Predicting resprouting ofPlatanus×hispanicafollowing branch pruning by means of machine learning

Abstract

Resprouting is a crucial survival strategy following the loss of branches, being it by natural events or artificially by pruning. The prediction of resprouting patterns on a physiological basis is a highly complex approach. However, trained gardeners try to predict a tree's resprouting after pruning purely based on their empirical knowledge and a visual check of the tree's geometry. In this study, we explore in how far such predictions can also be made by algorithms, especially using machine learning. Table-topped annually pruned Platanus × hispanica trees at a nursery were documented with terrestrial LiDAR scanners in two consecutive years. Topological structures for these trees were abstracted from point clouds by cylinder fitting. Then, new shoots and trimmed branches were labelled on corresponding cylinders. Binary and multiclass classification models were tested for predicting the location and number of new sprouts. The accuracy for predicting whether having or not new shoots on each cylinder reaches 90.8% with the LGBMClassifier, the balanced accuracy is 80.3%. The accuracy for predicting the exact numbers of new shoots with GaussianNB model is 82.1% but its balanced accuracy is reduced to 42.9%. The results were validated with a separate evaluation dataset. It proves a feasibility in predicting resprouting patterns after pruning using this approach. Different tree species, tree forms, and other variables should be addressed in further research.