM AUTHOR]

• The ability of AGB estimation based on RGB-VIs and H-VIs was compared. • The potential of RGB-VIs, SFs and TFs to estimate AGB was evaluated. • The potato AGB estimation model was built based on multiple types of features. • UAV RGB images could be used to monitor potato crop growth. Rapid and non-destructive potato above ground biomass (AGB) monitoring is a crucial step in the development of smart agriculture because AGB is closely related to crop growth, yield, and quality. Compared to time-consuming and laborious field surveys, unmanned aerial vehicle (UAV) remote sensing provides a new direction for large-scale AGB monitoring. However, estimating AGB using an optical remote sensing technique usually does not work well because of spectral saturation, but multi-source remote sensing feature fusion (e.g., fusing spectral and structural features) can mitigate that problem. Due to potato crop canopy structure and AGB change greatly during growth, the potential of fusing optical, textural (TFs), and structural features (SFs) for calculating potato AGB at multiple growth stages was unknown. In addition, the ability of optical features, TFs, and SFs and their combinations to estimate potato AGB had not been examined. Vegetation indices (RGB-VIs), TFs, and SFs were extracted from ultra-high spatial resolution RGB images and compared their performances for estimating potato AGB with those of hyperspectral vegetation indices (H-VIs) obtained from UAV hyperspectral images. The results revealed that each type of feature had its own advantages and limitations for potato AGB estimation. Except for canopy volume (CV) in SFs, the best H-VI, RGB-VI, and TF for estimating AGB in both single growth stages and the entire growth period were inconsistent. When estimating AGB with only a single type of feature, the model accuracy in descending order was SFs, TFs, H-VIs, and RGB-VIs. The fusion of any two types of remote sensing features improved AGB estimation model accuracy. Among them, TFs combined with SFs provided the best estimation performance. The fusion of RGB-VIs, TFs, and SFs produced the best AGB estimates precision (R2 = 0.81, RMSE = 207 kg/hm2, NRMSE = 17.40%). Since AGB was effectively estimated under different treatments in the field, the model applicability was confirmed. Using different types of remote sensing features, the Gaussian process regression method produced better estimation results than the partial least squares regression method did. This study provides an economic and effective method for monitoring the potato growth in the field, and thus helps improve farmland production and guide fertilization management. [ABSTRACT FROM AUTHOR]

• A-BiGRU enables more accurate modeling of crop growth to achiever high accuracy. • The Attention mechanism can focus on the critical growth period of the crop. • The bidirectional GRU structure is better than GRU. • Image reconstruction can effectively fill the data gap. Timely and accurate access to regional scale crop plant area and spatial distribution is essential for regional agricultural production and food security, especially in the context of global population growth and climate change. Deep learning has become prevalent in crop mapping under complex conditions due to its powerful feature extraction and nonlinear ability. This study proposes a time-series image classification framework using Attention-based Bidirectional Gated Recurrent Unit (A-BiGRU) to map rice, maize, and soybean in Fujin, China, from reconstructed Sentinel-2 time-series images. Firstly, the reconstructed Sentinel-2 time-series images with 10 spectral dimensions and 22 temporal dimensions were obtained by linear interpolation and Savitzky-Golay (SG) filter. Then, a neural network, the A-BiGRU was developed to identify different crops by taking advantage of their unique growth patterns. The attention mechanism enables temporal neural networks to focus on the critical growth periods of crops. Additionally, the structure of GRU is simpler than that of long short-term memory (LSTM) and simple recurrent neural network (SRNN), which reduces the number of parameters and alleviates overfitting. Compared to GRU, BiGRU can fully uses the time-series information of the entire crop growth cycle. To assess the effectiveness of the proposed method, we compared two deep learning methods (LSTM and SRNN) and three widely used non-deep learning classifiers (Spectral Angle Mapping (SAM), Support Vector Machine (SVM)) and eXtreme Gradient Boosting (XGBoost). The results demonstrate that A-BiGRU achieved the highest accuracy, with an overall accuracy of 0.9804, a macro F1 score of 0.9788 and a kappa score of 0.9714. We also selected four typical regions and compared the classification results with optical images, which showed that the proposed method has a good recognition effect. Therefore, the A-BiGRU method is capable of achieving high-precision crop mapping. [ABSTRACT FROM AUTHOR]

