Due to the complex growth positions of dragon fruit and the difficulty in robotic picking, this paper proposes a six degrees of freedom dragon fruit picking robot and investigates the manipulator’s motion characteristics to address the adaptive motion issues of the picking manipulator. Based on the agronomic characteristics of dragon fruit cultivation, the structural design of the robot and the dimensions of its manipulator were determined. A kinematic model of the dragon fruit picking robot based on screw theory was established, and the workspace of the manipulator was analyzed using the Monte Carlo method. Furthermore, a dynamic model of the manipulator based on the Kane equation was constructed. Performance experiments under trajectory and non-trajectory planning showed that trajectory planning significantly reduced power consumption and peak torque. Specifically, Joint 3’s power consumption decreased by 62.28%, and during the picking, placing, and resetting stages, the peak torque of Joint 4 under trajectory planning was 10.14 N·m, 12.57 N·m, and 16.85 N·m, respectively, compared to 12.31 N·m, 15.69 N·m, and 22.13 N·m under non-trajectory planning. This indicated that the manipulator operates with less impact and smoother motion under trajectory planning. Comparing the dynamic model simulation and actual testing, the maximum absolute error in the joint torques was −2.76 N·m, verifying the correctness of the dynamic equations. Through field picking experiments, it was verified that the machine’s picking success rate was 66.25%, with an average picking time of 42.4 s per dragon fruit. The manipulator operated smoothly during each picking process. In the study, the dragon fruit picking manipulator exhibited good stability, providing the theoretical foundation and technical support for intelligent dragon fruit picking.

Carrying out quality grading detection on the harvested dragon fruit is an important step in the dragon fruit industry. To reduce the high costs and damage rates caused by this process, an online spectral sensor and a weighing sensor embedded at the end effector of the dragon fruit-picking robot were designed to detect the sugar content, hardness and weight of the dragon fruits in real time during the picking process, thereby achieving the quality classification of the dragon fruits. After collecting the spectral data of dragon fruit, typical linear and nonlinear machine learning methods were used to establish prediction models for SSC-edge, SSC-center and hardness of dragon fruit. The results showed that PLSR models were selected as optimal models for prediction sugar content and hardness, and R² of test set for SSC-edge, SSC-center and hardness are 0.876, 0.826 and 0.902, respectively. Subsequently, the dragon fruits were classified based on the weighing sensor, and the SSC-center and hardness were predicted. The results showed that the established quality prediction model and the prototype could achieve the integrated operation of non-destructive quality detection and grading of dragon fruit during picking. The study provides technical support for the intelligent upgrade of fruit-harvesting equipment and the grading operations.