The United States (US) apple industry relies heavily on semi‐skilled manual labor force for essential field operations such as training, pruning, blossom and green fruitlet thinning, and harvesting. Blossom thinning is one of the crucial crop‐load management practices to achieve desired crop load, fruit quality, and return bloom. While several techniques such as chemical and mechanical thinning are available for large‐scale blossom thinning, such approaches often yield unpredictable thinning results and may damage the canopy, spurs, and leaf tissue. Hence, growers still depend on laborious, labor‐intensive, and expensive manual hand blossom thinning for desired thinning outcomes. This research presents a robotic solution for precision blossom thinning in apple orchards using a deep learning‐based computer vision system, a six‐degrees‐of‐freedom UR5e robotic manipulator, and an electrically actuated miniature end‐effector. The integrated robotic system was evaluated in a commercial apple orchard which showed promising results for targeted and selective blossom thinning. Two thinning approaches, center and boundary thinning, were investigated to evaluate the system's ability to remove varying proportions of flowers from apple flower clusters. During boundary thinning, the end‐effector was actuated around the cluster boundary, while center thinning involved end‐effector actuation only at the cluster centroid for a fixed duration of 2 s. Field evaluation results showed that the boundary thinning approach thinned 67.2% of flowers from the targeted clusters with a cycle time of 9.0 s per cluster, whereas the center thinning approach thinned 59.4% of flowers with a cycle time of 7.2 s per cluster. Upon further improvement for commercial adoption, the proposed system could help address problems faced by apple growers with current hand, chemical, and mechanical blossom thinning approaches.

中文翻译：

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木果作物机器人疏花系统的设计、集成和现场评估

美国苹果产业严重依赖半熟练体力劳动力进行必要的田间作业，如培训、修剪、开花和青果间疏以及收获。疏花是实现所需作物负荷、果实质量和返花的关键作物负荷管理措施之一。虽然化学和机械疏伐等多种技术可用于大规模的花朵疏伐，但这些方法通常会产生不可预测的疏伐结果，并可能损坏冠层、枝刺和叶子组织。因此，种植者仍然依靠费力、劳动密集型且昂贵的手工疏花来获得所需的疏花结果。这项研究提出了一种用于苹果园精确疏花的机器人解决方案，使用基于深度学习的计算机视觉系统、六自由度 UR5e 机器人操纵器和电动微型末端执行器。集成机器人系统在商业苹果园中进行了评估，显示出有针对性和选择性的疏花效果。研究了两种稀疏方法（中心稀疏和边界稀疏），以评估系统从苹果花簇中去除不同比例的花朵的能力。在边界细化过程中，末端执行器在簇边界周围驱动，而中心细化涉及末端执行器仅在簇质心处驱动，持续时间固定为 2 s。田间评估结果显示，边界疏伐方法疏除了目标簇中 67.2% 的花朵，每簇循环时间为 9.0 秒，而中心疏伐方法疏除了 59.4% 的花朵，每簇循环时间为 7.2 秒。在进一步改进商业应用后，所提出的系统可以帮助解决苹果种植者采用当前手工、化学和机械疏花方法所面临的问题。