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.

中文翻译：

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使用监督机器学习和主成分分析 (PCA) 确定大规模机械苹果收获的关键冠层参数

随着熟练采摘劳动力的减少，美国新鲜市场苹果生产的可持续性受到威胁。大规模机械收获苹果提供了一种替代且有前景的解决方案。除了收割机设计元素外，了解苹果树的关键树冠参数也很重要，因为它们在收获过程中紧密结合并相互作用。在这项研究中，研究了在收获试验期间垂直训练的“Scifresh”和 V 型格架种植的“Envy”树木的 11 种树冠参数对机械收获的影响。采用具有加权 k 最近邻 (kNN) 的监督机器学习算法来分析我们的冠层数据集。总体而言，2678 个地面实况数据点（苹果）被分为水果去除状态的两个二元类：“机械收获”和“机械未收获”的苹果。对于训练数据集 (85%)，所采用的算法对“Scifresh”和“Envy”的总体预测准确率分别为 76-92% 和 62-74%。对于剩余的 15% 数据集，“Scifresh”的整体测试准确率为 81-91%，但“Envy”的整体测试准确率仅为 36-79%。采用主成分分析（PCA）通过计算主成分（PCs）的系数来确定关键的冠层参数。PC1-PC5 解释了至少 80% 的数据差异。假设系数大于 0.5 为高度相关，每枝果实载量、枝基直径和枝条长度是最相关的。这些结果为种植者的树冠管理提供了指导，可以提高机械收割系统的效率。