1. Home
  2. Abstract

A Convolutional Neural Network (CNN) Based Classification Framework for Multi-Crop Disease Detection Using Leaf Images

Vinay Sampatrao Mandlik1* and Lenina Vithalrao Birgale2

1Department of Electronics and Telecommunication Engineering, Swami Ramanand Teerth Marathwada University, Nanded, Maharashtra, India

2Department of Electronics and Telecommunication Engineering, SGGSIE and T Vishnupuri, Nanded, Maharashtra, India

Corresponding Author E-mail:vinaymandlik@gmail.com

DOI : http://dx.doi.org/10.12944/CARJ.13.3.15

Article Publishing History

Received: 31 Oct 2025
Accepted: 05 Dec 2025
Published Online: 16 Dec 2025

Review Details

Reviewed by: Dr. Syed Sumera Ali
Second Review by: Dr. Monica Dutta
Final Approval by: Dr. José Luis da Silva Nunes

Article Metrics

Views 

    PDF Download PDF Downloads: 64

Google Scholar

Abstract:

Early and precise diagnosis of crop diseases is crucial for global food security, particularly in developing countries where agriculture still plays a dominant role. This study presents a deep learning approach for labelling ten different plant disease conditions across three principal crops—maize, potato, and soybean. The Convolutional Neural Network (CNN) model incorporates multiple convolutional and batch normalization layers, achieving an overall classification accuracy of 95 %. Class-wise F1-scores range from 0.84 to 0.96, with notably strong performance for the Potato-Healthy and Soybean-Healthy categories. The model demonstrates robust generalization to variations in background, lighting, and leaf orientation, highlighting its suitability for real-world agricultural environments. This work supports the development of automated, scalable, and accurate multi-crop disease detection systems. The study also examines challenges such as class imbalance and overfitting, and proposes improvements including the integration of attention mechanisms and transfer learning. However, the model’s performance is still limited by the relatively small dataset size and restricted environmental diversity, suggesting future scope for expansion through larger field-based datasets, multimodal sensing, and advanced hybrid architectures.

Keywords:

Convolutional Neural Networks; Deep Learning; Plant Disease Detection; Precision Farming; Smart Agriculture

Download this article as: 

Copy the following to cite this article:

Mandlik V. S, Birgale L. V. A Convolutional Neural Network (CNN) Based Classification Framework for Multi-Crop Disease Detection Using Leaf Images. Curr Agri Res 2025; 13(3). doi : http://dx.doi.org/10.12944/CARJ.13.3.15

Copy the following to cite this URL:

Mandlik V. S, Birgale L. V. A Convolutional Neural Network (CNN) Based Classification Framework for Multi-Crop Disease Detection Using Leaf Images. Curr Agri Res 2025; 13(3). Available from: https://bit.ly/48X6jgr

[ HTML Full Text]


Back to TOC