基于改良YOLOv5s的黄瓜霜霉病孢子检测

IF 7.7 Q1 AGRICULTURE, MULTIDISCIPLINARY
Chen Qiao , Kaiyu Li , Xinyi Zhu , Jiaping Jing , Wei Gao , Lingxian Zhang
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引用次数: 0

摘要

黄瓜霜霉病是由霜霉病孢子侵染叶片引起的。然而,黄瓜霜霉病的防治研究往往集中在叶片出现症状后的阶段,即病斑已经形成的阶段。由于霜霉病的发生与孢子数量密切相关,因此早期研究霜霉病孢子数量对黄瓜霜霉病的防治具有重要意义。因此,开发一种快速、准确、高效的黄瓜霜霉病孢子检测方法对推进黄瓜霜霉病防治具有重要意义。本文介绍了一种改进的YOLOv5s孢子检测模型。该模型将一个变压器模块集成到YOLOv5s的主干中,增强了全局特征信息的提取。它还增加了一个小型目标检测头,以应对YOLOv5s的广泛降采样和难以学习小目标的特征。与卷积块注意模块(CBAM)的集成进一步提高了对霉菌孢子等小物体的检测精度。通过显微镜收集的图像数据集进行评估后,改进的YOLOv5s模型在各种分辨率下都表现出卓越的性能指标。在1440px × 1440px的分辨率下,它达到了95.4%的最高平均平均精度([email protected]),精度(P)得分为89.1%,召回率(R)为90.3%。在相同的1440px × 1440px分辨率下,这些指标在[email protected]上比原始的YOLOv5s模型高出1.6%,在P上高出1.6%,在r上高出0.5%。此外,该模型在各种分辨率尺度上的[email protected]表明,与YOLOv7等其他领先模型相比,该模型的检测精度更高。在孢子小、背景复杂的显微图像下,改进的YOLOv5s模型能有效检测出黄瓜霜霉病孢子,为推进黄瓜霜霉病的防治措施提供了有价值的见解和技术支持。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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.
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来源期刊
Information Processing in Agriculture
Information Processing in Agriculture Agricultural and Biological Sciences-Animal Science and Zoology
CiteScore
21.10
自引率
0.00%
发文量
80
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
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