Deploying an early-stage Cyber-Physical System for the implementation of Forestry 4.0 in a New Zealand timber harvesting context
Patrick Humphrey , Campbell Harvey, Rien Visser
iForest - Biogeosciences and Forestry, Volume 17, Issue 6, Pages 353-359 (2024)
doi: https://doi.org/10.3832/ifor4651-017
Published: Nov 13, 2024 - Copyright © 2024 SISEF
Research Articles
Abstract
Industry 4.0 is a concept using enabling technologies to increase efficiency for industries that can digitalise production processes. Industry 4.0 is intended to be an interconnected system, shifting from centralised to decentralised production control, with optimisation completed at multiple levels in real time. It facilitates communication between humans and machines with data. Forestry 4.0 is the adaption to the forest industry where high mechanisation rates in forest harvesting operations provide a clear opportunity for digitalisation and optimisation. A Cyber-Physical System (CPS) is an enabling technology that connects the physical and virtual domains. Implementing a CPS across a mechanised harvesting operation presents opportunities such as real-time optimisation of machine tasking or predicting machine maintenance needs. While economic benefits are commonly cited as the main driver for Forestry 4.0, the literature indicates that barriers like technology, costs, education, and organisational structure have hindered progress to date. This paper develops a CPS for harvesting systems. Using a New Zealand-based case study, it demonstrates early-stage implementation where Controller Area Network data was live-streamed from a felling machine, analysed and presented on an interactive online dashboard. This system allows logging contractors to monitor the operations of their machines in real time outside the area of work, while also storing data for future analyses. However, without linking the entirety of the harvesting operations, the economic benefits and realisation of Forestry 4.0 are limited.
Keywords
Forestry 4.0, Cyber-Physical Systems, CANbus, New Zealand, Forest Harvesting, Industry 4.0, J1939
Authors’ Info
Authors’ address
Campbell Harvey 0000-0002-2445-4942
Rien Visser 0000-0003-2137-9198
School of Forestry, University of Canterbury (New Zealand)
Corresponding author
Paper Info
Citation
Humphrey P, Harvey C, Visser R (2024). Deploying an early-stage Cyber-Physical System for the implementation of Forestry 4.0 in a New Zealand timber harvesting context. iForest 17: 353-359. - doi: 10.3832/ifor4651-017
Academic Editor
Marco Borghetti
Paper history
Received: May 27, 2024
Accepted: Aug 31, 2024
First online: Nov 13, 2024
Publication Date: Dec 31, 2024
Publication Time: 2.47 months
Copyright Information
© SISEF - The Italian Society of Silviculture and Forest Ecology 2024
Open Access
This article is distributed under the terms of the Creative Commons Attribution-Non Commercial 4.0 International (https://creativecommons.org/licenses/by-nc/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
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References
Machine learning and knowledge extraction to support work safety for smart forest operations (Holzinger A, Kieseberg P, Tjoa AM, Weippl E eds). Series “Lecture Notes in Computer Science”, vol. 13480, Springer, Cham, Switzerland, pp. 362-375.
CrossRef | Gscholar
Automatic reverse engineering of CAN Bus data using machine learning techniques. In: “Advances on P2P, Parallel, Grid, Cloud and Internet Computing - 3PGCIC 2017” (Xhafa F, Caballé S, Barolli L eds). Series “Lecture Notes on Data Engineering and Communications Technologies”, vol. 13, Springer, Cham, Switzerland, pp. 751-761.
CrossRef | Gscholar
Predictive maintenance of mining machines using advanced data analysis system based on the cloud technology. In: Proceedings of the “27th International Symposium on Mine Planning and Equipment Selection - MPES 2018” (Widzyk-Capehart E, Hekmat A, Singhal R eds). Springer eBooks, Cham, Switzerland, pp. 459-470.
CrossRef | Gscholar
Design and implementation of a real-time fleet management system for a courier operator. In: “Engineering Asset Lifecycle Management” (Kiritsis D, Emmanouilidis C, Koronios A, Mathew J eds). Springer, London, UK, pp. 197-206.
CrossRef | Gscholar