{"title":"基于改良YOLOv5s的黄瓜霜霉病孢子检测","authors":"Chen Qiao , Kaiyu Li , Xinyi Zhu , Jiaping Jing , Wei Gao , Lingxian Zhang","doi":"10.1016/j.inpa.2024.05.002","DOIUrl":null,"url":null,"abstract":"<div><div>Cucumber downy mildew is caused by the infection of leaves with downy mildew spores. However, research on the prevention and control of cucumber downy mildew often focuses on the stage after symptoms have appeared on the leaves, that is, once disease spots have already formed. Since the occurrence of downy mildew is closely related to the quantity of spores, early-stage research on the quantity of downy mildew spores is of great significance for the prevention and control of cucumber downy mildew. Consequently, developing a rapid, accurate, and efficient method for detecting cucumber downy mildew spores is critical for advancing disease control. This study introduces an improved YOLOv5s model for spore detection. The model incorporates a transformer module into YOLOv5s’s backbone, enhancing global feature information extraction. It also adds a small object detection head to counter YOLOv5s’s extensive down-sampling and difficulty in learning features of small objects. Integration with the Convolutional Block Attention Module (CBAM) further refines detection precision for small objects like mildew spores. Upon evaluation with an image dataset collected through a microscope, the improved YOLOv5s model demonstrated superior performance metrics across various resolutions. At a resolution of 1440px × 1440px, it achieved the highest mean Average Precision ([email protected]) of 95.4 %, a precision (P) score of 89.1 %, and a recall (R) rate of 90.3 %. These metrics surpassed the original YOLOv5s model at the same 1440px × 1440px resolution by 1.6 % in [email protected], 1.6 % in P, and 0.5 % in R. Additionally, the model’s [email protected] across various resolution scales indicates superior detection precision compared to other leading models like YOLOv7. In the context of microscopic images with small spores and complex backgrounds, the improved YOLOv5s model effectively detects cucumber downy mildew spores, offering valuable insights and technical support for advancing the prevention and control measures against cucumber downy mildew.</div></div>","PeriodicalId":53443,"journal":{"name":"Information Processing in Agriculture","volume":"12 2","pages":"Pages 179-194"},"PeriodicalIF":7.7000,"publicationDate":"2024-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Detection of cucumber downy mildew spores based on improved YOLOv5s\",\"authors\":\"Chen Qiao , Kaiyu Li , Xinyi Zhu , Jiaping Jing , Wei Gao , Lingxian Zhang\",\"doi\":\"10.1016/j.inpa.2024.05.002\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Cucumber downy mildew is caused by the infection of leaves with downy mildew spores. However, research on the prevention and control of cucumber downy mildew often focuses on the stage after symptoms have appeared on the leaves, that is, once disease spots have already formed. Since the occurrence of downy mildew is closely related to the quantity of spores, early-stage research on the quantity of downy mildew spores is of great significance for the prevention and control of cucumber downy mildew. Consequently, developing a rapid, accurate, and efficient method for detecting cucumber downy mildew spores is critical for advancing disease control. This study introduces an improved YOLOv5s model for spore detection. The model incorporates a transformer module into YOLOv5s’s backbone, enhancing global feature information extraction. It also adds a small object detection head to counter YOLOv5s’s extensive down-sampling and difficulty in learning features of small objects. Integration with the Convolutional Block Attention Module (CBAM) further refines detection precision for small objects like mildew spores. Upon evaluation with an image dataset collected through a microscope, the improved YOLOv5s model demonstrated superior performance metrics across various resolutions. At a resolution of 1440px × 1440px, it achieved the highest mean Average Precision ([email protected]) of 95.4 %, a precision (P) score of 89.1 %, and a recall (R) rate of 90.3 %. These metrics surpassed the original YOLOv5s model at the same 1440px × 1440px resolution by 1.6 % in [email protected], 1.6 % in P, and 0.5 % in R. Additionally, the model’s [email protected] across various resolution scales indicates superior detection precision compared to other leading models like YOLOv7. In the context of microscopic images with small spores and complex backgrounds, the improved YOLOv5s model effectively detects cucumber downy mildew spores, offering valuable insights and technical support for advancing the prevention and control measures against cucumber downy mildew.