Design of Shallow Neural Network Based Plant Disease Detection System

Fatai O Sunmola,Olaide A Agbolade

doi:10.24018/ejece.2021.5.4.337

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%.

Highlights

Food security is an important problem in most nations of the world
We developed an artificial neural network based plant disease detection system that is able to detect certain diseases and provide early warning to the farmer
We demonstrate the capability of a shallow neural network when trained with limited training data

Summary

Introduction

Food security is an important problem in most nations of the world. Estimates show that up to 40% of annual global production of the top five most cultivated crops of wheat, rice, maize, potato and soybean is lost to disease and pest [1]. Tomato (Lycopersicon esculentum) production in Nigeria recorded a similar experience between 2016 and 2017 when Tuta Absoluta popularly known as Tomato leaf miner broke out in Northern Nigeria, leading to over 80% loss of all tomato plantation and more than 400% hike in the cost of tomato in the country [4]. Another source of concern is the rapid rate at which plant diseases tend to spread. The Spreading rate of the Uganda outbreak was estimated to be between 20 to 50 kilometer per year, initiating similar infection in neighboring countries like Sudan, Kenya, Tanzania and Congo sparking up yield losses of up to 95% in some of these countries [3]

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