New Zealand forest harvesting operations are highly mechanised, with all stages of the harvesting process (from felling to loading out) being achieved by machines. All the functions of these machines are controlled using the Controller Area Network (CAN) bus, a standard designed to enable efficient communication primarily between electronic control units. As a result, a substantial amount of data are generated by these machines, including parameters such as engine speed, joystick movements, and hydraulic pressures. Harnessing these data in real time offers significant benefits for operational monitoring and decision-making for operators, contractors, and forest managers.
One of the projects in the FGR Automation & Robotics programme involves collecting machine data and transforming it into management information. In order to do this a system was developed to tap into the harvesting machinery’s diagnostic ports to extract CAN bus data. Using Wi-Fi (Starlink), data is sent to the cloud where it is processed and then displayed in an interactive dashboard (Figure 1). The dashboard enables real-time machine monitoring, as well as access to historical data from when the system was first installed and linked to GPS to display machine location and tracking over time. Several parameters are able to be visualised, including operating and idle time, fuel consumption, and the terrain on which the machine is operating.
Without contextualising the significant amounts of data available (that is, understanding what the machine is actually doing) the way operations are managed will not improve. To address this, a method was developed to use CAN bus data to predict machine activity during an operation. This method was then trialled on a John Deere forwarder. Results showed that the system achieved an accuracy of over 95% in predicting whether the forwarder was idling, loading, unloading, travelling loaded or unloaded. Once the machine activity is known, it becomes possible to dive deeper into actual operations.
For example, work cycles can be broken down into elements and then into individual movements. A method was developed based on an operator’s joystick movements to determine how many movements were required to complete a task. This approach allows standardised comparison of operator behaviour across different environments, operators, and machines. Such analysis will be useful for training new operators, identifying processes that may increase or reduce workload, and enable self-improvement through accurate and timely feedback to the operator.
Overall, utilising machine data presents the opportunity to enhance our understanding of harvesting machinery and operations. By transforming CAN bus data into meaningful insights, we can improve decision-making, operation monitoring and the training of new operators. As the industry moves towards greater connectivity across the forest products supply chain, harnessing machine data is an important first step.
This article is based on a presentation on “Capturing Data from Harvesting Operations” by Patrick Humphrey at the 2025 FGR Conference, held in Auckland 14th-16th October. Funding from the Forest Grower Levy Trust and the Ministry for Primary Industries for this work is acknowledged.
Click here to see Patrick’s presentation from the FGR Annual Conference
Click here for FGR Conference 2025 highlights including speaker presentations, videos and photos
