Maize Disease Model

--- language: en license: mit tags: - image-classification - plant-disease - agriculture - efficientnet - transfer-learning - maize - ghana metrics: - accuracy - f1 --- # Maize Disease & Pest Classifier — EfficientNet-B0 Part of the MSc thesis: **"Application of Deep Learning for Low-Resource Maize Disease Diagnosis in Heterogeneous Agro-Ecological Zones"** ## Model details | | | |---|---| | Architecture | EfficientNet-B0 | | Training strategy | Two-phase transfer learning (ImageNet → maize diseases) | | Test accuracy | 90.94% | | Weighted F1 | 0.9198 (validation) | | Classes | 7 | | Input size | 224 × 224 | ## Classes 0. Healthy 1. Northern Leaf Blight 2. Common Rust 3. Gray Leaf Spot 4. Ear Rot 5. Fall Armyworm 6. Stem Borer ## Dataset Combined from: - **PlantVillage** (4,188 images, 4 classes) — [Kaggle](https://www.kaggle.com/datasets/abdallahalidev/plantvillage-dataset) - **Ghana field images** (406 images, 3 classes) — custom field-collected dataset Total: 4,594 images | Class imbalance: 10.75:1 ## Usage ```python from huggingface_hub import hf_hub_download import torch from src.deployment.inference import MaizeClassifier from PIL import Image ckpt = hf_hub_download(repo_id="moro23/maize-disease-model", filename="best_val_weighted_f1_0.919762_epoch_22.pth") classifier = MaizeClassifier(ckpt, "configs/data_config.yaml") image = Image.open("maize_leaf.jpg") print(classifier.predict(image)) ``` ## Deployment Live demo: [HuggingFace Spaces](https://huggingface.co/spaces/moro23/maize-disease-diagnosis)

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