Manara - Qatar Research Repository
10.1038_s41598-023-37087-z.pdf (1.92 MB)

Deep learning supported machine vision system to precisely automate the wild blueberry harvester header

Download (1.92 MB)
journal contribution
submitted on 2023-12-11, 06:33 and posted on 2023-12-13, 10:46 authored by Zeeshan Haydar, Travis J. Esau, Aitazaz A. Farooque, Qamar U. Zaman, Patrick J. Hennessy, Kuljeet Singh, Farhat Abbas

An operator of a wild blueberry harvester faces the fatigue of manually adjusting the height of the harvester’s head, considering spatial variations in plant height, fruit zone, and field topography affecting fruit yield. For stress-free harvesting of wild blueberries, a deep learning-supported machine vision control system has been developed to detect the fruit height and precisely auto-adjust the header picking teeth rake position. The OpenCV AI Kit (OAK-D) was used with YOLOv4-tiny deep learning model with code developed in Python to solve the challenge of matching fruit heights with the harvester’s head position. The system accuracy was statistically evaluated with R2 (coefficient of determination) and σ (standard deviation) measured on the difference in distances between the berries picking teeth and average fruit heights, which were 72, 43% and 2.1, 2.3 cm for the auto and manual head adjustment systems, respectively. This innovative system performed well in weed-free areas but requires further work to operate in weedy sections of the fields. Benefits of using this system include automated control of the harvester’s head to match the header picking rake height to the level of the fruit height while reducing the operator’s stress by creating safer working environments.

Other Information

Published in: Scientific Reports
See article on publisher's website:



  • English


Springer Nature

Publication Year

  • 2023

License statement

This Item is licensed under the Creative Commons Attribution 4.0 International License.

Institution affiliated with

  • University of Doha for Science and Technology
  • College of Engineering and Technology - UDST