A Novel CNN-Based Approach for Classification of Tomato Plant Diseases
DOI:
https://doi.org/10.30812/matrik.v24i3.4464Keywords:
Accuracy, Convolution Neural Network, Model, Performance, TomatoAbstract
Tomatoes are one of the most widely cultivated and consumed crops globally, but they are highly susceptible
to various diseases that can significantly reduce yield and quality. Early detection of these
diseases is crucial for effective management and prevention. The objective of this study is to develop
an accurate early detection system for tomato diseases using deep learning to support effective crop
management. The research method employed is a modified Convolutional Neural Network trained
on the PlantVillage dataset, which consists of 21,000 images across 10 disease classes. The study
evaluates three training scenarios using different epoch values (25, 50, and 75) to optimize model
performance. Data preprocessing included image resizing and augmentation, followed by Convolutional
Neural Network training and validation. The study’s results showed that increasing epochs
improved the model’s accuracy: 98.18% at 25 epochs, 98.53% at 50 epochs, and 99.19% at 75 epochs.
Precision, recall, and F1-score also increased, from 90.95% at 25 epochs to 95.80% at 75 epochs, indicating
enhanced model reliability. However, longer training times were required as the epoch count
increased. This research concludes that a modified Convolutional Neural Network can accurately
classify tomato diseases, providing a reliable and practical tool for early disease detection. The proposed
system has the potential to be integrated into mobile applications for real-time use in the field.
It contributes to sustainable agriculture by enabling timely disease intervention and improving crop
productivity.
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