INTERNATIONAL JOURNAL OF SCIENTIFIC DEVELOPMENT AND RESEARCH International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2455-2631 | Impact factor: 8.15 | ESTD Year: 2016
open access , Peer-reviewed, and Refereed Journals, Impact factor 8.15
SHREEVATSA S
, P SAI CHARAN , D ROHITH , CH SHIVA PRASAD , K MUTHULAKSHMI
Unique Id:
IJSDR2404124
Published In:
Volume 9 Issue 4, April-2024
Abstract:
This study introduces a novel approach to automate the classification of rice leaf diseases using deep learning. Leveraging Convolutional Neural Networks (CNNs), our system analyzes high-resolution images to distinguish between various rice varieties. The model demonstrates high accuracy in differentiating grains based on size, shape, and color. The technology is integrated into a user-friendly application, allowing easy classification using standard smartphones or cameras. This research contributes to streamlining the rice grading process, reducing errors, and enhancing efficiency in the agricultural supply chain The proposed method involves several stages: preprocessing of rice grain images to enhance features, training of ResNet and DenseNet models using a large dataset of annotated rice grain images, and evaluation of the models' performance using standard metrics such as accuracy, precision, recall, and F1-score.
Keywords:
Agriculture, DenseNet121, Deep Learning, Diseases, Classification, Detection.
Cite Article:
"RICE LEAF DISEASE CLASSIFICATION SYSTEM", International Journal of Science & Engineering Development Research (www.ijsdr.org), ISSN:2455-2631, Vol.9, Issue 4, page no.870 - 876, April-2024, Available :http://www.ijsdr.org/papers/IJSDR2404124.pdf
Downloads:
000338172
Publication Details:
Published Paper ID: IJSDR2404124
Registration ID:210858
Published In: Volume 9 Issue 4, April-2024
DOI (Digital Object Identifier):
Page No: 870 - 876
Publisher: IJSDR | www.ijsdr.org
ISSN Number: 2455-2631
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