The segmentation of plant disease images enables researchers to quantify the proportion of disease spots on leaves, known as disease severity. Current deep learning methods predominantly focus on single diseases, simple lesions, or laboratory-controlled environments. In this study, we established and publicly released image datasets of field scenarios for three diseases: soybean bacterial blight (SBB), wheat stripe rust (WSR), and cedar apple rust (CAR). We developed Plant Disease Segmentation Networks (PDSNets) based on LinkNet with ResNet-18 as the encoder, including three versions: ×1.0, ×0.75, and ×0.5. The ×1.0 version incorporates a 4 × 4 embedding layer to enhance prediction speed, while versions ×0.75 and ×0.5 are lightweight variants with reduced channel numbers within the same architecture. Their parameter counts are 11.53 M, 6.50 M, and 2.90 M, respectively. PDSNetx0.5 achieved an overall F1 score of 91.96%, an Intersection over Union (IoU) of 85.85% for segmentation, and a coefficient of determination (R²) of 0.908 for severity estimation. On a local central processing unit (CPU), PDSNetx0.5 demonstrated a prediction speed of 34.18 images (640 × 640 pixels) per second, which is 2.66 times faster than LinkNet. Our work provides an efficient and automated approach for assessing plant disease severity in field scenarios.

Nitrogen content is a crucial index for crop growth diagnosis and the exact estimation of nitrogen content is of great significance for grasping crop growth status in real-time. This paper takes winter wheat as the study object and the precision agriculture demonstration area of the Jiaozuo Academy of Agricultural and Forestry Sciences in Henan Province as the research area. The hyperspectral reflectance data of the wheat canopy in different growth periods are obtained with the ASD ground object hyperspectral instrument, and the original canopy spectral data are preprocessed by fractional differential and continuous wavelet transform; then, the vegetation indices are established, correlation analysis with nitrogen content is conducted, and the fractional differential spectra are selected; finally, based on the wavelet energy coefficient and the vegetation indices with strong correlations, the methods of support vector machine (SVM), ridge regression, stepwise regression, Gaussian process regression (GPR), and the BP neural network are used to construct the estimation model for nitrogen content in wheat at different growth stages. By adopting the ${\mathrm{R}}^2$ and root mean square error (RMSE) indices, the best nitrogen content estimation model at every growth stage is selected. The overall analysis of the nitrogen content estimation effect indicated that for the four growth periods, the maximum modeling and validation ${\mathrm{R}}^2$ of the nitrogen content estimation models of the SVM, ridge regression, stepwise regression, GPR, and BP neural network models reached 0.95 and 0.93, the average reached 0.76 and 0.71, and the overall estimation effect was good. The average values of the modeling and validation ${\mathrm{R}}^2$ of the nitrogen content estimation model at the flag picking stage were 0.85 and 0.81, respectively, which were 37.10% and 44.64%, 1.19% and 3.85%, and 14.86% and 17.39% higher than those at the jointing stage, flowering stage, and filling stage, respectively. Therefore, the model of the flag picking stage has higher estimation accuracy and a better estimation effect on the nitrogen content. For the different growth stages, the optimal estimation models of nitrogen content were different. Among them, continuous wavelet transform combined with the BP neural network model can be the most effective method for estimating the N content in wheat at the flagging stage. The paper provides an effective method for estimating the nitrogen content in wheat and a new idea for crop growth monitoring.