This study utilizes hyperspectral imaging technology to capture spectral images of three grades of green tea. Reflectance spectra of the three quality grades of tea, namely Grade A, Grade B, and Grade C, were collected between 370 nm and 1040 nm. Three methods, namely Savitzky-Golay (SG) smoothing, standard normal variable (SNV), and first derivative (FD), were employed to preprocess the raw spectra. Dimensionality reduction and visual display were accomplished using t-distributed stochastic neighbor embedding, and the preprocessed spectra with the most optimal visualization effect were selected to extract the key wavelengths using principal components analysis (PCA). The extracted key bands could aid in achieving the detection of tea quality grades, as tea polyphenols are more sensitive between 650 and 800 nm, where 664 nm and 765 nm represent the key wavelengths of catechins. K-nearest neighbor (KNN) and support vector machine (SVM) discriminative models were deployed to model the key wavelengths. The model built by FD-PCA-SVM exhibited the best discriminant effect, with an accuracy of 93.8 % in the training set and 98.2 % in the test set. FD-PCA-SVM discriminant model was used to identify and visualize the tea quality grades with good results. Hyperspectral imaging technology is well-suited to identifying the quality of green tea. [Display omitted] • Hyperspectral imaging achieved rapid non-destructive testing of tea quality. • The PCA can diminish the superfluity of spectral data, but ameliorate the accuracy. • The key wavelength of the biochemical compounds present in tea was investigated. • FD-PCA-SVM was used to identify and visualize the tea quality grades with good results. [ABSTRACT FROM AUTHOR]

• Using digital photos can reconstruct high-quality 3D point clouds of maize tassels. • The TreeQSM is effective in extracting phenotypes from maize tassels. • Low light has a greater impact on the reconstruction of maize point clouds. • Spatial calibration is necessary for each tassel. • The quality of point clouds obtained using image-based reconstruction methods is superior. Maize tassel is a crucial pollen producing organ that plays an important role in maize production. It is challenging to investigate the morphological and structural traits of maize tassels for breeding programs, since traditional manual measurements of organ-scale phenotypic traits are labor-intensive and prone to human errors. It is, therefore, urgent to design and develop new phenotyping systems for maize tassels to improve throughput and measurement accuracy. This study first introduced the TreeQSM in crop phenotyping at organ scale and developed TIPS (TreeQSM based Image Phenotyping System for maize tassels). The system mainly consists of three digital cameras for acquiring multi-view images of individual maize tassel. These cameras were vertically arranged with different angles of view. The acquired images were used to reconstruct the 3D point cloud data of individual maize tassel, which were fed into the TreeQSM to extract four tassel phenotypic parameters including trunk length, branch number, branch length, and branch angle. The performance analyses of the developed system were conducted on 52 tassel samples from 37 maize materials with different canopy geometric structures. The experimental results showed that the TIPS could achieve accurate tassel parameters estimation with the R2 of 0.964, 0.973, 0.935, and 0.857, and the RMSE of 1.72, 1.28, 19.94, and 2.73 for the trunk length, branch number, total branch length, and first node branch angle, respectively. This study also investigated the advantages of the data acquisition with the TIPS and the effects of different shooting angles, number of images, lighting conditions and tassel types on the four tassel parameters estimation accuracy. The comparison results indicated that the four influencing factors reduced the estimation accuracy to varying degree. Compared with the other three parameters, the estimation accuracy of branch angles less than 20 were largely affected. The higher the degree of compactness, the worse the estimation accuracy. Compared with shooting angles, the reduction of the number of images in a certain range had less impact on the quality of 3D point cloud reconstruction. The influence of low light was obviously greater than that of strong light. This study provides a valuable guide to the collection and quantitative analysis of high-throughput phenotype information of maize tassels in breeding program. [ABSTRACT FROM AUTHOR]

• Our method focuses on exploring the effect of texture indices to estimate potato PNC. • An texture index was developed for PNC estimation throughout the potato growth cycle. • O-TTI3 can further improve the estimation accuracy of potato PNC. Plant nitrogen content (PNC) is vital for evaluating crop nitrogen nutrient status and for net primary productivity. Therefore, rapid and accurate acquisition of crop PNC information can help reduce fertilizer and increase efficiency in modern agriculture. This study investigated whether an optimized texture index constructed from hyperspectral characteristic wavelength images acquired from a unmanned aerial vehicle (UAV) may be used to estimate PNC in potatoes over multiple growth stages. A potato field trial conducted in 2019 in Beijing, China, included different planting densities, nitrogen fertilizer gradients, and potato varieties with three replicates. A UAV remote sensing platform served to acquire hyperspectral images during three critical N demand periods for potatoes. In addition, we simultaneously conducted field sampling to obtain ground-truth PNC measurements. Following the classical form of vegetation indices, 12 texture indices were constructed based on hyperspectral texture features, and an arbitrary variable combination optimization algorithm was used to optimize these indices. Finally, the texture index, which had the highest correlation with potato PNC, was used as the best indicator for estimating the PNC status of potato at multiple growth stages and whether this indicator, in combination with spectral information, could further improve the accuracy of potato PNC estimation was subsequently explored. The results showed that (i) the optimal texture index TTI3 (R 494 -Cor, R 578 -Hom, R 514 -Sem) maintained a good linear relationship with PNC at the late stage of potato growth, and the accuracy and stability of the PNC estimation models constructed based on it was significantly better than that of a single texture metric. (ii) Compared with spectral information alone, the texture index combined with spectral features improved the accuracy of potato PNC estimation. More specifically, TTI3 (R 494 -Cor, R 578 -Hom, R 514 -Sem) combined with the three-band spectral index TBI 5 (530, 734, 514) achieved the best estimation accuracy with calibrated R2 , root mean square error (RMSE), and normalized RMSE of 0.77%, 0.28%, and 9.88%, respectively. The results of this study showed that the texture index constructed by combining multiple texture metrics enhanced the association between texture features and potato PNC over multiple growth stages, thus improving the monitoring accuracy of potato nitrogen nutrition status. This study can provide a reference for accurately managing crop nitrogen nutrition. [ABSTRACT FROM AUTHOR]

