Leaf Disease Detection Using Deep Learning
Our project uses deep learning to detect leaf diseases from images. By training models like VGG16 and EfficientNet on a robust dataset, we accurately diagnose plant conditions, aiding farmers in early disease detection and promoting healthier crops.
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Project Outcomes
By achieving accurate leaf disease detection, this project offers numerous practical benefits for agriculture and beyond. Here are the effective outcomes:
- Obtained 99.4% accuracy in the recognition of leaf disease with the EfficientNet-B4 model.
- Built a deep learning model using VGG16, VGG19 and EfficientNet-B4 architectures for robust performance.
- Efficiently processed and analyzed a dataset of over 10,000 leaf images across 26 categories of diseases and healthy plants.
- Successfully utilized transfer learning, allowing the model to generalize well even with limited training data.
- Used CM and reports to assess the model performance on each category.
- Created the principles of cutting-edge approaches for precision agriculture and smart farming implementation.
- Supported the growth of AI instruments in agriculture that can help to prevent crop damage and increase productivity.
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