农业机械学报2025，Vol.56Issue(5)：433-442,10.DOI:10.6041/j.issn.1000-1298.2025.05.041
基于改进YOLO v8的复杂温室环境黄瓜果实分割方法
Improved YOLO v8 Method for Cucumber Fruit Segmentation in Complex Greenhouse Environments
摘要
Abstract
The detection and segmentation of cucumber fruits are crucial for phenotypic analysis and the management of cucumber growth.However,in complex greenhouse environments,fruits are often occluded by stems and leaves,and their color may be similar to the background,making it difficult for traditional methods to accurately identify fruit boundaries and achieve efficient segmentation.To address this issue,an improved YOLO v8-based method for cucumber fruit segmentation was proposed.This method incorporated deformable convolution network v4(DCNv4)to enhance the model's spatial adaptability and utilized the RepNCSPELAN4 module in combination with an additional C2F module to refine feature extraction and fusion,thereby improving the model's segmentation performance for cucumber fruit images in complex greenhouse environments.Experimental results showed outstanding performance across multiple categories in two experimental settings:a glass greenhouse and a plastic greenhouse.Specifically,in the glass greenhouse scenario,the model achieved a precision of 96.3％,recall of 93.1％,mean average precision(mAP50)of 96.2％,and mAP50-95 of 85.3％.In the plastic greenhouse scenario,the precision was 86.8％,recall was 81.9％,mAP50 was 90.0％,and mAP50-95 was 77.0％.The proposed method demonstrated stronger robustness and generalization in handling boundary issues,multiple occlusions,and multi-scale segmentation,enabling the model to adapt to diverse and complex cultivation environments and accurately segment cucumber fruits.Accurate fruit image segmentation facilitated the acquisition of phenotypic parameters and provides reliable technical support for further phenotypic analysis of cucumber fruits,thereby promoting the application of agricultural phenotyping robots and the intelligent development of agricultural production.关键词
黄瓜/果实遮挡/设施温室/图像分割/YOLO v8/可变形卷积Key words
cucumber/fruit occlusion/greenhouse/image segmentation/YOLO v8/deformable convolution分类
信息技术与安全科学引用本文复制引用
夏天,谢纯,李琳一,陆声链,钱婷婷..基于改进YOLO v8的复杂温室环境黄瓜果实分割方法[J].农业机械学报,2025,56(5):433-442,10.基金项目
上海市农业科技创新项目(2023-02-08-00-12-F04621)和国家自然科学基金项目(61762013) （2023-02-08-00-12-F04621）
