A novel technique predicting the rice leaf diseases using convolutional neural network

A. V. Subba Rao; G. Jagadeeswar Reddy; V. Madhuri; A. Venkata Srinivasa Rao

Author(s)

A. V. Subba Rao Newton's Institute of Engineering,Macherla, Andhra Pradesh,India

G. Jagadeeswar Reddy Newton's Institute of Engineering,Macherla, Andhra Pradesh,India

V. Madhuri Newton's Institute of Engineering,Macherla, Andhra Pradesh,India

A. Venkata Srinivasa Rao Elecotronic and Communication Engineering, Sasi Institute of Technology and Engineering,Tadepalligudem,Andhra Pradesh,India

Abstract

Various ailments affect rice, a staple crop in India, across different stages of its growth. Identification of these diseases manually poses a significant challenge, especially for farmers lacking in-depth knowledge. Recently, there's been promising advancement in deep learning research through automated picture identification systems employing Convolutional Neural Network (CNN) models. To tackle the scarcity of rice leaf disease image datasets, we developed a deep learning model using Transfer Learning on a limited dataset. Our approach leverages VGG-16 to train and evaluate the proposed CNN architecture, drawing from rice field and internet datasets. Impressively, the model achieves a 95 percent accuracy rate. Key terms in this study include Deep Learning, Convolutional Neural Network (CNN), fine-tuning, and rice leaf diseases.

Publication Details

Page(s) 232-240

DOI DOI not available

Published Journal: ARPN Journal of Engineering and Applied Sciences, Volume: 19, Issue: 4, Year: 2024

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Keywords

Deep learning, CNN, leaf disease

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