Greenhouse production of fruits and vegetables in developed countries is challenged by labor 12 scarcity and high labor costs. Robots offer a good solution for sustainable and cost-effective 13 production. Acquiring accurate spatial information about relevant plant parts is vital for 14 successful robot operation. Robot perception in greenhouses is challenging due to variations in 15 plant appearance, viewpoints, and illumination. This paper proposes a keypoint-detection-based 16 method using data from an RGB-D camera to estimate the 3D pose of peduncle nodes, which 17 provides essential information to harvest the tomato bunches. 18 19 Specifically, this paper proposes a method that detects four anatomical landmarks in the color 20 image and then integrates 3D point-cloud information to determine the 3D pose. A 21 comprehensive evaluation was conducted in a commercial greenhouse to gain insight into the 22 performance of different parts of the method. The results showed: (1) high accuracy in object 23 detection, achieving an Average Precision (AP) of [email protected]=0.96; (2) an average Percentage of 24 Detected Joints (PDJ) of the keypoints of [email protected]=94.31%; and (3) 3D pose estimation 25 accuracy with mean absolute errors (MAE) of 11.38o and 9.93o for the relative upper and lower 26 angles between the peduncle and main stem, respectively. Furthermore, the capability to handle 27 variations in viewpoint was investigated, demonstrating the method was robust to view changes. 28 However, canonical and higher views resulted in slightly higher performance compared to other 29 views. Although tomato was selected as a use case, the proposed method is also applicable to 30 other greenhouse crops like pepper.

翻译：发达国家果蔬温室生产面临着劳动力短缺和成本高昂的挑战。机器人技术为可持续且经济高效的生产提供了良好解决方案。获取植物相关部位的精准空间信息对于机器人成功作业至关重要。由于植物外观、视角和光照条件的变化，温室中的机器人感知面临挑战。本文提出一种基于关键点检测的方法，利用RGB-D相机数据估计果柄节点的三维姿态，为番茄串采摘提供关键信息。 具体而言，本文提出一种方法：在彩色图像中检测四个解剖学标志点，然后结合三维点云信息确定三维姿态。为探究该方法各组成部分的性能，在商业温室中开展了综合评估。结果表明：（1）目标检测精度高，平均精度（AP）达到[email protected]=0.96；（2）关键点的平均检测关节百分比（PDJ）为[email protected]=94.31%；（3）三维姿态估计精度方面，果柄与主茎相对上、下角度的平均绝对误差（MAE）分别为11.38°和9.93°。此外，对视角变化的适应能力进行了研究，表明该方法对视角变化具有鲁棒性。但相较于其他视角，正视观察和较高视角下的表现略优。尽管以番茄为应用案例，该方法同样适用于辣椒等其他温室作物。