Abstract
Cucumber disease seriously affects the yield and quality of cucumber planting, so quickly and accurately identifying the type of cucumber disease is the premise of cucumber disease control. In view of the complexity of feature extraction in existing cucumber disease recognition methods based on disease leaf image, and the vulnerability of extracted features to the diversity of disease leaf image, light and background, a cucumber disease recognition method based on depthwise separable convolutional network was proposed. This method can automatically obtain the classification features of the image of disease leaf, which overcomes the shortage of the existing crop disease recognition methods that need to extract the classification features manually, and the recognition rate has been greatly improved. The method was applied to the cucumber leaf disease data set and the average recognition accuracy reached 99.23%. The results show that this method has high recognition accuracy and can provide reference for other crops.
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Wang, X., Wang, Z., Zhang, S. (2020). Cucumber Disease Recognition Based on Depthwise Separable Convolution. In: Huang, DS., Bevilacqua, V., Hussain, A. (eds) Intelligent Computing Theories and Application. ICIC 2020. Lecture Notes in Computer Science(), vol 12463. Springer, Cham. https://doi.org/10.1007/978-3-030-60799-9_19
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DOI: https://doi.org/10.1007/978-3-030-60799-9_19
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