基于改进YOLOv5的番茄品质识别技术  

Tomato quality recognition technology based on improved YOLOv5

作　　者：吴秀君[1] 丁琼[1] WU Xiujun;DING Qiong(Urumqi Vocational University,Urumqi Xinjiang 830002,China)

机构地区：[1]乌鲁木齐职业大学,新疆乌鲁木齐830002

出　　处：《农业工程》2025年第3期34-40,共7页AGRICULTURAL ENGINEERING

摘　　要：为提高番茄品质识别的精度与速度,提出一种基于改进YOLOv5的识别方法。以YOLOv5为基础识别框架,通过Bottleneck Transformer(BoT3)模块替换YOLOv5中CSPDarknet53网络的空间卷积模块,从而提高网络计算速度;引入注意力机制增强YOLOv5的特征表达能力,并使用SIoU损失函数替换YOLOv5的CIoU损失函数;最后,利用改进YOLOv5对番茄品质进行识别。结果表明,所提方法对红色和绿色番茄品质的平均检测精确率、召回率、全类平均精度分别达到90.11%、95.21%和95.10%,平均检测速度129帧/s;相较于SSD、CNN和VGG,改进模型具有明显的识别优势。研究表明,该改进方法可提高番茄等农作物品质的识别精度和识别速度。To improve accuracy and speed of tomato quality recognition,a recognition method based on an improved YOLOv5 was proposed.Method was based on YOLOv5 as a recognition framework.It replaced spatial convolution module of CSPDarknet53 network in YOLOv5 with Bottleneck Transformer(BoT3)module to improve network computing speed.Attention mechanism to enhance feature expression ability of YOLOv5 was introduced,and replacing CIoU loss function of YOLOv5 with SIoU.Finally,improved YOLOv5 was used to identify tomato quality.Results showed that proposed method achieved an average recognition accuracy,recall,and accuracy of 90.11%,95.21%and 95.10%for red and green tomatoes'quality,with an average detection speed of 129 frames per second.Compared with SSD,CNN and VGG,improved YOLOv5 had obvious recognition advantages.It was proved that improved model could improve recognition accuracy and speed of crop quality of such as tomatoes.

关 键 词：番茄 品质识别 YOLOv5 识别精度 识别速度 注意力机制 

分 类 号：S24[农业科学—农业电气化与自动化]