Huang Yuhua, Xiong Juntao, Yao Zhaoshen, Huang Qiyin, Tang Kun, Jiang Dandan, Yang Zhengang
College of Mathematics and Informatics, South China Agricultural University, Guangzhou, China.
J Sci Food Agric. 2024 Aug 30;104(11):6615-6625. doi: 10.1002/jsfa.13486. Epub 2024 Apr 2.
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.
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.
番茄品质的视觉分级受到表皮光滑、光照不均以及难以识别的隐形缺陷等问题的极大影响。实现采后表皮缺陷的智能检测有助于进一步提高采后番茄的经济价值。
设计了一种利用荧光技术的图像采集装置，用于获取番茄表皮缺陷数据集，包括腐烂缺陷、裂纹缺陷和不易察觉的缺陷等类别。通过引入卷积块注意力模块并用可切换空洞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模型的精确率、召回率和平均精度均值分别为89.93%、82.33%和87.57%，高于YOLOv5m、Faster R-CNN和YOLOv7，且平均检测时间减少了47.04毫秒/图片。
本研究利用荧光成像和改进的YOLOv5m模型检测番茄表皮缺陷，能够更好地识别不易察觉的缺陷并检测多种类别的缺陷。这为番茄的智能检测和品质分级提供了有力的技术支持。© 2024化学工业协会。
