A COMPREHENSIVE REVIEW ON DIAGNOSIS AND CLASSIFICATION OF PADDY LEAF DISEASES USING ADVANCED COMPUTER VISION TECHNOLOGIES
Abstract
vioft2nntf2t|tblJournal|Abstract_paper|0xf4ffa4c8320000006c40130001000700
Food is required for human survival. Paddy is a vital food crop serving 60% of the Indian population. Food quality is determined by the plant yield. Unfavorable environmental circumstances, soil fertility, bacteria, viruses, nematodes, fertilizer use, and the absence of nutritional shortages substantially influence plant yield. As a result, it is critical to protect the plants from illness. Crop yield must be improved to meet food scarcity of growing population. Although disease symptoms are apparent in various parts of plant like leaves, stem, fruits and stem, the infections are commonly observed in the leaves. Understanding plant pathology plays a vital role in disease detection. Early detection of diseases is a prompt intervention that aids the farmers in controlling disease spread, resulting in increased agricultural quantity and quality. Image processing techniques with advanced computer vision technologies like machine learning and deep learning have proven the automation of plant disease diagnosis precisely. The main objectives of this research are to investigate computer vision technologies for the early identification of plant diseases and help novice researchers in the same domain learn about plant diseases and the methodologies for disease detection in paddy plant leaves. Consequently this manuscript reviews significant paddy plant infections, highlights related study of tools and techniques, current research, limitations and conclusions for future research in this field.

Authors
B. Sowmiya1, K. Saminathan2, M. Chithra Devi3
A.V.V.M. Sri Pushpam College, Affiliated to Bharathidasan University, India1,2, Queens College of Arts and Science for Women, Affiliated to Bharathidasan University, India3

Keywords
Paddy Disease Detection, Machine Learning, Deep Learning, Preprocessing, Segmentation, Feature Extraction, Classification, Convolutional Neural Network
Yearly Full Views
JanuaryFebruaryMarchAprilMayJuneJulyAugustSeptemberOctoberNovemberDecember
101000000000
Published By :
ICTACT
Published In :
ICTACT Journal on Image and Video Processing
( Volume: 13 , Issue: 4 , Pages: 2973 - 2986 )
Date of Publication :
May 2023
Page Views :
353
Full Text Views :
16

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.