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Showcase · Bronze medal

Crop-Inspection Robot (v1)

The final-year project robot where M-Tech started. It drives along crop rows, finds each cabbage and tells healthy leaves from diseased ones in real time.

How it works

  1. 1

    Drive

    A 4-wheel omni-wheel base, driven with a PS5 controller, moves along the crop rows.

  2. 2

    See

    A camera on a 6-axis myCobot 280 Pi arm streams live video over Wi-Fi to the control station.

  3. 3

    Find

    Stage 1: a YOLOv8 detector finds the cabbage in each frame.

  4. 4

    Classify

    Stage 2: a YOLOv8 classifier labels the crop as Healthy, Alternaria Spot, Black Rot or Downy Mildew.

Results

disease classification accuracy (validation set, YOLOv8n-cls)
99.87%
disease classification accuracy (validation set, YOLOv8n-cls)
cabbage detection mAP@0.5 (YOLOv8n)
99.5%
cabbage detection mAP@0.5 (YOLOv8n)
deployed model size
≈ 11.5 MB
deployed model size

Track record

Experience & Achievements

  • Award · 2026

    Bronze Medal — AI & Robotics category

    [Competition / exhibition name]

    Awarded for our founder's final-year project: a semi-autonomous mobile robot with a robot arm and camera that detects cabbage leaf diseases in real time using deep learning (YOLOv8).

  • Conference paper · 2026 Submitted

    Lightweight Two-Stage YOLOv8 Cabbage Disease Recognition on a Teleoperated Mobile Manipulator for Real-Time Crop Inspection

    ICM Conference 2026, Sarawak

    Conference paper on the robot's two-stage vision system and its wireless link between robot and control station.

This robot led to AgriBot, our next-generation field robot.

Meet AgriBot