Predicting the occurrence of root rot in tree stumps based on harvester data

Root rot (<i>Heterobasidion spp</i>.) causes substantial losses for forest owners due to decreased wood quality in Norway spruce (<i>Picea abies</i>). Containing root rot spread in regeneration can be achieved by planting resistant species around infected stumps. However, detecting rotten trees remains challenging. In this study, ground truth data for root rot was collected by seven contractors by adding assortments for rotten pulpwood and cutoffs, with all energy wood assumed rotten. Root rot occurrence was estimated in two ways: (1) by developing Extreme Gradient Boosting (XGB) models from all data (XGB-only); and (2) trough binary classification for bucking patterns containing only rotten or healthy trees, followed by developing XGB models for remaining trees (combined). XGB models were developed nationwide and for two specific contractors. Classifications showed sensitivity of 83–87% (rot) and specificity of 95–99% (healthy). Whether nationwide, contractor-specific, XGB-only or combined classification was better varied by situation. Compared to prior studies, predictions from harvester data outperformed UAV images in classification but were surpassed by handheld camera images. Despite lower sensitivity compared to previous XGB applications, more rotten trees were detected than when using only energy wood as an indicator. As estimations are almost cost-free, the results may be acceptable.