Abstract
This research aims to develop the EfficientNet-B0 Deep Learning model for detecting diseases in tomato leaves and integrate it into a mobile application based on Flutter. The research method includes training the model using an image dataset with optimization techniques such as quantization and pruning for efficiency on resource-limited devices. The results show a loss value of 0.0939 and an accuracy of 0.9955 on test data, with detection times ranging from 0.150 to 0.554 seconds. The implementation in the application allows farmers to detect tomato leaf diseases in real-time with high accuracy, supporting sustainable agricultural practices. The application is designed to be user-friendly, enabling users to capture images of suspected diseased tomato leaves and obtain quick diagnostic results. Through optimization techniques, this model operates efficiently on resource-constrained devices without sacrificing accuracy. This research provides a significant contribution to the application of artificial intelligence technology in the agricultural sector, offering practical and innovative solutions for detecting plant diseases via mobile devices, and potentially enhancing efficiency and effectiveness in managing tomato plant diseases.Keywords: Deep Learning, EfficientNet-B0, Tomato Leaf Disease Detection, Flutter, TensorFlow Lite.
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