农业机械学报2025，Vol.56Issue(5)：130-140,11.DOI:10.6041/j.issn.1000-1298.2025.05.013
基于改进YOLO v8的轻量化棉铃识别模型与产量预测方法研究
Lightweight Cotton Boll Detection Model and Yield Prediction Method Based on Improved YOLO v8
摘要
Abstract
Cotton boll count is a critical phenotypic trait for estimating cotton yield and plays a vital role in precision agricultural management.However,accurately detecting cotton bolls in densely planted fields remained challenging due to complex backgrounds,occlusion,and varying illumination conditions.High-resolution UAV imagery was employed to capture cotton field scenes in a densely planted area of Xinjiang.A comprehensive dataset was developed through image segmentation and augmentation techniques,ensuring diverse representations of field conditions.To address the trade-off between detection accuracy and computational efficiency,an improved lightweight detection model IML-YOLO was proposed.The model integrated a novel GRGCE module that combined efficient ghost convolution with a RepGhostCSPELAN structure for feature extraction,a CAHSFPN feature fusion mechanism to enhance multi-scale representation,and a Focaler-MPDIoU loss function to refine localization accuracy.Extensive experiments demonstrated that IML-YOLO reduced computational complexity by 32.1％,decreased model size by 47.5％,and lowered parameter count by 50％compared with that of the baseline YOLO v8n,while boosting mean average precision by 10.1 percentage points.Furthermore,when applied to cotton yield prediction,the model achieved an average relative error of only 7.22％.These findings indicated that the proposed IML-YOLO model and yield prediction methodology can offer an effective solution for real-time cotton boll detection and significantly contribute to the advancement of intelligent cotton management.关键词
棉铃检测/产量预测/YOLO v8/特征融合/无人机遥感Key words
cotton boll detection/yield prediction/YOLO v8/feature fusion/UAV remote sensing分类
农业科技引用本文复制引用
刘祥,项若雪,班成龙,田敏,谭明天,黄凯文..基于改进YOLO v8的轻量化棉铃识别模型与产量预测方法研究[J].农业机械学报,2025,56(5):130-140,11.基金项目
新一代人工智能国家科技重大专项(2022ZD0115803)、国家重点研发计划项目(2022YFD2002400)和兵团科技攻关计划项目(2023AB014) （2022ZD0115803）
