Determination of key canopy parameters for mass mechanical apple harvesting using supervised machine learning and principal component analysis (PCA)

As availability of skilled harvest labour is in decline, the sustainability of fresh market apple production in the USA is threatened. A mass mechanical harvesting of apples offers an alternative and promising solution. In addition to harvester design elements, it is important to understand the key canopy parameters of apple trees as they are closely integrated and interact with each other during the harvest process. In this study, the impact of eleven canopy parameters on mechanical harvesting were investigated for vertically-trained “Scifresh” and V-trellis grown “Envy” trees during harvesting trials. A supervised machine learning algorithm with weighted k-nearest neighbours (kNN) was adopted to analyse our canopy datasets. Overall, 2678 ground-truth data points (apples) were classified into two binary classes of fruit removal status: “mechanically harvested” and “mechanically unharvested” apples. For the training dataset (85%), the adopted algorithm achieved overall prediction accuracies of 76–92% and 62–74% for “Scifresh” and “Envy”. With the remaining 15% dataset, the overall test accuracies were 81–91% on “Scifresh” but only 36–79% on “Envy”. The principal components analysis (PCA) was adopted to determine the key canopy parameters by calculating the coefficients of principal components (PCs). The PC1–PC5 explained at least 80% of the data variance. By assuming a coefficient greater than 0.5 as being highly relevant, fruit load per branch, branch basal diameter, and shoot length were the most relevant among all. These results provide guidance for growers in canopy management that could improve efficiency of a mechanical harvesting system.