Root and tuber vegetables, including potatoes, radishes, sweet potatoes, and carrots, are important components of Korean upland agriculture, yet harvesting and yield determination remain less mechanized and digitally integrated than cereal production. This review integrated contact- and weight-based yield measurement technologies applicable to Korean root and tuber crop harvesting, with emphasis on load-cell systems, conveyor weighing, hopper/bin weighing, impact-force sensing, and integrated multi-sensor approaches. Korean and international studies were checked in relation to sensing principles, harvester integration, calibration, dynamic measurement, spatial yield mapping, and field applicability. The literature shows that direct load-cell-based measurement provides the most physically interpretable basis for harvested mass estimation, while conveyor systems are particularly suitable for continuously conveyed crops such as potatoes and radishes. Hopper and cart weighing provide practical alternatives for semi-mechanized operations, including sweet potato harvesting, whereas impact sensing offers compact measurement but is more sensitive to crop trajectory, vibration, inclination, and mechanical damage. Korean studies have demonstrated promising sensor-level performance for potatoes and radishes; however, field-scale implementation remains constrained by vibration, slope, soil and debris contamination, tare drift, crop transport delay, limited standardization, and differences in harvester architecture. Integration of direct mass sensing with GNSS, IMU, belt-speed sensing, machine-state information, and machine vision provides a promising pathway toward reliable yield mapping. The synthesis indicates crop-specific pathways: continuous conveyor weighing after soil separation for potatoes; dynamically compensated event/impact sensing for radishes; low-impact hopper, cart, or batch weighing for sweet potatoes; and post-cleaning weighing for carrots. The main remaining gap is integrated field validation under realistic throughput, vibration, slope, headland transitions, and field-scale conditions. Future studies should report accuracy together with throughput, calibration burden, spatial-lag error, contamination effects, cost, and field repeatability.

Reliable operation of smart vertical farms requires coordinated sensing, actuation, communication, and control, yet sensor and actuator faults can compromise environmental regulation. This structured narrative review examined signal-processing methods for operational monitoring, anomaly detection, and fault isolation in vertical farms and related controlled-environment systems. The PRISMA-style diagram reports 410 identified records and 78 cited sources. A four-tier framework distinguished direct vertical-farm evidence, adjacent controlled-environment studies, transferable engineering research, and foundational methods. Statistical monitoring and change detection were suitable for slowly varying environmental and nutrient measurements, whereas frequency and time-frequency analyses were more appropriate for electrical, vibration, and acoustic signals. Sensor redundancy, correlation analysis, and model-based residuals supported fault isolation by comparing actuator commands with measured responses. Available evidence was stronger for hydroponic sensor faults than for progressive actuator degradation in commercial vertical farms. Crop development, photoperiod, irrigation, and operating-state changes could also produce anomalies without equipment failure. Reliable diagnosis therefore requires synchronized signals, contextual metadata, state-specific baselines, and independent fault confirmation. Evaluation should consider false alarms, detection delay, isolation accuracy, computational demand, and generalization across facilities. Future priorities include benchmark datasets, multimodal diagnosis, edge implementation, digital-twin-supported evaluation, and safe fault-tolerant control under varying crop stages, equipment conditions, and control schedules.