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Objectives: The micro‐nano structure of 3D‐printed porous titanium (Ti) alloy with excellent performance in avoiding stress shielding and promoting bone tissue differentiation provides a new opportunity for the development of bone implants, but it necessitates higher requirements for bone tissue differentiation and the antibacterial properties of bone implants in clinical practice. Methods: This study investigated the preparation, antimicrobial properties, and osteogenesis‐promoting ability of the 3D printed porous Ti alloy anodic oxidized Ag‐carrying (Ag@3D‐TiO2) scaffolds. The 3D printed porous Ti alloy (3D‐Ti), anodized 3D printed porous Ti alloy (3D‐TiO2), and Ag@3D‐TiO2 scaffolds were synthesized using electron beam melting. The antimicrobial properties of the scaffolds were examined using antibacterial tests and their cytocompatibility was assessed using a cell proliferation assay and acridine orange/ethidium bromide (AO/EB) staining. In vitro cellular assays were used to investigate the effects of the scaffold microstructural features on cell activity, proliferation, and osteogenesis‐related genes and proteins. In vivo animal experiments were used to evaluate the anti‐inflammatory and osteogenesis‐promoting abilities of the scaffolds. Results: The Ag@3D‐TiO2 scaffolds exhibited sustained anti‐microbial activity over time, enhanced cell proliferation, facilitated osteogenic differentiation, and increased extracellular matrix mineralization. In addition, alkaline phosphatase (ALP), collagen type I (COL‐I), and osteocalcin (OCN)‐related genes and proteins were upregulated. In vivo animal implantation experiments, the anti‐inflammatory effect of the Ag@3D‐TiO2 scaffolds were observed using histology, and a large amount of fibrous connective tissue was present around it; the Ag@3D‐TiO2 scaffolds were more bio‐compatible with the surrounding tissues compared with 3D‐Ti and 3D‐TiO2; a large amount of uniformly distributed neoplastic bone tissue existed in their pores, and the chronic systemic toxicity test showed that the 3D‐Ti, 3D‐TiO2, and Ag@3D‐TiO2 scaffolds are biologically safe. Conclusion: The goal of this study was to create a scaffold that exhibits antimicrobial properties and can aid bone growth, making it highly suitable for use in bone tissue engineering. [ABSTRACT FROM AUTHOR]

Global warming and algal blooms have been two of the most pressing problems faced by the world today. In recent decades, numerous studies indicated that global warming promoted the expansion of algal blooms. However, research on how algal blooms respond to global warming is scant. Global warming coupled with eutrophication promoted the rapid growth of phytoplankton, which resulted in an expansion of algal blooms. Algal blooms are affected by the combined effects of global warming, including increases in temperatures, CO2 concentration, and nutrient input to aquatic systems by extreme weather events. Since the growth of phytoplankton requires CO2, they appear to act as a carbon sink. Unfortunately, algal blooms will release CH4, CO2, and inorganic nitrogen when they die and decompose. As substrate nitrogen increases from decompose algal biomass, more N2O will be released by nitrification and denitrification. In comparison to CO2, CH4 has 28-fold and N2O has 265-fold greenhouse effect. Moreover, algal blooms in the polar regions may contribute to melting glaciers and sea ice (will release greenhouse gas, which contribute to global warming) by reducing surface albedo, which consequently would accelerate global warming. Thus, algal blooms and global warming could form feedback loops which prevent human survival and development. Future researches shall examine the mechanism, trend, strength, and control strategies involved in this mutual feedback. Additionally, it will promote global projects of environmental protection combining governance greenhouse gas emissions and algal blooms, to form a geoengineering for regulating the cycles of carbon, nitrogen, and phosphorus. [ABSTRACT FROM AUTHOR]

Currently, the main methods for detecting plant diseases are sampling and manual visual inspection. However, these methods are time-consuming, laborious and prone to misinterpretation. Rapid advances in Deep Learning (DL) techniques offer new possibilities. This study focused on analysing the confounding factors among three types of wheat rust (stripe rust, leaf rust and stem rust) and aimed to achieve higher classification accuracy. The following approaches were used: (1) Images were collected from several crops and diseases: Wheat Rusts Dataset (WRD), Wheat Common Disease Dataset (WDD), and Common Poaceae Disease Dataset (PDD); (2) Seven common convolutional neural network (CNN) models were made and their performance compared. DenseNet121 was selected as the base model, and its classification results further analysed. The results of the above analyses were then considered using phenotypic morphology and model structure analysis, as well as potential confounder discussions; (3) Adjustments and optimisations were made based on the identified confounding factors. The final improved model, designated Imp-DenseNet, achieved the following accuracies with different datasets: Top-1 accuracy = 98.32% (WRD), Top-3 accuracy = 97.30% (WDD) and Top-5 accuracy = 96.60% (PDD) (Top-x Accuracy refers to the accuracy of the top-ranked category that matches or containing the actual results). The study revealed the potential factors contributing to the confusion among the three wheat rusts and successfully achieved higher accuracy. It can provide a new perspective for future research on other diseases of wheat or other crops. [Display omitted] • Confusion factors of wheat rust were analysed from different perspectives • Combine knowledges of plant protection, plant phenotype morphology analysis, DL and CV • Remodel DenseNet using attention mechanism, SPP, and factorisation thoughts • Verify validity of methods with Grad-CAM, Yolo v8 and Transformer [ABSTRACT FROM AUTHOR]

Titanium alloy Ti6Al4V is a commonly used bone implant material, primarily prepared as a porous material to better match the elastic modulus of human bone. However, titanium alloy is biologically inert and does not have antibacterial properties. At the same time, the porous structure with a large specific surface area also increases the risk of infection, leading to surgical failure. In this paper, we prepared three porous samples with different porosities of 60%, 75%, and 85%, respectively (for short, 3D-60, 3D-75, and 3D-85) using 3D printing technology and clarified the mechanical properties. Through tensile experiments, when the porosity was 60%, the compressive modulus was within the elastic modulus of human bone. Anodic oxidation technology carried out the surface modification of a 3D-printed porous titanium alloy with 60% porosity. Through change, the different voltages and times on the TiO2 oxide layer on the 3D-printed porous titanium alloy are different, and it reveals the growth mechanism of the TiO2 oxide layer on a 3D-printed unique titanium alloy. The surface hydrophilic and antibacterial properties of 3D-printed porous titanium alloy were significantly improved after modification by anodic oxidation. [ABSTRACT FROM AUTHOR]

The acquisition of maize tassel phenotype information plays a vital role in studying maize growth and improving yield. Unfortunately, detecting maize tassels has proven challenging because of the complex field environment, including image resolution, varying sunlight conditions, plant varieties, and planting density. To address this situation, the present study uses unmanned aerial vehicle (UAV) remote sensing technology and a deep learning algorithm to facilitate maize tassel identification and counting. UAVs are used to collect maize tassel images in experimental fields, and RetinaNet serves as the basic model for detecting maize tassels. Small maize tassels are accurately identified by optimizing the feature pyramid structure in the model and introducing attention mechanisms. We also study how mapping differences in image resolution, brightness, plant variety, and planting density affect the RetinaNet model. The results show that the improved RetinaNet model is significantly better at detecting maize tassels than the original RetinaNet model. The average precision in this study is 0.9717, the precision is 0.9802, and the recall rate is 0.9036. Compared with the original model, the improved RetinaNet improves the average precision, precision, and recall rate by 1.84%, 1.57%, and 4.6%, respectively. Compared with mainstream target detection models such as Faster R-CNN, YOLOX, and SSD, the improved RetinaNet model more accurately detects smaller maize tassels. For equal-area images of differing resolution, maize tassel detection becomes progressively worse as the resolution decreases. We also analyze how detection depends on brightness in the various models. With increasing image brightness, the maize tassel detection worsens, especially for small maize tassels. This paper also analyzes the various models for detecting the tassels of five maize varieties. Zhengdan958 tassels prove the easiest to detect, with R2 = 0.9708, 0.9759, and 0.9545 on 5, 9, and 20 August 2021, respectively. Finally, we use the various models to detect maize tassels under different planting densities. At 29,985, 44,978, 67,466, and 89,955 plants/hm2, the mean absolute errors for detecting Zhengdan958 tassels are 0.18, 0.26, 0.48, and 0.63, respectively. Thus, the detection error increases gradually with increasing planting density. This study thus provides a new method for high-precision identification of maize tassels in farmland and is especially useful for detecting small maize tassels. This technology can be used for high-throughput investigations of maize phenotypic traits. [ABSTRACT FROM AUTHOR]

The Loess Plateau is a typical ecologically sensitive area that can easily be perturbed by the effects of human activities and global climate change. Therefore, it is necessary to develop tools to monitor the environmental quality in the LP quickly and accurately. To reveal the spatio-temporal changes in environmental quality in the LP from 2000 to 2020, we used the Moderate-Resolution Imaging Spectroradiometer (MODIS) products on the Google Earth Engine platform and constructed the remote sensing ecological index (RSEI) through principal component analysis (PCA). Then, Sen–Mann–Kendall methods were applied to determine the changing trend of the environmental quality of the LP. Finally, natural and anthropogenic factors affecting the environmental quality were probed using a geographical detector model. The results showed that: (1) the average RSEI values in 2000, 2010 and 2020 were 0.396, 0.468 and 0.511, respectively, displaying an upward trend from 2000 to 2020, with a growth rate of 0.005 year − 1 . The overall environment quality was moderate (0.4–0.6). (2) In terms of spatial distribution, the environmental quality was excellent in the southeast and poor in the northwest of the LP. The areas with improved environmental quality (84.51%) were located in all the counties, whereas the areas with degraded environmental quality (8.11%) occurred in the north and southeast of the study area. (3) Greenness, heat, wetness, dryness and land use types were prominent factors affecting RSEI throughout the study period; additionally, the total industrial gross domestic product showed a growing influence. The contribution of multi-factor interaction was stronger than that of single factors. The results will provide a reference and a new research perspective for local environmental protection and regional planning. [ABSTRACT FROM AUTHOR]

