Authors

Abstract

In the process of agricultural intelligence, precise detection of plant organs serves as the foundation for core tasks such as crop phenotyping analysis and yield prediction. However, in complex field environments, small targets such as citrus flowers and shoots face challenges including scale variation, background interference, and dense occlusion, which severely impact detection accuracy. This study improves the YOLOv10 model by introducing the BAM (Bottleneck Attention Module) attention mechanism and GIoU (Generalized Intersection over Union) loss function, constructing a YOLOv10s-BAM-GIoU model suitable for citrus flower, fruit, and shoot recognition. The BAM attention mechanism enhances the model's feature extraction capability for small target organs under complex backgrounds through parallel channel and spatial attention branches; the GIoU loss function improves the localization accuracy of densely occluded targets by optimizing the geometric alignment between predicted and ground-truth boxes. Validation experiments were conducted on a self-constructed dataset. The experimental results show that the improved YOLOv10s achieves significant advantages in comprehensive detection accuracy, with an mAP50 of 89.1%, representing an improvement of 2.9%~9.5% over the original YOLOv10s and other comparative models. In fine-grained category detection, the model achieves mAP50 of 91.2%, 83.6%, and 92.5% for shoots, flowers, and fruits, respectively. Furthermore, while maintaining high detection accuracy, the model achieves a detection speed of 23.6 ms per frame, meeting real-time detection requirements. The research results demonstrate that the improved YOLOv10s model integrating the BAM attention mechanism and GIoU loss function achieves an optimal balance between accuracy and speed in citrus organ detection tasks, providing a preferred solution for field real-time detection systems.

Abstract in Chinese

在农业智能化进程中，植物器官精准检测是作物表型分析、产量预测等核心任务的基础。然而，田间复杂环境下柑橘花、梢等小目标存在尺度多变、背景干扰及密集遮挡等问题，严重影响检测精度。本研究基于YOLOv10模型，通过引入BAM注意力机制和GIoU损失函数对其进行改进，构建了适用于柑橘花果梢识别的YOLOv10s-BAM-GIoU模型。其中，BAM注意力机制通过通道与空间的并行注意力分支，增强模型对复杂背景下小目标器官的特征提取能力；GIoU损失函数则通过优化预测框与真实框的几何对齐，提升模型对密集遮挡目标的定位精度。在本研究自建数据集上进行验证，实验结果显示，改进后的YOLOv10s在综合检测精度上展现显著优势，其mAP50达到89.1%，较原始YOLOv10s及其他对比模型提升2.9%~9.5%。在细分类别检测中，该模型对梢（shoot）、花（flower）、果实（fruit）的mAP50分别达到91.2%、83.6%、92.5%。此外，模型在保持较高检测精度的同时，检测速度达到23.6ms/帧，满足实时检测需求。研究结果表明，融合BAM注意力机制与GIoU损失函数的改进YOLOv10s模型在柑橘器官检测任务中实现了精度与速度的最佳平衡，为田间实时检测系统提供了优选方案。