🍀 🌿 🌱 Drone Based Plant Monitoring System.

📝 Description.

This project demonstrates a proof of concept towards solving the challenge of proper plant monitoring and adequate data collection on large farms. By using a drone programmed to fly along a specified path, the drone transmits the video stream to a laptop for processing via wifi and the following steps are carried out;

1. Object detection is performed on each frame of the video stream to locate the crops by drawing bounding boxes.

2. Next is Object Tracking which associates a unique id to each crop and also keeps track of the number of crops on its path i.e crop counting.

   

3. and lastly the system takes a snapshot of the detected crops for data collection and further examination by farmer

⏳ Hardware.

• Flight Performance.
  • Max Flight Distance: 100m
  • Max Speed: 8m/s
  • Max Flight Height: 30m (without wifi repeater)
• Camera.
  • Photo: 5MP (2592x1936).
  • Format: JPG(Photo); MP4(Video)
  • FOV: 82.6°

⏳ Dataset.

To get enough images for this task, we converted frames from a 5 minutes video taken by the drone to get about 150 images, which was then used for training and validation. Data Annotations was done using LabelImg.

🏽‍Custom Object Detection using Yolov4.

The object detection algorithm used was the YoloV4 which offers great training speed and impressive performance.

🏽‍Multiple Object Tracking Using DeepSort.

To prevent taking multiple snapshots of the same crops, a unique id has to be assigned to each detected crop. DeepSort is the state of the art for object tracking.

🎯 Demo.

Project demo

Data Science Nigeria 2021 AI Bootcamp Presentation.

Project Poster

Acknowledgement.

Obafemi Awolowo University Teaching and Research Farm.

Reference

The AI Guy's Object tracking implementation using Tensorflow