Nutrients and hydrodynamic mixing are both important environmental factors affecting the growth of Microcystis. Effects of nutrient combined with hydrodynamic mixing in the dominance of Microcystis in phytoplankton community is unclear. Here we present data on the effects of hydrodynamic mixing (HM), nutrient addition (+ N + P), and hydrodynamic mixing combined nutrient addition (HM + N + P) on dominance of Microcystis in a microcosm experiments. Results showed that the average number of Microcystis in HM + N + P was 10.09, 3.79, and 1.95 times that of the control (9.19 × 106 cells L−1), + N + P, and HM group, respectively. Moreover, the mean colony sizes of Microcystis in HM + N + P group were 2.08, 1.91, and 1.21 times that of the control (24.42 μm), + N + P,HM group, respectively. Pseudanabaena limnetica replaced Microcystis as the dominant species in control, + N + P, and HM group at end of the experiment, with 82.21%, 90.65%, and 50.37% of total algae number, respectively. However, in HM + N + P group, Microcystis dominated throughout the experimental period, accounting for 73.56–94.67% of the total algae abundance. Results demonstrated that N and P combined with hydrodynamic mixing promoted abundance, colony size of Microcystis, which plays an important role in the dominance of Microcystis in phytoplankton community in natural ecosystems. [ABSTRACT FROM AUTHOR]

• A novel model (LabTNet) is proposed to estimate crop biochemical traits. • LabTNet jointly estimates multiple wheat traits by hyperspectral data. • LabTNet exhibits superior predictive performance than existing methods. • Grad-CAM analyzed hyperspectral attention regions of LabTNet. Accurate estimation of crop leaf and canopy biochemical traits, such as leaf dry matter content (Cm), leaf equivalent water thickness (Cw), leaf area index (LAI), dry leaf biomass (DLB), leaf total water content (LW), and fresh leaf biomass (FLB), is essential for monitoring crop growth accurately. The vegetation spectral feature technique combined with statistical regression methods is widely employed for remote sensing crop biochemical traits mapping. However, the crop canopy spectral reflectance is influenced by various crop biochemical traits and uncertainties in geometric changes of light and soil background effects. Consequently, the remote-sensing estimation of crop biochemical traits is limited. A potential solution involves training a deep learning model to understand the physical relationship between crop biochemical traits and canopy spectral reflectance based on a physical radiative transfer model (RTM). The primary focus of this study is to propose a winter-wheat leaf and canopy biochemical traits analysis and mapping method based on hyperspectral remote sensing, utilizing a deep learning network for leaf area index and leaf biochemical traits deep learning network (LabTNet). This study consists of four main tasks: (1) Field-based measurements of winter-wheat spectra and biochemical traits were conducted in two growing seasons. A PROSAIL RTM was also employed to generate a simulated dataset representing comprehensive and complex winter-wheat field conditions. (2) The LabTNet deep learning model was pre-trained using the simulated spectra dataset to acquire knowledge of the physical relationship between crop biochemical traits and canopy spectral reflectance derived from the RTM. Subsequently, the model was re-trained using the field-based spectra dataset from two growing seasons, employing a transfer learning technique. (3) An analysis was conducted to assess the performance of LabTNet against traditional statistical regression methods in estimating crop leaf and canopy biochemical traits. The study used the gradient-weighted class activation mapping (Grad-CAM) technique to analyze the attention regions of input spectra (454:8:950 nm, 960:10:1300 nm, 1450:10:1750 nm, 2000:10:2350 nm) by different convolutional neural network layers in LabTNet, aiming to enhance the interpretability of deep learning models. (4) Winter-wheat leaf and canopy biochemical traits (Cw, Cm, LAI, DLB, LW, and FLB) were mapped using the LabTNet deep learning model. Our research has the following conclusions: (1) Combining the RTM and deep learning techniques yields higher winter-wheat biochemical trait estimates than traditional statistical regression methods. (2) Different LabTNet deep learning model layers focus on distinct areas of canopy reflectance, corresponding to the sensitive regions for various winter-wheat biochemical traits. (3) LabTNet demonstrates similar winter-wheat leaf and canopy biochemical traits estimation performance using visible and near-infrared (VNIR) reflectance data and full-spectral (FS) range hyperspectral reflectance as inputs (Cw: R 2 = 0.603–0.653, RMSE = 0.0015–0.0015 cm; Cm: R 2 = 0.511–0.560, RMSE = 0.0006–0.0007 g/m2; LAI: R 2 = 0.773–0.793, RMSE = 0.65–0.66 m2/m2; LW: R 2 = 0.842–0.847, RMSE = 67.93–70.73 g/m2; DLB: R 2 = 0.747–0.762, RMSE = 21.10–21.89 g/m2; FLB: R 2 = 0.831–0.840, RMSE = 86.26–90.30 g/m2). The combined use of UAV hyperspectral remote sensing and the LabTNet model proves effective in providing high-performance winter-wheat leaf and canopy biochemical trait maps, offering valuable insights for agricultural management. [ABSTRACT FROM AUTHOR]

As a key functional trait, leaf photosynthetic pigment content (LPPC) plays an important role in the health status monitoring and yield estimation of apples. Hyperspectral features including vegetation indices (VIs) and derivatives are widely used in retrieving vegetation biophysical parameters. The fractional derivative spectral method shows great potential in retrieving LPPC. However, the performance of fractional derivatives and machine learning (ML) for retrieving apple LPPC still needs to be explored. The objective of this study is to test the capacity of using fractional derivative and ML methods to retrieve apple LPPC. Here, the hyperspectral data in the 400–2500 nm domains was used to calculate the fractional derivative order of 0.2–2, and then the sensitive bands were screened through feature dimensionality reduction to train ML to build the LPPC estimation model. Additionally, VIs-based ML methods and empirical regression models were developed to compare with the fractional derivative methods. The results showed that fractional derivative-driven ML methods have higher accuracy than the ML methods driven by the original spectra or vegetation index. The results also showed that the ML methods perform better than empirical regression models. Specifically, the best estimates of chlorophyll content and carotenoid content were achieved using support vector regression (SVR) at the derivative order of 0.2 (R2 = 0.78) and 0.4 (R2 = 0.75), respectively. The fractional derivative maintained a good universality in retrieving the LPPC of multiple phenological periods. Therefore, this study highlights that the fractional derivative and ML improved the estimation of apple LPPC. [ABSTRACT FROM AUTHOR]

• The increasing trend of vapor pressure deficit (VPD) change in 1997–2020 is ten times higher than in 1981–1996. • Increasing VPD promoted cropland and grassland growth in Arid, while inhibited the growth of shrubland. • Significant positive or negative difference in the sensitivity to VPD for the same vegetation under different climatic zones. • The vast majority of vegetation showed a significant increase in the inter-annual trend of the sensitivity to VPD. • The relationship of higher sensitivity with lower VPD is not stable under different vegetation and climatic zones. Vapor pressure deficit (VPD) shows significant spatial and temporal variability in the context of global climate change, which is important for studying the implications of climate change on the structure and function of ecosystems to analyze the effects of VPD on vegetation dynamics. Spatial patterns of vegetation sensitivity to VPD have been recently investigated, however, the feedback of different vegetation types to VPD may vary depending on physiological characteristics, it is unclear how different types influence the sensitivity to VPD. In this study, the ERA5-Land reanalysis time-series dataset was used to analyze the spatial and temporal trends of VPD under different vegetation types. It was found that VPD showed an increasing trend in vegetated areas over the past 20 years with large spatial heterogeneity, generally increasing with drying conditions. On this basis, the spatial patterns of vegetation sensitivity to VPD and temporal trends in sensitivity were evaluated over the past 20 years in China using the enhanced vegetation index (EVI) and near-infrared reflectance of vegetation (NIRv) which can describe vegetation dynamics. The results show that the sensitivities under the two indices have high spatial consistency, with northeastern and central China showing positive sensitivities and southern China showing negative sensitivities, respectively. The positive sensitivities are relatively high for Deciduous Broadleaf Forests (DBF), Deciduous Needleleaf Forests (DNF), Grasslands (GL), and Croplands (CL) types, while the negative sensitivities are larger for Shrublands (SL) and Savannas (SA) types. Under different climatic zones, the sensitivity of CL and GL are independent of climatic zones (both showing positive), while the sensitivity of SL is negative in the Humid zone and positive in the Semi-Arid zone. Temporally, the sensitivity showed a slow increasing trend over the last 20 years. In terms of vegetation types, sensitivities of Evergreen Broadleaf Forests (EBF), DBF, GL and CL types showed a significant increasing trend (p < 0.05), except for the SL type, which showed a significant decreasing trend (p < 0.05). The trends of sensitivity are not affected by the differences in vegetation types (all of them show an increasing trend) under arid and semi-arid conditions, while dry sub-humid and humid have a greater impact on sensitivity trends. The finding of an overall increase in sensitivity suggests a mechanism of erratic change in vegetation growth under climate change. Notably, the increased sensitivity of certain vegetation types (especially GL and CL) suggests that these may become progressively vulnerable to increased VPD as global climate change persists, with the risk of moving from facilitation to inhibition of photosynthesis. [ABSTRACT FROM AUTHOR]

Phase II clinical trials in oncology are used to initially evaluate the therapeutic efficacy of a new treatment. In the past, the total response was a frequently used endpoint to access the effectiveness of the treatment. When the total response is modest, clinicians may also be interested in disease control (defined as the total response or stable disease) since it may better predict clinical outcomes. Thus, formally testing both the total response and disease control in a phase II trial has an important clinical implication. In this paper, we propose a new method to construct optimal subset designs for one-stage and two-stage phase II clinical trials with two binary endpoints, i.e. the total response and disease control. A new set of hypotheses under the framework of intersection-union tests is provided in which the treatment is considered promising if both the total response and disease control are good. We show that the power function for each test in a large family of tests is nondecreasing in both the total response rate and disease control rate; identify the parameter configurations at which the maximum Type I error rate and minimum power are achieved and derive level- α tests. We also provide optimal one-stage designs with the minimum sample size and optimal two-stage designs with the least expected total sample size. • A new method of constructing optimal subset designs is proposed. • The new method has potential of establishing more precise inferential conclusions. • The proposed optimal design usually yields a smaller sample size. [ABSTRACT FROM AUTHOR]

Conjugated polymers have attracted growing attention for versatile applications in energy storage due to their potential benefits including low‐cost processing, molecular tunability, environmental benignity, and high mechanical flexibility. In particular, polymer‐based organic electrode materials have shown significant progress in supercapacitor (SC) applications with superior electrochemical behaviors. The performances of SCs are closely related to the intrinsic characteristics of different polymers in the nanoscale and the morphological features of the polymer‐based electrode materials obtained by different fabrication techniques in the macroscale. This review summarizes the design and synthesis of both p‐type and n‐type conjugated polymers, highlighting the pros and cons of three synthesis techniques: electrochemical polymerization, chemical polymerization, and in situ polymerization. The performances of conjugated polymers in SCs, their cycling stabilities, and structure‐performance relationships are discussed. Moreover, the existing challenges and future directions of polymer‐based SCs are considered with respect to energy density, stability, and large‐scale production to promote commercialization. [ABSTRACT FROM AUTHOR]

