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• 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]

• 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]

• 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]

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]

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]

• Canopy-scale chlorophyll content (CCC) estimation conducted on open-canopy orchards. • A simplified 3D radiative transfer model is coupled with PROSPECT-D model. • Vegetation indices and model inversion methods are assessed with UAV data. • Look-up table including vegetation indices can estimate CCC of ITCs from image spectroscopy. • The inversion method based on the prior knowledge provides accurate estimation of CCC. Chlorophyll content is a key trait for understanding the functioning of agroforestry ecosystems and has important implications for leaf and canopy photosynthesis. However, fine-scale monitoring of canopy chlorophyll content (CCC) of individual fruit trees is rather challenging. This study aims to use a 3D radiative transfer model (RTM) and proposes a joint inversion model based on prior knowledge to estimate the CCC of individual tree crowns (ITCs) in apple orchards. The widely recognized 3D RTM LESS (large-scale remote sensing data and image simulation framework over heterogeneous 3D scenes) was adopted for large-scale apple orchard 3D scenes radiative transfer computing and image simulation. LESS was first evaluated with unmanned aerial vehicle (UAV) multispectral imagery and the results showed that it reasonably characterized the reflectance of apple tree canopies (RMSE = 0.02). An original look-up table (LUT) with reflectance was then produced using LESS, and the final vegetation indices LUT (VI LUT) including Normalized Difference Vegetation Index (NDVI), Green Chlorophyll Index (CIgreen), Red edge Chlorophyll Index (CIred edge) and Green NDVI (GNDVI) was generated from the original LUT form VI interpolation. A physically-based joint inversion model coupling prior knowledge of leaf pigments and leaf area index (LAI) was developed to estimate the CCC of ITCs from high-resolution UAV images. The solution first used linear interpolation to produce a weighted VI LUT corresponding to the sample based on estimated LAI. Linear interpolation was then adopted to screen multiple combinations of leaf chlorophyll a+b (C ab) and leaf carotenoids (C xc) contents from the VI LUT. A prior relationship between C ab and C xc was finally used to regularize the constraints on multiple VI combinations and determine the estimation of C ab and CCC. The joint inversion model demonstrated an accurate estimation of CCC of ITCs. The model driven by GNDVI yielded the highest result for CCC estimation (R2 = 0.84, RMSE = 24.12 μg/cm2). In addition, CIgreen (R2 = 0.82, RMSE = 32.22 μg/cm2) and CIred edge (R2 = 0.81, RMSE = 34.05 μg/cm2) also achieved satisfactory results. The proposed model facilitates CCC estimation of ITCs from high-resolution imagery in heterogeneous orchard canopies, which is important for advancing the precise nutrition management of fruit trees. [ABSTRACT FROM AUTHOR]

• Potential of land surface temperature to predict flowering phenology was explored. • A spatio-temporal reconstruction approach is developed to compenstate the missing LST. • The predicted first flowering date are validated with field measurements at multiple sites. • Feasibility for generating spatially continuous information on flowering phenology. The first flowering date (FFD) is a critical phenological parameter closely related to the apple yield, so the accurate prediction of the FFD is important for precise orchard production management. Existing methods to predict the FFD are mostly based on air temperature (Ta) measured at meteorological stations, but to great differences in meteorological variations and the ecological conditions, these methods cannot present the differences of FFD under complex meteorological conditions and provide spatially continuous FFD information at the level of a region. Therefore, we propose a method to predict spatially continuous apple FFD from remote sensing land surface temperature (LST) based on flowering prediction model. Firstly, the missing LST data were reconstructed by using spatio-temporal reconstruction (STR) approach developed. Next, new air temperature (NAT) data were generated by using the daily Ta estimation (DTE) model and the reconstructed LST. Finally, apple FFD was predicted by the NAT data and the apple flowering prediction model established based on random forest (RF) algorithm and the phenology sequential model, and the prediction accuracy was verified by comparison with the independently measured apple FFD. The LST reconstructed by using the STR approach has mean absolute error (MAE) ranging from 0.51 to 0.68 °C, and root mean square error (RMSE) ranging from 1.07 to 1.21 °C. The MAE between the NAT data and the High-Resolution Land Surface Data Assimilation System (HR-CLDAS) meteorological data ranges from 2.15 to 3.23 °C, and the RMSE ranges from 2.81 to 4.27 °C. In addition, the determination coefficient (R2) and RMSE between the predicted and measured FFD is 0.72 and 2.96 days, respectively. These results demonstrate that the developed method maximizes the potential of MODIS LST in predicting spatially continuous apple FFD, which is valuable for flower and fruit thinning, to defend against frost disasters, and in general for refined orchard production management. [ABSTRACT FROM AUTHOR]

