This repository contains code and resources for a disease detection system tailored for Solanum Lycopersicum, commonly known as tomatoes. The project focuses on leveraging machine learning techniques to identify and classify diseases affecting tomato plants, providing a valuable tool for early detection and mitigation.
Key Features:
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Machine Learning Models: Implementation of AlexNet architecture for image classification to accurately identify diseases in tomato plants.
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Dataset: Inclusion of datasets used for training and testing the model with 10 classes of diseased leaves and one class of healthy leaves ensuring transparency and reproducibility.
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Pre-processing Scripts: Code snippets for data pre-processing to enhance model accuracy and efficiency.
Requirements:
- Python3
- Keras
- TensorFlow
- Flask (for deployment)