Performance and Accuracy Analysis of Deep Learning Models for Automated Tomato Plant Disease Classification.
Keywords:
Tomato plant disease, Deep learning, Convolutional Neural Network, Image classification, Precision agriculture, Plant disease detection.Abstract
Tomato (Solanum lycopersicum) is one of the most widely cultivated and economically important vegetable crops worldwide. However, tomato production is severely affected by various diseases that reduce yield and quality. Early and accurate disease detection is essential for effective crop management, but traditional visual inspection methods are time-consuming, subjective, and require expert knowledge. Recent advancements in artificial intelligence, particularly deep learning and neural network techniques, have shown significant potential for automated plant disease detection using leaf images. This study presents a comprehensive analysis of deep learning models for automated tomato plant disease classification. Convolutional Neural Networks (CNNs) and pre-trained deep learning architectures were evaluated in terms of classification accuracy, precision, recall, F1-score, and computational efficiency. Experimental results demonstrate that deep learning models achieve high accuracy in identifying tomato leaf diseases, outperforming traditional machine learning approaches. The study highlights the effectiveness of deep learning-based systems for real-time disease diagnosis and their potential application in smart agriculture and precision farming
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