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This is a Human Clinical Trial study.

Tomato Fruit Diseases Classification using MobileNetV2

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The model achieved a test accuracy of 91%, demonstrating remarkable effectiveness in identifying tomato fruit diseases.
Using a lightweight convolutional neural network, the system is optimized for speed and can operate on limited computing resources.
Data preprocessing techniques enhanced the model's robustness, showing the importance of quality data handling.
This automated classification tool may transform agricultural practices by enabling smarter disease detection and management.

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Deep learning model classifies fruit diseases in tomatoes with 91% accuracy, suggesting improved detection methods.

Abdelkarim Boulahya (2025) studied this question.

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