Jun Wang

Agricultural sustainability is becoming increasingly critical in the face of climate change and resource scarcity. This study presents an innovative method for maize yield estimation, integrating remote sensing data and machine learning techniques to promote sustainable agricultural development. By combining Sentinel-2 optical imagery and Sentinel-1 radar data, accurate maize classification masks were created, and the Weighted Least Squares (WLS) model achieved a coefficient of determination (R²) of 0.89 and a root mean square error (RMSE) of 12.8%. Additionally, this study demonstrates the significant role of water and fertilizer optimization in enhancing agricultural sustainability, with water usage reduced by up to 14.76% in Wuwei and 10.23% in Zhangye, and nitrogen application reduced by 5.5% and 8.5%, respectively, while maintaining stable yields. This integrated approach not only increases productivity and reduces resource waste, but it also promotes environmentally friendly and efficient resource use, supporting sustainable agriculture in water-scarce regions.

This study addresses the challenges of redundant crop identification features and low computational efficiency in complex agricultural environments, particularly in arid regions. Focusing on the Hexi region of Gansu Province, we utilized the Google Earth Engine (GEE) to integrate Sentinel-2 optical imagery (10 bands) and Sentinel-1 radar data (VV/VH polarization), constructing a 96-feature set that comprises spectral, vegetation index, red-edge, and texture variables. The recursive feature elimination random forest (RF-RFE) algorithm was employed for feature selection and model optimization. Key findings include: (1) Variables driven by spatiotemporal differentiation were effectively selected, with red-edge bands (B5–B7) during the grain-filling stage in August accounting for 56.7% of the top 30 features, which were closely correlated with canopy chlorophyll content (p < 0.01). (2) A breakthrough in lightweight modeling was achieved, reducing the number of features by 69%, enhancing computational efficiency by 62.5% (from 8 h to 3 h), and decreasing memory usage by 66.7% (from 12 GB to 4 GB), while maintaining classification accuracy (PA: 97.69%, UA: 97.20%, Kappa: 0.89). (3) Multi-source data fusion improved accuracy by 11.54% compared to optical-only schemes, demonstrating the compensatory role of radar in arid, cloudy regions. This study offers an interpretable and transferable lightweight framework for precision crop monitoring in arid zones.

Efficient and accurate weed detection in wheat fields is critical for precision agriculture to optimize crop yield and minimize herbicide usage. The dataset for weed detection in wheat fields was created, encompassing 5967 images across eight well-balanced weed categories, and it comprehensively covers the entire growth cycle of spring wheat as well as the associated weed species observed throughout this period. Based on this dataset, PMDNet, an improved object detection model built upon the YOLOv8 architecture, was introduced and optimized for wheat field weed detection tasks. PMDNet incorporates the Poly Kernel Inception Network (PKINet) as the backbone, the self-designed Multi-Scale Feature Pyramid Network (MSFPN) for multi-scale feature fusion, and Dynamic Head (DyHead) as the detection head, resulting in significant performance improvements. Compared to the baseline YOLOv8n model, PMDNet increased mAP@0.5 from 83.6% to 85.8% (+2.2%) and mAP@0.50:0.95 from 65.7% to 69.6% (+5.9%). Furthermore, PMDNet outperformed several classical single-stage and two-stage object detection models, achieving the highest precision (94.5%, 14.1% higher than Faster-RCNN) and mAP@0.5 (85.8%, 5.4% higher than RT-DETR-L). Under the stricter mAP@0.50:0.95 metric, PMDNet reached 69.6%, surpassing Faster-RCNN by 16.7% and RetinaNet by 13.1%. Real-world video detection tests further validated PMDNet’s practicality, achieving 87.7 FPS and demonstrating high precision in detecting weeds in complex backgrounds and small targets. These advancements highlight PMDNet’s potential for practical applications in precision agriculture, providing a robust solution for weed management and contributing to the development of sustainable farming practices.

Due to current global population growth, resource shortages, and climate change, traditional agricultural models face major challenges. Precision agriculture (PA), as a way to realize the accurate management and decision support of agricultural production processes using modern information technology, is becoming an effective method of solving these challenges. In particular, the combination of remote sensing technology and machine learning algorithms brings new possibilities for PA. However, there are relatively few comprehensive and systematic reviews on the integrated application of these two technologies. For this reason, this study conducts a systematic literature search using the Web of Science, Scopus, Google Scholar, and PubMed databases and analyzes the integrated application of remote sensing technology and machine learning algorithms in PA over the last 10 years. The study found that: (1) because of their varied characteristics, different types of remote sensing data exhibit significant differences in meeting the needs of PA, in which hyperspectral remote sensing is the most widely used method, accounting for more than 30% of the results. The application of UAV remote sensing offers the greatest potential, accounting for about 24% of data, and showing an upward trend. (2) Machine learning algorithms displays obvious advantages in promoting the development of PA, in which the support vector machine algorithm is the most widely used method, accounting for more than 20%, followed by random forest algorithm, accounting for about 18% of the methods used. In addition, this study also discusses the main challenges faced currently, such as the difficult problems regarding the acquisition and processing of high-quality remote sensing data, model interpretation, and generalization ability, and considers future development trends, such as promoting agricultural intelligence and automation, strengthening international cooperation and sharing, and the sustainable transformation of achievements. In summary, this study can provide new ideas and references for remote sensing combined with machine learning to promote the development of PA.

The frequent occurrence of global climate change and natural disasters highlights the importance of precision agricultural monitoring, yield forecasting, and early warning systems. The data assimilation method provides a new possibility to solve the problems of low accuracy of yield prediction, strong dependence on the field, and poor adaptability of the model in traditional agricultural applications. Therefore, this study makes a systematic literature retrieval based on Web of Science, Scopus, Google Scholar, and PubMed databases, introduces in detail the assimilation strategies based on many new remote sensing data sources, such as satellite constellation, UAV, ground observation stations, and mobile platforms, and compares and analyzes the progress of assimilation models such as compulsion method, model parameter method, state update method, and Bayesian paradigm method. The results show that: (1) the new remote sensing platform data assimilation shows significant advantages in precision agriculture, especially in emerging satellite constellation remote sensing and UAV data assimilation. (2) SWAP model is the most widely used in simulating crop growth, while Aquacrop, WOFOST, and APSIM models have great potential for application. (3) Sequential assimilation strategy is the most widely used algorithm in the field of agricultural data assimilation, especially the ensemble Kalman filter algorithm, and hierarchical Bayesian assimilation strategy is considered to be a promising method. (4) Leaf area index (LAI) is considered to be the most preferred assimilation variable, and the study of soil moisture (SM) and vegetation index (VIs) has also been strengthened. In addition, the quality, resolution, and applicability of assimilation data sources are the key bottlenecks that affect the application of data assimilation in the development of precision agriculture. In the future, the development of data assimilation models tends to be more refined, diversified, and integrated. To sum up, this study can provide a comprehensive reference for agricultural monitoring, yield prediction, and crop early warning by using the data assimilation model.