CustomBottleneck-VGGNet: Advanced tomato leaf disease identification for sustainable agriculture

Abstract

In recent years, sustainable agriculture has become increasingly important due to challenges such as climate change, population growth, and the need for food security. Tomato plants, being highly susceptible to various diseases, require accurate and timely diagnosis to maintain crop quality. Deep learning, particularly convolutional neural networks (CNNs), has shown great potential in addressing this challenge. This study introduces an advanced method for identifying tomato diseases using the CustomBottleneck-VGGNet model, enhanced through transfer learning techniques. The objective is to develop a highly accurate and computationally efficient model that can be deployed on resource-constrained devices for real-time disease detection. The proposed model achieves a remarkable accuracy of 99.12% with just 1.4 million parameters, outperforming classical models such as MobileNetV2, ResNet50, GoogleNet, VGG16, and VGG19 in terms of accuracy, precision, recall, and F1-score. Additionally, a mobile application has been developed to deploy this model, enabling real-time disease detection using a smartphone camera or images from the gallery, even in offline environments. The study also introduces a novel method for model comparison, focusing on differences between models trained under identical conditions to ensure fair evaluations.