Abstract
Dormant pruning, or the removal of unproductive portions of a tree while a tree is not actively growing, is an important orchard task to help maintain yield, requiring years to build expertise. Because of long training periods and an increasing labor shortage in agricultural jobs, pruning could benefit from robotic automation. However, to program robots to prune branches, we first need to understand how pruning decisions are made, and what variables in the environment (e.g., branch size and thickness) we need to capture. Working directly with three pruning stakeholders -- horticulturists, growers, and pruners -- we find that each group of human experts approaches pruning decision-making differently. To capture this knowledge, we present three studies and two extracted pruning protocols from field work conducted in Prosser, Washington in January 2022 and 2023. We interviewed six stakeholders (two in each group) and observed pruning across three cultivars -- Bing Cherries, Envy Apples, and Jazz Apples -- and two tree architectures -- Upright Fruiting Offshoot and V-Trellis. Leveraging participant interviews and video data, this analysis uses grounded coding to extract pruning terminology, discover horticultural contexts that influence pruning decisions, and find implementable pruning heuristics for autonomous systems. The results include a validated terminology set, which we offer for use by both pruning stakeholders and roboticists, to communicate general pruning concepts and heuristics. The results also highlight seven pruning heuristics utilizing this terminology set that would be relevant for use by future autonomous robot pruning systems, and characterize three discovered horticultural contexts (i.e., environmental management, crop-load management, and replacement wood) across all three cultivars.
Abstract (translated)
休眠修剪，即在树木非积极生长期间，修剪掉无产量的树段，是保持产量的园艺重要任务，需要多年时间来培养专业知识。由于长时间的培训期和农业就业岗位劳动力的不断减少，修剪可能会从机器人自动化中受益。然而，要编程机器人进行修剪，我们首先需要了解修剪决定的制定过程，以及我们需要捕捉的环境变量的内容。与三个修剪 stakeholders（即园艺师、种植者和修剪者）直接合作，我们发现每个团队的人类专家在修剪决策上有所不同。为了捕捉这一知识，我们在2022年1月和2023年的华盛顿州普雷西园艺中心现场工作中，展示了三个研究和从2022年1月和2023年的普雷西园艺中心现场工作中提取的两个修剪协议。我们对六个利益相关者（每个组别两人）进行了采访，并观察了来自三种栽培品种—— Bing 樱桃、Envy 苹果和Jazz 苹果的修剪情况，以及两种树架构——立式果树出芽和V形平顶树架构。利用参与者的访谈和视频数据，本分析使用 grounded coding 提取了修剪术语，发现了影响修剪决策的园艺背景，并找到了可应用于自治系统的实现性修剪技巧。结果包括验证的术语集，我们将其提供给修剪利益相关者和机器人使用，以传达一般修剪概念和技巧。结果还突出了七个利用此术语集的修剪技巧，这些技巧对于未来自主机器人修剪系统具有重要意义，并描述了所有三个栽培品种中发现的三个园艺背景（即环境管理、负荷管理和替换木）。
URL
https://arxiv.org/abs/2405.04030
