Modern Computer Vision for Oil Palm Tree Health Surveillance using YOLOv5

Yohanes Nuwara*, W. K. Wong, Filbert H. Juwono

*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review

2 Citations (Scopus)

Abstract

Oil palm (Elaeis guineensis) is an important species of palm vegetation for bio-energy agribusiness. Although oil palm tree plantations have expanded rapidly, especially in tropical countries to meet the need for biofuels, issues like diseases can have a negative impact on the industry by reducing productivity and survival rates of palm trees. Therefore, a regular tree counting is needed for inventory and health monitoring. The rapid advancement of deep learning-based computer vision and remote sensing technology has made it possible to automate tree counting. In this paper, we use YOLOv5 model for counting oil palm trees from Papua, Indonesia. The image data are divided into five classes, namely healthy, smallish, yellowish, mismanaged, and dead palms. We achieve average Fl-score of 0.895, which outperformed Faster R-CNN (0.706) and CNN ResNet-101 (0.493). The strength of YOLOv5 model is high precision for all the five classes, which is above 0.961. This application provides fast, robust, and accurate oil palm tree counting that can be applied elsewhere in the world.

Original languageEnglish
Title of host publication2022 International Conference on Green Energy, Computing and Sustainable Technology, GECOST 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages404-409
Number of pages6
ISBN (Electronic)9781665486637
DOIs
Publication statusPublished - 2022
Externally publishedYes
Event2022 International Conference on Green Energy, Computing and Sustainable Technology, GECOST 2022 - Virtual, Online, Malaysia
Duration: 26 Oct 202228 Oct 2022

Publication series

Name2022 International Conference on Green Energy, Computing and Sustainable Technology, GECOST 2022

Conference

Conference2022 International Conference on Green Energy, Computing and Sustainable Technology, GECOST 2022
Country/TerritoryMalaysia
CityVirtual, Online
Period26/10/2228/10/22

Keywords

  • Automation
  • Unmanned Aerial Vehicle
  • YOLOv5
  • computer vision
  • oil palm
  • tree count

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