A fluorescence detection method for postharvest tomato epidermal defects based on improved YOLOv5m.

Abstract

Tomato quality visual grading is greatly affected by the problems of smooth skin, uneven illumination and invisible defects that are difficult to identify. The realization of intelligent detection of postharvest epidermal defects is conducive to further improving the economic value of postharvest tomatoes. An image acquisition device that utilizes fluorescence technology has been designed to capture a dataset of tomato skin defects, encompassing categories such as rot defects, crack defects and imperceptible defects. The YOLOv5m model was improved by introducing Convolutional Block Attention Module and replacing part of the convolution kernels in the backbone network with Switchable Atrous Convolution. The results of comparison experiments and ablation experiments show that the Precision, Recall and mean Average Precision of the improved YOLOv5m model were 89.93%, 82.33% and 87.57%, which are higher than YOLOv5m, Faster R-CNN and YOLOv7, and the average detection time was reduced by 47.04 ms picture-1. The present study utilizes fluorescence imaging and an improved YOLOv5m model to detect tomato epidermal defects, resulting in better identification of imperceptible defects and detection of multiple categories of defects. This provides strong technical support for intelligent detection and quality grading of tomatoes. © 2024 Society of Chemical Industry.