Intelligent Agriculture

A Convolutional Approach to Early Detection and Classification of Tomato Foliar Pathogens

George Princess Thomas

Department of Computing Technologies, SRM Institute of Science and Technology, Kattankulathur, Chennai 603203, India

Poovammal Easwaran

Department of Computing Technologies, SRM Institute of Science and Technology, Kattankulathur, Chennai 603203, India

Heartlin Maria Hermas

Department of ECE, SRM Institute of Science and Technology, Kattankulathur, Chennai 603203, India

Kothai Ganesan

Department of CSE (Artificial Intelligence and Machine Learning), KPR Institute of Engineering and Technology, Coimbatore 641407, India

DOI: https://doi.org/10.36956/ia.v1i1.1486

Received: 19 November 2024 | Revised: 15 March 2025 | Accepted: 23 March 2025 | Published Online: 29 March 2025

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Abstract

Food security remains a critical global concern. The rising world population has led to a continuous increase in foGlobal food security relies significantly on the agricultural sector, with tomatoes being a vital dietary component worldwide. However, various diseases pose an ongoing threat to tomato crop yield and quality. Prompt and accurate identification of these diseases is crucial for sustainable agriculture and effective management practices. This study introduces an innovative approach using Convolutional Neural Networks (CNNs) to enable rapid detection and classification of tomato leaf diseases through image analysis. The system utilizes a high-resolution dataset comprising images of tomato leaves showing symptoms of common diseases such as bacterial wilt, early blight, and late blight. Before training, the dataset undergoes preprocessing to enhance image clarity and eliminate noise, followed by division into training and testing subsets. A custom CNN architecture is developed and trained to automatically learn and extract hierarchical features from the images. Additionally, transfer learning methods are explored to improve the model’s efficiency and generalization. The model’s performance is evaluated using various metrics including accuracy, precision, recall, and F1 score. Results indicate that the CNN model demonstrates high accuracy and robustness in early disease detection. This approach holds substantial potential for practical implementation, offering farmers and agricultural professionals a powerful tool for timely and precise disease management. By enabling targeted responses and supporting precision agriculture, the proposed method represents a significant advancement in integrating modern technology with sustainable farming, ultimately contributing to agricultural stability and global food security.

Keywords: Importing Libraries and Datasets; Tomato Disease Classification; Convolutional Neural Networks (CNN); Transfer Learning; Data Augmentation; Pre-Processing and Feature Extraction; Testing and Training; Prevention; Recommendation of Pesticides

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