Great question — segmentation polygons are a gold mine for improving VLM accuracy on fine-grained agri tasks. Below are practical, progressively “deeper” ways to inject those masks into a Gemma-like 4B VLM pipeline, from no-finetune tricks to light adapters and region-supervised finetuning. Pick the depth that matches your constraints.

1) No-finetune: make the model look where it matters

These are drop-in inference tweaks that often yield big gains on disease/pest tasks.

A. Multi-view inputs (original + ROI crops + overlay)

• For each polygon mask:
  • Compute a tight bbox, dilate by ~10–20% to include context.
  • Produce 2 derivatives:
    1. ROI crop (high-res patch of just the diseased area).
    2. Overlay view (original image with the polygon filled or outlined; background optionally dimmed to 30–50%).
• Feed the VLM a small set of images in one prompt (if the API supports multi-image), or stitch them into a single canvas horizontally.
• Prompt template (disease):

  text
  You’ll see the original leaf and a zoomed patch. The colored region marks the suspected lesion.
  Task 1: Plant species (top-1).
  Task 2: Disease vs. pest vs. healthy.
  Task 3: If disease/pest, name it and cite key visual cues in the colored region.
  Output JSON with keys: species, class, name, cues.

• Why it helps: it reduces background bias (soil, pot type, lab benches) and gives the model a high-resolution look at small lesions or insects.

B. Background suppression (“soft masking”)

• Create a masked image where pixels outside polygons are blurred/desaturated, while the lesion stays sharp.
• Keep lesion color/texture intact; randomize overlay color (to prevent color→label leakage).

C. Test-time ensembling

• Run the VLM on: (i) original, (ii) overlay, (iii) ROI crop (1×, 1.5×, 2× zoom).
• Average class logits if you can access them; otherwise, take majority vote with a confidence heuristic (e.g., prefer answers that repeat across ≥2 views and contain lesion-based “cues”).

D. Structure the output

• Force a schema so the model doesn’t hedge:

  json
  {"species":"", "class":{"type":"disease|pest|healthy"}, "name":"", "cues":["…","…"], "confidence":0-1}

2) Light integration: use the mask to steer attention (no full retrain)

If you can touch the vision encoder or do parameter-efficient finetuning:

A. Mask-guided attention bias (encoder-level)

• If the vision tower is ViT-like, identify patch tokens overlapping the polygon(s).
• Add a learnable bias +λ to QK attention for ROI tokens (or rescale their key/value norms).
• If you can’t change attention, do masked average pooling: compute an extra ROI embedding by averaging only patch tokens inside the mask, then concatenate [CLS, ROI_EMB] before the LLM.

B. Extra input channel without breaking 3-channel contracts

• Render the binary mask as a grayscale overlay concatenated side-by-side to the RGB image (so the network still sees 3-channel tiles), or encode as a subtle alpha matte on the original. In practice this preserves plug-compatibility while making the ROI explicit.

C. Prompt-conditioned visual tokens

• Derive a short “mask descriptor” from polygon geometry (area %, centroid, long/short axis). Inject into the prompt:

  Region of interest covers ~12% of the leaf near the right margin; elongated shape with irregular edges.
  

  This gives the LLM spatial priors without needing internal changes.

D. Few-shot exemplars with overlays

• Show 3–5 labeled examples (original + overlay + zoom), each with “cues” grounded in the highlighted region. Keep overlays stylistically consistent with your inference overlays.

3) Finetuning: teach the VLM to use the mask

Where you’ll get the biggest, most stable gains.

A. Multi-task objective

• For each training sample (image, polygons, labels) create three views: original, overlay, ROI crop.
• Train with losses:
  • Disease/pest class CE (or seq-to-seq tokens if you generate text).
  • Species class CE.
  • Region-grounding loss: encourage cross-attention maps or Grad-CAM on lesion tokens to overlap the mask (KL or Dice between normalized attention and mask).
  • Optional caption loss: short rationale like “tan oval lesions with concentric rings inside ROI”.
• Parameter-efficient: LoRA on projector/vision MLP + LLM input layers (e.g., r=8–16). QLoRA if VRAM is tight.