In recent years, porcine epidemic diarrhea (PED) has become widespread and caused huge economic losses for the global pig industry. There is growing evidence that frequent outbreaks of diarrhea are caused by the variants of porcine epidemic diarrhea virus (PEDV) with high pathogenicity. Herein, an epidemic strain of PEDV HLJ strain was isolated and characterized from Heilongjiang Province of China, and the whole genomic expression profile of intestinal porcine epithelial cells (IPEC-J2) infected with HLJ strain was investigated in comparison with classical CV777 strain. A total of 26,851 genes were identified, of these, 25,880 were known genes and 971 were novel genes. There were 258 differentially expressed genes (DEGs) identified between PEDV HLJ-infected and uninfected cells at 24 h post infection (hpi), and 201 DEGs between PEDV HLJ and CV777 infection. A comparative analysis revealed that 258 DEGs were enriched in 468 gene ontology (GO) terms and mapped to 179 KEGG pathways, and 201 DEGs in 1120 GO terms and mapped to 115 KEGG pathways for HLJ-infected cells in contrast to the uninfected and CV777-infected cells, respectively. Specifically, PEDV HLJ strain could activate anti-viral innate immune response and inflammation more intensively than CV777, in which mRNA levels of interferon (IFN-β), chemokines (CCL5 and CXCL10) and pro-inflammatory cytokines (IL-8 and TNF-α) were induced earlier and more strongly. Subsequently, 20 DEGs and 5 proteins were selected and validated by real-time fluorescence quantitative PCR (RT-qPCR) and western blot, and the results were consistent with the transcriptomic analysis. Overall, this study may be helpful for understanding the pathogenesis mechanism of PEDV variants, and contribute to the effective prevention and control of PEDV infection. • PEDV HLJ strain could adapt and propagate on IPEC-J2 cells with a highest titer of 105.6 TCID 50 at 48 h post infection after serial passages. • The epidemic PEDV HLJ strain, as classified into GI b subgroup, has obvious mutations in its spike and ORF3 protein alignment. • Comparative transcriptomic analysis showed PEDV HLJ strain infection could trigger earlier and stronger responses of inflammation, apoptosis, and innate immunity, in comparison with CV777 strain in cell mitosis and metabolism. This will provide new clues for the further study of the pathogenesis mechanism and PED prevention. [ABSTRACT FROM AUTHOR]

• An apple tree yield prediction model was developed by using ensemble learning. • The contribution of spectral and structural features to orchard yield was discussed. • An automatic channel was developed to extract growth characteristics. As one of the world's most popular fruit, apple tree yield prediction before harvest plays an important role in optimizing orchard nutrition management, especially at the individual tree level. However, few studies focus on fruit-tree yield prediction with remote-sensing technology whereas most of them aim at field crops. Current fruits identifying and counting methods often fail to produce the expected result due to light and occlusion in complex orchard conditions. Since both the spectral and morphological characteristics of tree canopy can reflect the growth and development of fruit trees and are directly related to its potential yield. In this study, we develop a channel for automatic extraction of spectral and morphological features of apple trees using light detection and ranging (LiDAR) and multispectral imagery data from unmanned aerial vehicles. The contribution of spectral and morphological characteristics to the yield prediction of individual apple trees is discussed. With the combination of spectral and morphological features, an ensemble machine learning yield prediction model was developed by combining two widely used basic learners: support vector regression (SVR) and K-nearest neighbor (KNN). Then through extrapolating the ensemble model, the yield map was produced at the orchard level and individual tree level, respectively. The results show that the data processing channels developed in this study can accurately extract the morphological and spectral features of individual apple trees. Three features (Crown Volume 1, Ratio Vegetation Index, and CPA1) contribute most in apple tree yield prediction. The ensemble learning model outperforms all base learners with R2 = 0.813 for the validation and 0.758 for the test when using the selected three features. This study thus provides a practical example of predicting the yield of individual apple trees based on multi-source remote-sensing data and ensemble learning. [ABSTRACT FROM AUTHOR]