In order to solve the pollution problem of organic pollutants in water, this study proposed a photocatalyst that efficiently utilizes sunlight, and TiO 2 /HTiOF 3 photocatalytic materials were successfully prepared by a hydrothermal method and used for the degradation of rhodamine B (RhB) solution under simulated sunlight. The effect of time and urea addition on the performance of the material was investigated, and the results showed that the sample with a reaction time of 12 h and a urea addition of 0.05 g exhibited 97 % degradation efficiency and the best photocatalytic degradation activity for RhB solution, which was attributed to the construction of a traditional type II heterojunction of the TiO 2 /HTiOF 3 nanocomposite, which improved the carrier separation rate and maintained a strong redox capacity. This work contributes to the construction of efficient semiconductor photocatalytic nano-heterostructures. The preparation of TiO 2 /HTiOF 3 composites by one-step hydrothermal method provides a new idea for the development of catalysts. • Stable HTiOF 3 intermediates were synthesized by introducing urea, and TiO 2 /HTiOF 3 photocatalytic materials were constructed by a one-step hydrothermal method. • The construction of type II heterojunction effectively reduces the interfacial resistance while greatly extending the lifetime of the photogenerated carriers. • Photocatalytic performance of TiO 2 /HTiOF 3 composites is greatly improved over pure TiO 2. [ABSTRACT FROM AUTHOR]

• Introduction of a novel model that integrates OBIA and the attention mechanism for precise orchard mapping. • The contribution of SAR is studied in orchard classification. • The model obtains more accurate classification results than RNN-based and CNN-based deep learning models. • The uncertainty of the super-pixel size on classification accuracy is investigated. Reliable and accurate classification of orchards is important for the dynamic monitoring of large-scale orchards and food security evaluation. At present, the very similar spectral profiles of different fruit trees and the high susceptibility of optical data to interference by weather conditions limit the resolution of orchard classification. Synthetic Aperture Radar (SAR) imagery provides an advanced solution to this problem, with the advantages of being immune to weather conditions and advances in deep learning techniques. This paper presents an orchard classification model (STCM) with optical and SAR fusion, which integrates the advantages of the Simple Non-Iterative Clustering (SNIC) super-pixel algorithm with a deep learning algorithm based on a multi-headed attention mechanism, and it achieves the best classification accuracy of above 0.82 in comparison with existing deep learning models. Meanwhile, the model has exceptional classification accuracy and robustness in a study area with fragmented plots, many fruit tree species, and various distributions of optical images. In the absence of optical data, the classification accuracy of the STCM model using only SAR data is around 0.70, which makes the model have a promising potential application value. This study provides a technical solution for accurately obtaining different orchard categories in high-resolution remotely sensed orchard images. The study helps to improve the management and operation of orchards and provides a strong basis for decision-making in the fruit industry to enhance the sustainability of the global fruit industry. [ABSTRACT FROM AUTHOR]

Polyvinylidene fluoride‐hexafluoropropylene (PVDF‐HFP) is a standard electrolyte membrane and binder material for lithium‐ion batteries (LIBs) because of the benefits of membrane formation and strong mechanical properties. Still, its use is limited due to unavoidable defects such as high crystallinity and poor electrochemical performance. In this paper, a larger specific surface area and three‐dimensional (3D) permeable lamellae carboxylated g‐C3N4 (HCN) were successfully prepared by HNO3 and introduced into the PVDF‐HFP to obtain a high‐performance composite separator. The wettability and electrolyte absorption of PVDF‐HFP separator can be improved through the carboxylated HCN. More crucially, because PVDF‐HFP has several active sites and low crystallinity, adding HCN can greatly increase the ionic conductivity of separators. The ionic conductivity of PH‐HCN at 25 °C is 0.52 S cm−1, 4.7 times higher than the initial PVDF‐HFP membrane, and the oxidation potential can reach 4.8 V. The assembled lithium‐ion batteries half‐cell has a capacity of 71 % at a 5 C rate and 94 % after 400 cycles at a 2 C rate. Therefore, the improved separator has the potential to be applied to high‐performance LIBs. [ABSTRACT FROM AUTHOR]

Herein, the synthesis of MnO 2 nanowires decorated with transition metal-doped carbon quantum dots (MCQDs) is described for use as binder-free flexible solid-state supercapacitors. In particular, the water-soluble Fe and N co-doped MCQDs prepared by amidation reaction with ferric ammonium citrate and urea had ultrafine particle morphologies and showed super-hydrophilicity in aqueous solution, which greatly improved the contact probability between the electrode and the electrolyte. In addition, the co-doped MCQDs increased both the electrical conductivity and specific capacitance of the MnO 2 /carbon-based nanocomposites and limited the collapse and exfoliation of the MnO 2 structure during extended cycling. Compared with pristine MnO 2 electrode, the MCQDs/MnO 2 composite electrode exhibited faster charge-discharge rate and improved cycle stability with increased specific capacitance. The flexible symmetric supercapacitor constructed from the MCQDs/MnO 2 composite had a capacitance retention of 85.6 % after 10,000 charge/discharge cycles at a current density of 2 mA cm−2. In addition, the device showed a maximum power density of 4.8 W cm−2 at an energy density of 2.3 mW h cm−2, and a maximum energy density of 11.16 mW h cm−2 at a power density of 311.6 mW cm−2. The data that support the findings of this study are available from the corresponding author upon reasonable request. • A metal carbon quantum dots (MCQDs)/MnO 2 nanowire composite is described. • The MCQDs/MnO 2 composite was used to construct flexible solid-state supercapacitors. • Super-hydrophilic MCQDs improved the contact probability of the interface. • MCQDs improved the conductivity, specific capacitance and cycling stability of MnO 2. • The flexible symmetric supercapacitor exhibited good electrochemical performance. [ABSTRACT FROM AUTHOR]

Accurate and high-throughput identification of the initial anthesis of soybean varieties is important for the breeding and screening of high-quality soybean cultivars in field trials. The objectives of this study were to identify the initial day of anthesis (IADAS) of soybean varieties based on remote sensing multispectral time-series images acquired by unmanned aerial vehicles (UAVs), and analyze the differences in the initial anthesis of the same soybean varieties between two different climatic regions, Shijiazhuang (SJZ) and Xuzhou (XZ). First, the temporal dynamics of several key crop growth indicators and spectral indices were analyzed to find an effective indicator that favors the identification of IADAS, including leaf area index (LAI), above-ground biomass (AGB), canopy height (CH), normalized-difference vegetation index (NDVI), red edge chlorophyll index (CIred edge), green normalized-difference vegetation index (GNDVI), enhanced vegetation index (EVI), two-band enhanced vegetation index (EVI2) and normalized-difference red-edge index (NDRE). Next, this study compared several functions, like the symmetric gauss function (SGF), asymmetric gauss function (AGF), double logistic function (DLF), and fourier function (FF), for time-series curve fitting, and then estimated the IADAS of soybean varieties with the first-order derivative maximal feature (FDmax) of the CIred edge phenology curves. The relative thresholds of the CIred edge curves were also used to estimate IADAS, in two ways: a single threshold for all of the soybean varieties, and three different relative thresholds for early, middle, and late anthesis varieties, respectively. Finally, this study presented the variations in the IADAS of the same soybean varieties between two different climatic regions and discussed the probable causal factors. The results showed that CIred edge was more suitable for soybean IADAS identification compared with the other investigated indicators because it had no saturation during the whole crop lifespan. Compared with DLF, AGF and FF, SGF provided a better fitting of the CIred edge time-series curves without overfitting problems, although the coefficient of determination (R2) and root mean square error (RMSE) were not the best. The FDmax of the SGF-fitted CIred edge curve (SGF_CIred edge) provided good estimates of the IADAS, with an RMSE and mean average error (MAE) of 3.79 days and 3.00 days, respectively. The SGF-fitted_CIred edge curve can be used to group the soybean varieties into early, middle and late groups. Additionally, the accuracy of the IADAS was improved (RMSE = 3.69 days and MAE = 3.09 days) by using three different relative thresholds (i.e., RT50, RT55, RT60) for the three flowering groups compared to when using a single threshold (RT50). In addition, it was found that the IADAS of the same soybean varieties varied greatly when planted in two different climatic regions due to the genotype–environment interactions. Overall, this study demonstrated that the IADAS of soybean varieties can be identified efficiently and accurately based on UAV remote sensing multispectral time-series data. [ABSTRACT FROM AUTHOR]

• We introduce leaf N profile characteristic coefficient K m on its remote estimates. • Diverse datasets are used for developing robust models for remote estimates of K m. • Four regression algorithms are tested with various possible sets of predictors. • A robust method is proposed to estimate K m from canopy reflectance for winter wheat. The characteristic coefficient of vertical leaf nitrogen (N) profile is a canopy parameter that indicates the attenuation steepness of leaf N from the top of canopy downward. It is sensitive not only to crop production, grain yield and quality, and light- and N-use efficiency but also to N deficiency in the crop. We introduce this coefficient by exploring a robust method to estimate it from canopy spectral reflectance. We analyze comprehensively various approaches based on diverse datasets of winter wheat. We test and compare the accuracy and stability of models by using the adjusted and weighted coefficient of determination (wR adj 2), the mean absolute error (MAE), and the mean and coefficient of variation (CV) over multiple seasons. The analysis focuses mainly on the coefficient of mass-based leaf N profile (K m); nevertheless, a comparison with the coefficient of area-based leaf N profile is presented. The results indicate that the most robust model to estimate K m of winter wheat is K m = (1.8037 R G V I - 0.9702 + 0.0786 e x p (0.6315 / D A S F)) / 2 , where RGVI is the red and green ratio vegetation index, and DASF is the directional area scattering factor. The mean wR adj 2 is 0.663 with CV = 8.2% and the mean MAE is 0.117 with CV = 12.8% over three seasons including various situations. This makes it possible to assess K m at large areas and follow its dynamics over multiple periods in a timely and nondestructive manner. [ABSTRACT FROM AUTHOR]

