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Design of Shallow Neural Network Based Plant Disease Detection System

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  • Fatai O. Sunmola
  • Olaide A. Agbolade

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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    References listed on IDEAS

    as
    1. Olaide Ayodeji Agbolade & Fatai Olaoluwa Sunmola, 2021. "Cellular Internet of Things Based Power Monitoring System for Networking Devices," European Journal of Electrical Engineering and Computer Science, European Open Science, vol. 5(1), pages 80-84, January.
    2. Olaide Ayodeji Agbolade, 2020. "Vowels and Prosody Contribution in Neural Network Based Voice Conversion Algorithm with Noisy Training Data," European Journal of Engineering and Technology Research, European Open Science, vol. 5(3), pages 229-233, March.
    3. Christine L. Carroll & Colin A. Carter & Rachael E. Goodhue & C.-Y. Cynthia Lin Lawell, 2017. "Crop Disease and Agricultural Productivity," NBER Working Papers 23513, National Bureau of Economic Research, Inc.
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