Coupling remote sensing data with a crop growth model has become an effective tool for estimating grain yields and assessing grain quality. In this study, a data assimilation approach using a particle swarm optimization algorithm was developed to integrate remotely sensed data into the DSSAT-CERES model for estimating the grain yield and protein content of winter wheat. Our results showed that the normalized difference red edge index (NDRE) produced the most accurate selection of spectral indices for estimating canopy N accumulation (CNA), with R 2 and RMSE values of 0.663 and 34.05 kg ha −1 , respectively. A data assimilation method ( R 2 = 0.729 and RMSE = 32.02 kg ha −1 ) performed better than the spectral indices method for estimation of canopy N accumulation. Simulation of grain yield by the data assimilation method agreed well with the measured grain yield, with R 2 and RMSE values of 0.711 and 0.63 t ha −1 , respectively. Estimating grain protein content by gluten type could improve the estimation accuracy, with R 2 and RMSE of 0.519 and 1.53%, respectively. Our study showed that estimating wheat grain yield, and especially quality, could be successfully accomplished by assimilating remotely sensed data into the DSSAT-CERES model. [ABSTRACT FROM AUTHOR]

• The changes of potato textures and plant heights were analyzed. • The estimation models of potato above ground biomass were constructed. • The underestimation of above ground biomass when using VIs alone was solved. • The potato growth monitoring at the field scale was realized. Above-ground biomass (AGB) is a significant phenotypic index for evaluating photosynthesis capacity, healthy growth, and estimating crop yield. Accurately monitoring the AGB helps improve agricultural fertilization management and optimize planting patterns. Numerous studies have confirmed that canopy spectrum saturation causes optical vegetation indices (VIs) to underestimate the AGB of crops at multiple growth periods. To solve this problem, the present research used a remote sensing method to obtain RGB images of potato tuber formation-, tuber growth-, and starch accumulation- periods by a high-definition digital camera sensor on the unmanned aerial vehicle (UAV). From the ultrahigh spatial resolution RGB images, we then extracted RGB-VIs, textures (based on the gray level co-occurrence matrix, GLCM), and crop height (Hdsm) and analyzed the correlation between the three image features and the potato AGB for single and multiple growth periods. Finally, we estimated potato AGB at multiple growth periods based on (1) RGB-VIs, (2) RGB-VIs + GLCM-based textures, (3) RGB-VIs + Hdsm, and (4) RGB-VIs + GLCM-based textures + Hdsm by applying multiple stepwise regression (MSR) and extreme learning machine (ELM). The results showed that (i) unlike the texture features of wheat and maize that increased with growth period, the texture features and crop height of the potato canopy both increased first and then decreased with the growth period. (ii) The potato AGB was poorly estimated when using RGB-VIs, CLCM-based textures, or Hdsm individually; (iii) combining GLCM-based textures, Hdsm, and RGB-VIs solved the problem of underestimating the high AGB values of potato samples by the RGB-VIs model alone. Therefore, combining GLCM-based textures, Hdsm, and RGB-VIs obtained from UAV digital images could enhance the accuracy of potato AGB estimation under high coverage. [ABSTRACT FROM AUTHOR]

