Improved Neural Network based Plant Diseases Identification

TL;DR

This paper enhances neural network architecture for plant disease identification, achieving high accuracy and reducing overfitting by optimizing training algorithms and network parameters.

Contribution

It introduces an improved neural network model with optimized training algorithms and neuron configuration for accurate plant disease detection.

Findings

98.30% accuracy on general plant leaf disease

100% accuracy on specific plant leaf disease

Reduced overfitting through Bayesian regularization

Abstract

The agriculture sector is essential for every country because it provides a basic income to a large number of people and food as well, which is a fundamental requirement to survive on this planet. We see as time passes, significant changes come in the present era, which begins with Green Revolution. Due to improper knowledge of plant diseases, farmers use fertilizers in excess, which ultimately degrade the quality of food. Earlier farmers use experts to determine the type of plant disease, which was expensive and time-consuming. In today time, Image processing is used to recognize and catalog plant diseases using the lesion region of plant leaf, and there are different modus-operandi for plant disease scent from leaf using Neural Networks (NN), Support Vector Machine (SVM), and others. In this paper, we improving the architecture of the Neural Networking by working on ten different types of training algorithms and the proper choice of neurons in the concealed layer. Our proposed approach gives 98.30% accuracy on general plant leaf disease and 100% accuracy on specific plant leaf disease based on Bayesian regularization, automation of cluster and without over-fitting on considered plant diseases over various other implemented methods.