Agricultural machinery operates under variable loads, impacts, dust, and changing soil–crop interactions, allowing faults to propagate through energy, material, information, and control pathways. This qualitative review synthesizes 195 research publications across fault formation, trustworthy diagnosis, prognostics and proactive risk control, maintenance and recovery, and case-based evidence assessment. Empirical findings are reported separately from review-derived recommendations, the authors’ conceptual requirements, and mandatory provisions of applicable standards or law. The literature most consistently supports controlled-fault identification, selected single-machine field monitoring, and localized operational compensation. Evidence is weaker for transfer across machines and seasons, calibrated prognostics, safety authorization, post-repair verification, and fleet-scale deployment. On this basis, the review recommends mission profile-specific fault boundaries, traceable diagnostic outputs, explicit uncertainty and abstention, and risk decisions linked to remaining work and resources. It also proposes a diagnostic passport, risk evolution trajectory, repair-effectiveness label, case-evidence matrix, and closed-loop repair workflow as review-level organizing constructs. No included study continuously followed the same machine or fleet through diagnosis, prognosis, authorization, maintenance, acceptance, and feedback; the framework therefore connects evidence-supported stages theoretically rather than claiming end-to-end validation.

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.

Strawberry production is economically important but remains highly dependent on labour-intensive harvesting. The delicate texture, irregular distribution, and non-uniform maturity of strawberry fruit create substantial challenges for mechanised and robotic operations. This review examines the development of strawberry-harvesting technologies from the broader perspectives of crop value, cultivation management, harvesting methods, robotic systems, post-harvest handling, and sustainable production. The nutritional and economic significance of strawberries is first outlined, followed by an analysis of cultivation environments, production patterns, and crop-management practices that influence fruit accessibility and robotic operation. The historical transition from manual harvesting to mechanised and intelligent harvesting is then reviewed. Particular attention is given to the principal technologies of strawberry-harvesting robots, including mobile platforms, robotic manipulators, path planning and obstacle avoidance, end-effectors, visual recognition, multispectral sensing, and software control. Robotic systems designed for elevated and ridge-based cultivation are also compared to clarify the influence of cultivation layout on platform configuration and harvesting strategy. In addition, the integration of harvesting with fruit transfer, post-harvest handling, and sustainable cultivation is discussed. The reviewed studies indicate that effective robotic harvesting depends on the coordinated design of cultivation systems, perception, motion planning, compliant manipulation, and system control. Future research should prioritise robust perception under occlusion, low-damage harvesting, improved operational speed, scenario adaptability, cost reduction, and closer integration between agronomic practices and robotic design.

Control of agricultural machinery is shifting from direct operator input toward closed-loop regulation coordinated by sensors, controllers, and actuators. This transition places greater demands on real-time performance, reliability, and practical deployability because field environments are complex and operating loads vary continuously. Existing reviews generally address automated navigation, electrohydraulic control, electrification, or control algorithms in isolation; few systematically compare coupling among all elements of the electrical control loop or the strength of the supporting evidence. This study therefore combines a topic-oriented structured narrative review with evidence mapping and critical synthesis for field agricultural machinery and implements. The proposed framework comprises five functional domains: sensing and state estimation, controllers and control hardware, control methods, actuation and drives, and communication and power supply. These domains are mapped to representative applications, including tractor–implement coordination, seeding and fertilization, crop protection, harvesting, and autonomous operation. Current closed-loop applications predominantly regulate vehicle motion, actuators, and process states. By contrast, operation-quality loops that directly use agronomic outcomes, such as seed placement quality, spray deposition, or harvest loss, as feedback remain uncommon. Overall system performance is jointly constrained by sensing, computation, communication, actuation, and power delivery, whereas heterogeneity in machines and validation conditions complicates direct comparison across technologies. By linking component-level performance to whole-machine outcomes within a common analytical framework, this review provides a consistent basis for assessing the practical applicability of electrical control technologies in agricultural machinery. Future work should prioritize coordinated design across functional domains, real-time deployment under resource constraints, safe degradation after faults, and long-term, multi-site, and multi-season field validation using final operation quality as the primary evaluation endpoint.