Timely monitoring of above-ground biomass (AGB) is essential for indicating the crop growth status and predicting grain yield and carbon dynamics. Non-destructive remote sensing techniques with a large spatial coverage have become a promising method for crop biomass monitoring. However, most existing crop biomass models have only been tested at a single growth stage or only at a small number of growth stages at a single location. This has limited the ability of these AGB models to be transferred spatially, to other fields or regions, to predict AGB at any growth stage during the season, or to be potentially used with data from other sensing systems. Here, a new crop biomass algorithm (CBA-Wheat) was developed to estimate AGB over the entire growing season. It uses information on the crop growth stage, based on phenological scale observations (Zadoks scale or ZS), the day of the year or thermal indices (growing degree days), to correct AGB estimations from remotely sensed vegetation indices. The model transferability was evaluated across multiple regional test sites and different data sources (UAV and hand-held spectroscopic data). Results showed that the coefficient values [slope (k) and intercept (b) ] of ordinary least squares regression (OLSR) of AGB with vegetation indices had a strong relationship with ZS. These k and b relationships were used to correct the OLSR model parameters based on the observed phenological stage (ZS value). The two-band enhanced vegetation index (EVI2) was the best vegetation index for predicting AGB with the new CBA-Wheat ZS model, with R2 and RMSE values of 0.83 and 2.07 t/ha for an experimental trial site, 0.78 and 2.05 t/ha for multiple independent regional test sites, and 0.69 and 1.87 t/ha when transferred to EVI2 derived from UAV. Model performance was lower with the day of the year and thermal index corrections; however, the use of relative growing degree-days (RGS; CBA-Wheat RGS), instead of ZS information, to adjust the model parameters showed a high consistency with the CBA-Wheat ZS model, and a good potential for estimation of AGB at regional scales without the need for local phenological observations. The CBA-Wheat RGS had validated R2 and RMSE values of 0.82 and 2.01 t/ha for the experimental trial site, 0.76 and 2.39 t/ha for multiple independent regional test sites, and 0.66 and 2.14 t/ha for UAV hyperspectral imagery. These results demonstrated a good potential to estimate biomass from remotely sensed imagery at varying spatio-temporal scales in winter wheat. • A model for wheat crop biomass based on a single evolving relationship with phenological period was developed. • Biomass prediction can be achieved by combining phenological information and remote sensing data. • Model transferability at varying spatio-temporal scales and to different remote sensing data sources was tested successfully. [ABSTRACT FROM AUTHOR]

The feasibility of monitoring lodging of wheat fields by exploiting fully polarimetric C-band radar images has been investigated in this paper. A set of backscattering intensity features and polarimetric features, derived by target decomposition techniques, was extracted from 5 consecutive Radarsat-2 images. The temporal evolutions of these features of lodging wheat fields were investigated as a function of DAS (day after sowing) during the entire growing season. The temporal behavior was compared between typical lodging fields and normal fields in different growing stages. It was found that polarimetric feature from synthetic aperture radar (SAR) data was very sensitive to wheat lodging. Then a method called polarimetric index, availing the sensitivity of polarimetry to the structure, was put forward to monitor wheat lodging. The method was validated by two sets of in situ data collected in Shangkuli Farmland area, Inner Mongolia, China, at heading and ripe stages of spring wheat. Almost all the lodging fields were successfully distinguished from normal fields. Furthermore, the result revealed that the polarimetric index can reflect the intrinsic feature of lodging wheat with good anti-inference ability such as wheat growth difference. While optical sensors relied on its spectral features to monitor crop lodging, the proposed method based on radar data utilized polarimetric features to monitor crop lodging. [ABSTRACT FROM AUTHOR]

• Hyperspectral imaging monitored the quality of apple directly in orchards. • Relative spectral processing could reduce the unevenness of ambient light. • The accuracy of the prediction models met the requirements of orchard management. • The dynamic process of apple quality and maturity was visualized. Rapid and in-situ estimation of apple quality and maturity is of great significance to guarantee the quality of apples and the profit of the industry. This study explored the utility of hyperspectral imaging (HSI) in assessing the quality and maturity of apples. Hyperspectral images, in the range of 500–900 nm, were collected directly in the orchard. A total of 100 apples contained in the visual field of the camera were picked for destructive testing of firmness, soluble solids content (SSC) and starch pattern index (SPI). To reduce the complicated outdoor light interference, we adopted the PTEE ball as the white reference. Normalized reflectance spectrum (NR) and normalization different spectral index (NDSI) involving all possible two-band combinations were also extracted for apple quality estimation. Subsequently, partial least squares regression (PLSR) analyses were conducted using the characteristic wavelengths extracted by stability competitive adaptive reweighted sampling (SCARS). The PLSR model established by NDSI combined with SCARS achieved the best results. The correlation coefficient of validation set (R v) of firmness, SSC and SPI were 0.783, 0.901 and 0.834, respectively. The root-mean-square errors of the validation set (RMSEv) of firmness, SSC, SPI were 0.993 kg/cm2, 0.535% and 0.427, respectively. The Streif index was used to evaluate the maturity of apples, which was calculated from the predicted value of firmness, SSC and SPI. The RMSEv of the PLS model for Streif index was 0.0120. The results indicated the potential of the NDSI-SCARS-PLSR model based on hyperspectral imaging to observe the dynamic changes in the quality and maturity of the apples in site. This model could provide the spatial distribution of apple quality and maturity, which would be valuable for exploring the maturation pattern, grasping the growth of the area, and planning harvest time. [ABSTRACT FROM AUTHOR]

