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.