jabbrv-ltwa-all.ldf jabbrv-ltwa-en.ldf With the aging population and labor shortages, the proportion of labor costs in tomato harvesting is increasing, making the development of tomato harvesting robots imperative. This study developed an integrated tomato bunch harvesting robotic system for cherry tomatoes. A combined cutting and gripping end-effector powered by a single actuator, achieving a cutting success rate of 93.33% and a gripping capacity of 1600 g. A parameterized camera arrangement was employed to match the robotic arm’s field of view, thereby avoiding mutual interference. A tomato bunch and stalk recognition model was constructed based on the YOLOv4 algorithm to enable precise localization of picking points. The proposed tomato bunch–stalk matching method achieved a recall rate of 99.22%, while the fuzzy discrimination method for stalk growth posture attained an accuracy of 97%. Field experiments demonstrated that the system achieved an average harvesting time of 12.23 seconds per tomato bunch and an overall picking success rate of 70.77% in unstructured environments, improving automation and operational efficiency compared to existing solutions. This research offers a solution integrating hardware optimization and perception algorithms for greenhouse harvesting robots, demonstrating potential for commercial application.