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.
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Liu et al. (2026) studied this question.
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