现代信息科技2026，Vol.10Issue(17)：18-25,8.DOI:10.19850/j.cnki.2096-4706.2026.17.004
基于深度学习的番茄加工不合格原料智能识别系统
Intelligent Recognition System for Unqualified Raw Materials in Tomato Processing Based on Deep Learning
摘要
Abstract
Quality control of raw materials is crucial for ensuring the quality of tomato products.Traditional manual sorting is inefficient and its accuracy is easily affected by human factors,making it difficult to adapt to industrial large-scale production.Therefore,according to the actual needs of the tomato processing industry in Xinjiang,this study investigates the intelligent recognition of unqualified tomatoes based on deep learning and develops a supporting recognition system.This study constructs a dataset containing six types of tomato fruits through on-site photography and online collection,and expands it to 10,429 images through data augmentation.Four mainstream object detection models,namely Faster R-CNN,SSD,YOLOv8 and YOLOv13,are selected for comparative experiments,and YOLOv13 is finally determined as the optimal model.Experimental results show that the model achieves an mAP@0.5 of 94.8%,an inference speed of 32 FPS and a small object miss rate of 4.8%,and can accurately complete tomato localization,disease differentiation and qualification determination.In addition,a corresponding intelligent recognition system is developed to visualize the detection results,which supports four detection algorithms,namely YOLOv13,YOLOv8,Faster R-CNN and SSD,effectively replacing manual sorting and providing technical support and implementation solutions for intelligent quality control in the tomato processing industry.关键词
番茄加工/不合格原料/智能识别/深度学习/Faster R-CNN/SSD/YOLOv8/YOLOv13/目标检测Key words
tomato processing/unqualified raw materials/intelligent recognition/deep learning/Faster R-CNN/SSD/YOLOv8/YOLOv13/object detection分类
信息技术与安全科学引用本文复制引用
保婉婷,张云龙,王心恬,张雨静,孟小艳..基于深度学习的番茄加工不合格原料智能识别系统[J].现代信息科技,2026,10(17):18-25,8.基金项目
2025年新疆农业大学自治区级大学生创新训练计划项目(S202510758002) （S202510758002）
