Data fusion for timber bundle volume measurement

In many industrial fields, the direct measurement of a specific parameter of interest can be too costly to be feasible. Using data from other parameters about the process, a data driven approach can instead be used for a cost-efficient estimation of the parameter of interest. For the Swedish forest industry, the measurement of the timber bundles volume sets the price for the customers. Volume is in this case an expensive parameter to measure, where manual measurements have been used as reference to a remote imaging estimation system involving operators. In this paper a data driven approach is presented for the estimation of timber bundles volume, using correlated features such as weight, date of cut, storage time etc., to demonstrate a costefficient software estimation system. The proposed approach, based on neural networks, shows comparable results to the existing remote estimation method when using a few features and even better performance when adding feature information from the timber harvesters.