Land-use maps are thematic materials reflecting the current situation, geographical diversity, and classification of land use and are an important scientific foundation that can assist decision-makers in adjusting land-use structures, agricultural zoning, regional planning, and territorial improvement according to local conditions. Spectral reflectance and radar signatures of time series are important in distinguishing land-use types. However, their impact on the accuracy of land-use mapping and decision making remains unclear. Also, the many spatial and temporal heterogeneous landscapes in southern Xinjiang limit the accuracy of existing land-use classification products. Therefore, our objective herein is to develop reliable land-use products for the highly heterogeneous environment of the southern Xinjiang Uygur Autonomous Region using the freely available public Sentinel image datasets. Specifically, to determine the effect of temporal features on classification, several classification scenarios with different temporal features were developed using multi-temporal Sentinel-1, Sentinel-2, and terrain data in order to assess the importance, contribution, and impact of different temporal features (spectral and radar) on land-use classification models and determine the optimal time for land-use classification. Furthermore, to determine the optimal method and parameters suitable for local land-use classification research, we evaluated and compared the performance of three decision-tree-related classifiers (classification and regression tree, random forest, and gradient tree boost) with respect to classifying land use. Yielding the highest average overall accuracy (95%), kappa (95%), and F1 score (98%), we determined that the gradient tree boost model was the most suitable for land-use classification. Of the four individual periods, the image features in autumn (25 September to 5 November) were the most accurate for all three classifiers in relation to identifying land-use classes. The results also show that the inclusion of multi-temporal image features consistently improves the classification of land-use products, with pre-summer (28 May–20 June) images providing the most significant improvement (the average OA, kappa, and F1 score of all the classifiers were improved by 6%, 7%, and 3%, respectively) and fall images the least (the average OA, kappa, and F1 score of all the classifiers were improved by 2%, 3%, and 2%, respectively). Overall, these analyses of how classifiers and image features affect land-use maps provide a reference for similar land-use classifications in highly heterogeneous areas. Moreover, these products are designed to describe the highly heterogeneous environments in the study area, for example, identifying pear trees that affect local economic development, and allow for the accurate mapping of alpine wetlands in the northwest. [ABSTRACT FROM AUTHOR]

• Multispectral data are effective in discriminating tea plant varieties. • Texture information are essential elements in discriminating tea plant varieties. • Data fusion of multispectral image can improve tea plant discrimination rate. Discrimination of tea plant (Camellia sinensis L.) varieties is of significant value for the efficient management of cultivation and resources optimization of tea industry. Phenotypic and spectral characteristics of tea plants are important indicators for determining the quality of tea cultivars to a certain extent. However, few studies have used spectral image information to identify tea plant varieties. The aim of this research was the discrimination of 16 types of high-yield tea plant varieties using a multispectral camera. This methodology involved image registration, calibration, segmentation, information extraction, and data fusion. The hue (H), saturation (S), and value (V), texture information, and several spectral vegetation indices were acquired from the multispectral image of the tea plant canopy. The successive projection algorithm (SPA) was used to analyze the original parameters. Three classification methods were applied to tea plant variety discrimination: Bayes discriminant analysis (BDA), support vector machine (SVM), and extreme learning machine (ELM). The results indicated that SPA based on fusing data combined with the SVM classification model, achieved a feasible method to identify tea plant varieties. Additionally, the method achieved accuracy in the training, test, and validation sets, reaching 97.00%, 90.52%, and 88.67%, respectively. This study proposed a new perspective on multispectral image information as an identifier of tea plant varieties. The explored model will be helpful for the development of portable instruments for commercial applications in variety identification and phenotype recognition of tea plants. [ABSTRACT FROM AUTHOR]

• Vegetation indices show a strong correlation with nitrogen status in tea plants. • The combination of multispectral data and hyperspectral data is effective in monitoring nitrogen level. • Fusing data of image information and spectral information has remarkable ability to predict nitrogen content in tea plants. Nitrogen (N) plays a pivotal role in management of tea plantation, with significant impacts on the growth, productivity, and nutrition status of tea plants. The existing methods for N content monitoring of tea leaves are complicated and can not realize in suite and in real time way. This study proposed a method for estimating the N content of tea plants in field conditions based on a combination of a multispectral imaging system and hyperspectral data. A total of 32 parameters were extracted from five tea gardens using calibrated multispectral images of the tea plant canopy, and 27 indices were selected by Pearson correlation analysis. A total of 28 wavelengths selected by competitive adaptive reweighted sampling from hyperspectral data were combined with 27 multispectral indices as the original data. Subsequently, five variables of fused data (H, VOG, BGI, 1664 nm and 1665 nm) were selected by variable combination population analysis based on the 55 combination parameters. Partial least squares regression, random forest regression, and support vector machine regression (SVR) models all showed excellent performance for both the calibration and prediction sets. The overall results indicated that the infused data of multispectral and hyperspectral data combined with SVR are effective in monitoring the N level under field conditions, and the R2 (coefficient of determination) and root mean square error values of the prediction were 0.9186 and 0.0560, respectively. The findings of this study are important in retaining the nutritional and quality attributes of agricultural commodities. [ABSTRACT FROM AUTHOR]

• Accurately forecast the first flowering period of two typical apple producing areas in China. • It fills in the research of forecasting the first flowering period of apple from point scale to regional scale. • The phenological survey method of replacing time with space is very effective for the calibration of sequential model. • The prediction of the apple first flowering on a regional scale has important application value for optimizing orchard management. China is one of the largest apple-producing countries in the world, with large orchards and diverse climates. Accurately forecasting the first-flowering time of apple trees can assist orchard managers in their deciding when to apply anti-freeze. The temperature-driven sequential model from previous studies can be used to forecast the flowering phenology of deciduous fruit trees. However, this model requires many years of observational data for calibration, so flowering forecasts based on traditional phenological models cannot be implemented in areas that lack such historical data. To overcome this problem, the present work combines a spatial rather than a temporal phenological survey method with 1-km-gridded temperature products to calibrate the chill and heat requirement parameters of the sequential model. We then use the model to forecast the first-flowering on a regional scale for Luochuan and Linyi, which are two main apple-producing areas of China. The results show that the proposed method accurately forecasts regional flowering. The root mean squared errors (RMSE) for Luochuan and Linyi were 4.7 and 4.4 days, respectively, and the normalized RMSEs were all less than 5.19%. We expect the proposed regional first-flowering forecast method to be an important aid to optimize orchard management. [ABSTRACT FROM AUTHOR]

• Synthetic Minority Oversampling Technique and Edited Nearest Neighbors are applied. • A SMOTE-ENN-XGBoost model is developed to detect lodging in imbalanced datasets. • The developed model is explained with SHAP values. • Deeper insight is obtained into how the model behaves with these explanations. • Complex and interrelated impacts of the selected features are captured and analyzed. Remote sensing image is becoming an increasingly popular tool for crop lodging detection because it conveniently provides features for building machine learning models and predicting lodging. However, difficulties in interpreting machine learning models and their predictions limit the confidence of using remote sensing images to detect lodging. In addition, the lodging datasets used for modeling are difficult to balance under natural conditions. Designing a robust and interpretable classification model for the detection of lodging in an imbalanced distribution dataset poses a particularly difficult challenge. In this study, visible and multi-spectral images were collected with a UAV to extract relevant features from remote sensing images. In a preliminary step, Synthetic Minority Oversampling Technique (SMOTE) and Edited Nearest Neighbors (ENN) method were used to treat imbalanced datasets. The SMOTE-ENN-XGBoost model is proposed for the efficient identification of maize lodging at the plot scale. The SMOTE-ENN-XGBoost model achieved an F1-score of 0.930 and a recall of 0.899 on a testing set, suggesting that it can be used for modeling lodging detection. Additionally, the SHapley Additive exPlanations (SHAP) approach was employed to interpret the identification and prioritization of features that determine lodging classification and activity prediction. The results showed that canopy structure and textural features are relatively stable compared with spectral features, which are susceptible to the external environment when modeling is employed to detect lodging. This work also showed that canopy structural, spectral, and textural information should be considered simultaneously rather than separately when detecting crop lodging in a crop breeding program in order to prevent differences in expression controlled by the interaction between genotype and environment obscuring the change in a single feature before and after lodging. For practical applications of machine learning models in crop lodging detection, such insights are of critical relevance. Taken together, the results of this study encourage further applications of remote sensing techniques to build interpretable machine learning models. [ABSTRACT FROM AUTHOR]

Ear height (EH) and ear–plant height ratio (ER) are important agronomic traits in maize that directly affect nutrient utilization efficiency and lodging resistance and ultimately relate to maize yield. However, challenges in executing large-scale EH and ER measurements severely limit maize breeding programs. In this paper, we propose a novel, simple method for field monitoring of EH and ER based on the relationship between ear position and vertical leaf area profile. The vertical leaf area profile was estimated from Terrestrial Laser Scanner (TLS) and Drone Laser Scanner (DLS) data by applying the voxel-based point cloud method. The method was validated using two years of data collected from 128 field plots. The main factors affecting the accuracy were investigated, including the LiDAR platform, voxel size, and point cloud density. The EH using TLS data yielded R2 = 0.59 and RMSE = 16.90 cm for 2019, R2 = 0.39 and RMSE = 18.40 cm for 2021. In contrast, the EH using DLS data had an R2 = 0.54 and RMSE = 18.00 cm for 2019, R2 = 0.46 and RMSE = 26.50 cm for 2021 when the planting density was 67,500 plants/ha and below. The ER estimated using 2019 TLS data has R2 = 0.45 and RMSE = 0.06. In summary, this paper proposed a simple method for measuring maize EH and ER in the field, the results will also offer insights into the structure-related traits of maize cultivars, further aiding selection in molecular breeding. [ABSTRACT FROM AUTHOR]

A better understanding of wheat nitrogen status is important for improving N fertilizer management in precision farming. In this study, four different sensors were evaluated for their ability to estimate winter wheat nitrogen. A Gaussian process regression (GPR) method with the sequential backward feature removal (SBBR) routine was used to identify the best combinations of vegetation indices (VIs) sensitive to wheat N indicators for different sensors. Wheat leaf N concentration (LNC), plant N concentration (PNC), and the nutrition index (NNI) were estimated by the VIs through parametric regression (PR), multivariable linear regression (MLR), and Gaussian process regression (GPR). The study results reveal that the optical fluorescence sensor provides more accurate estimates of winter wheat N status at a low-canopy coverage condition. The Dualex Nitrogen Balance Index (NBI) is the best leaf-level indicator for wheat LNC, PNC and NNI at the early wheat growth stage. At the early growth stage, Multiplex indices are the best canopy-level indicators for LNC, PNC, and NNI. At the late growth stage, ASD VIs provide accurate estimates for wheat N indicators. This study also reveals that the GPR with SBBR analysis method provides more accurate estimates of winter wheat LNC, PNC, and NNI, with the best VI combinations for these sensors across the different winter wheat growth stages, compared with the MLR and PR methods. [ABSTRACT FROM AUTHOR]

