Christorious/ghana-cmf-vlm

Three-stream multimodal fusion for plant disease detection in Twi # ghana-cmf-vlm Three-stream multimodal fusion for plant disease detection in Twi # Ghana CMF-VLM: Multimodal Plant Disease Detection Three-stream fusion model combining vision, Twi language, and sensors for Ghanaian smallholder farmers. Implementation of Liu et al. 2025 adapted for low-resource languages. ## Results - **Overall accuracy:** 99.94% on 5,431 held-out PlantVillage images (38 classes) - **Critical crops (previously 0-25% with CLIP):** - Tomato Late Blight: 191/191 (100%) - Maize Northern Blight: 99/99 (100%) - Maize Healthy: 116/116 (100%) ## Structure - `notebooks/` — Kaggle training notebooks - `src/model.py` — EfficientNet + AfroXLMR + LSTM fusion - `results/` — confusion matrices and metrics ## Reproduce 1. Add PlantVillage dataset from Kaggle 2. `pip install -r requirements.txt` 3. Run `notebooks/02_fusion_training.ipynb` on GPU ## Limitations PlantVillage uses lab images. Field performance in Ghana expected lower. See Issues for field-test plan. ## Citation Based on CMDF-VLM (Liu et al., 2025). Uses AfroXLMR-base for Twi.