Edible flowers, with their increasing demand in the market, face a challenge in labor-intensive hand-picking practices, hindering their attractiveness for growers. This study explores the application of artificial intelligence vision for robotic harvesting, focusing on the fundamental elements: detection, pose estimation, and plucking point estimation. The objective was to assess the adaptability of this technology across various species and varieties of edible flowers. The developed computer vision framework utilizes YOLOv5 for 2D flower detection and leverages the zero-shot capabilities of the Segmentation Anything Model for extracting points of interest from a 3D point cloud, facilitating 3D space flower localization. Additionally, we provide a pose estimation method, a key factor in plucking point identification. The plucking point is determined through a linear regression correlating flower diameter with the height of the plucking point. The results showed effective 2D detection. Further, the zero-shot and standard machine learning techniques employed achieved promising 3D localization, pose estimation, and plucking point estimation.

Keywords: computer vision; deep neural networks; edible flowers; precision farming.

随着市场上可食用花卉需求的增加，这些花卉面临着手工采摘这一劳动密集型实践带来的挑战，这阻碍了其对种植者的吸引力。本研究探讨了人工智能视觉技术在机器人收获中的应用，重点关注检测、姿态估计和摘取点估算这三个基本要素。该研究的目标是评估这项技术在不同种类和品种的可食用花卉中的适应性。开发的计算机视觉框架利用YOLOv5进行二维花检测，并借助Segmentation Anything Model的零样本能力从三维点云中提取感兴趣点，从而实现花卉在三维空间中的定位。此外，我们还提供了一种姿态估计方法，这是摘取点识别的关键因素。通过将花朵直径与摘取点高度之间的线性关系建立回归模型来确定摘取点的位置。结果显示二维检测效果良好。进一步地，所采用的零样本和标准机器学习技术在三维定位、姿态估计和摘取点估算方面取得了令人满意的结果。

关键词：计算机视觉；深度神经网络；可食用花卉；精准农业。