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Design of Shallow Neural Network Based Plant Disease Detection System

Author

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  • Fatai O. Sunmola

    (Federal University of Technology, Nigeria)

  • Olaide A. Agbolade

    (Federal University of Technology, Nigeria)

Abstract

— In this work, we proposed the use of a shallow neural network for plant disease detection. The study focuses on four major diseases that are known to attack some of the most cultivated crops globally. The diseases considered include Bacterial Blight, Anthracnose, Cercospora leaf spot and Alternaria Alternata. In developing the disease detection model, K-means algorithm was used for plant segmentation while color co-occurrence method was used for feature analysis. A shallow neural network trained on 145 training samples was used as a classifier. The detection accuracy of 98.34 %, 98.48%, 98.03% and 98.14% were recorded for Bacterial Blight, Anthracnose, Cercospora leaf spot and Alternaria Alternata diseases respectively. The overall detection accuracy of the model is 98.25%.

Suggested Citation

  • Fatai O. Sunmola & Olaide A. Agbolade, 2021. "Design of Shallow Neural Network Based Plant Disease Detection System," European Journal of Electrical Engineering and Computer Science, European Open Science, vol. 5(4), pages 5-9, July.
    • Handle: RePEc:epw:ejece0:v:5:y:2021:i:4:id:19337
    DOI: 10.24018/ejece.2021.5.4.337
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