The normalized difference vegetation index (NDVI) is an important agricultural parameter that is closely correlated with crop growth. In this study, a novel method combining the dynamic time warping (DTW) model and the long short-term memory (LSTM) deep recurrent neural network model was developed to predict the short and medium-term winter wheat NDVI. LSTM is well-suited for modelling long-term dependencies, but this method may be susceptible to overfitting. In contrast, DTW possesses good predictive ability and is less susceptible to overfitting. Therefore, by utilizing the combination of these two models, the prediction error caused by overfitting is reduced, thus improving the final prediction accuracy. The combined method proposed here utilizes the historical MODIS time series data with an 8-day time resolution from 2015 to 2020. First, fast Fourier transform (FFT) is used to decompose the time series into two parts. The first part reflects the inter-annual and seasonal variation characteristics of winter wheat NDVI, and the DTW model is applied for prediction. The second part reflects the short-term change characteristics of winter wheat NDVI, and the LSTM model is applied for prediction. Next, the results from both models are combined to produce a final prediction. A case study in Hebei Province that predicts the NDVI of winter wheat at five prediction horizons in the future indicates that the DTW–LSTM model proposed here outperforms the LSTM model according to multiple evaluation indicators. The results of this study suggest that the DTW–LSTM model is highly promising for short and medium-term NDVI prediction. [ABSTRACT FROM AUTHOR]

Metal–organic framework materials(ZIF-9) were loaded on Sodium alginate/polyvinyl(PVA/SA) hydrogel by in situ growth method for adsorption of heavy metal Cu (II). The structure of hydrogels composited with MOFs and polymers was designed to improve the poor mechanical properties of natural polymer gel materials and the inconvenience of powdered MOFs materials in practical applications. The adsorption results showed that the optimum adsorption process of Cu (II) pH was 5.0. The adsorption kinetics and isotherm suggest that the adsorption process follows the Freundlich isotherm and the pseudo-second-order models. The experimental maximum adsorption capacity was 98.98 mg/g, 2.6 times and 1.5 times higher than ordinary SA and PVA/SA hydrogel spheres. Synthesized hydrogel spheres were characterized by FT-IR, SEM, XRD, and XPS, which confirmed that MOF materials have grown in situ on PVA/SA hydrogel spheres. More importantly, PVA/SA@ZIF-9 exhibited exceptional mechanical stability and showed excellent recycling capability in the cyclic adsorption process. [ABSTRACT FROM AUTHOR]

Nitrogen(N) is one of the most important elements for crop growth and yield formation. Insufficient or excessive application of N fertilizers can limit crop yield and quality, especially as excessive N fertilizers can damage the environment and proper fertilizer application is essential for agricultural production. Efficient monitoring of crop N content is the basis of precise fertilizer management, and therefore to increase crop yields and improve crop quality. Remote sensing has gradually replaced traditional destructive methods such as field surveys and laboratory testing for crop N diagnosis. With the rapid advancement of remote sensing, a review on crop N monitoring is badly in need of better summary and discussion. The purpose of this study was to identify current research trends and key issues related to N monitoring. It begins with a comprehensive statistical analysis of the literature on remote sensing monitoring of N in rice and wheat over the past 20 years. The study then elucidates the physiological mechanisms and spectral response characteristics of remote sensing monitoring of canopy N. The following section summarizes the techniques and methods applied in remote sensing monitoring of canopy N from three aspects: remote sensing platforms for N monitoring; correlation between remotely sensed data and N status; and the retrieval methods of N status. The influential factors of N retrieval were then discussed with detailed classification. However, there remain challenges and problems that need to be addressed in the future studies, including the fusion of multisource data from different platforms, and the uncertainty of canopy N inversion in the presence of background factors. The newly developed hybrid model integrates the flexibility of machine learning with the mechanism of physical models. It could be problem solving, which has the advantages of processing multi-source data and reducing the interference of confounding factors. It could be the future development direction of crop N inversion with both high precision and universality. [ABSTRACT FROM AUTHOR]

[Display omitted] • Design strategies for 2D/2D NH 2 -TiO 2 /ReS 2 heterojunction photocatalyst. • The transition of NH 2 -TiO 2 /ReS 2 from type-Ⅰ to S-scheme heterojunction by amination modification. • An innovative construction engineering for 2D/2D heterojunction The 2D/2D heterojunction photocatalyst demonstrates a broad range of applications owing to its large interface contact area. At present, most of the current 2D/2D structures are constructed by self-assembly, which mainly forms a relatively weak van der Waals force at the interface. This poses a challenge to achieving effective carrier separation. Here, we proposed an innovative strategy of molecular-connected heterojunctions to address this challenge. The strategy uses (3-aminopropyl) trimethoxysilane (APTMS) to modify the surface of TiO 2 and construct NH 2 -TiO 2 /ReS 2 heterojunction, which not only alters the material's Zeta potential but also uses molecular chains connecting TiO 2 and ReS 2. Simultaneously, the design of molecular-connected heterojunction changes the work function of TiO 2 , leading to the transformation of the TiO 2 /ReS 2 heterojunction from type-Ⅰ to S-scheme, thereby achieving a selective carrier separation. The constructed NH 2 -TiO 2 /ReS 2 photocatalytic heterojunction composite has a hydrogen production rate of 451.3 μmol g-1 h−1, which is over 11 and 3 times than TiO 2 and TiO 2 /ReS 2 , respectively, and exhibits excellent long-term photocatalytic stability. This study presents a novel approach for achieving strong interface bonding and introduces an innovative approach for constructing 2D/2D photocatalytic heterojunctions. [ABSTRACT FROM AUTHOR]

• Determined the optimal UAV wide-band vegetation indices (VIs) for LAI and LCC retrievals. • Analyzed the sensitivity of VIs used for retrieval to influencing factors. • Constructed a hybrid inversion method of LAI and chlorophyll for individual apple tree crowns. • Quantitatively characterized the effect of canopy shading on inversion. The structural and chemical characteristics of individual apple tree crowns can indicate the nutritional and growth status of the trees, making them crucial for advancing orchard management practices. In this study, we collected multispectral imagery and ground validation data from two representative apple orchards in Beijing, China. We employed a hybrid inversion method to estimate the Leaf Area Index (LAI), Leaf Chlorophyll Content (LCC), and Canopy Chlorophyll Content (CCC) of individual apple tree crowns. Furthermore, we quantitatively evaluated the impact of canopy shading on the inversion results and mapped these traits at the scale of individual tree crowns (ITCs). To determine the optimal broad-band Vegetation Indices (VIs) for estimating LAI and LCC, we empirically analyzed 22 VIs using the PROSAIL simulation dataset. We constructed two measured datasets of canopy reflectance by masking canopy shadows, one containing shaded pixels and the other consisting of sunlit-only pixels. Using the Artificial Neural Network (ANN) algorithm and PROSAIL model, we developed a hybrid inversion model to assess the performance of the filtered VIs on the two measured datasets. The results demonstrated that TCARI/OSAVI and SR3 were the most accurate VIs for estimating LAI (including shaded pixels: R2 = 0.67, RMSE = 0.31 m2/m2; sunlit-only pixels: R2 = 0.74, RMSE = 0.28 m2/m2) and LCC (including shaded pixels: R2 = 0.70, RMSE = 7.11 μg/cm2; sunlit-only pixels: R2 = 0.73, RMSE = 6.63 μg/cm2) in the two measured reflectance datasets, respectively. Removing canopy shadows significantly improved the accuracy of LAI and LCC retrieval, although there was no significant difference in CCC retrieval accuracy (including shaded pixels: R2 = 0.78, RMSE = 31.25 μg/cm2; sunlit-only pixels: R2 = 0.79, RMSE = 28.48 μg/cm2). Moreover, we utilized UAV imaging multispectral data to map the estimated variability of leaf and canopy traits. The results revealed trait variability among different apple tree canopies, highlighting the potential of UAV imaging multispectral techniques in characterizing and mapping individual apple tree crown traits while capturing variability among crowns. We recommend performing canopy shading pixel masking to enhance the accuracy of ITCs trait retrieval. [ABSTRACT FROM AUTHOR]

Chlorophyll, as a key component of crop leaves for photosynthesis, is one significant indicator for evaluating the photosynthetic efficiency and developmental status of crops. Fractional-order differentiation (FOD) enhances the feature spectral information and reduces the background noise. In this study, we analyzed hyperspectral data from grape leaves of different varieties and fertility periods with FOD to monitor the leaves' chlorophyll content (LCC). Firstly, through sensitive analysis, the fractional-order differential character bands were identified, which was used to construct the typical vegetation index (VI). Then, the grape LCC prediction model was built based on the random forest regression algorithm (RFR). The results showed the following: (1) FOD differential spectra had a higher sensitivity to LCC compared with the original spectra, and the constructed VIs had the best estimation performance at the 1.2th-order differential. (2) The accuracy of the FOD-RFR model was better than that of the conventional integer-order model at different fertility periods, but there were differences in the number of optimal orders. (3) The LCC prediction model for whole fertility periods achieved good prediction at order 1.3, R 2 = 0.778, RMSE = 2.1, and NRMSE = 4.7%. As compared to the original reflectance spectra, R2 improved by 0.173; RMSE and NRMSE decreased, respectively, by 0.699 and 1.5%. This indicates that the combination of FOD and RFR based on hyperspectral data has great potential for the efficient monitoring of grape LCC. It can provide technical support for the rapid quantitative estimation of grape LCC and methodological reference for other physiological and biochemical indicators in hyperspectral monitoring. [ABSTRACT FROM AUTHOR]

