An improved YOLOv5-based method for tomato fruit identification and yield estimation
YANG Jian
YANG Xiaozhi
XIONG Chuan
LIU Li
Abstract：In pursuit of intelligent real-time yield estimation for tomatoes in greenhouse environments,we introduce an enhanced YOLOv5 tomato recognition algorithm aimed at the statistical assessment and estimation of tomato fruit yield in their natural growth conditions.Our approach involved two key enhancements:firstly,we substituted the final layer of the backbone network with a Separable Vision Transformer to augment the connectivity between the backbone network and global context,thereby facilitating tomato feature extraction;secondly,we incorporated the WIOU loss function and employ the Mish activation function to enhance convergence speed and accuracy.Experimental findings demonstrate that the improved detection model achieves a mAP score of 99.5％,reflecting 1.1 percentage points enhancement compared to the conventional YOLOv5 model,and the processing time for every image is 15ms.Furthermore,the improved YOLOv5 algorithm exhibits superior recognition performance for densely populated and occluded tomato fruits.
Keywords：Greenhouse tomatoYOLOv5Attention mechanismLoss function
Publication Date：2024-06-05
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：8( 61-68 )