During the processing and planting of soybeans, it is greatly significant that a reliable, rapid, and accurate technique is used to detect soybean varieties. Traditional chemical analysis methods of soybean variety sampling (e.g., mass spectrometry and high-performance liquid chromatography) are destructive and time-consuming. In this paper, a robust and accurate method for nondestructive soybean classification is developed through hyperspectral imaging and ensemble machine learning algorithms. Image acquisition, preprocessing, and feature selection are used to obtain different types of soybean hyperspectral features. Based on these features, one of ensemble classifiers-random subspace linear discriminant (RSLD) algorithm is used to classify soybean seeds. Compared with the linear discrimination (LD) and linear support vector machine (LSVM) methods, the results show that the RSLD algorithm in this paper is more stable and reliable. In classifying soybeans in 10, 15, 20, and 25 categories, the RSLD method achieves the highest classification accuracy. When 155 features are used to classify 15 types of soybeans, the classification accuracy of the RSLD method reaches 99.2%, while the classification accuracies of the LD and LSVM methods are only 98.6% and 69.7%, respectively. Therefore, the ensemble classification algorithm RSLD can maintain high classification accuracy when different types and different classification features are used.

Currently, the demand for refined crop monitoring through remote sensing is increasing rapidly. Due to the similar spectral and morphological characteristics of different crops and vegetation, traditional methods often rely on deeper neural networks to extract meaningful features. However, deeper networks face a key challenge: while extracting deep features, they often lose some boundary details and small-plot characteristics, leading to inaccurate farmland boundary classifications. To address this issue, we propose the Detail and Deep Feature Multi-Branch Fusion Network for High-Resolution Farmland Remote-Sensing Segmentation (DFBNet). DFBNet introduces an new three-branch structure based on the traditional UNet. This structure enhances the detail of ground objects, deep features across multiple scales, and boundary features. As a result, DFBNet effectively preserves the overall characteristics of farmland plots while retaining fine-grained ground object details and ensuring boundary continuity. In our experiments, DFBNet was compared with five traditional methods and demonstrated significant improvements in overall accuracy and boundary segmentation. On the Hi-CNA dataset, DFBNet achieved 88.34% accuracy, 89.41% pixel accuracy, and an IoU of 78.75%. On the Netherlands Agricultural Land Dataset, it achieved 90.63% accuracy, 91.6% pixel accuracy, and an IoU of 83.67%. These results highlight DFBNet’s ability to accurately delineate farmland boundaries, offering robust support for agricultural yield estimation and precision farming decision-making.

Traditional trophic state index assessment methods tend to weaken the recognition of interactions and joint risks among water quality factors, making it difficult to comprehensively capture the multivariate dependencies in complex aquatic environments. To overcome this limitation, this study coupled the Sparrow Search Algorithm-Deep Neural Network (SSA-DNN) and Copula function models, establishing an integrated framework from remote sensing-based water quality retrieval to eutrophication risk assessment. This approach achieves an organic integration of remote sensing-driven retrieval and multivariate modeling. The results show that, compared with traditional machine learning methods (Random Forest and Support Vector Regression) and empirical models, the SSA-DNN model achieved higher accuracy in water quality parameter retrieval, with testing set R² values of 0.81, 0.81, and 0.80 for chlorophyll-a (Chl-a), total phosphorus (TP), and total nitrogen (TN), respectively. Between 2021 and 2024, Chl-a, TP, and TN concentrations in Dianchi and Qilu Lakes remained high, highlighting severe eutrophication issues. Copula-based risk assessment further revealed that TN was the key factor influencing eutrophication risk probability, with the maximum risk probability increasing by up to 68 %. The proposed integrated framework enables effective monitoring of water quality status and provides scientific and systematic technical support for ecological risk assessment in typical plateau lake regions.