• Proposed a tea plantation phenological feature index (TPI). • Constructed a mapping algorithm based on multitemporal spectral features. • Tea plantations were accurately distinguished from evergreen vegetation. • The tea plantation mapping algorithm is temporally reusable. Tea is a highly demanded and valuable commodity in both domestic and international markets, with China being the world's largest producer and exporter, resulting in the expansion of tea tree cultivation in China over the past few decades. Accurate mapping of tea plantations is vital for tea markets, food security, poverty alleviation, and ecosystem value assessment. However, research on vegetation plantation monitoring has focused primarily on food crops, and the spatial information for the perennial tea tree is often neglected. Moreover, monitoring tea plantations poses significant challenges in terms of the diverse cultivation management measures, the heterogeneity of tea garden landscapes, and the limited availability of clear imagery due to cloud cover. In this study, we constructed a novel tea plantation mapping algorithm based on multitemporal spectral features. In addition, a Tea Plantation Phenological Feature Index (TPI) is proposed to enable easy and reliable monitoring of tea plantations. The algorithm harmonized the Landsat7/8 and Sentinel-2 satellite images on the Google Earth Engine (GEE) platform, utilizing a semi-automatic decision tree model to identify tea gardens in Yingde City, Guangdong Province. Our results show that the tea plantation phenological feature index (TPI), green band in October (Green Oct) and cumulative band in April (CB Apr) are the most effective multitemporal spectral features for identifying tea gardens. The F1-score, OA, and kappa coefficients (Kappa) of the 2020 tea plantation map generated by the tea plantation mapping algorithm based on multitemporal spectral features are 90.75%, 92.71%, and 0.844, respectively. Furthermore, the algorithm is time-reusable, as evidenced by the F1-score, OA, and kappa coefficients of the generated 2021 tea plantation map, which are 85.99%, 88.86%, and 0.7675, respectively. Our results underscore the exceptional ability of the multitemporal spectral features to separate tea gardens from evergreen forests. The tea plantation mapping algorithm based on multitemporal spectral features is a robust, user-friendly, and effective tool suitable for regional or national annual tea plantation mapping, enabling sustainable production and implementing. [ABSTRACT FROM AUTHOR]

• The MP-Net model aggregates context information. • The MP-Net model reduces the feature losses. • The MP-Net model efficiently and precisely classifies crops. Accurate crop classification map is of great significance in various fields such as the survey of agricultural resource, the analysis of existing circumstance on land application, the yield estimation of crop and the disaster warning. The methods based on machine learning and deep learning are popularly used in crop classification and recognition of remote sensing images. However, the crop classification task based on neural networks still faces significant challenges due to the spatial and temporal distribution of crops and the inherent characteristics of remote sensing images. Therefore, this study proposes the multi-layer pyramid crop classification network (MP-Net) to solve the above problems. To reduce the feature loss during the crop extraction, the proposed model uses the pyramid pooling module to improve the ability of global information acquisition, and the information concatenation module to retain the upper features. Using the GF-6 and Sentinel-2 satellite data, the proposed model was tested in Erhai Lake Basin and Beian City. Compared with other five deep learning models, such as FCN, SegNet, U-Net, PSPNet and DeepLabv3+, the experimental results indicate that the proposed model achieves the highest accuracy in both study areas. Meanwhile, the proposed model has the advantages of short training time and high efficiency under the same running conditions. Overall, this study is beneficial to improve the efficiency and accuracy of crop classification task in the unbalanced temporal and spatial distribution. It also brings a feasible scheme for crop classification tasks in complex growing areas. The code has been publicly available at https://github.com/Xu-Chang-Hong/MP-Net. [ABSTRACT FROM AUTHOR]

