Swarm Robot Agricultural Monitoring Simulation

We are starting the development of a swarm robot based agricultural monitoring simulation system. The goal of this project is to simulate multiple robots in Webot/simulation enviornments (min 5)monitoring crops, detecting plant diseases, and estimating weed density using deep learning models, with results displayed on a live dashboard.

Project Overview
The simulation will run in Webots with 5 robots. Each robot will have a camera that captures images of the crop environment. Every 2 seconds, the system should capture the camera image and process it using two AI models.

AI Models

1. ResNet50 model (resnet50_disease.pth) for plant disease classification. The output should include the disease name and confidence score.
2. U-Net model (unet_weed.pth) for weed segmentation. The output should be a mask identifying weed pixels in the image.

Weed Density Calculation
Using the segmentation mask, weed density should be calculated as:

weed_density = (weed_pixels / total_pixels) * 100

Based on this percentage, the severity should be classified as:

* Low: 0–10%
* Moderate: 10–30%
* High: above 30%

Dashboard
We need a live dashboard built with Flask that automatically refreshes and displays the following for each robot:

* Live camera image with weed mask overlay
* Detected disease name
* Confidence score displayed as a bar
* Weed percentage
* Severity badge (Green for Low, Amber for Moderate, Red for High)
* Soil pH value

All 5 robots should appear as separate cards on the same dashboard page and update in real time.

Main Components

1. Webots robot controllers to capture images from each robot camera.
2. AI inference module to run ResNet50 and U-Net models.
3. Image processing module to calculate weed density and overlay masks.
4. Flask server to host the live dashboard.
5. Frontend dashboard to display results and auto-update.

Update Frequency
The system should process and update data every 2 seconds.

Expected Outcome
A fully working simulation where robots analyze crop images in real time and display disease detection and weed severity on a live monitoring dashboard.