The Yellow River Basin (YB) acts as a key barrier to ecological security and is an important experimental region for high-quality development in China. There is a growing demand to assess the ecological status in order to promote the sustainable development of the YB. The eco-environmental quality (EEQ) of the YB was assessed at both the regional and provincial scales utilizing the remote sensing-based ecological index (RSEI) with Landsat images from 2000 to 2020. Then, the Theil–Sen (T-S) estimator and Mann–Kendall (M-K) test were utilized to evaluate its variation trend. Next, the optimal parameter-based geodetector (OPGD) model was used to examine the drivers influencing the EEQ in the YB. Finally, the geographically weighted regression (GWR) model was utilized to further explore the responses of the drivers to RSEI changes. The results suggest that (1) a lower RSEI value was found in the north, while a higher RSEI value was found in the south of the YB. Sichuan (SC) and Inner Mongolia (IM) had the highest and the lowest EEQ, respectively, among the YB provinces. (2) Throughout the research period, the EEQ of the YB improved, whereas it deteriorated in both Henan (HA) and Shandong (SD) provinces. (3) The soil-available water content (AWC), annual precipitation (PRE), and distance from impervious surfaces (IMD) were the main factors affecting the spatial differentiation of RSEI in the YB. (4) The influence of meteorological factors (PRE and TMP) on RSEI changes was greater than that of IMD, and the influence of IMD on RSEI changes showed a significant increasing trend. The research results provide valuable information for application in local ecological construction and regional development planning. [ABSTRACT FROM AUTHOR]

Solar-induced chlorophyll fluorescence (SIF) is closely related to the light-reaction process and has been recognized as a good indicator for tracking gross primary productivity (GPP). Nevertheless, it has not been widely examined how SIF and GPP respond to temperature. Here, we explored the linkage mechanisms between SIF and GPP in winter wheat based on continuous measurements of canopy SIF ( c S I F ), GPP, and meteorological data. To separately explore the structural and physiological mechanisms underlying the SIF–GPP relationship, we studied the temperature responses of the estimated light use efficiency ( L U E p ), canopy-level chlorophyll fluorescence yield ( c S I F y i e l d ) and photosystem-level chlorophyll fluorescence yield ( Φ F ) estimated using canopy-scale remote sensing measurements. We found that GPP, red canopy SIF ( c S I F 688 ) and far-red canopy SIF ( c S I F 760 ) all exhibited a decreasing trend during overwintering periods. However, GPP and c S I F 688 showed relatively more obvious changes in response to air temperature ( T a ) than c S I F 760 did. In addition, the L U E p responded sensitively to T a (the correlation coefficient, r = 0.83, p-value < 0.01). The c S I F y i e l d _ 688 and Φ F _ 688 ( Φ F at 688 nm) also exhibited significantly positive correlations with T a (r > 0.7, p-value < 0.05), while c S I F y i e l d _ 760 and Φ F _ 760 ( Φ F at 760 nm) were weakly correlated with T a (r < 0.3, p-value > 0.05) during overwintering periods. The results also show that L U E p was more sensitive to T a than Φ F , which caused changes in the L U E p / Φ F ratio in response to T a . By considering the influence of T a , the GPP estimation based on the total SIF emitted at the photosystem level ( t S I F ) was improved (with R2 increased by more than 0.12 for t S I F 760 and more than 0.05 for t S I F 688 ). Therefore, our results indicate that the L U E p / Φ F ratio is affected by temperature conditions and highlights that the SIF–GPP model should consider the influence of temperature. [ABSTRACT FROM AUTHOR]

Fast and accurate counting of wheat ears in field conditions is a key element for determining wheat yield. To obtain the number of wheat ears in a field, we propose a new counting algorithm based on computer vision. This algorithm counts wheat ears in remote images through semantic segmentation regression network (SSRNet). SSRNet is a multistage convolutional neural network that we propose to achieve counting problems through regression. In SSRNet, first, the original image is cropped to increase the amount of data. This method effectively solves the small sample dataset. Next, based on the cropping results, we build a fully convolutional neural network (FCNN) to segment wheat ears in field conditions. FCNN increases the accuracy of wheat ears counting by accurately segmenting wheat ears in a complex background. Then, we build a regression convolutional neural network (RCNN) to count wheat ears based on the segmentation results of FCNN. In RCNN, we propose a new activation function positive rectification linear unit (PrLU) to process the last layer of the fully connected layer, so that RCNN can effectively count the number of wheat ears in the image. Finally, a counting strategy is proposed to count the number of wheat ears in the original image. To verify the counting performance of SSRNet, we compare the counting result of SSRNet with the real value of manual statistics. The results show that the average accuracy (Acc), $R^{2}$ , and root mean squared error (RMSE) of the SSRNet count results on the test set in this article are 0.980, 0.996, and 9.437, respectively. It can be seen from the results that our proposed method can accurately count wheat ears in field conditions. At the same time, the counting time (0.11 s) shows that SSRNet can quickly estimate the number of wheat ears in field conditions. We concluded that this study can provide important technical support for the high-throughput field wheat ears counting task in large-scale phenotyping work. [ABSTRACT FROM AUTHOR]

Timely and accurate rice spatial distribution maps play a vital role in food security and social stability. Early-season rice mapping is of great significance for yield estimation, crop insurance, and national food policymaking. Taking Tongjiang City in Heilongjiang Province with strong spatial heterogeneity as study area, a hierarchical K-Means binary automatic rice classification method based on phenological feature optimization (PFO-HKMAR) is proposed, using Google Earth Engine platform and Sentinel-1/2, and Landsat 7/8 data. First, a SAR backscattering intensity time series is reconstructed and used to construct and optimize polarization characteristics. A new SAR index named VH-sum is built, which is defined as the summation of VH backscattering intensity for specific time periods based on the temporal changes in VH polarization characteristics of different land cover types. Then comes feature selection, optimization, and reconstruction of optical data. Finally, the PFO-HKMAR classification method is established based on Simple Non-Iterative Clustering. PFO-HKMAR can achieve early-season rice mapping one month before harvest, with overall accuracy, Kappa, and F1 score reaching 0.9114, 0.8240 and 0.9120, respectively (F1 score is greater than 0.9). Compared with the two crop distribution datasets in Northeast China and ARM-SARFS, overall accuracy, Kappa, and F1 scores of PFO-HKMAR are improved by 0.0507–0.1957, 0.1029–0.3945, and 0.0611–0.1791, respectively. The results show that PFO-HKMAR can be promoted in Northeast China to enable early-season rice mapping, and provide valuable and timely information to different stakeholders and decision makers. [ABSTRACT FROM AUTHOR]

Timely and accurately monitoring leaf nitrogen content (LNC) is essential for evaluating crop nutrition status. Currently, Unmanned Aerial Vehicles (UAV) imagery is becoming a potentially powerful tool of assessing crop nitrogen status in fields, but most of crop nitrogen estimates based on UAV remote sensing usually use single type imagery, the fusion information from different types of imagery has rarely been considered. In this study, the fusion images were firstly made from the simultaneously acquired digital RGB and multi-spectral images from UAV at three growth stages of rice, and then couple the selecting methods of optimal features with machine learning algorithms for the fusion images to estimate LNC in rice. Results showed that the combination with different types of features could improve the models' accuracy effectively, the combined inputs with bands, vegetation indices (VIs) and Grey Level Co-occurrence Matrices (GLCMs) have the better performance. The LNC estimation of using fusion images was improved more obviously than multispectral those, and there was the best estimation at jointing stage based on Lasso Regression (LR), with R2 of 0.66 and RMSE of 11.96%. Gaussian Process Regression (GPR) algorithm used in combination with one feature-screening method of Minimum Redundancy Maximum Correlation (mRMR) for the fusion images, showed the better improvement to LNC estimation, with R2 of 0.68 and RMSE of 11.45%. It indicates that the information fusion from UAV multi-sensor imagery can significantly improve crop LNC estimates and the combination with multiple types of features also has a great potential for evaluating LNC in crops. [ABSTRACT FROM AUTHOR]

Accurate and rapid estimation of the crop yield is essential to precision agriculture. Critical to crop improvement, yield is a primary index for selecting excellent genotypes in crop breeding. Recently developed unmanned aerial vehicle (UAV) platforms and advanced algorithms can provide powerful tools for plant breeders. Genotype category information such as the maturity group information (M) can significantly influence soybean yield estimation using remote sensing data. The objective of this study was to improve soybean yield prediction by combining M with UAV-based multi-sensor data using machine learning methods. We investigated three types of maturity groups (Early, Median and Late) of soybean, and collected the UAV-based hyperspectral and red–green–blue (RGB) images at three key growth stages. Vegetation indices (VI) and texture features (Te) were extracted and combined with M to predict yield using partial least square regression (PLSR), Gaussian process regression (GPR), random forest regression (RFR) and kernel ridge regression (KRR). The results showed that (1) the method of combining M with remote sensing data could significantly improve the estimation performances of soybean yield. (2) The combinations of three variables (VI, Te and M) gave the best estimation accuracy. Meanwhile, the flowering stage was the optimal single time point for yield estimation (R2 = 0.689, RMSE = 408.099 kg/hm2), while using multiple growth stages produced the best estimation performance (R2 = 0.700, RMSE = 400.946 kg/hm2). (3) By comparing the models constructed by different algorithms for different growth stages, it showed that the models built by GPR showed the best performances. Overall, the results of this study provide insights into soybean yield estimation based on UAV remote sensing data and maturity information. [ABSTRACT FROM AUTHOR]

Generating spatial crop yield information is of great significance for academic research and guiding agricultural policy. Existing public yield datasets have a coarse spatial resolution, spanning from 1 to 43 km. Although these datasets are useful for analyzing large-scale temporal and spatial change in yield, they cannot deal with small-scale spatial heterogeneity, which happens to be the most significant characteristic of the Chinese farmers' economy. Hence, we generated a 30 m Chinese winter wheat yield dataset (ChinaWheatYield30m) for major winter-wheat-producing provinces in China for the period 2016–2021 with a semi-mechanistic model (hierarchical linear model, HLM). The yield prediction model was built by considering the wheat growth status and climatic factors. It can estimate wheat yield with excellent accuracy and low cost using a combination of satellite observations and regional meteorological information (i.e., Landsat 8, Sentinel 2 and ERA5 data from the Google Earth Engine (GEE) platform). The results were validated using in situ measurements and census statistics and indicated a stable performance of the HLM based on calibration datasets across China, with a correlation coefficient (r) of 0.81 and a relative root mean square error (rRMSE) of 12.59 %. With regards to validation, the ChinaWheatYield30m dataset was highly consistent with in situ measurement data and statistical data (p<0.01), indicated by an r (rRMSE) of 0.72** (15.34 %) and 0.69** (19.16 %). The ChinaWheatYield30m is a sophisticated dataset with both high spatial resolution and excellent accuracy; such a dataset will provide basic knowledge of detailed wheat yield distribution, which can be applied for many purposes including crop production modeling and regional climate evaluation. The ChinaWheatYield30m dataset generated from this study can be downloaded from 10.5281/zenodo.7360753 (Zhao et al., 2022b). [ABSTRACT FROM AUTHOR]