Di (2-ethyl) hexyl phthalate (DEHP) is a common environmental endocrine disruptor that induces oxidative stress, posing a significant threat to human and animal health. Oxidative stress can activate the PTEN/PI3K/AKT pathway, which is closely related to cell apoptosis. However, it is unclear whether DEHP induces apoptosis of chicken liver cells by regulating the PTEN/PI3K/AKT pathway through oxidative stress. In this experiment, male laying hens were continuously exposed to 400 mg/kg, 800 mg/kg, and 1600 mg/kg DEHP for 14 d, 28 d, and 42 d. The results showed that liver injury was aggravated with the dose of DEHP gavage, and the ROS/MDA levels in L, M, and H DEHP exposure groups were significantly increased, while the T-AOC/T-SOD/GSH-PX levels were decreased. Meanwhile, DEHP exposure up-regulated the mRNA and protein expression levels of PTEN/Bax/Caspase-9/Caspase-3 and down-regulated the mRNA and protein expression levels of PI3K/AKT/BCL-2, indicating that DEHP may lead to hepatocyte apoptosis through ROS regulation of PTEN/PI3K/AKT axis. In order to further clarify the relationship between oxidative stress and liver injury, we treated chicken hepatocellular carcinoma cell line (LMH) with 2.5 mM N -acetylcysteine (NAC). NAC attenuated these phenomena. In summary, our study suggests that DEHP can induce apoptosis of chicken liver through ROS activation of the PTEN/PI3K/AKT axis. [Display omitted] • Di-(2-ethylhexyl) phthalate (DEHP) induced apoptosis. • DEHP induced oxidative stress in the liver. • DEHP induced damage to liver through ROS/PTEN/PI3K/AKT axis. [ABSTRACT FROM AUTHOR]

• Hyperspectral-to-image transform technique converts hyperspectral reflectance into images. • A 2D CNN architecture was proposed for leaf chlorophyll content (LCC) estimation. • An LCCNet was pre-trained based on a PROSAIL RTM dataset. • Reusing CNN layer information of LCCNet improved the soybean LCC estimation. Leaf chlorophyll content (LCC) is a distinct indicator of crop health status used to estimate nutritional stress, diseases, and pests. Thus, accurate LCC information can assist in the monitoring of crop growth. The combined use of hyperspectral and deep learning techniques (e.g., convolutional neural network [CNN] and transfer learning [TL]) can improve the performance of crop LCC estimation. We propose a hyperspectral-to-image transform (HIT) technique for converting canopy hyperspectral reflectance into 2D images. We designed a CNN architecture called LCCNet that fuses the deep and shallow features of CNNs to improve soybean LCC estimation. This study evaluated the combined use of hyperspectral remote sensing (RS), HIT, CNN, and TL techniques to estimate soybean LCC for multiple growth stages. The LCCNet was pre-trained based on a simulated dataset (n = 114,048) from the PROSAIL radiative transfer model (RTM) and used as prior knowledge for this work. The soybean canopy hyperspectral RS dataset (n = 910) was obtained using a FieldSpec 3 spectrometer. The knowledge gained while learning to estimate LCC from PROSAIL RTM was applied when estimating field soybean LCC (Dualex readings). TL was used to enhance the soybean estimation model, called the Soybean-LCCNet (RTM + HIT + CNN + TL) model. We tested the LCC (Dualex readings) estimation performance using (a) HIT + CNN, (b) LCCNet (RTM + HIT + CNN), (c) Soybean-LCCNet (RTM + HIT + CNN + TL), and (d) widely used LCC spectral features + partial least squares regression (PLSR). Four methods were ranked based on their LCC estimation performance: Soybean-LCCNet (R2 = 0.78, RMSE = 4.13 (Dualex readings)) > HIT + CNN (R2 = 0.75, RMSE = 4.41 (Dualex readings)) > PLSR-based method (R2 = 0.61, RMSE = 5.39 (Dualex readings)) > LCCNet (R2 = 0.53, RMSE = 7.11 (Dualex readings)). The main conclusions of this work are as follows: (1) HIT + CNN can provide a more robust LCC estimation performance than the widely used LCC SIs; (2) Fusing the deep and shallow features of CNNs can improve the performance of RS soybean LCC (Dualex readings) estimation; and (3) Soybean-LCCNet can reuse the CNN layer information of a pre-trained LCCNet based on a PROSAIL RTM dataset and improve the soybean LCC estimation performance. [ABSTRACT FROM AUTHOR]

Grain protein content (GPC) is an important indicator of wheat quality. Earlier estimation of wheat GPC based on remote sensing provided effective decision to adapt optimized strategies for grain harvest, which is of great significance for agricultural production. The objectives of this field study are: (i) To assess the ability of spectral vegetation indices (VIs) of Sentinel 2 data to detect the wheat nitrogen (N) attributes related to the grain quality of winter wheat production, and (ii) to examine the accuracy of wheat N status and GPC estimation models based on different VIs and wheat nitrogen parameters across Analytical Spectra Devices (ASD) and Unmanned Aerial Vehicle (UAV) hyper-spectral data-simulated sentinel data and the real Sentinel-2 data. In this study, four nitrogen parameters at the wheat anthesis stage, including plant nitrogen accumulation (PNA), plant nitrogen content (PNC), leaf nitrogen accumulation (LNA), and leaf nitrogen content (LNC), were evaluated for their relationship between spectral parameters and GPC. Then, a multivariate linear regression method was used to establish the wheat nitrogen and GPC estimation model through simulated Sentinel-2A VIs. The coefficients of determination ( R 2 ) of four nitrogen parameter models were all greater than 0.7. The minimum R 2 of the prediction model of wheat GPC constructed by four nitrogen parameters combined with VIs was 0.428 and the highest R 2 was 0.467. The normalized root mean square error (nRMSE) of the four nitrogen estimation models ranged from 26.333% to 29.530% when verified by the ground-measured data collected from the Beijing suburbs, and the corresponding nRMSE for the GPC-predicted models ranged from 17.457% to 52.518%. The accuracy of the estimated model was verified by UAV hyper-spectral data which had resized to different spatial resolution collected from the National Experimental Station for Precision Agriculture. The normalized root mean square error (nRMSE) of the four nitrogen estimation models ranged from 16.9% to 37.8%, and the corresponding nRMSE for the GPC-predicted models ranged from 12.3% to 13.2%. The relevant models were also verified by Sentinel-2A data collected in 2018 while the minimum nRMSE for GPC invert model based on PNA was 7.89% and the maximum nRMSE of the GPC model based on LNC was 12.46% in Renqiu district, Hebei province. The nRMSE for the wheat nitrogen estimation model ranged from 23.200% to 42.790% for LNC and PNC. These data demonstrate that freely available Sentinel-2 imagery can be used as an important data source for wheat nutrition and grain quality monitoring. [ABSTRACT FROM AUTHOR]

