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watermelon-elite/
aaagriculture-models

Name: aaagriculture-models
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AAAgriculture ML Models

This repository contains the trained models for the AAAgriculture precision agriculture platform.

Models Included

PlantDiseaseModel_best.pth (81 MB)
- Architecture: ResNet34
- Task: Plant disease classification
- Classes: 38 diseases across 14 crop types
- Accuracy: 95%+
crop_yield_model_best.pth
- Architecture: Custom Neural Network
- Task: Crop yield prediction (regression)
- Features: 8 input features (soil, weather, farming practices)
- R² Score: 0.92+
preprocessing_objects.pkl
- Contains: Label encoders and scalers for crop yield model
- Used for: Feature preprocessing
soil_type_model.pth (130 MB)
- Architecture: ResNet18
- Task: Soil type classification
- Classes: 6 soil types (Sandy, Clay, Loamy, Silt, Peat, Chalky)
- Accuracy: 90%+
soil_moisture_model.pth (246 MB)
- Architecture: ResNet34
- Task: Soil moisture level classification
- Classes: 3 levels (Dry, Moderate, Wet)
- Accuracy: 93%+

Usage

python

from huggingface_hub import hf_hub_download
import torch

# Download a model
model_path = hf_hub_download(
    repo_id="watermelon-elite/aaagriculture-models",
    filename="soil_moisture_model.pth"
)

# Load the model
checkpoint = torch.load(model_path, map_location='cpu')
# ... load into your model architecture

Source Code

Full application code: https://github.com/watermelon-elite/AAAgriculture

License

MIT License - Free to use for research and commercial purposes.

Citation

If you use these models, please cite:

@software{aaagriculture2026,
  author = {Your Name},
  title = {AAAgriculture: ML-Powered Precision Agriculture Platform},
  year = {2026},
  url = {https://github.com/watermelon-elite/AAAgriculture}
}