A mobile-first smart agriculture app built with React Native and hosted using Expo and will be up for deployment. Soil Scan allows users to scan and analyze soil using their phone's camera. The app uses a ResNet50 convolutional neural network to classify soil color and a trained XGBoost model to recommend the most suitable crops based on the detected soil type.
The machine learning logic is powered by a Python backend hosted on Hugging Face Spaces, which exposes an API to handle image classification and crop prediction tasks. All model inference is handled server-side to keep the app lightweight and efficient.
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React Native (Expo) – for building cross-platform mobile UI
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ResNet50 – for soil color classification from images
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XGBoost – for crop recommendation based on soil classification
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Python – backend logic and ML model hosting
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Hugging Face Spaces – lightweight cloud backend for API hosting
Soil Image Classification
The user can upload a photo or take a photo. This results in the following:
- Munsell color code
- Description of soil color
- Soil properties
Crop Recommendation
The user can input NPK values, pH, temperature, humidity, and rainfall. It results in the following:
- Top 5 crop recommendations
- Confidence level in the crop recommendation
