Cut-to-Length Harvesting Prediction Tool: Machine Learning Model Based on Harvest and Weather Features

Abstract

Weather is a significant factor influencing forest health, productivity, and the carbon cycle. However, our understanding of these effects is limited for many regions and ecosystems. Assessing the impact of weather variability on harvester productivity from plantation forests may assist in forest planning through the use of data modeling. We investigated whether weather data combined with timber harvesting attributes could be used to create a high-performance model that could accurately predict harvester productivity in Eucalyptus plantations using machine learning. Furthermore, we aimed to provide an online application to assist forest managers in applying the model. For the modeling, we considered 15 weather and timber harvesting attributes. We considered productivity as the target attribute. We subjected the database to 24 common algorithms in default mode and compared them according to error metrics and accuracy. From the timber harvesting features combined with weather features, the Catboost model can predict the productivity of harvesters in a tuned mode, with a coefficient of determination of 0.70. The use of weather data combined with timber harvesting attributes in the model is an accurate approach for predicting harvester productivity in Eucalyptus plantations, allowing for the creation of an online, free application to assist forest managers.