Early-stage identification of fruit flowers that are in both opened and unopened condition in an orchard environment is significant information to perform crop load management operations such as flower thinning and pollination using automated and robotic platforms. These operations are important in tree-fruit agriculture to enhance fruit quality, manage crop load, and enhance the overall profit. The recent development in agricultural automation suggests that this can be done using robotics which includes machine vision technology. In this article, we proposed a vision system that detects early-stage flowers in an unstructured orchard environment using YOLOv5 object detection algorithm. For the robotics implementation, the position of a cluster of the flower blossom is important to navigate the robot and the end effector. The centroid of individual flowers (both open and unopen) was identified and associated with flower clusters via K-means clustering. The accuracy of the opened and unopened flower detection is achieved up to mAP of 81.9% in commercial orchard images.

翻译：摘要：在果园环境中，识别处于开放与未开放状态的早花期果实花朵，对于利用自动化及机器人平台实施作物负载管理操作（如疏花与授粉）具有重要信息价值。这些操作在果树农业中至关重要，能够提升果实品质、调控作物负载并增加整体收益。近年来农业自动化的发展表明，结合机器视觉技术的机器人系统可实现此类操作。本文提出了一种基于YOLOv5目标检测算法的视觉系统，用于在非结构化果园环境中检测早花期花朵。在机器人实现中，花簇在花朵盛开期的空间定位对于导航机器人及末端执行器至关重要。通过K均值聚类算法，分别识别了开放与未开放花朵的质心，并将其与花簇关联。在商业果园图像中，开放与未开放花朵的检测精度最高可达平均精度均值（mAP）81.9%。