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Simulation Tools and Techniques. 13th EAI International Conference, SIMUtools 2021, Virtual Event, November 5-6, 2021, Proceedings

Research Article

A Crop Disease Recognition Algorithm Based on Machine Learning

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  • @INPROCEEDINGS{10.1007/978-3-030-97124-3_38,
        author={Yuchao Zhou and Kailiang Zhang and Yi Shi and Ping Cui},
        title={A Crop Disease Recognition Algorithm Based on Machine Learning},
        proceedings={Simulation Tools and Techniques. 13th EAI International Conference, SIMUtools 2021, Virtual Event, November 5-6, 2021, Proceedings},
        proceedings_a={SIMUTOOLS},
        year={2022},
        month={3},
        keywords={Machine learning Crop disease recognition Support vector machine},
        doi={10.1007/978-3-030-97124-3_38}
    }
    
  • Yuchao Zhou
    Kailiang Zhang
    Yi Shi
    Ping Cui
    Year: 2022
    A Crop Disease Recognition Algorithm Based on Machine Learning
    SIMUTOOLS
    Springer
    DOI: 10.1007/978-3-030-97124-3_38
Yuchao Zhou1, Kailiang Zhang1,*, Yi Shi1, Ping Cui1
  • 1: Jiangsu Province Key Laboratory of Intelligent Industry Control Technology, Xuzhou University of Technology
*Contact email: zhangkailiang@xzit.edu.cn

Abstract

There are many related diseases in the process of crop planting, which reduces the quality and yield of crops. Faced with such a situation, the prevention of crop diseases has become a hot spot and has broad application prospects. This experiment uses the image recognition technology of machine vision to analyze and recognize crop diseases. Based on the features of machine vision that can capture details that cannot be observed by the human eye, with high accuracy and high efficiency, it provides accurate image recognition of crop diseases. In accordance with. In the process of selecting the SVM classifier for image classification, the kernel function and gamma parameters in the classifier were adjusted, and the kernel function and high accuracy parameter interval suitable for crop disease analysis were found.

Keywords
Machine learning Crop disease recognition Support vector machine
Published
2022-03-31
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-030-97124-3_38
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