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# https://github.com/huggingface/huggingface.jsIsokoLink: VLM Crop Disease Diagnostic Module
IsokoLink is a fine-tuned vision-language model (VLM) specialized for identifying and localizing diseases in staple crops (*Maize and Beans) within smallholder farming systems in *Rwanda.
Developed as the core AI engine for the Digital-to-Physical (D2P) Agri-Platform, this model transforms visual field data into verifiable quality scores that anchor financial transactions and fair trade for farmers.
Model Details
- Model Type: Vision-Language Model (VLM)
- Base Model: Google PaliGemma 3B (224px)
- Language(s): English
- Fine-tuning Method: Parameter-Efficient Fine-Tuning (PEFT) targeting attention layers only.
Intended Use
Direct Use
IsokoLink is designed to be used by farmers,hub operators and extension officers at aggregation hubs (e.g., Nyabugogo) to: 1. Identify specific crop diseases from mobile imagery. 2. Localize disease clusters using bounding boxes. 3. Generate an objective "Quality Certificate" for crop batches.
Training Data & Methodology
Dataset
The model was trained on a hybrid dataset: • PlantVillage: Baseline labels for common agricultural diseases. • Musanze Field Dataset: Locally collected images from smallholder farms in Rwanda’s Northern Province to account for regional environmental variations and lighting.
Target Classes
• maize streak virus • maize stalk borer • bean leaf disease • healthy
Training Hyperparameters
• Batch Size: 8 • Learning Rate: 0.005 (Cosine schedule with 10% warmup) • Training Steps: 64 • Sequence Length: 128 tokens • Hardware: NVIDIA T4 GPU
Performance & Limitations
Accuracy Target
The model is optimized to achieve a classification accuracy of ≥ 85% for the target crops.
Limitations
• Resolution: As a 224px model, it may struggle with very small, microscopic symptoms (e.g., early-stage fungal spores) without close-up photography. • Scope: Currently limited to Maize and Beans; performance on other Rwandan staples like Irish potatoes or Cassava is not guaranteed. • Hallucination: Like all VLMs, the model may occasionally generate bounding boxes for non-existent symptoms in extremely noisy backgrounds.
Ethical Considerations
IsokoLink was built to address the structural trust deficit between farmers and middlemen. By providing objective evidence of crop health, the model aims to prevent under-pricing of healthy yields. Users are advised that AI diagnostics should supplement, not entirely replace, the expertise of agricultural agronomists in critical food security decisions.
How to Get Started
To run inference in JAX/big_vision, load the paligemma-3b-pt-224.f16.npz weights and use the prompt: detect maize streak virus ; maize stalk borer ; bean leaf disease ; healthy
Project Context: This module is part of the D2P Agri-Platform, aiming to modernize Rwandan agriculture through transparent, AI-backed market linkages.
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