数字农科院2.0

Development of an enhanced hybrid attention YOLOv8s small object detection method for phenotypic analysis of root nodules

文献类型： 外文期刊

作者： Ya Zhao;Wen Zhang;Liangxiao Zhang;Xiaoqian Tang;Du Wang;Qi Zhang;Peiwu Li

作者机构：

关键词： Hybrid attention mechanism;Phenotypic characteristics;Root nodules;Small object detection;YOLOv8s

期刊名称： Artificial Intelligence in Agriculture

ISSN： 2589-7217

年卷期： 2025 年 16 卷 1 期

页码：

收录情况： SCIE（2025版） ; ; EI（2025版） ; ; CSCD（2025-2026年度） ; ; 农林核心（2024版）

摘要： Nodule formation and their involvement in biological nitrogen fixation are critical features of leguminous plants, with phenotypic characteristics closely linked to plant growth and nitrogen fixation efficiency. However, the phenotypic analysis of root nodules remains technically challenging due to their small size, weak texture, dense clustering, and occlusion. To address these challenges, this study constructed a scanner-based imaging platform and optimized data acquisition conditions for high-resolution, high-consistency root nodule images under field conditions. In addition, A hybrid small-object detection method, SCO-YOLOv8s, was proposed, integrating Swin Transformer and CBAM attention mechanisms into the YOLOv8s framework to enhance global and local feature representation. Furthermore, an Otsu segmentation-based post-processing module was incorporated to validate and refine detection results based on geometric features, boundary sharpness, and image entropy, effectively reducing false positives and enhancing robustness in complex scenes. Using this integrated approach, over 3375 nodules were identified from a single plant sample in under 1 min, with extracted phenotypic features such as diameter, color, and texture. A total of 10,879 high-quality annotated images were collected from 39 peanut varieties across 14 provinces and 31 soybean varieties across 12 provinces in China, addressing the current lack of large-scale datasets for legume root nodules. The SCO-YOLOv8s model achieved a precision of 97.29 %, a mAP of 98.23 %, and an overall identification accuracy of 95.83 %. This integrated approach provides a practical and scalable solution for high-throughput nodule phenotyping, and may contribute to a deeper understanding of nitrogen fixation mechanisms.

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