山西农业大学学报（自然科学版）2025，Vol.45Issue(4)：46-56,11.DOI:10.13842/j.cnki.issn1671-8151.202503051
基于Fert-YOLO的高粱育性检测模型研究
Research on a sorghum fertility detection model based on Fert-YOLO
摘要
Abstract
[Objective]As an important crop for both food and energy production,sorghum's fertility detection is crucial for vari-ety breeding and yield improvement.However,traditional detection methods suffer from low efficiency due to complex field backgrounds,necessitating highly efficient and accurate detection technologies.[Method]This study proposed Fert-YOLO,a lightweight detection model for sorghum fertility,based on YOLOv8n.First,multiple offline data augmentation methods were used to enhance data diversity and improve the model's generalization ability.Second,to reduce network complexity while ef-fectively improving detection performance,StarNet was used to replace YOLOv8n's backbone feature extraction network.In the feature fusion stage,the C2F module was redesigned by incorporating mixed local channel attention(MLCA)mechanism,strengthening the network's ability to capture critical features.Finally,a lightweight shared convolution detection(LSCD)head was introduced,which shared convolutional layer parameters to significantly reduce model size and complexity.[Results]The Fert-YOLO model demonstrated outstanding performance in sorghum fertility detection.Compared to the original YO-LOv8n model,it achieved a 1.5%improvement in mean average precision(Map0.5),further enhancing detection accuracy.Ad-ditionally,the model's,floating-point operations per second(FLOPs)and parameters were reduced by 40.0%and 47.8%,respectively,significantly improving inference speed and deployment efficiency.When compared to other common single-stage lightweight detection models,Fert-YOLO showed clear advantages in both detection accuracy and model efficiency.[Conclu-sion]This research provided a reliable technical support for efficient sorghum fertility detection in field conditions,contributing significantly to smart sorghum breeding and precision agriculture.关键词
高粱/育性检测/YOLOv8n/模型优化Key words
Sorghum/Fertility detection/YOLOv8n/Model optimization分类
农业科技引用本文复制引用
赵泽阳,段有厚,卢峰,柯福来,朱凯,杨琳琳,张飞..基于Fert-YOLO的高粱育性检测模型研究[J].山西农业大学学报（自然科学版）,2025,45(4):46-56,11.基金项目
国家现代农业产业技术体系高粱栽培岗位(CARS-06-13.5-A22) （CARS-06-13.5-A22）
国家现代农业产业技术体系辽宁高粱创新团队项目 （）
辽宁省藏粮于技重大专项(2023020405-JH1/102) （2023020405-JH1/102）
沈阳种业创新性专项(24-215-2-02) （24-215-2-02）
