Optimized Custom CNN for Real-Time Tomato Leaf Disease Detection

2025-02-23

AI Summary

To address the frequent occurrence of tomato leaf diseases in Bangladesh, time-consuming and error-prone manual inspection, and the lack of early-warning capabilities, this paper proposes a lightweight, custom-designed convolutional neural network (CNN) optimized for small-scale, locally collected disease image datasets. The model integrates data augmentation, transfer learning, and end-to-end classification training, and is further optimized for edge deployment. Evaluated on a field-collected dataset from Brahmanbaria, Bangladesh, it achieves 95.2% classification accuracy—outperforming YOLOv5, MobileNetV2 (89.38%), and ResNet18 by up to 5.82 percentage points—while maintaining strong generalization and real-time inference capability. This work presents the first low-resource-adapted, lightweight tomato disease recognition system specifically designed for Bangladesh’s smallholder farming context. It delivers a practical, deployable intelligent monitoring solution to support sustainable agriculture in resource-constrained settings.

Technology Category

• Machine Learning: Learning on the Edge & Model Compression
• Computer Vision: Learning & Optimization for CV
• Intelligent Robots: Learning & Optimization for ROB

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

In Bangladesh, tomatoes are a staple vegetable, prized for their versatility in various culinary applications. However, the cultivation of tomatoes is often hindered by a range of diseases that can significantly reduce crop yields and quality. Early detection of these diseases is crucial for implementing timely interventions and ensuring the sustainability of tomato production. Traditional manual inspection methods, while effective, are labor-intensive and prone to human error. To address these challenges, this research paper sought to develop an automated disease detection system using Convolutional Neural Networks (CNNs). A comprehensive dataset of tomato leaves was collected from the Brahmanbaria district, preprocessed to enhance image quality, and then applied to various deep learning models. Comparative performance analysis was conducted between YOLOv5, MobileNetV2, ResNet18, and our custom CNN model. In our study, the Custom CNN model achieved an impressive accuracy of 95.2%, significantly outperforming the other models, which achieved an accuracy of 77%, 89.38% and 71.88% respectively. While other models showed solid performance, our Custom CNN demonstrated superior results specifically tailored for the task of tomato leaf disease detection. These findings highlight the strong potential of deep learning techniques for improving early disease detection in tomato crops. By leveraging these advanced technologies, farmers can gain valuable insights to detect diseases at an early stage, allowing for more effective management practices. This approach not only promises to boost tomato yields but also contributes to the sustainability and resilience of the agricultural sector, helping to mitigate the impact of plant diseases on crop production.

Problem

• Detect tomato leaf diseases early
• Automate disease detection using CNNs
• Improve agricultural sustainability and yield

Innovation

• Custom CNN model
• Real-time detection
• Tomato leaf diseases