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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%.

Keywords: Neural Network, Plant disease detection, K-means algorithm, color co-occurrence method

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How to Cite
[1]
Sunmola, F.O. and Agbolade, O.A. 2021. Design of Shallow Neural Network Based Plant Disease Detection System. European Journal of Electrical Engineering and Computer Science. 5, 4 (Jul. 2021), 5-9. DOI:https://doi.org/10.24018/ejece.2021.5.4.337.