Plant nitrogen (N) information has widely been estimated through empirical techniques using hyperspectral data. However, the physical model inversion approach on N spectral response has seldom developed and remains a challenge. In this study, an N-PROSAIL model based on the N-based PROSPECT model and the SAIL model canopy model was constructed and used for retrieving crop N status both at leaf and canopy scales. The results show that the third parameter (3rd-par) retrieving strategy (leaf area index (LAI) and leaf N density (LND) optimized where other parameters in the N-PROSAIL model are set at different values at each growth stage) exhibited the highest accuracy for LAI and LND estimation, which resulted in R2 and RMSE values of 0.80 and 0.69, and 0.46 and 21.18 µg·cm−2, respectively. It also showed good results with R2 and RMSE values of 0.75 and 0.38% for leaf N concentration (LNC) and 0.82 and 0.95 g·m−2 for canopy N density (CND), respectively. The N-PROSAIL model retrieving method performed better than the vegetation index regression model (LNC: RMSE = 0.48 − 0.64%; CND: RMSE = 1.26 − 1.78 g·m−2). This study indicates the potential of using the N-PROSAIL model for crop N diagnosis on leaf and canopy scales in wheat. [ABSTRACT FROM AUTHOR]

• Optimized multiple lookup tables (Multi-LUTs) for improving computing efficiency and estimation accuracy. • Multi-LUTs method based on the N-PROSAIL model for canopy nitrogen density estimation. • Canopy nitrogen density estimation from field and UAV hyperspectral data. Unmanned aerial vehicle (UAV) based hyperspectral images linked to a radiative transfer model can provide a promising approach for high throughput monitoring of plant nitrogen (N) status. In this study, multiple lookup tables (Multi-LUTs), each LUT corresponding to one growth stage, were constructed based on the N-PROSAIL model, a radiative transfer model, and LUT size was optimized for improving computing efficiency. The objective is to use the constructed Multi-LUTs for estimating canopy N density (CND) in winter wheat. Results suggest that Multi-LUTs of leaf area index, leaf N density and two spectral indices (MSR and MCARI/MTVI2) in winter wheat demonstrate good performance of CND estimation; and LUTs with the optimal size of 6000 rows can yield good accuracy. The R2 and nRMSE values of the regression relationship between estimated and measured CND were 0.83 and 0.23 from field hyperspectral data, and 0.69 and 0.27 from UAV based hyperspectral imagery during the 2014–2015 growing season. CND by Multi-LUTs method was also accurately estimated from field hyperspectral data during the 2013–2014 growing season, with R2 and nRMSE values of 0.74 and 0.26. The estimation accuracy of CND based UAV data was a slightly lower than based field data. The resultant thematic CND map accurately exhibits CND variability at varying spatial and temporal scales. Results from this study confirmed the potential of combining UAV based hyperspectral imagery and physical optics approach for estimating CND in winter wheat. [ABSTRACT FROM AUTHOR]

