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

IF 3.3 2区农林科学 Q1 AGRICULTURE, MULTIDISCIPLINARY

Journal of the Science of Food and Agriculture Pub Date : 2024-03-24 DOI:10.1002/jsfa.13486

Yuhua Huang, Juntao Xiong, Zhaoshen Yao, Qiyin Huang, Kun Tang, Dandan Jiang, Zhengang Yang

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引用次数: 0

Abstract

BACKGROUND

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.

RESULTS

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.

CONCLUSION

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.

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基于改进型 YOLOv5m 的采后番茄表皮缺陷荧光检测方法。

背景：番茄品质目测分级受表皮光滑、光照不均、隐形缺陷难以识别等问题影响较大。实现采后表皮缺陷的智能检测，有利于进一步提高采后番茄的经济价值：结果：设计了一种利用荧光技术的图像采集装置，用于采集番茄表皮缺陷的数据集，包括腐烂缺陷、裂纹缺陷和不易察觉的缺陷等类别。然后，通过整合卷积块注意力模块并用可切换阿特柔斯卷积替换主干网络中的部分卷积核，对 YOLOv5m 模型进行了改进。对比实验和消融实验结果表明，改进后的 YOLOv5m 模型的精确度、召回率和平均精确度分别为 89.93 %、82.33 % 和 87.57 %，高于 YOLOv5m、Faster R-CNN 和 YOLOv7，平均检测时间缩短了 47.04 ms/pic：本文利用荧光成像技术和改进的 YOLOv5m 模型检测番茄表皮缺陷，能更好地识别不易察觉的缺陷，并能检测出多种类别的缺陷。这为番茄的智能检测和质量分级提供了强有力的技术支持。本文受版权保护。版权所有，不得转载。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of the Science of Food and Agriculture 工程技术-农业综合

CiteScore

8.10

自引率

4.90%

发文量

634

审稿时长

3.1 months

期刊介绍： The Journal of the Science of Food and Agriculture publishes peer-reviewed original research, reviews, mini-reviews, perspectives and spotlights in these areas, with particular emphasis on interdisciplinary studies at the agriculture/ food interface. Published for SCI by John Wiley & Sons Ltd. SCI (Society of Chemical Industry) is a unique international forum where science meets business on independent, impartial ground. Anyone can join and current Members include consumers, business people, environmentalists, industrialists, farmers, and researchers. The Society offers a chance to share information between sectors as diverse as food and agriculture, pharmaceuticals, biotechnology, materials, chemicals, environmental science and safety. As well as organising educational events, SCI awards a number of prestigious honours and scholarships each year, publishes peer-reviewed journals, and provides Members with news from their sectors in the respected magazine, Chemistry & Industry . Originally established in London in 1881 and in New York in 1894, SCI is a registered charity with Members in over 70 countries.