Challenges in strawberry picking made selective harvesting robotic technology demanding. However, selective harvesting of strawberries is complicated forming a few scientific research questions. Most available solutions only deal with a specific picking scenario, e.g., picking only a single variety of fruit in isolation. Nonetheless, most economically viable (e.g. high-yielding and/or disease-resistant) varieties of strawberry are grown in dense clusters. The current perception technology in such use cases is inefficient. In this work, we developed a novel system capable of harvesting strawberries with several unique features. The features allow the system to deal with very complex picking scenarios, e.g. dense clusters. Our concept of a modular system makes our system reconfigurable to adapt to different picking scenarios. We designed, manufactured, and tested a picking head with 2.5 DOF (2 independent mechanisms and 1 dependent cutting system) capable of removing possible occlusions and harvesting targeted strawberries without contacting fruit flesh to avoid damage and bruising. In addition, we developed a novel perception system to localise strawberries and detect their key points, picking points, and determine their ripeness. For this purpose, we introduced two new datasets. Finally, we tested the system in a commercial strawberry growing field and our research farm with three different strawberry varieties. The results show the effectiveness and reliability of the proposed system. The designed picking head was able to remove occlusions and harvest strawberries effectively. The perception system was able to detect and determine the ripeness of strawberries with 95% accuracy. In total, the system was able to harvest 87% of all detected strawberries with a success rate of 83% for all pluckable fruits. We also discuss a series of open research questions in the discussion section.
翻译:草莓的选择性采摘挑战使得有选择的采摘机械技术要求很高。 然而,选择性采摘草莓的系统非常复杂,形成一些科学研究问题。 多数现有解决方案只涉及特定的采摘情景, 例如, 孤立地采摘单一种类的水果。 尽管如此, 大部分经济上可行的草莓品种( 如高产和/或抗病性抗药性) 生长在密集的组群中。 目前这种使用案例中的感知技术效率低下。 在这项工作中, 我们开发了一个新颖的感知系统, 能够捕捉具有若干独特特点的草莓。 这些特性使得系统能够处理非常复杂的采摘情景, 例如密集的集群。 我们的模块系统概念使我们的系统可以重新配置, 适应不同的采摘情况。 最后, 我们设计、 制造和测试了2.5 DOF(2个独立机制和1个依赖的切削系统), 能够消除可能存在的隐蔽和收获的草莓, 以避免破坏和淤烂。 此外, 我们开发了一个新型的感知系统, 用所有易碎点来检测, 并且确定它们的成熟性。 为此, 我们引入了两个新的数据讨论, 我们用新的数据结构, 正确性 正在研究 测试了一个稳定的系统, 我们用一个稳定的系统, 测试了一个稳定的系统, 测试了一个稳定的系统,, 测试了一个稳定的系统,,, 一个稳定的, 一个稳定的, 一个稳定的, 一个稳定的, 一个稳定的,, 一个系统, 一个稳定的,, 一个稳定的,, 一个稳定的,, 测试了一个稳定的,,, 一个稳定的, 一个稳定的, 一个系统, 一个稳定的, 一个稳定的, 一个稳定的, 一个稳定的, 一个稳定的, 一个稳定的, 一个稳定的, 一个系统, 一个系统, 一个稳定的, 一个稳定的, 一个系统, 一个稳定的, 一个稳定的, 一个系统, 一个稳定的, 一个稳定的, 一个稳定的, 一个, 一个稳定的, 一个系统, 一个稳定的, 一个系统, 一个稳定的, 一个系统, 一个系统, 一个系统, 一个系统, 一个稳定的, 一个系统, 一个系统, 一个稳定的, 一个稳定的, 一个系统, 一个系统, 一个系统, 一个, 一个, 一个, 一个, 一个, 一个, 一个, 一个系统, 一个系统, 一个系统, 一个, 一个, 一个, 一个, 一个, 一个, 一个, 一个, 一个, 一个, 一个, 一个, 一个