Indian Journal of Agricultural Research
SCOPUSWeb of Science
  • Year: 2026
  • Volume: 60
  • Issue: 1

Convolutional Neural Network Architectures for Enhanced Tomato Leaf Disease Classification using ResNet-152 and VGGNet Model

  • Author:
  • L.N. Swamy1, Prakash V. Parande2, H.P. Mohan Kumar3, V. Rakshitha1, S.N. Thimmaraju1, T. Yogesha1*
  • Total Page Count: 5
  • Page Number: 130 to 134

1Department of Computer Science and Engineering, Centre for Post Graduation Studies, Visvesvaraya Technological University, Mysuru-570 019, Karnataka, India.

2Department of Computer Science and Engineering, Centre for Post Graduation Studies, Visvesvaraya Technological University, Belagavi-590 018, Karnataka, India.

3PES College of Engineering, Mandya-571 401, Karnataka, India.

*Corresponding Author: T. Yogesha, Department of Computer Science and Engineering, Centre for Post Graduation Studies, Visvesvaraya Technological University, Mysuru-570 019, Karnataka, India. Email: yogesh@vtu.ac.in

Abstract

Tomato is one of the most extensively grown and consumed crops in the world. Diseases, such as bacterial spots and bacterial specks, cause significant economic losses by reducing both yield and quality. These diseases damage and destroy the leaves of tomato plants, making it difficult for the plant to produce fruit.

The purpose of this work is to use Convolutional Neural Network (CNN) models to diagnose diseases in tomato plants more quickly and accurately. This paper compares the ResNet-152 and VGGNet models for the classification of bacterially-induced tomato leaf diseases.

An accuracy of 98% is achieved using the ResNet-152 model for disease classification.

Keywords

Bacteria, CNN model, Diagnose, Diseases, Tomato