CropScan - Plant Disease Detection Model

CropScan is a plant disease detection model based on RF-DETR, designed to help farmers quickly identify health issues in their crops.

Why CropScan?

Farming is hard work. Farmers face countless daily challenges: unpredictable weather, economic pressures, and most critically, crop diseases that can devastate entire harvests in just a few days.

CropScan was built to:

• Help farmers detect diseases early, before they spread
• Reduce crop losses through rapid and targeted intervention
• Optimize treatment usage by precisely identifying affected areas
• Democratize access to advanced diagnostic tools, once reserved for experts

Whether you're a small-scale farmer or a large producer, CropScan gives you the power to protect your crops with artificial intelligence.

Detection Example

The left image shows a leaf with disease symptoms. The right image shows CropScan's result: each diseased region is identified and segmented with precision using SAM2 integration.

Technical Details

Usage

Installation

pip install rfdetr torch torchvision

Inference

import torch
from rfdetr import RFDETRBase
from PIL import Image

# Load the model
model = RFDETRBase()
checkpoint = torch.load("checkpoint_best_total.pth", map_location="cpu")
model.load_state_dict(checkpoint)
model.eval()

# Load an image
image = Image.open("your_image.jpg")

# Run detection
with torch.no_grad():
    predictions = model(image)

# predictions contains bounding boxes of diseased regions

SAM2 Integration (Recommended)

For precise segmentation masks instead of bounding boxes, combine CropScan with SAM2:

from sam2.sam2_image_predictor import SAM2ImagePredictor

# Use CropScan boxes as prompts for SAM2
predictor = SAM2ImagePredictor.from_pretrained("facebook/sam2-hiera-small")
predictor.set_image(image)

for box in predictions.boxes:
    masks, scores, _ = predictor.predict(box=box, multimask_output=False)
    # masks contains the precise segmentation mask

Training Data

This model was trained on the Plant Disease dataset from Roboflow Universe, containing images of leaves with various diseases.

@misc{plant-disease-faxnj_dataset,
    title = { Plant disease Dataset },
    type = { Open Source Dataset },
    author = { Project },
    howpublished = { \url{ https://universe.roboflow.com/project-oklwn/plant-disease-faxnj } },
    url = { https://universe.roboflow.com/project-oklwn/plant-disease-faxnj },
    journal = { Roboflow Universe },
    publisher = { Roboflow },
    year = { 2024 },
    month = { feb },
}

Use Cases

• Precision Agriculture: Automated crop monitoring via drone or fixed camera
• Field Diagnosis: Mobile app for rapid disease identification
• Agricultural Research: Study of plant disease propagation
• Education: Teaching tool for agronomy students

Limitations

• Trained primarily on PlantVillage-style images
• Best performance on individual leaf images with clear backgrounds
• SAM2 recommended for precise segmentation masks
• Does not replace expert agronomist diagnosis

License

This model is distributed under the MIT license. You are free to use, modify, and distribute it for commercial or non-commercial purposes.

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Built with passion to support those who feed us.