Manuscript Number : SHISRRJ2472115

Paddy Disease Detection Using Machine Learning

Authors(2) :-S. Girinath, Hemanth Chalamkota

Rice is a staple food in our daily diet, and holds immense agricultural significance in India. Traditionally, farmers rely on visual examination or group discussions to identify leaf diseases, later resorting to time-consuming laboratory tests for confirmation. To address this, a model has been developed – A Deep Convolutional Neural Network (DCNN) combined with an Ensemble Model utilising AdaBoost and Bagging Classifier. This model proficiently classifies five paddy leaf diseases: Bacterial Leaf Blight (BLB), Hispa , Brown Spot (BS), and Leaf Blast (LS).Notably, it outperforms traditional models such as Combined Contrast Limited Adaptive Histogram Equalization (CLAHE), Gray Level Co-occurrence Matrix (GLCM) with Convolutional Neural Network (CNN).This innovation has the potential for extension into a practical rice plant disease identification system for real-world agriculture applications.

Authors and Affiliations

S. Girinath
Mohan Babu University, Tirupati, Andhra Pradesh, India
Hemanth Chalamkota
Mohan Babu University, Tirupati, Andhra Pradesh, India

Paddy leaf diseases, DCNN, Ensemble Model, AdaBoost, Bagging Classifier, Agriculture.

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Publication Details

Published in : Volume 7 | Issue 3 | May-June 2024
Date of Publication : 2024-05-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 35-41
Manuscript Number : SHISRRJ2472115
Publisher : Shauryam Research Institute

ISSN : 2581-6306

Cite This Article :

S. Girinath, Hemanth Chalamkota, "Paddy Disease Detection Using Machine Learning", Shodhshauryam, International Scientific Refereed Research Journal (SHISRRJ), ISSN : 2581-6306, Volume 7, Issue 3, pp.35-41, May-June.2024
URL : https://shisrrj.com/SHISRRJ2472115

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