构建了高精度大豆棉叶级检测数据集，助力田间杂草智能识别。
A Leaf-Level Dataset for Soybean-Cotton Detection and Segmentation
- 采集640张高清图像，标注7221个大豆叶与5190个棉叶实例。
- 在重叠叶片、小尺寸和形态相似等挑战下实现先进识别性能。
- 适合农业自动化、精准施药与病虫害监测研究者使用。
大豆和棉花是多国农业经济的重要支柱，虽带来可观收益，却长期受残留植株与杂草困扰，影响可持续管理。有效控制残留植株与杂草需先进识别技术，在复杂作物冠层中精准定位。尽管深度学习在叶级检测与分割上表现良好，现有数据集难以反映真实农田的复杂性。为此，我们从商业化农场采集640张高分辨率图像，覆盖多个生长阶段、杂草压力及光照变化。每张图像均进行叶实例级标注，共包含7,221个大豆叶和5,190个棉叶，通过边界框与分割掩码标注，涵盖重叠叶、小叶尺寸及形态相似性。利用YOLOv11验证该数据集，展现出先进的重叠叶识别与分割性能。本公开数据集可支持选择性除草剂喷洒、病虫害监测等应用，推动更鲁棒、数据驱动的大豆-棉田管理策略。
原文摘要 · Abstract (English)
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.
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