</div></div>\",\"PeriodicalId\":53443,\"journal\":{\"name\":\"Information Processing in Agriculture\",\"volume\":\"12 2\",\"pages\":\"Pages 179-194\"},\"PeriodicalIF\":7.7000,\"publicationDate\":\"2024-05-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Information Processing in Agriculture\",\"FirstCategoryId\":\"1091\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2214317324000507\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"AGRICULTURE, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Information Processing in Agriculture","FirstCategoryId":"1091","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2214317324000507","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AGRICULTURE, MULTIDISCIPLINARY","Score":null,"Total":0}
Detection of cucumber downy mildew spores based on improved YOLOv5s
Cucumber downy mildew is caused by the infection of leaves with downy mildew spores. However, research on the prevention and control of cucumber downy mildew often focuses on the stage after symptoms have appeared on the leaves, that is, once disease spots have already formed. Since the occurrence of downy mildew is closely related to the quantity of spores, early-stage research on the quantity of downy mildew spores is of great significance for the prevention and control of cucumber downy mildew. Consequently, developing a rapid, accurate, and efficient method for detecting cucumber downy mildew spores is critical for advancing disease control. This study introduces an improved YOLOv5s model for spore detection. The model incorporates a transformer module into YOLOv5s’s backbone, enhancing global feature information extraction. It also adds a small object detection head to counter YOLOv5s’s extensive down-sampling and difficulty in learning features of small objects. Integration with the Convolutional Block Attention Module (CBAM) further refines detection precision for small objects like mildew spores. Upon evaluation with an image dataset collected through a microscope, the improved YOLOv5s model demonstrated superior performance metrics across various resolutions. At a resolution of 1440px × 1440px, it achieved the highest mean Average Precision ([email protected]) of 95.4 %, a precision (P) score of 89.1 %, and a recall (R) rate of 90.3 %. These metrics surpassed the original YOLOv5s model at the same 1440px × 1440px resolution by 1.6 % in [email protected], 1.6 % in P, and 0.5 % in R. Additionally, the model’s [email protected] across various resolution scales indicates superior detection precision compared to other leading models like YOLOv7. In the context of microscopic images with small spores and complex backgrounds, the improved YOLOv5s model effectively detects cucumber downy mildew spores, offering valuable insights and technical support for advancing the prevention and control measures against cucumber downy mildew.
期刊介绍:
Information Processing in Agriculture (IPA) was established in 2013 and it encourages the development towards a science and technology of information processing in agriculture, through the following aims: • Promote the use of knowledge and methods from the information processing technologies in the agriculture; • Illustrate the experiences and publications of the institutes, universities and government, and also the profitable technologies on agriculture; • Provide opportunities and platform for exchanging knowledge, strategies and experiences among the researchers in information processing worldwide; • Promote and encourage interactions among agriculture Scientists, Meteorologists, Biologists (Pathologists/Entomologists) with IT Professionals and other stakeholders to develop and implement methods, techniques, tools, and issues related to information processing technology in agriculture; • Create and promote expert groups for development of agro-meteorological databases, crop and livestock modelling and applications for development of crop performance based decision support system. Topics of interest include, but are not limited to: • Smart Sensor and Wireless Sensor Network • Remote Sensing • Simulation, Optimization, Modeling and Automatic Control • Decision Support Systems, Intelligent Systems and Artificial Intelligence • Computer Vision and Image Processing • Inspection and Traceability for Food Quality • Precision Agriculture and Intelligent Instrument • The Internet of Things and Cloud Computing • Big Data and Data Mining