Cotton is a crucial crop in the global textile industry, with major production regions including China, India, and the United States. While smart agricultural mechanization technologies, such as automated irrigation and precision pesticide systems, have improved crop management, weeds remain a significant challenge. These weeds not only compete with cotton for nutrients but can also serve as hosts for diseases, affecting both cotton yield and quality. Existing weed detection models perform poorly in the complex environment of cotton fields, where the visual features of weeds and crops are similar and often overlap, resulting in low detection accuracy. Furthermore, real-time deployment on edge devices is difficult. To address these issues, this study proposes an improved lightweight weed detection model, YOLO-WL, based on the YOLOv8 architecture. The model leverages EfficientNet to reconstruct the backbone, reducing model complexity and enhancing detection speed. To compensate for any performance loss due to backbone simplification, CA (cross-attention) is introduced into the backbone, improving feature sensitivity. Finally, AFPN (Adaptive Feature Pyramid Network) and EMA (efficient multi-scale attention) mechanisms are integrated into the neck to further strengthen feature extraction and improve weed detection accuracy. At the same time, the model maintains a lightweight design suitable for deployment on edge devices. Experiments on the CottonWeedDet12 dataset show that the YOLO-WL model achieved an mAP of 92.30%, reduced the detection time per image by 75% to 1.9 ms, and decreased the number of parameters by 30.3%. After TensorRT optimization, the video inference time was reduced from 23.134 ms to 2.443 ms per frame, enabling real-time detection in practical agricultural environments.

The expertly rectified extraction of cropland parcel boundaries is a critical basis for precision agriculture deployment and land resource management. However, the scarcity of large-scale, high-quality, cross-regional reference datasets limits the generalizability of deep learning models. To address this data bottleneck, we created a standardized, multi-source cropland parcel boundary benchmark dataset. The collection, which covers Europe (Denmark, the Netherlands, Spain) and Asia (Central China), encompasses over 180,000 expert-rectified parcels, with average parcel sizes ranging from 0.05 to 10 hectares and spatial resolutions spanning 0.2 to 10 m. Based on this dataset, we evaluated nine representative deep learning models belonging to three architectural paradigms (edge detection, multi-task semantic segmentation, and instance segmentation) and analyzed the effects of spatial resolution, parcel scale, and topographic relief on extraction accuracy. The experimental results indicate the following: (1) Spatial resolution exerts a non-linear influence on boundary extraction fidelity, where some medium-resolution data conditions (such as 4.8 m imagery in large-scale fields) retained competitive boundary extraction performance for certain models. (2) Even small but regular parcels of merely 0.22 hectares can be extracted with high accuracy, achieving a maximum F1 score of 86.58%. (3) Topographic variations lead to contrasting model performance. In plain and hilly terrain, multi-task semantic segmentation models exhibit obvious advantages. BsiNet yields the highest F1 score of 86.58%, but this value drops to 36.56% in complex mountainous regions. By comparison, edge detection models perform better in mountainous areas, where RCF attains the maximum F1 of 70.85%. In summary, this study establishes a standardized, expertly refined reference parcel dataset and provides an empirical evaluation of how different model architectures perform across varied observational settings, offering an empirical baseline for future model development and evaluation in cropland boundary extraction.

Farmland changes have a profound impact on agricultural ecosystems and global food security, making the timely and accurate detection of these changes crucial. Remote sensing image change detection provides an effective tool for monitoring farmland dynamics, but existing methods often struggle with high-resolution images due to complex scenes and insufficient multi-scale information capture, particularly in terms of missed detections. Missed detections can lead to underestimating land changes, which affects key areas such as resource allocation, agricultural decision-making, and environmental management. Traditional CNN-based models are limited in extracting global contextual information. To address this, we propose a CNN-Transformer-based Multi-Scale Attention Siamese Network (MT-SiamNet), with a focus on reducing missed detections. The model first extracts multi-scale local features using a CNN, then aggregates global contextual information through a Transformer module, and incorporates an attention mechanism to increase focus on key change areas, thereby effectively reducing missed detections. Experimental results demonstrate that MT-SiamNet achieves superior performance across multiple change detection datasets. Specifically, our method achieves an F1 score of 65.48% on the HRSCD dataset and 75.02% on the CLCD dataset, significantly reducing missed detections and improving the reliability of farmland change detection, thereby providing strong support for agricultural decision-making and environmental management.