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| title: Plant Disease Assistant | |
| emoji: 🌱 | |
| colorFrom: green | |
| colorTo: yellow | |
| sdk: gradio | |
| sdk_version: 5.0.0 | |
| app_file: app.py | |
| pinned: false | |
| license: apache-2.0 | |
| short_description: Diagnose 22 crop diseases from a leaf. DINOv2-L on MI300X. | |
| tags: | |
| - plant-disease | |
| - agriculture | |
| - dinov2 | |
| - amd | |
| - mi300x | |
| - rocm | |
| - image-classification | |
| # 🌱 Plant Disease Assistant | |
| Snap a leaf, name the disease, get a fix. | |
| Upload a photo of a plant leaf and this Space will identify which of **22 crop diseases** it has, rate the model's confidence, and return a structured treatment + prevention guide. | |
| ## What's under the hood | |
| | Component | Details | | |
| |---|---| | |
| | **Classifier** | DINOv2-Large (304M params, ViT-L/14), fine-tuned with a linear head | | |
| | **Accuracy** | 97.06% top-1, 0.9713 macro F1 on the held-out test split | | |
| | **Dataset** | [CCMT Crop Pest and Disease Detection](https://www.kaggle.com/datasets) — cashew, cassava, maize, tomato | | |
| | **Hardware** | Fine-tuned on a single AMD Instinct **MI300X** (192 GB HBM3) via the AMD Developer Cloud | | |
| | **Framework** | PyTorch 2.x + ROCm, [timm](https://github.com/huggingface/pytorch-image-models) | | |
| | **Knowledge base** | Hand-curated treatment, prevention, and severity notes per class | | |
| The classifier runs on CPU here for accessibility — inference takes a few seconds per image. The original training run used ROCm on MI300X. | |
| ## Crops & diseases covered | |
| - **Cashew** — anthracnose, gumosis, leaf miner, red rust, healthy | |
| - **Cassava** — bacterial blight, brown spot, green mite, mosaic, healthy | |
| - **Maize** — fall armyworm, grasshopper, leaf beetle, leaf blight, leaf spot, streak virus, healthy | |
| - **Tomato** — leaf blight, leaf curl, septoria leaf spot, verticilium wilt, healthy | |
| ## How it was built | |
| This is **Track 2** of a multi-track entry in the lablab.ai AMD Developer Hackathon: | |
| - **Track 2** — Fine-tune DINOv2-L on CCMT for plant disease classification (this Space) | |
| - **Track 3** — Fine-tune Llama 3.2 11B Vision (LoRA) on the same data for conversational diagnosis ([adapter on HF](https://huggingface.co/iamcode6/llama32-vision-ccmt-mi300x)) | |
| - **Build in Public** — Documented the journey end-to-end on social | |
| Both tracks were trained on the same MI300X droplet, demonstrating that a single AMD GPU can comfortably handle both a 304M-param classifier and an 11B-param vision-language model in the same workflow. | |
| ## Limitations | |
| - Trained on a single dataset (CCMT) — performance on field photos with very different lighting, angles, or unseen crops will degrade. | |
| - The treatment guidance is informational only and **not a substitute for advice from a qualified agronomist or extension officer**. | |
| - CPU inference is intentionally slow (~5–10s/image). The original GPU pipeline runs in milliseconds. | |
| ## License | |
| Apache 2.0. Model weights and code are open. CCMT dataset licensing applies to the training data only. | |
| ## Acknowledgements | |
| - AMD for the Developer Cloud credits and MI300X access | |
| - Meta for [DINOv2](https://github.com/facebookresearch/dinov2) | |
| - The CCMT dataset authors | |
| - lablab.ai for organizing the hackathon | |