[Display omitted] • Multi-source environmental data were used to predict wheat yield in China. • We developed a robust machine learning model to predict wheat yield. • The random forest model using NIRv had better performance than models using NDVI and EVI. • NIRv from jointing to heading was the most important predictor in determining yield. Crop yield is controlled by different environmental factors. Multi-source data for site-specific soils, climates, and remotely sensed vegetation indices are essential for yield prediction. Algorithms of data-model fusion for crop growth monitoring and yield prediction are complicated and need to be optimized to deal with model uncertainty. This study integrated multi-source environmental variables (e.g., satellite-based vegetation indices, climate data, and soil properties) into random forest (RF) and support vector machine (SVM) models for wheat yield prediction in China. The performance of both RF and SVM models was investigated using different types of vegetation indices associated with other predictors. Relative importance and partial dependence analyses were used to identify the main predictors and their relationships with wheat yield. We found that using remotely sensed vegetation indices improved our model precision, and that near-infrared reflectance of terrestrial vegetation (NIRv) was slightly better than normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) in predicting yield. NIRv was better in detecting climate stress on crops, and could capture more information regarding crop growth and yield formation. Compared with the SVM model, the RF model with NIRv and other covariates had better performance in wheat yield prediction, with R2 and RMSE being 0.74 and 758 kg/ha respectively. We also found that NIRv from jointing to heading was the most important predictor in determining yield, followed by solar radiation (especially during tillering–heading), relative humidity (during planting–tillering), soil organic carbon, and wind speed (throughout the growing season). In addition, wheat yield exhibited threshold-like responses to most factors based on our RF model. These threshold values can help to better understand how different environmental factors limit wheat yield, which will provide useful information for climate-adaptive crop management. Our findings demonstrated the potential of using NIRv for yield prediction. This approach is broadly applicable to other regions globally using publicly available data. [ABSTRACT FROM AUTHOR]

Timely and accurate forecasting of crop grain protein content (GPC) is helpful in planning to acquire the desired target protein levels. A geographically weighted regression (GWR) model was estimated based on meteorological factors to predict the winter wheat GPC at the county level. In the Huang-Huai-Hai region, the grain protein content of winter wheat increased by 0.29% for every 1° increase in latitude. GPC prediction with this model was more precise than that of the multiple linear regressions (MLR) model. The correlation coefficient (R) and Akaike information criterion (AIC) value ranges were 0.26 ~ 0.66 and 1573.86 ~ 1710.70 for the GWR, and 0.06 ~ 0.46 and 1670.18 ~ 1939.76 for the MLR, respectively. Except for radiation in March (RAD03), radiation in April (RAD04) and radiation in May (RAD05), the sensitivity index of other monthly weather indicators to GPC had a high correlation with latitude. With 36° north latitude (L) as the limit, the correlation between RAD03 (R L<36 ° = 0.36, R L>36 ° = −0.29), RAD04 (R L<36 ° = 0.31, R L>36 ° = −0.35) and RAD05 (R L<36 ° = 0.20, R L>36 ° = −0.20) with latitude all showed an opposite trend. We highlight that spatial information needs to be considered when predicting county-level winter wheat GPC. • The grain protein content (GPC) is positively correlated with latitude. • The effect of meteorological factors on winter GPC showed spatial heterogeneity. • Geographically weighted model has advantages in winter GPC in heterogeneous region. [ABSTRACT FROM AUTHOR]

Highlights: [•] We detected yellow rust in winter wheat with hyperspectral data. [•] Disease sensitive features were extracted by continuous wavelet analysis. [•] Wavelet features outperformed conventional spectral features in disease detection. [•] The disease can be better detected at Zodaks 70 stage than Zodaks 37 stage. [ABSTRACT FROM AUTHOR]