• The averaged dry stem mass content (C sm) is defined. • The crop leaf dry matter content (C m), height (C h), and density (C d) were considered. • C sm , C h , and C d improved vertically growing crop stem AGB (VGC-AGB) estimation. • The VGC-AGB = LAI C m + C d C h C sm did not saturate at medium-to-high crop covers. The accurate estimation of crop above-ground biomass (AGB) can assist in crop growth monitoring and grain yield prediction. Remote sensing has been widely used for AGB estimation at regional and local scales in recent years. However, optical remote sensing spectral indices (SIs) become saturated at medium-to-high crop covers. The combined use of remote sensing techniques and statistical regression models is not based on an understanding of how crop leaves and vertical organs contribute to the crop AGB. This causes difficulties in measuring the biomass stored in vertical organs (e.g., plant stem, wheat-spike, maize-tassel; abbreviated as AGB v) using optical remote sensing. This study aims to develop an unmanned aerial vehicle (UAV)-based vertically growing crop AGB (VGC-AGB) model. We defined C sm (g/m) to describe the crop stem and reproductive organs' average dry mass content. This was done to improve the estimation of AGB v. The crop leaf area index (LAI, m2/m2), leaf dry matter content (C m , g/m2), height (C h , m), and density (C d , m−2) were used in the VGC-AGB. The VGC-AGB calculated crop leaf AGB (AGB l) using LAI × C m (g/m2) and AGB v using C d × C h × C sm (g/m2). The proposed VGC-AGB (AGB = LAI × C m + C d × C h × C sm) was verified using field and UAV-based hyperspectral datasets of winter-wheat and summer-maize at three growth stages. Our results indicate that UAV-based VGC-AGB (R 2 = 0.92–0.93, RMSE = 68.82–75.15 g/m2) is superior to the statistical regression model that is based on remote sensing SIs and CSMs (R 2 = 0.77, RMSE = 134.94 g/m2). The results indicate that the UAV-based VGC-AGB supports the analysis of crop photosynthetic product transfers and high-performance UAV-based high-performance non-destructive AGB monitoring. [ABSTRACT FROM AUTHOR]

The resulting maps of land use classification obtained by pixel-based methods often have salt-and-pepper noise, which usually shows a certain degree of cluttered distribution of classification image elements within the region. This paper carries out a study on crop classification and identification based on time series Sentinel images and object-oriented methods and takes the crop recognition and classification of the National Modern Agricultural Industrial Park in Jalaid Banner, Inner Mongolia, as the research object. It uses the Google Earth Engine (GEE) cloud platform to extract time series Sentinel satellite radar and optical remote sensing images combined with simple noniterative clustering (SNIC) multiscale segmentation with random forest (RF) and support vector machine (SVM) classification algorithms to classify and identify major regional crops based on radar and spectral features. Compared with the pixel-based method, the combination of SNIC multiscale segmentation and random forest classification based on time series radar and optical remote sensing images can effectively reduce the salt-and-pepper phenomenon in classification and improve crop classification accuracy with the highest accuracy of 98.66 and a kappa coefficient of 0.9823. This study provides a reference for large-scale crop identification and classification work. [ABSTRACT FROM AUTHOR]

LNC (leaf nitrogen content) in crops is significant for diagnosing the crop growth status and guiding fertilization decisions. Currently, UAV (unmanned aerial vehicles) remote sensing has played an important role in estimating the nitrogen nutrition of crops at the field scale. However, many existing methods of evaluating crop nitrogen based on UAV imaging techniques usually have used a single type of imagery such as RGB or multispectral images, seldom considering the usage of information fusion from different types of UAV imagery for assessing the crop nitrogen status. In this study, GS (Gram–Schmidt Pan Sharpening) was utilized to fuse images from two sensors of digital RGB and multispectral cameras mounted on UAV, and the specific bands of the multispectral cameras are blue, green, red, rededge and NIR. The color space transformation method, HSV (Hue-Saturation-Value), was used to separate soil background noise from crops due to the high spatial resolution of UAV images. Two methods of optimizing feature variables, the Successive Projection Algorithm (SPA) and the Competitive Adaptive Reweighted Sampling method (CARS), combined with two regularization regression algorithms, LASSO and RIDGE, were adopted to estimate the LNC, compared to the commonly used Random Forest algorithm. The results showed that: (1) the accuracy of LNC estimation using the fusion image is improved distinctly by a comparison to the original multispectral image; (2) the denoised images performed better than the original multispectral images in evaluating LNC in rice; (3) the RIDGE-SPA combined method, using SPA to select the MCARI, SAVI and OSAVI, had the best performance for LNC in rice, with an R2 of 0.76 and an RMSE of 10.33%. It can be demonstrated that the information fusion of multiple-sensor imagery from UAV coupling with the methods of optimizing feature variables can estimate the rice LNC more effectively, which can also provide a reference for guiding the decision making of fertilization in rice fields. [ABSTRACT FROM AUTHOR]

The estimation of physicochemical crop parameters based on spectral indices depend strongly on planting year, cultivar, and growing period. Therefore, the efficient monitoring of crop growth and nitrogen (N) fertilizer treatment requires that we develop a generic spectral index that allows the rapid assessment of the plant nitrogen content (PNC) of crops and that is independent of year, cultivar, and growing period. Thus, to obtain the best indicator for estimating potato PNC, herein, we provide an in-depth comparative analysis of the use of hyperspectral single-band reflectance and two- and three-band spectral indices of arbitrary bands for estimating potato PNC over several years and for different cultivars and growth periods. Potato field trials under different N treatments were conducted over the years 2018 and 2019. An unmanned aerial vehicle hyperspectral remote sensing platform was used to acquire canopy reflectance data at several key potato growth periods, and six spectral transformation techniques and 12 arbitrary band combinations were constructed. From these, optimal single-, two-, and three-dimensional spectral indices were selected. Finally, each optimal spectral index was used to estimate potato PNC under different scenarios and the results were systematically evaluated based on a correlation analysis and univariate linear modeling. The results show that, although the spectral transformation technique strengthens the correlation between spectral information and potato PNC, the PNC estimation model constructed based on single-band reflectance is of limited accuracy and stability. In contrast, the optimal three-band spectral index TBI 5 (530,734,514) performs optimally, with coefficients of determination of 0.67 and 0.65, root mean square errors of 0.39 and 0.39, and normalized root mean square errors of 12.64% and 12.17% for the calibration and validation datasets, respectively. The results thus provide a reference for the rapid and efficient monitoring of PNC in large potato fields. [ABSTRACT FROM AUTHOR]

Crop failure detection using UAV images is helpful for precision agriculture, enabling the precision management of failure areas to reduce crop loss. For wheat failure area detection at the seedling stage using UAV images, the commonly used methods are not sufficiently accurate. Thus, herein, a new tool for precision wheat management at the seedling stage is designed. For this purpose, field experiments with two wheat cultivars and four nitrogen (N) treatments were conducted to create different scenarios for the failure area, and multispectral UAV images were acquired at the seedling growth stage. Based on the above data, a new failure detection method was designed by assimilating prior knowledge and a filter analysis strategy and compared with classical filter-based methods and Hough transform-based methods for wheat failure area detection. The results showed that the newly proposed assimilation method had a detection accuracy between 83.86% and 97.67% for different N levels and cultivars. In contrast, the filter-based methods and Hough transform-based methods had detection accuracies between 53.73% and 83.95% and between 20.71% and 75.79%, respectively. Thus, the assimilation method demonstrated the best failure detection performance. • An assimilation method was proposed for wheat failure area detection. • Different methods were compared for wheat failure area detection. • Wheat failure area can be detected using unmanned aerial vehicles imagery. [ABSTRACT FROM AUTHOR]

Crop production is one of the major interactions between humans and the natural environment, in the process, carbon is translocated cyclically inside the ecosystem. Data assimilation algorithm has advantages in mechanism and robustness in yield estimation, however, the computational efficiency is still a major obstacle for widespread application. To address the issue, a novel hybrid method based on the combination of the Crop Biomass Algorithm of Wheat (CBA-Wheat) to the Simple Algorithm For Yield (SAFY) model and the transfer learning method was proposed in this paper, which enables winter wheat yield estimation with acceptable accuracy and calculation efficiency. The transfer learning techniques learn the knowledge from the SAFY model and then use the knowledge to predict wheat yield. The main results showed that: (1) The comparison using CBA-Wheat between measured AGB and predicted AGB all reveal a good correlation with R2 of 0.83 and RMSE of 1.91 t ha−1, respectively; (2) The performance of yield prediction was as follows: transfer learning method (R2 of 0.64, RMSE of 1.05 t ha−1) and data assimilation (R2 of 0.64, RMSE of 1.01 t ha−1). At the farm scale, the two yield estimation models are still similar in performance with RMSE of 1.33 t ha−1 for data assimilation and 1.13 t ha−1 for transfer learning; (3) The time consumption of transfer learning with complete simulation data set is significantly lower than that of the other two yield estimation tests. The number of pixels to be simulated was about 16,000, and the computational efficiency of the data assimilation algorithm and transfer learning without complete simulation datasets. The transfer learning model shows great potential in improving the efficiency of production estimation. [ABSTRACT FROM AUTHOR]

Above-ground biomass (AGB) is an important indicator for monitoring crop growth and plays a vital role in guiding agricultural management, so it must be determined rapidly and nondestructively. The present study investigated the extraction from UAV hyperspectral images of multiple variables, including canopy original spectra (COS), first-derivative spectra (FDS), vegetation indices (VIs), and crop height (CH) to estimate the potato AGB via the machine-learning methods of support vector machine (SVM), random forest (RF), and Gaussian process regression (GPR). High-density point clouds were combined with three-dimensional spatial information from ground control points by using structures from motion technology to generate a digital surface model (DSM) of the test field, following which CH was extracted based on the DSM. Feature bands in sensitive spectral regions of COS and FDS were automatically identified by using a Gaussian process regression-band analysis tool that analyzed the correlation of the COS and FDS with the AGB in each growth period. In addition, the 16 Vis were separately analyzed for correlation with the AGB of each growth period to identify highly correlated Vis and excluded highly autocorrelated variables. The three machine-learning methods were used to estimate the potato AGB at each growth period and their results were compared separately based on the COS, FDS, VIs, and combinations thereof with CH. The results showed that (i) the correlations of COS, FDS, and VIs with AGB all gradually improved when going from the tuber-formation stage to the tuber-growth stage and thereafter deteriorated. The VIs were most strongly correlated with the AGB, followed by FDS, and then by COS. (ii) The CH extracted from the DSM was consistent with the measured CH. (iii) For each growth stage, the accuracy of the AGB estimates produced by a given machine-learning method depended on the combination of model variables used (VIs, FDS, COS, and CH). (iv) For any given set of model variables, GPR produced the best AGB estimates in each growth period, followed by RF, and finally by SVM. (v) The most accurate AGB estimate was achieved in the tuber-growth stage and was produced by combining spectral information and CH and applying the GPR method. The results of this study thus reveal that UAV hyperspectral images can be used to extract CH and crop-canopy spectral information, which can be used with GPR to accurately estimate potato AGB and thereby accurately monitor crop growth. [ABSTRACT FROM AUTHOR]

