| --- |
| 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 |
|
|
