Optimized Custom CNN for Real-Time Tomato Leaf Disease Detection

• URL: http://arxiv.org/abs/2502.18521v1
• Date: Sun, 23 Feb 2025 18:27:08 GMT
• Title: Optimized Custom CNN for Real-Time Tomato Leaf Disease Detection
• Authors: Mangsura Kabir Oni, Tabia Tanzin Prama,
• Abstract summary: In Bangladesh, tomatoes are a staple vegetable, prized for their versatility in various culinary applications.<n>Early detection of diseases is crucial for implementing timely interventions and ensuring the sustainability of tomato production.<n>Traditional manual inspection methods, while effective, are labor-intensive and prone to human error.<n>This research paper sought to develop an automated disease detection system using Convolutional Neural Networks (CNNs)
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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