Aiming to address the challenges of dynamic end-effector payload variation, insufficient environmental interaction compliance, and limited trajectory tracking accuracy in strawberry harvesting robots, a fully decoupled parallel wrist joint based on an RPU-RPR-UPR configuration was designed, and its inverse kinematic model was established. To improve the positioning accuracy of the manipulator’s end-effector, a complete system dynamics model was developed, and an adaptive sliding mode control (ASMC) algorithm was implemented. Model uncertainties were estimated and compensated in real time by the ASMC, effectively alleviating the chattering phenomenon associated with conventional sliding mode control. As a result, the average tracking errors of the wrist joint’s α, β, and h degrees of freedom were reduced to 0.0758°, 0.0771°, and 0.0414mm, respectively, significantly outperforming both PID control and traditional sliding mode control (SMC) in terms of trajectory tracking accuracy and system robustness. To enhance harvesting smoothness, a fuzzy inference-based variable admittance controller was developed. Admittance parameters were dynamically adjusted according to interaction forces and system state errors, enabling autonomous modulation of the robot’s stiffness-compliance characteristics and thereby improving its disturbance rejection capability and environmental adaptability. Simulation results showed that, under external disturbances of 10N and 20N, the proposed fuzzy variable admittance control reduced overshoot by 12.5% and 17.8%, and shortened settling time by 0.14s and 0.25s, respectively, compared with fixed-parameter admittance control-demonstrating superior dynamic performance. The designed parallel wrist joint was integrated into a mobile strawberry harvesting platform. Acceleration response tests revealed that, during end-effector load variations, compliant control reduced the average acceleration fluctuation amplitude by 45.5% compared with position control, indicating excellent dynamic compliance. In harvesting trials, a success rate of 93% was achieved with an average cycle time of 15s per fruit, satisfying practical requirements for harvesting efficiency and reliability.