复杂大田场景下基于改进YOLOv8的小麦幼苗期叶片数快速检测方法  

A Rapid Detection Method for Wheat Seedling Leaf Number in Complex Field Scenarios Based on Improved YOLOv8

作　　者：侯依廷 饶元[1,2,3] 宋贺 聂振君[1,2,3] 王坦 何豪旭 HOU Yiting;RAO Yuan;SONG He;NIE Zhenjun;WANG Tan;HE Haoxu(College of Information and Artificial Intelligence,Anhui Agricultural University,Hefei 230036,China;Key Laboratory of Agricultural Sensors,Ministry of Agriculture and Rural Affairs,Hefei 230036,China;Key Laboratory of Smart Agriculture Technology and Equipment in Anhui Province,Hefei 230036,China;College of Agriculture,Anhui Agricultural University,Hefei 230036,China)

机构地区：[1]安徽农业大学信息与人工智能学院,安徽合肥230036 [2]农业农村部农业传感器重点实验室,安徽合肥230036 [3]智慧农业技术与装备安徽省重点实验室,安徽合肥230036 [4]安徽农业大学农学院,安徽合肥230036

出　　处：《智慧农业（中英文）》2024年第4期128-137,共10页Smart Agriculture

基　　金：国家自然科学基金项目(32371993);安徽省重点研究与开发计划项目(202204c06020026,2023n06020057);安徽省高校自然科学研究重大项目(2022AH040125,2023AH040135)。

摘　　要：[目的/意义]小麦叶片数是衡量植株生长状况、确定茎蘖动态、调节群体结构的重要指标之一。目前大田环境下小麦叶片计数主要依靠人工、耗时耗力,而现有的自动化检测计数方法的效率与精度难以满足实际应用需求。为提高小麦叶片数检测的准确性,设计了一种复杂大田环境下高效识别小麦叶尖的算法。[方法]本研究以手机和田间摄像头获取的可见光图像构建了两种典型光照条件下出苗期、分蘖期、越冬期等多个生长期的小麦叶片图像数据集。以YOLOv8为基础网络,融合坐标注意力机制降低背景环境的干扰,提高模型对小麦叶尖轮廓信息的提取能力;替换损失函数加快模型收敛速度;增加小目标检测层提高对小麦叶尖的识别效果,降低漏检率。设计了一种适用于叶尖小目标识别的深度学习网络,通过检测图像叶尖数量从而得出叶片数。[结果与讨论]本研究提出的方法对小麦叶尖的识别精确率和mAP0.5分别达到91.6%和85.1%,具有良好的检测效果。在复杂大田环境下该方法具有更好的适应能力,能够在不同光照条件下实现自适应检测,模型鲁棒性强。小麦幼苗期叶片检测漏检率低,说明该方法能够满足复杂大田场景下小麦叶尖识别的需求,提高了小麦叶片数检测的准确性。[结论]本研究可为复杂大田场景下小麦叶片数检测的研究提供参考,为小麦长势高质量评估提供技术支撑。[Objective]The enumeration of wheat leaves is an essential indicator for evaluating the vegetative state of wheat and predicting its yield potential.Currently,the process of wheat leaf counting in field settings is predominantly manual,characterized by being both time-consuming and labor-intensive.Despite advancements,the efficiency and accuracy of existing automated detection and counting methodologies have yet to satisfy the stringent demands of practical agricultural applications.This study aims to develop a method for the rapid quantification of wheat leaves to refine the precision of wheat leaf tip detection.[Methods]To enhance the accuracy of wheat leaf detection,firstly,an image dataset of wheat leaves across various developmental stages—seedling,tillering,and overwintering—under two distinct lighting conditions and using visible light images sourced from both mobile devices and field camera equipmen,was constructed.Considering the robust feature extraction and multi-scale feature fusion capabilities of YOLOv8 network,the foundational architecture of the proposed model was based on the YOLOv8,to which a coordinate attention mechanism has been integrated.To expedite the model's convergence,the loss functions were optimized.Furthermore,a dedicated small object detection layer was introduced to refine the recognition of wheat leaf tips,which were typically difficult for conventional models to discern due to their small size and resemblance to background elements.This deep learning network was named as YOLOv8-CSD,tailored for the recognition of small targets such as wheat leaf tips,ascertains the leaf count by detecting the number of leaf tips present within the image.A comparative analysis was conducted on the YOLOv8-CSD model in comparison with the original YOLOv8 and six other prominent network architectures,including Faster R-CNN,Mask R-CNN,YOLOv7,and SSD,within a uniform training framework,to evaluate the model's effectiveness.In parallel,the performance of both the original and YOLOv8-CSD models was

关 键 词：小麦叶片 叶尖识别 叶片计数 注意力机制 YOLOv8 深度学习 

分 类 号：S24[农业科学—农业电气化与自动化]