---
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)