Highlights • An unmanned aerial vehicle (UAV) was optimized and used for non-destructive high-throughput phenotyping of NDVI. • UAV-NDVI measurements were highly consistent with ground data captured by a handheld Greenseeker. • UAV-NDVI explained significant variations in biomass and grain yield. • UAV-NDVI was accurate and can be used for selection of high yielding genotypes during grain-filling stages in large breeding programs. Abstract Wheat improvement programs require rapid assessment of large numbers of individual plots across multiple environments. Vegetation indices (VIs) that are mainly associated with yield and yield-related physiological traits, and rapid evaluation of canopy normalized difference vegetation index (NDVI) can assist in-season selection. Multi-spectral imagery using unmanned aerial vehicles (UAV) can readily assess the VIs traits at various crop growth stages. Thirty-two wheat cultivars and breeding lines grown in limited irrigation and full irrigation treatments were investigated to monitor NDVI across the growth cycle using a Sequoia sensor mounted on a UAV. Significant correlations ranging from R2 = 0.38 to 0.90 were observed between NDVI detected from UAV and Greenseeker (GS) during stem elongation (SE) to late grain gilling (LGF) across the treatments. UAV-NDVI also had high heritabilities at SE (h2 = 0.91), flowering (F)(h2 = 0.95), EGF (h2 = 0.79) and mid grain filling (MGF) (h2 = 0.71) under the full irrigation treatment, and at booting (B) (h2 = 0.89), EGF (h2 = 0.75) in the limited irrigation treatment. UAV-NDVI explained significant variation in grain yield (GY) at EGF (R2 = 0.86), MGF (R2 = 0.83) and LGF (R2 = 0.89) stages, and results were consistent with GS-NDVI. Higher correlations between UAV-NDVI and GY were observed under full irrigation at three different grain-filling stages (R2 = 0.40, 0.49 and 0.45) than the limited irrigation treatment (R2 = 0.08, 0.12 and 0.14) and GY was calculated to be 24.4% lower under limited irrigation conditions. Pearson correlations between UAV-NDVI and GY were also low ranging from r = 0.29 to 0.37 during grain-filling under limited irrigation but higher than GS-NDVI data. A similar pattern was observed for normalized difference red-edge (NDRE) and normalized green red difference index (NGRDI) when correlated with GY. Fresh biomass estimated at late flowering stage had significant correlations of r = 0.30 to 0.51 with UAV-NDVI at EGF. Some genotypes Nongda 211, Nongda 5181, Zhongmai 175 and Zhongmai 12 were identified as high yielding genotypes using NDVI during grain-filling. In conclusion, a multispectral sensor mounted on a UAV is a reliable high-throughput platform for NDVI measurement to predict biomass and GY and grain-filling stage seems the best period for selection. [ABSTRACT FROM AUTHOR]

Fe2O3, TiO2, TiO2‐Fe2O3 and TiO2‐Fe2O3/Cu nanomaterials were synthesized by hydrothermal methods. The crystal structure and chemical valence of the samples were characterized by X‐ray diffractometry (XRD), transmission electron microscopy (TEM), photoelectron spectroscopy (XPS) and ultraviolet‐visible diffuse reflectance spectroscopy (UV‐vis DRS). The results show that the TiO2‐Fe2O3/Cu nanocomposites have been successfully synthesized and have good UV‐Vis absorption properties. The electrocatalytic properties of the samples were characterized by cyclic voltammetry (CV), electrochemical impedance spectra(EIS) and Tafel polarization curve (Taf). The TiO2‐Fe2O3/Cu nanocomposites have the highest cathode potential and current density, and the charge transfer resistance in EIS is TiO2 > Fe2O3 > TiO2‐Fe2O3 > TiO2‐Fe2O3/Cu. It is indicated that TiO2‐Fe2O3/Cu nanocomposites had good electrocatalytic properties. Finally, the photocatalytic properties of Fe2O3, TiO2, TiO2‐Fe2O3 and TiO2‐Fe2O3/Cu for the degradation of methyl orange (MO) were studied under UV‐vis irradiation. The results show that the reaction rate constants k of TiO2‐Fe2O3/Cu is the relatively highest(12.91×10−3 min−1), indicating the synthetic of TiO2‐Fe2O3/Cu have good photocatalytic properties, which are due to the highest light utilization of TiO2‐Fe2O3/Cu and the best charge transfer performance. The TiO2‐Fe2O3/Cu nanocomposites were successfully synthesized by one‐step hydrothermal method.The presence of Fe element causes a large red shift in the UV‐visible absorption spectrum, Cu element could be benefit the charge separation. It is known from electrochemical and catalytic tests that the TiO2‐Fe2O3/Cu nanocomposites exhibit good photoelectrocatalytic activity. [ABSTRACT FROM AUTHOR]

Wheat is one of the staple crops of the world. With the wide application of remote-sensing methods in agriculture, the use of data from synthetic aperture radar has attracted increasing attention for monitoring wheat growth. Most of previous studies estimated wheat biomass based on a water cloud model (WCM). However, when no data are available on soil moisture content, the applicability of such models is greatly reduced because of insufficient parameters. Thus, this study proposed a new polarized water cloud model (PWCM) called APWCM, which is a physical model and no auxiliary ground data was needed including soil moisture data. APWCM and WCM model was compared to estimate the above ground biomass of wheat in two different study areas. The results revealed that the WCM has a slightly lower root mean squared error (RMSE = 131.63 g m−2, and 645.17 g m−2) in two different study areas. However, the APWCM has lower relative error (RE = 17.91%, and 13.53%) for wheat biomass estimation in areas with higher biomsass. The final result indicates that the APWCM can replace the WCM for estimating wheat biomass based on Gaofen-3 (GF-3) data when soil-moisture data are not available. [ABSTRACT FROM AUTHOR]

