---
license: apache-2.0
language:
- es
tags:
- agriculture
- maize
- plant-disease
- image-classification
- vision-transformer
- mobilenet
- pytorch
library_name: pytorch
pipeline_tag: image-classification
---

# Maize Disease and Pest Classification Models

Pre-trained deep learning models for the joint classification of foliar diseases
and arthropod pests in maize (Zea mays L.), trained on a multi-source dataset
of 29,075 images covering nine classes.

## Models included

| Model | Parameters | F1 macro | Use case |
|---|---|---|---|
| `mobilenetv3_best.pth` | 4.21 M | 0.9482 ± 0.0036 | Edge deployment, mobile applications |
| `vit_base_best.pth`    | 85.81 M | 0.9579 ± 0.0032 | High-accuracy server inference |

## Classes (9 total)

**Diseases (7):** healthy, leaf_blight, leaf_spot, lethal_necrosis, rust, streak_virus
**Pests (2):** fall_armyworm, grasshopper, leaf_beetle

## Training details

- **Framework:** PyTorch 2.x + timm
- **Optimizer:** AdamW (lr=1e-4, wd=0.01)
- **Scheduler:** Cosine annealing
- **Augmentation:** Albumentations (RandomResizedCrop, HorizontalFlip, Rotation, ColorJitter)
- **Mixed precision:** Yes (torch.cuda.amp)
- **Multi-seed protocol:** 3 independent seeds (42, 123, 7) under deterministic mode
- **Hardware:** NVIDIA L4 GPU

## Dataset sources

- Ghana smartphone field captures (multi-class, including pests)
- CIMMYT/Kenya (maize lethal necrosis)
- PlantVillage (healthy + rust)
- Pandian et al. 2019 (additional rust samples)

## How to use

```python
import torch, timm

# MobileNetV3
model = timm.create_model("mobilenetv3_large_100", num_classes=9)
ckpt = torch.load("mobilenetv3_best.pth", map_location="cpu")
model.load_state_dict(ckpt["model_state_dict"] if "model_state_dict" in ckpt else ckpt)
model.eval()
```

## Citation

If you use these models, please cite:

```
@article{julians30_2026,
  title={Local convolutions vs. global attention: CNN-Vision Transformer benchmark
         for joint maize disease and pest classification},
  author={Julians30 and Co-authors},
  journal={Agriculture (MDPI)},
  year={2026}
}
```

## License

Apache 2.0