Multi-Disease Detection and Classification in Paddy Using Deep Convolutional Neural Networks

Sornalakshmi K., Valadri Pardhavan Reddy, Ravipati Deepthi

2024

Abstract

The world is expecting an exponential growth in food production in the recent future. Rice, a staple food for a large part of the world's population, faces the threat of various diseases that can seriously affect the crop. The proposed solution uses advanced deep learning algorithms on images of paddy leaves to predict leaf diseases. Using data containing high-resolution images of healthy and diseased leaves, convolutional neural network (CNN) model was implemented to accurately identify the disease. Preprocessing is used to improve the quality of the image and remove features that hinder accurate classification. The system has been shown to be useful in diagnosing many types of foliar diseases, providing good results for early disease detection and good agronomic management. The Resnet-50, efficient net B3 architectures of Convolutional Neural Networks (CNNs), a specialized deep learning architecture, has been trained on diverse datasets containing images of healthy and diseased rice leaves for the diseases bacterial leaf blight, Hispa and brown spot. Once trained, these models can accurately classify diseases with up to 90% accuracy thereby supporting timely interventions, ultimately preventing extensive crop losses and fostering sustainable practices. In addition to this, deep learning's image recognition capabilities is also used in sorting and grading rice leaves based on various parameters such as size, color, and ripeness. A user interface using Streamlit is developed for uploading test images and the system would identify the diseases.

Paper Citation

in Harvard Style

K. S., Reddy V. and Deepthi R. (2024). Multi-Disease Detection and Classification in Paddy Using Deep Convolutional Neural Networks. In Proceedings of the 1st International Conference on Emerging Innovations for Sustainable Agriculture - Volume 1: ICEISA; ISBN 978-989-758-714-6, SciTePress, pages 32-39. DOI: 10.5220/0012881000004519

in Bibtex Style

@conference{iceisa24,
author={Sornalakshmi K. and Valadri Reddy and Ravipati Deepthi},
title={Multi-Disease Detection and Classification in Paddy Using Deep Convolutional Neural Networks},
booktitle={Proceedings of the 1st International Conference on Emerging Innovations for Sustainable Agriculture - Volume 1: ICEISA},
year={2024},
pages={32-39},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012881000004519},
isbn={978-989-758-714-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Emerging Innovations for Sustainable Agriculture - Volume 1: ICEISA
TI - Multi-Disease Detection and Classification in Paddy Using Deep Convolutional Neural Networks
SN - 978-989-758-714-6
AU - K. S.
AU - Reddy V.
AU - Deepthi R.
PY - 2024
SP - 32
EP - 39
DO - 10.5220/0012881000004519
PB - SciTePress