Abstract: Crop growth and production are dependent not only on the amount of total nitrogen (N) absorbed by plants, but also on the vertical leaf N distribution within canopies. The non-uniform leaf N distribution has been reported for various plant canopies. Remote sensing has been widely used for determination of crop N status, but such analysis seldom takes N distribution into consideration, ultimately leading to limited accuracy and decreased practical value of the related results. This paper has reviewed the results of previous studies that investigated the ecophysiological aspects of non-uniform N distribution, and the remote sensing methods that have been proposed to monitor this phenomenon. Additionally, this study used field data to analyze the differences in leaf N distribution in wheat canopies with different plant types (i.e. spread type, semi-spread type, and erect type), and provided insights into the estimation of vertical leaf N distribution by means of remote sensing. The process of reviewing research related to the ecophysiological issues of leaf N distribution led to identification of several important inadequacies in the current body of research. We propose that future work should aim to strengthen an understanding of the dynamic response of vertical N distribution within canopies to the various related environment factors and field management strategies. When a more thorough understanding of vertical N distribution is achieved, researchers will be able to improve related quantitative modeling, and may be able to comprehensively reveal the effect of vertical N distribution on canopy photosynthesis performances. In addition, through a comparison of the leaf N profiles of spread, semi-spread, and erect wheat canopies, it was found here that the semi-spread wheat canopy had a more non-uniform N distribution than did the other two types, despite all types having large and full vegetation coverage at the booting stage. Regarding detection of leaf N distribution using remote sensing, the few existing studies can be grouped into three classes according to the hyperspectral data used. One class employed the spectral data obtained from top-view observations; another class used multi-angle canopy reflectance data, while the third mainly focused on the relationships between spectral reflectance and fluorescence characteristics and the leaf N or chlorophyll content for different vertical layers. Despite important progress having been made, the results of the studies and the methods therein face key limitations in practical application. The present paper suggests two possibilities for the estimation of vertical leaf N distribution with remote sensing. One possibility is based upon hyperspectral imaging, and the other possibility combines a vertical N distribution model and canopy reflectance data. The former method requires investigation of the ability of hyperspectral imaging to obtain pure spectral information of different vertical leaf layers. The latter method requires that the key determinants of vertical N distribution be identified to improve quantitative modeling, and that the parameters of the N distribution model be determined from remotely sensed data. Because of the current lack of adequate data, a concrete case study and a very thorough test analysis could not be presented here. But it is still hoped that this work can provide helpful information for future studies. [Copyright &y& Elsevier]

Abstract: To mitigate the mass transfer limitations of a highly viscous room temperature ionic liquid (RTIL) electrolyte, systematic characterizations of viscosity, ionic conductivity, apparent triiodide diffusion coefficient and photovoltaic response of an alternative cost-effective and highly conductive binary RTIL mixture of 1-ethyl-3-methylimidazolium trifluoroacetate (EMIATF) and 1-methyl-3-propylimidazolium iodide (PMII) were investigated. An emphasis was placed on the dynamics of electron transport and charge recombination processes; specifically, a focus was placed on the effective electron diffusion length and charge collection efficiency in the devices. Notably, the introduction of perfluorinated anions, with a strong delocalization of the negative charge over the anion backbone, was able to weaken the hydrogen bonding with the imidazolium cations and, thus, decrease the viscosity and increase the ionic conductivity of the electrolyte. A sealed device that employed the binary RTIL achieved an overall conversion efficiency of 5.22% under irradiation of 100mWcm−2, which increased the value by 30% compared to a device with a blank PMII-based RTIL electrolyte. Electrochemical impedance spectroscopy and intensity-modulated photovoltage/photocurrent spectroscopy analysis revealed that employment of this alternative binary RTIL electrolyte system was able to significantly decrease the diffusion resistance of triiodide species in the electrolyte and retard the charge recombination between the injected electrons with triiodide anions in the electrolyte, thus improving the effective electron diffusion length and charge collection efficiency in the device. The charge collection efficiency was certified to be the dominant parameter governing the short-circuit photocurrent density and the overall conversion efficiency of devices with RTIL electrolyte. [Copyright &y& Elsevier]