Highlights • Dynamic model of the DSSAT-CERES model for wheat production. • GLUE with a systematic approach for well calibrating parameters in DSSAT-CERES model. • Crop variables in time series were in agreement with measured values. • DSSAT-CERES model as optimization management tool in agriculture. Abstract Crop growth models integrate genotype, environment and management and can serve as an analytical tool by which to study the influences of these factors on crop growth, production, and agricultural planning. Parameter calibration is the primary step taken before the local application of crop growth models. In this study, experimental field data were collected by way of a five-year (2008–2013) set of field experiments at a field site in Beijing, China. The DSSAT-CERES model was calibrated by integrating the generalized likelihood uncertainty estimation (GLUE) method and a systematic approach, and used experimental data relating to two seasons 2009/2010 and 2012/2013. The calibrated model was evaluated for its prediction performance using experimental data relating to the three seasons 2008/2009, 2010/2011 and 2011/2012. The results showed that the GLUE method can accurately estimate the genotype parameters of wheat; that the simulated leaf area index (LAI), aboveground biomass (AGB), aboveground nitrogen (AGN) and grain yield (GY) were close to the measured values; and that the DSSAT-CERES-Wheat model can be used to schedule wheat seed sowing dates, and optimize N fertilizer application in areas around Beijing. In general, the DSSAT-CERES-Wheat model was proved to be a useful decision-making tool for winter wheat production in the Beijing area. [ABSTRACT FROM AUTHOR]

The main objective of this research was to examine the feasibility of Multi-GNSS precise point positioning (PPP) in precision agriculture (PA) through a series of experiments with different working modes (i.e. stationary and moving) under different observation conditions (e.g. open sky, with buildings or with canopy). For the stationary test carried out in open space in the UK, the positioning accuracy achieved was 13.9 mm in one dimension by a PPP approach, and the repeatability of positioning results was improved from 19.0 to 6.0 mm by using Multi-GNSS with respect to GPS only. For the moving test carried out in similar location in the UK, almost the same performance was achieved by GPS-only and by Multi-GNSS PPP. However, for a moving experiment carried out in China with obstruction conditions, Multi-GNSS improved the accuracy of baseline length from 126.0 to 35.0 mm and the repeatability from 110.0 mm to 49.0 mm, The results suggested that the addition of the BeiDou, Galileo and GLONASS systems to the standard GPS-only processing improved the positioning repeatability, while a positioning accuracy was achieved at about 20 mm level in the horizontal direction with an improvement against the GPS-only PPP results. In space-constrained and harsh environments (e.g. farms surrounded with dense trees), the availability and reliability of precise positioning decreased dramatically for the GPS-only PPP results, but limited impacts were observed for Multi-GNSS PPP. In addition, compared to real time kinematic (RTK) GNSS, which is currently most commonly used for high precision PA applications, similar accuracy has been achieved by PPP. In contrast to RTK GNSS, PPP can provide high accuracy positioning with higher flexibility and potentially lower capital and running costs. Hence, PPP might be a great opportunity for agriculture to meet the high accuracy requirements of PA in the near future. [ABSTRACT FROM AUTHOR]

To obtain an accurate count of wheat spikes, which is crucial for estimating yield, this paper proposes a new algorithm that uses computer vision to achieve this goal from an image. First, a home-built semi-autonomous multi-sensor field-based phenotype platform (FPP) is used to obtain orthographic images of wheat plots at the filling stage. The data acquisition system of the FPP provides high-definition RGB images and multispectral images of the corresponding quadrats. Then, the high-definition panchromatic images are obtained by fusion of three channels of RGB. The Gram-Schmidt fusion algorithm is then used to fuse these multispectral and panchromatic images, thereby improving the color identification degree of the targets. Next, the maximum entropy segmentation method is used to do the coarse-segmentation. The threshold of this method is determined by a firefly algorithm based on chaos theory (FACT), and then a morphological filter is used to de-noise the coarse-segmentation results. Finally, morphological reconstruction theory is applied to segment the adhesive part of the de-noised image and realize the fine-segmentation of the image. The computer-generated counting results for the wheat plots, using independent regional statistical function in Matlab R2017b software, are then compared with field measurements which indicate that the proposed method provides a more accurate count of wheat spikes when compared with other traditional fusion and segmentation methods mentioned in this paper. [ABSTRACT FROM AUTHOR]