Aboveground biomass (AGB) is an important indicator to evaluate crop growth, which is closely related to yield and plays an important role in guiding fine agricultural management. Compared with traditional AGB measurements, unmanned aerial vehicle (UAV) hyperspectral remote sensing technology has the advantages of being non-destructive, highly mobile, and highly efficient in precision agriculture. Therefore, this study uses a hyperspectral sensor carried by a UAV to obtain hyperspectral images of potatoes in stages of tuber formation, tuber growth, starch storage, and maturity. Linear regression, partial least squares regression (PLSR), and random forest (RF) based on vegetation indices (Vis), green-edge parameters (GEPs), and combinations thereof are used to evaluate the accuracy of potato AGB estimates in the four growth stages. The results show that (i) the selected VIs and optimal GEPs correlate significantly with AGB. Overall, VIs correlate more strongly with AGB than do GEPs. (ii) AGB estimates made by linear regression based on the optimal VIs, optimal GEPs, and combinations thereof gradually improve in going from the tuber-formation to the tuber-growth stage and then gradually worsen in going from the starch-storage to the maturity stage. Combining the optimal GEPs with the optimal VIs produces the best estimates, followed by using the optimal VIs alone, and using the optimal GEPs produces the worst estimates. (iii) Compared with the single-parameter model, which uses the PLSR and RF methods based on VIs, the combination of VIs with the optimal GEPs significantly improves the estimation accuracy, which gradually improves in going from the tuber-formation to the tuber-growth stage, and then gradually deteriorates in going from the starch-storage to the maturity stage. The combination of VIs with the optimal GEPs produces the most accurate estimates. (iv) The PLSR method is better than the RF method for estimating AGB in each growth period. Therefore, combining the optimal GEPs and VIs and using the PLSR method improves the accuracy of AGB estimates, thereby allowing for non-destructive dynamic monitoring of potato growth. [ABSTRACT FROM AUTHOR]

Aboveground biomass (AGB) is an important indicator for crop-growth monitoring and yield prediction, and accurate monitoring of AGB is beneficial to agricultural fertilization management and optimization of planting patterns. Imaging spectrometer sensors mounted on unmanned aerial vehicle (UAV) remote-sensing platforms have become an important technical method for monitoring AGB because the method is convenient, rapidly collects data and provides image data with high spatial and spectral resolution. To confirm the feasibility of UAV hyperspectral remote-sensing technology to estimate AGB, this study acquired hyperspectral images and measured AGB data over the potato bud, tuber formation, tuber growth, and starch-storage periods. The canopy spectrum obtained in each growth period was smoothed by using the Savitzky–Golay filtering method, and the spectral-reflection feature parameters, spectral-location feature parameters, and vegetation indexes were extracted. First, a Pearson correlation analysis was performed between the three types of characteristic spectral parameters and AGB, and the spectral parameters that reached a significant level of 0.01 in each growth period were selected. Next, the spectral parameters reaching a significance of 0.01 were optimized and screened by moving window partial least squares (MWPLS), Monte Carlo uninformative variable elimination (MC-UVE), and random frog (RF) methods, and the final model parameters were determined according to the thresholds of the root mean square error of cross-validation (RMSEcv), the reliability index, and the selected probability. Finally, the three optimal characteristic spectral parameters and their combinations were used to estimate the potato AGB in each growth period by combining the partial least squares regression (PLSR) and Gaussian process regression (GPR) methods. The results show that, (i) ranked from high to low, vegetation indexes, spectral-location feature parameters, and spectral-reflection feature parameters in each growth period are correlated with the AGB, and these correlations all first improve and then degrade in going from the budding period to the starch-storage period. (ii) The AGB estimation model based on the characteristic variables screened by the three methods in each growth period is most accurate with RF, less so with MC-UVE, and least accurate with MWPLS. (iii) Estimating the AGB with the same variables combined with the PLSR method in each growth period is more accurate than the corresponding GPR method, but the estimations produced by the two methods both show a trend of first improving and then worsening from the budding period to the starch-accumulation period. The accuracy of the estimation models constructed by PLSR and GPR from high to low is based on comprehensive variables, vegetation indexes, spectral-location feature parameters and spectral-reflection feature parameters. (iv) When combined with the RF-PLSR method to estimate AGB in each growth period, the best R2 values are 0.65, 0.68, 0.72, and 0.67, the corresponding RMSE values are 167.76, 162.98, 160.77, and 169.24 kg/hm2, and the corresponding NRMSE values are 19.76%, 16.01%, 15.04%, and 16.84%. The results of this study show that a variety of characteristic spectral parameters may be extracted from UAV hyperspectral images, that the RF method may be used for optimizing and screening, and that PLSR regression provides accurate estimates of the potato AGB. The proposed approach thus provides a rapid, accurate, and nondestructive way to monitor the growth status of potatoes. [ABSTRACT FROM AUTHOR]

Crop yields are important for food security and people's living standards, and it is therefore very important to predict the yield in a timely manner. This study used different vegetation indices and red-edge parameters calculated based on the canopy reflectance obtained from near-surface hyperspectral data and UAV hyperspectral data and used the partial least squares regression (PLSR) and artificial neural network (ANN) methods to estimate the yield of winter wheat at different growth stages. Verification was performed based on these two types of hyperspectral remote sensing data and the yield was estimated using vegetation indices and a combination of vegetation indices and red-edge parameters as the modeling independent variables, respectively, using PLSR and ANN regression, respectively. The results showed that, for the same data source, the optimal vegetation index for estimating the yield was the same in all of the studied growth stages; however, the optimal red-edge parameters were different for different growth stages. Compared with using only the vegetation indices as the modeling factor to estimate yield, the combination of the vegetation indices and red-edge parameters obtained superior estimation results. Additionally, the accuracy of yield estimation was shown to be improved by using the PLSR and ANN methods, with the yield estimation model constructed using the PLSR method having a better prediction effect. Moreover, the yield prediction model obtained using the near-surface hyperspectral sensors had a higher fitting and accuracy than the model obtained using the UAV hyperspectral remote sensing data (the results were based on the specific growth stressors, N and water supply). This study shows that the use of a combination of vegetation indices and red-edge parameters achieved an improved yield estimation compared to the use of vegetation indices alone. In the future, the selection of suitable sensors and methods needs to be considered when constructing models to estimate crop yield. [ABSTRACT FROM AUTHOR]

Grain protein content (GPC) is an important indicator of nutritional quality of rice. In this study, nitrogen fertilization experiments were conducted to monitor GPC for high-quality Indica rice varieties Meixiangzhan 2 (V1) and Wufengyou 615 (V2) in 2019 and 2020. Three types of parameters, including photosynthetic sensitive vegetation indices (VIs), canopy leaf area index (LAI), and crop plant nitrogen accumulation (PNA), obtained from UAV hyperspectral images were used to estimate rice GPC. Two-dimensional and three-dimensional GPC indices were constructed by combining any two of the three types of parameters and all three, respectively, based on the Euclidean distance method. The R2 and RMSE of the two-dimensional GPC index model for variety V1 at the tillering stage were 0.81 and 0.40% for modeling and 0.95 and 0.38% for validation, and 0.91 and 0.27% for modeling and 0.83 and 0.36% for validation for variety V2. The three-dimensional GPC index model for variety V1 had R2 and RMSE of 0.86 and 0.34% for modeling and 0.78 and 0.45% for validation, and 0.97 and 0.17% for modeling and 0.96 and 0.17% for validation for variety V2 at the panicle initiation stage. At the heading stage, the R2 and RMSE of the three-dimensional model for variety V1 were 0.92 and 0.26% for modeling and 0.91 and 0.37% for validation, and 0.96 and 0.20% for modeling and 0.99 and 0.15% for validation for variety V2. These results demonstrate that the GPC monitoring models incorporating multiple crop growth parameters based on Euclidean distance can improve GPC estimation accuracy and have the potential for field-scale GPC monitoring. [ABSTRACT FROM AUTHOR]

Although crop-growth monitoring is important for agricultural managers, it has always been a difficult research topic. However, unmanned aerial vehicles (UAVs) equipped with RGB and hyperspectral cameras can now acquire high-resolution remote-sensing images, which facilitates and accelerates such monitoring. To explore the effect of monitoring a single crop-growth indicator and multiple indicators, this study combines six growth indicators (plant nitrogen content, above-ground biomass, plant water content, chlorophyll, leaf area index, and plant height) into the new comprehensive growth index (CGI). We investigate the performance of RGB imagery and hyperspectral data for monitoring crop growth based on multi-time estimation of the CGI. The CGI is estimated from the vegetation indices based on UAV hyperspectral data treated by linear, nonlinear, and multiple linear regression (MLR), partial least squares (PLSR), and random forest (RF). The results are as follows: (1) The RGB-imagery indices red reflectance (r), the excess-red index (EXR), the vegetation atmospherically resistant index (VARI), and the modified green-red vegetation index (MGRVI), as well as the spectral indices consisting of the linear combination index (LCI), the modified simple ratio index (MSR), the simple ratio vegetation index (SR), and the normalized difference vegetation index (NDVI), are more strongly correlated with the CGI than a single growth-monitoring indicator. (2) The CGI estimation model is constructed by comparing a single RGB-imagery index and a spectral index, and the optimal RGB-imagery index corresponding to each of the four growth stages in order is r, r, r, EXR; the optimal spectral index is LCI for all four growth stages. (3) The MLR, PLSR, and RF methods are used to estimate the CGI. The MLR method produces the best estimates. (4) Finally, the CGI is more accurately estimated using the UAV hyperspectral indices than using the RGB-image indices. [ABSTRACT FROM AUTHOR]