中国瓜菜2026，Vol.39Issue(4)：60-71,12.DOI:10.16861/j.cnki.zggc.2025.0567
基于YOLO-TM的番茄采摘机器人感知算法
Sensing algorithm for tomato harvesting robot based on YOLO-TM
摘要
Abstract
To address the issue of insufficient detection accuracy in tomato harvesting robot operations caused by factors such as inter-class and intra-class occlusion,complex lighting variations,and larger scale differences,this paper proposes a tomato maturity detection model based on YOLO-TM(YOLO-transformer for tomatoes maturity detection).First,a multi-head self-attention(MHSA)mechanism is introduced into the backbone network to enhance global feature extrac-tion and suppress background interference.Then,a bidirectional adaptive feature pyramid network(BAFPN)is construct-ed to improve multi-scale feature fusion capability.Furthermore,a Lbox regression loss function is designed to optimize the localization accuracy of small-scale tomato targets.The experimental results show that YOLO-TM achieves a mean aver-age precision(mAP)of 95.3%and an inference speed of 94.6 frames per second(FPS)on a self-collected tomato maturity detection dataset.Compared with the baseline model YOLOv11,YOLO-TM improves mAP by 4.2 percentage points,and achieves a field picking success rate of 94.0%.Compared with Faster R-CNN and other mainstream YOLO series models,YOLO-TM significantly improves detection accuracy while maintaining high real-time performance,demonstrating supe-rior adaptability in complex greenhouse environments.This study provides a strong theoretical basis and technical support for the automated precise picking of tomato and the development of visual perception systems for intelligent agricultural equipment.关键词
番茄/成熟度/YOLO-TM/损失函数/小尺度目标Key words
Tomato/Ripeness/YOLO-TM/Loss function/Small-scale target分类
农业科技引用本文复制引用
张文娟,胡海州,杨聪敏..基于YOLO-TM的番茄采摘机器人感知算法[J].中国瓜菜,2026,39(4):60-71,12.基金项目
国家自然科学基金(61903341) （61903341）
