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GDE-YOLO:a robust and accurate method for real-time tea leaf disease detection in complex plantation environments 认领 引用
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作者 Lei GUO Hongbin CHEN +6 位作者 Zheming CUI Zhenhui LAI Weiliang HUANG Tianliang LIN Jufei WANG Shuhe ZHENG Wuxiong WENG 《ENGINEERING Agriculture》 CAS CSCD 2026年第4期23-44,共22页
To address the inherent problems of high labor costs and poor efficiency of current visual diagnosis methods for tea leaf diseases,this study proposes a GDE-YOLO-based real-time detection method for tea leaf diseases ... To address the inherent problems of high labor costs and poor efficiency of current visual diagnosis methods for tea leaf diseases,this study proposes a GDE-YOLO-based real-time detection method for tea leaf diseases identification in complex tea plantation environments.The proposed architecture integrates three key enhancements:(1)combination of the neck network with a global attention mechanism(GAM),(2)optimization of the C2f module through a diverse branch block(DBB),and(3)replacement of a complete intersection over union loss function with an efficient intersection over union loss function,collectively improving recognition accuracy and speed.Experimental validation demonstrates that GDE-YOLO achieved 91.7%precision(3.1%higher than YOLOv8n)across different tea plantation scenarios and disease types,with specific improvements of 0.7%for tea anthracnose and 12.4%for tea white spot detection.Also,the enhanced model attained 80 FPS real-time performance.The deployment test on the NVIDIA Jetson Orin Nano edge device showed that GDE-YOLO could achieve precise diseases identification with confidence threshold>0.8 and inference speed maintaining 18 FPS,satisfying edge computing requirements of accuracy and real-time performance.This research provides critical technical foundations for vision-guided precision sprayers in tea plantations,while promoting the practical implementation of machine vision in intelligent agricultural management. 展开更多
关键词 Attention mechanism deep learning precision agriculture tea leaf diseases YOLOv8
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