A rapid and accurate determination of large-scale winter wheat yield is significant for food security and policy formulation. In this study, meteorological data and enhanced vegetation index (EVI) were used to estimate the winter wheat yield in Henan Province, China, by constructing a deep learning model. The deep learning model combines CNN feature extraction and makes full use of the sequence data processing capability of the LSTM and a multi-head attention mechanism to develop a novel CNN–MALSTM estimation model, which can capture the information of input sequences in different feature subspaces to enhance the expressiveness of the model. A CNN–LSTM baseline model was also constructed for comparison. Compared with the baseline model (R² = 0.75, RMSE = 646.53 kg/ha, and MAPE = 8.82%), the proposed CNN–MALSTM model (R² = 0.79, RMSE = 576.01 kg/ha, MAPE = 7.29%) could more accurately estimate the yield. Based on the cross-validation with one year of left-out data and the input of the fertility period by fertility period to explore the sensitivity of the model to data from different fertility periods to the final yield, an annual yield distribution map of Henan Province was constructed. Through cross-validation, the stability of the model in different years was assessed. The results showed that the model could obtain the best prediction of the yield approximately 20 days in advance. In terms of the spatial distribution of the yield in Henan Province on a yearly basis, the estimated yield showed an overall uptrend from west to east, consistent with the trend in the statistical yearbook of the yield for Henan Province. Thus, it can be concluded that the proposed CNN–MALSTM model can provide stable yield estimation results.

The transformation of resource-exhausted urban land is an urgent problem for sustainable urban development in the world today. Obtaining the urban land use type and analyzing the changes in their land use can lead to better management of the relationship between economic development and resource utilization. In this paper, a residual-intelligent module network was proposed to solve the problems of low classification accuracy and missing objects edge information in traditional computer classification methods. The classification of four Landsat-TM/OLI images from 1993–2020 for Jiaozuo city (the first batch of resource-exhausted cities in China) was realized by this method. The results (overall accuracy was 98.61%, in 2020 images) were better than the comparison models (support vector machine, 2D-convolutional neural network, hybrid convolution networks; overall accuracy was 87.12%, 96.16%, 98.46%, respectively) and effectively reduced the loss of information on the edge of the ground objects. On this basis, six main land use types were constructed by combining field surveys and other methods. The characteristics and driving forces of spatial-temporal change in land use were explored from the aspect of social, economic and policy factors. The results showed that from 1993 to 2020 the cultivated land, forest land, water body and other land types in the study area decreased by 690.97 km², 57.54 km², 47.04 km² and 59.43 km², respectively. The construction land and bare land increased by 839.38 km² and 15.57 km², respectively. The transfer of land use types was mainly from cultivated land to construction land, with a cumulative conversion of 920.95 km² within 27 years. The driving forces of land use in the study area were analyzed by principal component analysis (PCA) and regression analysis. The spatial-temporal evolution of land use types was affected by policy changes, the level of social development and the adjustment in the economy, industry and agriculture structure. The investment in fixed assets and per capita net income in rural areas were the top two influencing factors and their cumulative contribution rate was 94.62%. The findings of this study can provide scientific reference and theoretical support for land use planning, land reclamation in mining areas, ecological protection and sustainable development in Jiaozuo and other resource-exhausted cities in the world.

Accurate and rapid access to crop distribution information is a significant requirement for the development of modern agriculture. Improving the efficiency of remote sensing monitoring of winter wheat planting area information, a new method of automatically updating training samples (AUTS), is proposed herein. Firstly, based on the Google Earth Engine (GEE) platform, a Sentinel-2 image with a spatial resolution of 10 m was selected to extract the distribution map of winter wheat in the city of Shijiazhuang in 2017. Secondly, combined with the NDVI time series, the weighted correlation coefficients from 2017, 2018, and 2019 were calculated. Then, the 2017 winter wheat distribution map and its most significant relevant areas were used to extract sample points from 2018 and 2019 automatically. Finally, the distribution map of winter wheat in Shijiazhuang in 2018 and 2019 was generated. In addition, to test the applicability of the automatically updating training sample at different scales and regions, the proposed method was applied to Landsat 8 image data with a spatial resolution of 30 m, as well as to Handan and Baoding. The results showed that the calculated winter wheat planting area is comparable with the officially published statistics, based on Sentinel-2, extracting three years of winter wheat, the R² values for all three years were above 0.95. The R² values for 2018 and 2019, based on Landsat 8 extractions, were 0.95 and 0.90, respectively. The R² values extracted from Handan and Baoding in 2018 were 0.94 and 0.86, respectively. These results indicate that the proposed method has high accuracy and can provide technical support and reference for winter wheat area monitoring and yield estimation.