• Develop a within-growing season yield forecast system with random forest model. • Random forest model performs well in predicting grain yield in China. • We identified the most important stage-specific predictors determining crop yield. • The most important variable influencing yields varied with crop types. Accurate and timely crop yield forecasts can provide essential information to make conclusive agricultural policies and to conduct investments. Recent studies have used different machine learning techniques to develop such yield forecast systems for single crops at regional scales. However, no study has used multiple sources of environmental predictors (climate, soil, and vegetation) to forecast yields for three major crops in China. In this study, we adopted 7-year observed crop yield data (2013–2019) for three major grain crops (wheat, maize, and rice) across China, and three major data sets including climate, vegetation indices, and soil properties were used to develop a dynamic yield forecasting system based on the random forest (RF) model. The RF model showed good performance for estimating yields of all three crops with correlation coefficient (r) higher than 0.75 and normalized root means square errors (nRMSE) lower than 18.0%. Our results also showed that crop yields can be satisfactorily forecasted at one to three months prior to harvest. The optimum lead time for yield forecasting depended on crop types. In addition, we found the major predictors influencing crop yield varied between crops. In general, solar radiation and vegetation indices (especially during jointing to milk development stages) were identified as the main predictor for winter wheat; vegetation indices (throughout the growing season) and drought (especially during emergence to tasseling stages) were the most important predictors for spring maize; soil moisture (throughout the growing season) was the dominant predictor for summer maize, late rice, and mid rice; precipitation (especially during booting to heading stages) was the main predictor for early rice. Our study provides insights into practical crop yield forecasting and the understanding of yield response to environmental conditions at a large scale across China. The methods undertaken in this research can be easily implemented in other countries with available information on climate, soil, and vegetation conditions. [ABSTRACT FROM AUTHOR]

• This research has proposed a precision agriculture technology adoption prediction model. • Need-technology fit, perceived benefit, facilitating conditions and perceived risk have significant impacts on farmers' intention to adopt precision agriculture. • Facilitating conditions (e.g. knowledge, resources and access to consultant services) were found to play the biggest role in improving the willingness of arable farmers in the North China Plain to adopt precision agriculture. • Social influence, was not found to play a significant impact on farmers' willingness to adopt precision agriculture in North China Plain agriculture. Precision agriculture has the potential to deliver improved and more sustainable food production. Despite the various Chinese policy initiatives to strengthen national food security, there is evidence that the adoption of precision agriculture technologies in China has been much lower when compared to other developed agricultural economies. This study therefore aims to explore factors that determine Chinese farmers' adoption of precision agriculture technologies in cropping systems and to provide recommendations on technology promotion in the future. The current status of precision agriculture adoption by smallholder farmers within crop farming systems in the North China Plain was explored. An integrated model of "Adapted Unified Theory of Acceptance and Usage of Technology (AUT2)" was developed to explain individual farmers' intention to adopt precision agriculture. 456 surveys were conducted via face to face interviews in the North China Plain and structural equation modelling analysis was used to estimate the proposed AUT2 model. The results showed that perceived need for technology characteristics (PNTC), perceived benefits, perception of the efficacy of facilitating conditions and perceived risks of adoption have significant impacts on farmers' intention to adopt precision agriculture. The facilitating conditions (e.g. knowledge, resources and access to consultant services) were the best predictor improving Chinese farmers' willingness to adopt these technologies. Policy makers and service providers need to consider these factors in the promotion of technologies. [ABSTRACT FROM AUTHOR]

