ML · Done
Tomato Leaf Disease Detection
Overview
Developed a lightweight deep learning model using TensorFlow Lite to detect diseases in tomato leaves from images. The model, based on transfer learning with MobileNetV2, achieves high accuracy with a compact size, suitable for deployment on resource-constrained devices.
Challenge
Optimizing a high-accuracy model to maintain performance within a compact 5MB size, handling a large dataset with 11 classes, and ensuring robust detection across varied image conditions such as lighting and angles.
Result
The model achieved 99.4% accuracy in detecting 11 classes of tomato leaf diseases using a dataset of over 17,000 images. Deployed on Streamlit, the application allows users to upload images and receive real-time disease predictions, demonstrating potential for agricultural diagnostic tools.
Key Statistics
99.4%
Model Accuracy
17,000+ images
Dataset Size
5MB
Model Size
11
Number of Classes
Technologies
Machine Learning
Data Processing
Visualization
Deployment
Gallery
Disease Detection Output
Visualization of detected tomato leaf disease with confidence scores.
Streamlit Interface
Interactive Streamlit app showing real-time disease prediction results.