IsokoLink: 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 ⁠

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Project Context: This module is part of the D2P Agri-Platform, aiming to modernize Rwandan agriculture through transparent, AI-backed market linkages.