Benchmarking Deep Learning for Multi-Class Plant Disease Diagnosis: A Critical Review

Manisha Bajpai; Deepshikha; Raj Gaurang Tiwari

doi:10.55524/ijircst.2025.13.3.15

Abstract

The performance of many deep learning models for the classification of multi-class plant diseases is examined in this study. Accurate and effective solutions are necessary because plant disease identification is crucial to agricultural productivity. Publicly accessible datasets of plant disease images are used to assess deep learning models, especially convolutional neural networks (CNNs), and transfer learning architectures such as ResNet and VGGNet. These models are compared in the study according to their generalizability, accuracy, and computing efficiency. The results are intended to shed light on the best deep learning methods for managing and detecting plant diseases in the real world.

Keywords

Machine Learning, Deep learning, CNNs, artificial intelligence, ResNet, VGGNet, Plant diseases detection.

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Cites this article as

M. Bajpai, Deepshikha, R. G. Tiwari, "Benchmarking Deep Learning for Multi-Class Plant Disease Diagnosis: A Critical Review", International Journal of Innovative Research in Computer Science and Technology (IJIRCST), Vol-13, Issue-3, Page No-89-94, 2025. Available from: https://doi.org/10.55524/ijircst.2025.13.3.15

Corresponding Author

Manisha Bajpai

M. Tech Scholar, Department of Computer Science and Engineering, Dr. A.P.J. Abdul Kalam Technical University, Lucknow, Uttar Pradesh, India

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