Timely and accurate estimation of crop yield before harvest to allow crop yields management decision-making at a regional scale is crucial for national food policy and security assessments. Modeling dynamic change of crop growth is of great help because it allows researchers to determine crop management strategies for maximizing crop yield. Remote sensing is often used to provide information about important canopy state variables for crop models of large regions. Crop models and remote sensing techniques have been combined and applied in crop yield estimation on a regional scale or worldwide based on the simultaneous development of crop models and remote sensing. Many studies have proposed models for estimating canopy state variables and soil properties based on remote sensing data and assimilating these estimated canopy state variables into crop models. This paper, firstly, summarizes recent developments of crop models, remote sensing technology, and data assimilation methods. Secondly, it compares the advantages and disadvantages of different data assimilation methods (calibration method, forcing method, and updating method) for assimilating remote sensing data into crop models and analyzes the impacts of different error sources on the different parts of the data assimilation chain in detail. Finally, it provides some methods that can be used to reduce the different errors of data assimilation and presents further opportunities and development direction of data assimilation for future studies. This paper presents a detailed overview of the comparative introduction, latest developments and applications of crop models, remote sensing techniques, and data assimilation methods in the growth status monitoring and yield estimation of crops. In particular, it discusses the impacts of different error sources on the different portions of the data assimilation chain in detail and analyzes how to reduce the different errors of data assimilation chain. The literature shows that many new satellite sensors and valuable methods have been developed for the retrieval of canopy state variables and soil properties from remote sensing data for assimilating the retrieved variables into crop models. Additionally, new proposed or modified crop models have been reported for improving the simulated canopy state variables and soil properties of crop models. In short, the data assimilation of remote sensing and crop models have the potential to improve the estimation accuracy of canopy state variables, soil properties and yield based on these new technologies and methods in the future. [ABSTRACT FROM AUTHOR]

Process-based models can be usefully employed for the assessment of field and regional-scale impact of drought on crop yields. However, in many instances, especially when they are used at the regional scale, it is necessary to identify the parameters and input variables that most influence the outputs and to assess how their influence varies when climatic and environmental conditions change. In this work, two different crop models, able to represent yield response to water, Aquacrop and SAFYE, were compared, with the aim to quantify their complexity and plasticity through Global Sensitivity Analysis (GSA), using Morris and EFAST (Extended Fourier Amplitude Sensitivity Test) techniques, for moderate to strong water limited climate scenarios. Although the rankings of the sensitivity indices was influenced by the scenarios used, the correlation among the rankings, higher for SAFYE than for Aquacrop, assessed by the top-down correlation coefficient (TDCC), revealed clear patterns. Parameters and input variables related to phenology and to water stress physiological processes were found to be the most influential for Aquacrop. For SAFYE, it was found that the water stress could be inferred indirectly from the processes regulating leaf growth, described in the original SAFY model. SAFYE has a lower complexity and plasticity than Aquacrop, making it more suitable to less data demanding regional scale applications, in case the only objective is the assessment of crop yield and no detailed information is sought on the mechanisms of the stress factors affecting its limitations. [ABSTRACT FROM AUTHOR]

Vertical heterogeneity of the canopy is being increasingly recognized in remote estimates of vegetative properties. Given the current limited knowledge of this issue, this paper investigated the effects of different vertical distributions of crop structure [e.g., leaf angle (LA) and leaf area index (LAI)] and biochemical parameters [e.g., chlorophyll a and b content (\textChl{a+{b}}) and water content (Wc)] on canopy reflectance and vegetation indices (VIs). A recently developed multiple-layer canopy reflectance model (MRTM) was tested for winter wheat and used to run a simulation analysis of different canopy scenarios. The results showed that the MRTM performed well to model winter wheat canopy reflectance with regard to spikes and vertical distributions of leaf properties. The vertical profiles of LA and LAI influenced canopy reflectance at almost all wavelengths, whereas the vertical profile of \textChl{a+{b}} mainly affected reflectance in the visible region, and that of Wc only affected reflectance in the near-infrared region. Changes in vertical distribution of the LA resulted in clear variations in VIs related to the LA, LAI, and \textChl{a+{b}} estimates. The vertical LAI and \textChl{a+{b}} profiles mainly influenced the VIs related to the LAI and \textChl{a+{b}} estimates. The Wc vertical profile primarily affected the VIs used to estimate crop water properties. The sensitivities of the VIs were mainly associated with the spectral responses and penetration characteristics of the bands they used. These findings suggest that the sensitivity of VIs to the vertical distributions of crop parameters should be considered when establishing models for remote crop monitoring. [ABSTRACT FROM PUBLISHER]

Remote sensing of rice traits has advanced significantly with regard to the capacity to retrieve useful plant biochemical, physiological and structural quantities across spatial scales. The rice leaf NDVI (normalized difference vegetation index) has been developed and applied in monitoring rice growth, yield prediction and disease status to guide agricultural management practices. This study combined rice canopy NDVI and environmental data to estimate rice leaf NDVI. The test site was a japonica rice experiment located in the eastern city of Shenyang, Liaoning Province, China. This paper describes (1) the use of multiple linear regression to establish four periods of rice leaf NDVI models with good accuracy (R2=0.782-0.903), and (2) how the key point of the rice growth period based on these models was determined. The techniques for modeling leaf NDVI at the point of remote canopy sensing were also presented. The results indicate that the rice leaf NDVI has a high correlation with the canopy NDVI and multisource environmental data. This research can provide an efficient method to detect rice leaf growth at the canopy scale in the future. [ABSTRACT FROM AUTHOR]