International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Call for Paper Volume 8, Issue 2 (March-April 2026) Submit your research before last 3 days of April to publish your research paper in the issue of March-April.
Submit Research Paper Join as a Reviewer

Plagiarism is checked by the leading plagiarism checker TurnItIn

Call for Paper

Volume 8 Issue 2
March-April 2026

Submit your research paper

Indexing Partners
Academia Advanced Sciences Index Bielefeld Academic Search Engine CiteSeer DRJI Google Scholar Independent Search Engine & Directory Network ISI (International Scientific Indexing) Issuu Mendeley Research Networks
RefSeek ResearcherId - Thomson Reuters ResearchGate Scirus Scribd Semantic Scholar UTeM - Universiti Teknikal Malaysia Melaka Wiki for Call for Papers WorldCat Zenodo

Crop Disease Detection Using Machine Learning Techniques: A Review

Author(s) Ms. SAYALI RAJENDRA JOSHI, Prof. Dr. MOHAMMAD ATIQUE M JUNAID
Country India
Abstract Crop diseases substantially lower agricultural output, endangering food security and farmers' livelihoods globally. Early and precise detection of diseases is critical to apply timely interventions, minimize loss of yield, and curtail pesticide consumption. Recent developments in machine learning (ML), deep learning (DL), and computer vision have made it possible to automatically classify plant diseases at high accuracy levels. This review article consolidates the literature on conventional image processing methods, ML-based classification, DL structures like convolutional neural networks (CNNs) and transformers, and IoT-based integrated systems for real-time identification. A comparative review identifies the merits and demerits of different approaches, stressing issues like data limitations, model generalizability, computational complexity, and explainability requirements. In addition, newly developing trends in research—such as multimodal methods, light models for edge deployment, and federated learning—are considered as possible directions towards scalable, field- deployable solutions. The survey seeks to provide researchers and practitioners with an integrated view of the state-of-the-art and open up research directions leading towards reliable, efficient, and trustworthy crop disease detection systems.
Keywords Crop disease detection, plant pathology, machine learning, deep learning, computer vision, IoT, precision agriculture
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 8, Issue 1, January-February 2026
Published On 2026-02-12
View / Download PDF File

Share this



E-ISSN 2582-2160
CrossRef

CrossRef DOI is assigned to each research paper published in our journal.

IJFMR DOI prefix is
10.36948/ijfmr

Downloads
Research Paper Format Copyright Permission Form and Undertaking Form Cover Page Vol 8 Isu 2Cover Page Vol 8 Isu 1Cover Page Vol 7 Isu 6

All research papers published on this website are licensed under Creative Commons Attribution-ShareAlike 4.0 International License, and all rights belong to their respective authors/researchers.

CC-BY-SA Open Access

About IJFMR
Fees & Payment
Current Issue
Publication Archive 		Submit Research Paper
Track Submission Status
Publication Guidelines
Publication Ethics
Peer Review & Plagiarism 		Join as a Reviewer
Editors & Reviewers
Reviewer Referral Program
Get Reviewer Membership Certi. 		Website/Journal Policies
Usage Policy
Content Policies
Privacy Policy
Contact Us +91-9687-828-838 editor@ijfmr.com
Powered by Sky Research Publication and Journals