Lithium titanate (Li 4 Ti 5 O 12 , LTO) has several disadvantages as active material for Li-ion batteries (LIBs) such as low conductivity and poor capacity. To overcome these drawbacks and specifically to improve electrical performance of LTO, we used solvothermal method to synthesis LTO and to dope it simultaneously with Al and Mn. Doped LTO was then incorporated into composites containing graphene (G), carbon nanotube (CNT) or carbon quantum dots (CQDs) to enhance specific capacity and conductivity of LIB anodes containing these composites as active materials. To have a further study of different carbon materials composite with doped LTO, these materials we obtained were thoroughly characterized. LTO doping and incorporation into composites not only increased its specific capacity and also decreased its impedance. Al3+ and Mn4+ substituted Ti4+ located at 16 d sites. First cycle specific capacity values and impedance values of anodes consisting of composites containing doped LTO and G, CNT or CQDs were 261.7, 286.2 and 296.5 mA h/g (at C/10 rate) and 13.99, 8.28, 18.79 Ω, respectively. Capacity retention after 100 cycles for these anodes was 84.6, 90.67 and 83.4%. LTO doping by Al3+ and Mn4+ and combining it with G, CNT or CQDs effectively improved its electrochemical performance. The best performance was obtained for anodes containing composites consisting of a combination of doped LTO and CNT. • Used carbon quantum dots to form a composite with Al3+ and Mn4+ co-doped LTO as anode material for the first time. • Compared the influence of composites with different nano-structure carbon materials. • The specific capacity of LTO-Al/Mn-CQDs increased to 296.5 mA⋅h⋅g−1 at 0.1 C. [ABSTRACT FROM AUTHOR]

• An interannual expandable wheat yield and quality predicting model. • Integrating spectral vegetative indices and meteorological data for yield and GPC prediction. • GPC prediction model was enhanced by adding a gluten type variable. The use of remote sensing data for predicting wheat yield and quality is becoming a more feasible alternative to destructive and post-harvest laboratory-based test methods. However, most prediction models which make use of remote sensing data are statistical rather than mechanistic, therefore difficult to extend at interannual and regional scales. In this work, an interannual expandable wheat yield and quality predicting model using hierarchical linear modeling (HLM) was developed, integrating hyperspectral and meteorological data. The results showed that the ordinary least squares (OLS) regression for predicting wheat yield and grain protein content (GPC), one key indicator of grain quality, had low stability at the interannual extension. The predictive power for yield by HLM method was higher than OLS, with R2, RMSEv and nRMSE values of 0.75, 1.10 t/ha, and 20.70 %, respectively. GPC prediction by the HLM method was enhanced when the gluten type was considered, with R2, RMSEv and nRMSE values of 0.85, 1.02 %, and 6.87 %, respectively. The results of this study confirmed that HLM can be a robust method for improving yield and GPC predicting stability under various growing seasons in winter wheat. [ABSTRACT FROM AUTHOR]

• The proposed method was successfully tested on datasets containing small images. • Measurements made with proposed method correlate strongly with ground truth data. • The proposed method automatically classifies different types of squid and evaluates squid freshness. • The proposed method provides robust, non-invasive, high-throughput squid morphological information. We developed and evaluated a rapid, low-cost system to classify squid in industrial production. This involved designing an easy-to-use handheld image-acquisition system combined with an automated, labor-saving, and efficient deep learning model (named "improved faster recurrent convolutional neural network") to identify three squid species from the North Pacific Ocean. Three indicators, Accuracy, Intersection-over-Union, and Average Running Time, are used to evaluate the classification, and the average results for the test samples are 85.7%, 80.1%, and 0.144 s, respectively. The proposed network provides better squid classification compared with four other approaches. In addition, to ensure quality, the freshness of the selected squid is also evaluated using global threshold segmentation analysis. This proposed method is demonstrated to be a robust, noninvasive, and high-throughput system for squid classification and can also be expanded to other fine processing of aquatic products. [ABSTRACT FROM AUTHOR]

• The hyperspectral data was decomposed using the wavelet transform algorithm. • The predictive accuracy based on the random forest algorithm was improved by 10.2%. • The high-frequency information enhanced the predictive accuracy of the SOM content. Soil organic matter (SOM) is one of the most important indicators of cultivated land fertility and greatly influences other soil nutrient factors and physicochemical characteristics. This study aimed to develop a universal method to detect SOM content within the plough layer of cultivated land using ground hyperspectral data. The hyperspectral data was decomposed using the wavelet transform algorithm. The sensitivity of the high-frequency information increased with the degree of the wavelet decomposition. SOM content was retrieved using the high-frequency coefficients created with the wavelet transform and random forest algorithm. The validation model showed a R2 of 0.748 and RMSE of 0.254. The predictive accuracy of the model based on the random forest algorithm was improved by 10.2% compared to that of the math transformations. Therefore, the high-frequency information decomposed by the wavelet technology effectively enhanced the predictive accuracy of the SOM content by coupling the wavelet technology and random forest algorithm. [ABSTRACT FROM AUTHOR]