soil-crop-recommendation-model

Agricultural AI Graph Embedding Models

Graph neural network models trained on Ugandan agricultural knowledge graph for crop recommendation.

Model Overview

This repository contains multiple graph embedding models trained on an agricultural knowledge graph with 175,318 triples representing crop-soil-climate relationships.

Models Included

Best Model: GCN (Graph Convolutional Network)

• File: best_model.pth
• Accuracy: 87.28%
• F1-Score: 85.71%
• ROC-AUC: 96.90%
• Embedding Dimension: 100
• Entities: 2,513
• Relations: 15

Individual Models

1. GCN Model (gcn_model.pth) - Best performing
2. TransE Model (transe_model.pth) - Translation-based
3. DistMult Model (distmult_model.pth) - Bilinear
4. ComplEx Model (complex_model.pth) - Complex embeddings
5. GraphSAGE Model (graphsage_model.pth) - Sampling-based

Model Metadata

The model_metadata.json file contains:

• Entity to ID mappings (2,513 entities)
• Relation to ID mappings (15 relations)
• ID to entity mappings
• Model configuration parameters

Usage

import torch
from huggingface_hub import hf_hub_download

# Download model
model_path = hf_hub_download(
    repo_id="Awongo/soil-crop-recommendation-model",
    filename="best_model.pth"
)

# Download metadata
metadata_path = hf_hub_download(
    repo_id="Awongo/soil-crop-recommendation-model",
    filename="model_metadata.json"
)

# Load model (pseudo-code - adjust to your model architecture)
# model = GCNModel(num_entities=2513, num_relations=15, embedding_dim=100)
# model.load_state_dict(torch.load(model_path, map_location='cpu'))
# model.eval()

Training Data

• Knowledge Graph: 175,318 triples
• Dataset: Ugandan agricultural data
• Literature: 52 research papers
• Crops: 8 major crops (maize, rice, beans, cassava, sweet potato, banana, coffee, cotton)

Application

Used in production for agricultural crop recommendations based on:

• Soil properties (pH, organic matter, nutrients)
• Climate conditions (temperature, rainfall)
• Knowledge graph embeddings

Citation

@misc{agricultural-ai-graph-models,
  title={Agricultural AI Graph Embedding Models for Crop Recommendation},
  year={2025},
  publisher={Hugging Face}
}