Study of Different Techniques for the Detection of Disease in Grape and Pomegranate Plants: A Review

Bhagawan N. Kadlag , Chandrakant G. Dighavkar , Arun V. Patil

Productivity, detection techniques, agriculture, artificial intelligence, diagnosis

Diseases pose a significant threat to the productivity and quality of grape and pomegranate crops, leading to economic losses for farmers and affecting global fruit production. This review paper aims to provide a comprehensive overview of the various disease detection techniques employed in the cultivation and management of grape and pomegranate plants. The review begins by discussing the importance of early disease detection in agriculture and the specific challenges associated with grape and pomegranate crops. It then delves into an extensive examination of different disease detection methods, including traditional visual inspection, modern imaging technologies, molecular techniques, and data-driven approaches. The current research paper explores the advantages and limitations of each technique and highlights recent advancements in the field. Special attention is given to the utilization of artificial intelligence and machine learning algorithms in automating disease detection processes, offering rapid and accurate diagnosis.

Published Paper   E-Certificate

"Study of Different Techniques for the Detection of Disease in Grape and Pomegranate Plants: A Review", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.8, Issue 9, page no.1069 - 1077, September-2023, Available :https://ijsdr.org/papers/IJSDR2309154.pdf

Volume 8 Issue 9, September-2023

Pages : 1069 - 1077

Paper Reg. ID: IJSDR_208743

Published Paper Id: IJSDR2309154

Downloads: 000347260

Research Area: Engineering

Country: -, -, -

Published Paper PDF: https://ijsdr.org/papers/IJSDR2309154

Published Paper URL: https://ijsdr.org/viewpaperforall?paper=IJSDR2309154

ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Publisher: IJSDR(IJ Publication) Janvi Wave

This work is licensed under a Creative Commons Attribution 4.0 International License and The Open Definition