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• 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]

• 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]

• Existing crop N estimation mainly relies on the correlation of chlorophyll with N. • A comprehensive survey of N-related hyperspectral VIs was made. • Combined use of feature mining method and machine learning was crucial. • Synergy of crop model and hyperspectra could help monitoring of dynamic N status. • Multi-source data integration might reduce uncertainty in crop N status retrieval. Nitrogen (N) is significantly related to crop photosynthetic capacity. Over-and-under-application of N fertilizers not only limits crop productivity but also leads to negative environment impacts. With such a dilemma, a feasible solution is to match N supply with crop needs across time and space. Hyperspectral remote sensing has been gradually regarded as a cost-effective alternative to traditional destructive field sampling and laboratory testing for crop N status determination. Hyperspectral vegetation indices (VIs) and linear nonparametric regression have been the dominant techniques used to estimate crop N status. Machine learning algorithms have gradually exerted advantages in modelling the non-linear relationships between spectral data and crop N. Physically-based methods were rarely used due to the lack of radiative transfer models directly involving N. The existing crop N retrieval methods rely heavily on the relationship between chlorophyll and N. The underlying mechanisms of using protein as a proxy of N and crop protein retrieval from canopy hyperspectral data need further exploration. A comprehensive survey of the existing N-related hyperspectral VIs was made with the aim to provide guidance in VI selection for practical application. The combined use of feature mining and machine learning algorithms was emphasized in the overview. Some feature mining methods applied in the field of classification and chemometrics might be adapted for extracting crop N-related features. The deep learning algorithms need further exploration in crop N status assessment from canopy hyperspectral data. Finally, the major challenges and further development direction in crop N status assessment were discussed. The overview could provide a theoretical and technical support to promote applications of hyperspectral remote sensing in crop N status assessment. [ABSTRACT FROM AUTHOR]

• In orchards, acquiring the maximum data possible is essential for growers. • Propose a deep learning model to help growers measure and acquire crop data. • Construct a large apple tree detection, segmentation, and measurement dataset. • The proposed model achieved a high count accuracy of 94.1% and a high segmentation accuracy of 97.1%. • The average measurement accuracy for the proposed model is over 92%. Manual measurement and visual inspection is a common practice for acquiring crop data in orchards and is a labor-intensive, time-consuming, and costly task. Accurate and rapid acquisition of crop data is vital for monitoring the dynamics of tree growth and optimizing farm management. In this work, we present a technique for orchard data acquisition and analysis that uses remote imagery acquired from unmanned aerial vehicles (UAVs) combined with deep learning convolutional neural networks to automatically detect and segment individual trees and measure the crown width, perimeter, and crown projection area of apple trees. By using an UAV platform, 50 high-resolution images of apple trees were collected from an orchard during dormancy (bare branches), and then each apple tree was detected by using a Faster R-CNN object detector. Based on these results, each tree was segmented by using a U-Net deep learning network. After convex tree boundaries were extracted from the semantic segmentation results by using an efficient pruning strategy, the crown parameters were automatically calculated, and the accuracy was compared with that obtained by manual delineation. The results show that the proposed remote sensing technique can be used to detect and count apple trees with precision and recall of 91.1% and 94.1%, respectively, segment their branches with an overall accuracy of 97.1%, and estimate crown parameter with an overall accuracy exceeding 92%. We conclude that this method not only saves labor by avoiding field measurements but also allows growers to dynamically monitor the growth of orchard trees. [ABSTRACT FROM AUTHOR]

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

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

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

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

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