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.

翻译：大豆和棉花是许多国家农业部门的重要经济作物，既能带来可观的经济回报，也长期面临自生植株和杂草的持续挑战，这些因素阻碍了可持续管理。有效控制自生植株和杂草需要先进的识别策略，以在复杂的作物冠层中准确辨识目标。尽管深度学习方法在叶片级检测与分割任务中已展现出良好效果，但现有数据集往往难以反映真实农田环境的复杂性。为此，我们从商业化农场采集了640张涵盖多个生长阶段、杂草压力及光照变化的高分辨率图像。每张图像均在叶片实例级别进行标注，通过边界框和分割掩码标记了7,221片大豆叶片与5,190片棉花叶片，完整呈现了叶片重叠、小尺寸叶片及形态相似性等特征。我们使用YOLOv11对该数据集进行验证，结果表明其在重叠叶片精确识别与分割方面达到先进水平。本公开数据集可支持选择性除草剂喷洒与病虫害监测等高级应用，并为大豆-棉花种植管理提供更鲁棒的数据驱动策略。