Deep Learning-Based Maize Leaf Disease Detection in Crops Using Images for Agricultural Applications

Registration ID: IJNRD_226673

Published ID: IJNRD2408378

DOI: Click Here to Get

Murasolimaran.R , Sowmiya.G

Corn Leaf Disease Detection, Deep Learning, Hybrid Models, Convolutional Neural Networks (CNNs)

The proliferation of corn leaf diseases poses a significant threat to global agricultural productivity. Diseases like Northern Leaf Blight, Gray Leaf Spot, and Common Rust lead to substantial yield losses if not detected and managed promptly. The advent of deep learning, particularly Convolutional Neural Networks (CNNs), has transformed the field of image classification, enabling more accurate and efficient detection of plant diseases. This paper investigates the application of a hybrid deep learning approach that combines four state-of-the-art CNN architectures: EfficientNetB0, MobileNetV2, InceptionResNetV2, and InceptionV3, for the detection of corn leaf diseases. By integrating these models, the proposed hybrid framework aims to leverage their unique strengths, thereby enhancing the accuracy of disease detection while optimizing computational efficiency. The research explores the development of a comprehensive hybrid model, detailing the preprocessing steps, model architecture, training procedures, and evaluation metrics. The hybrid model's performance is thoroughly analyzed and compared with that of individual architectures, demonstrating superior results in terms of accuracy, precision, recall, and F1- score. The study also delves into the practical implications of deploying such a model in real-world agricultural scenarios, including its potential to operate on mobile and edge devices. The paper concludes with a discussion on future research directions, emphasizing the scalability of the model to other crops and the challenges of real-world implementation.

Published Paper   E-Certificate

Murasolimaran.R & Sowmiya.G (August-2024). Deep Learning-Based Maize Leaf Disease Detection in Crops Using Images for Agricultural Applications. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 9(8), d384-d390. https://ijnrd.org/papers/IJNRD2408378.pdf

Volume 9 Issue 8, August-2024

Pages : d384-d390

Paper Reg. ID: IJNRD_226673

Published Paper Id: IJNRD2408378

Downloads: 000121981

Research Area: Information Technology 

Country: Cuddalure, Tamilnadu, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2408378.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2408378

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Publisher: IJNRD (IJ Publication) Janvi Wave | IJNRD.ORG | IJNRD.COM | IJPUB.ORG

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

Call For Paper - Volume 10 | Issue 10 | October 2025

IJNRD is a Scholarly Open Access, Peer-reviewed, and Refereed Journal with a High Impact Factor of 8.76 (calculated by Google Scholar & Semantic Scholar | AI-Powered Research Tool). It is a Multidisciplinary, Monthly, Low-Cost Journal that follows UGC CARE 2025 Peer-Reviewed Journal Policy norms, Scopus journal standards, and Transparent Peer Review practices to ensure quality and credibility. IJNRD provides indexing in all major databases & metadata repositories, a citation generator, and Digital Object Identifier (DOI) for every published article with full open-access visibility.

The INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT (IJNRD) aims to advance applied, theoretical, and experimental research across diverse fields. Its goal is to promote global scientific information exchange among researchers, developers, engineers, academicians, and practitioners. IJNRD serves as a platform where educators and professionals can share research evidence, models of best practice, and innovative ideas, contributing to academic growth and industry relevance.

Indexing Coverage includes Google Scholar, SSRN, ResearcherID-Publons, Semantic Scholar (AI-Powered Research Tool), Microsoft Academic, Academia.edu, arXiv.org, ResearchGate, CiteSeerX, ResearcherID (Thomson Reuters), Mendeley, DocStoc, ISSUU, Scribd, and many more recognized academic repositories.

How to submit the paper?

By Our website

Click Here to Submit Paper Online

By Mail

Send your paper to editor@ijnrd.org

Important Dates for Current issue

Paper Submission Open For: October 2025

Current Issue: Volume 10 | Issue 10 | October 2025

Impact Factor: 8.76

Last Date for Paper Submission: Till 31-Oct-2025

Notification of Review Result: Within 1-2 Days after Submitting paper.

Publication of Paper: Within 01-02 Days after Submititng documents.

Frequency: Monthly (12 issue Annually).

Journal Type: IJNRD is an International Peer-reviewed, Refereed, and Open Access Journal with Transparent Peer Review as per the new UGC CARE 2025 guidelines, offering low-cost multidisciplinary publication with Crossref DOI and global indexing.

Subject Category: Research Area

Call for Paper: More Details