Sweet Pepper Harvesting Robots: Advances in Perception, Decision-Making, Manipulation and Mobile Systems

Abstract

Sweet pepper is a high-value crop in protected horticulture whose robotic harvesting is complicated by canopy occlusion, asynchronous ripening, the need for precise peduncle cutting, and stringent damage limits. This review presents a systematic search and narrative synthesis guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 statement, with the revised evidence base comprising 262 eligible reports/publications. Reports describing the same robot platform, trial series, or overlapping evaluation dataset were linked as companion reports within a common study/evaluation family to avoid double counting in system-level comparisons. It examines crop constraints, perception, decision-making and system integration, manipulators and end-effectors, and mobile platforms across the complete harvesting cycle. Beyond a module-based synthesis, we propose a five-stage chain—visibility, assessability, reachability, operability, and sustained operation—supported by a unified failure taxonomy, a stage-wise funnel, and a scenario–technology matching matrix. This framework explains why strong module-level results do not necessarily yield effective whole-system harvesting. Representative system-level evaluations within the direct sweet-pepper/Capsicum evidence base report harvesting success rates of approximately 18–83% and source-reported cycle times of approximately 10–69 s per fruit under differing task boundaries, although direct ranking is inappropriate because environments, crop modifications, occlusion levels, sample sizes, task boundaries, denominators, and success criteria differ. As fruit detection and basic grasping improve, losses increasingly arise from incomplete action-ready information on maturity, pose, and peduncles; narrow approach and cutting tolerances; and weak recovery, unloading, and repositioning loops. Near-term development should investigate robot-friendly greenhouses, repeatable docking, local closed-loop control, and human-supervised or collaborative operation, while directly validating productivity, safety, workload, and economic effects. Medium-term priorities are active multimodal perception, fault-tolerant grasping and cutting, and coordinated mobile manipulation. Longer-term progress will depend on agronomy–equipment co-design and long-duration validation of reliability and economic viability.