A Leaf-Level Dataset for Soybean-Cotton Detection and Segmentation
This dataset addresses the need for better recognition strategies to control volunteer plants and weeds in soybean-cotton farming, supporting applications like selective herbicide spraying, but it is incremental as it builds on existing deep learning methods with a new dataset.
The paper tackled the problem of detecting and segmenting soybean and cotton leaves in complex agricultural fields by introducing a new dataset of 640 high-resolution images with 12,411 annotated leaves, and validated it with YOLOv11 to achieve state-of-the-art performance.
Soybean and cotton are major drivers of many countries' agricultural sectors, offering substantial economic returns but also facing persistent challenges from volunteer plants and weeds that hamper sustainable management. Effectively controlling volunteer plants and weeds demands advanced recognition strategies that can identify these amidst complex crop canopies. While deep learning methods have demonstrated promising results for leaf-level detection and segmentation, existing datasets often fail to capture the complexity of real-world agricultural fields. To address this, we collected 640 high-resolution images from a commercial farm spanning multiple growth stages, weed pressures, and lighting variations. Each image is annotated at the leaf-instance level, with 7,221 soybean and 5,190 cotton leaves labeled via bounding boxes and segmentation masks, capturing overlapping foliage, small leaf size, and morphological similarities. We validate this dataset using YOLOv11, demonstrating state-of-the-art performance in accurately identifying and segmenting overlapping foliage. Our publicly available dataset supports advanced applications such as selective herbicide spraying and pest monitoring and can foster more robust, data-driven strategies for soybean-cotton management.