B. Mask-conditioned adapters

• Feed a ROI embedding (masked average of vision tokens) to a small MLP and add it as a prefix visual token (a “region token”) before the normal sequence. Train LoRA on the cross-attention that reads this token.
• At inference, the region token is derived from your polygons; without polygons, omit it (model falls back gracefully).

C. Multi-instance learning for multiple polygons

• Treat each polygon as an instance. Predict per-instance class logits, then aggregate via:
  • Noisy-OR for presence detection (any lesion ⇒ positive).
  • Top-k pooling for class names (use top-1 or top-2 instances).
• This reduces misses when you have several small pests/lesions.

D. Negative sampling & bias control

• Add hard negatives: same species but healthy; same background but different plant.
• Randomize overlay color/thickness, and include a “mask present but healthy” case to prevent “mask ⇒ diseased” shortcut.

4) Pests: tiny objects need resolution & priors

• Use higher-zoom ROI crops than diseases (2–4× on the mask bbox).
• Add a size prior token to the prompt: “ROI spans ~0.6% of image; look for small insects, aphids, mites.”
• If you have bounding boxes for pests, pass multiple tiny crops; aggregate with majority vote.

5) Data pipeline notes (concrete)

Preprocess

• Normalize polygons to image space.
• Merge overlapping polygons; keep small ones if area ≥ 0.1–0.3% of image (tune).
• Create 3–5 views per sample: original, overlay (outline and/or fill, alpha=0.3–0.5), ROI crops at 1.0×/1.5×/2.0×.

Prompt template (unified, JSON-only)

text
You are an agronomy assistant.
Images: [original, overlay, 1.5x crop, 2.0x crop].
The colored region marks the area of interest (drawn from a segmentation mask).
Return JSON only:
{
  "species": "<plant species>",
  "class": "disease|pest|healthy",
  "name": "<diagnosis or pest>",
  "cues": ["short bullet cues grounded in the colored region"],
  "confidence": 0.0-1.0
}

Pseudo-code (PyTorch-style)

python
def make_views(img, polygons):
    mask = rasterize(polygons, img.size)              # HxW bool
    bbox = dilate_bbox(mask_to_bbox(mask), scale=1.2)
    crop1 = crop(img, bbox, scale=1.0)
    crop2 = crop(img, bbox, scale=1.5)
    overlay = draw_overlay(img, polygons, fill_alpha=0.35, outline=3)
    soft_bg = blur_outside(img, mask, sigma=7)        # optional variant
    return [img, overlay, crop1, crop2]

def vlm_infer(model, processor, views, prompt):
    # stitch views horizontally if single-image API
    canvas = hstack(views) if not model.supports_multiimage else views
    inputs = processor(prompt=prompt, images=canvas, return_tensors="pt")
    return model.generate(**inputs)

# Ensembling example
answers = [vlm_infer(model, proc, make_views(img, polys_variant), prompt) for polys_variant in augment(polygons)]
final = vote_or_logit_average(answers)

6) Region-aware evaluation (to see the gain)

• Report macro-F1 per class (diseases are often imbalanced).
• Slice metrics by lesion size and zoom to verify small-lesion gains.
• Run counterfactuals: original vs. masked-out-lesion; accuracy should drop if the model truly relied on the lesion (sanity check against background shortcuts).

7) Hyperparameters that usually work

• Overlay alpha: 0.35–0.5; outline width 2–4 px at 1K px image width.
• Dilation for ROI bbox: 10–20% each side.
• Ensembling: 3–5 views; abstain if model disagreement ≥2 classes and return “uncertain”.
• LoRA: r=8–16, α=16–32, lr 1e-4 to 3e-4 on adapters, 1–3 epochs; freeze vision backbone if data <20k samples.

8) Common pitfalls

• Color leakage: don’t fix overlay color per class; randomize.
• Over-cropping: include some healthy context; otherwise many diseases are ambiguous.
• Label noise: polygon may include veins/edges that mimic lesions; use slight erosion (1–2 px) to focus on core region for ROI pooling/attention supervision.
• Tiny pests: ensure at least one view preserves native sensor resolution over the ROI.

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If you tell me your current API constraints (multi-image or single-image only), and whether you can finetune with LoRA, I can sketch an exact prompt, view generator, and (if allowed) a small training loop tailored to your stack.