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.

LiDAR (Light Detection and Ranging) and RGB-D camera imaging have emerged as essential tools in agricultural applications, particularly for plant size and distance measurements, enabling non-destructive, cost-effective, and precise estimation. The objective of this study was to measure the plant canopy dimensions and distance between apples using commercial LiDAR, and an RGB-D camera with a speed sprayer platform was used to determine whether LiDAR provides a higher measurement accuracy under field conditions. Data were collected in an apple orchard in Muju, Republic of Korea. Commercial 3D LiDAR, a terminal box, an RGB-D camera, a microcontroller, a power supply, and individual display monitors were integrated into a customized data acquisition (DAQ) box for LiDAR point cloud (PCD), RGB, and depth imagery data collection. Commercial software was used for data acquisition, data conversion (pcap to PCD), segmentation of regions of interest (ROI), and pre-processing of data. PCD processing and measurement consisted of data frame selection, data conversion, outlier removal, downsampling, denoising, ground point removal by filtering, voxelization, and density map generation using an open access programming language script. Depth image processing included importing raw data, shaping metadata using intrinsic camera parameters, visualizing depth images, extracting depth points, and measuring the plant canopy at the pixel level. RGB image analysis involved grayscale conversion, thresholding, segmentation of ROI, contour preparation, noise removal, and binary masking for eliminating the background. Estimated results were compared to measured results. LiDAR measurements showed the closest agreement with the measured results for plant height, canopy volume, plant spacing, and row distance, outperforming both RGB and depth imaging. Under field conditions, plant spacing and row distance were estimated with accuracies of 97.5% and 94.7%, respectively, exhibiting higher measurement accuracies than RGB and depth imagery data results. Despite some discrepancies due to complex plant geometry and dynamic data collection, the results support data collection strategies critical for precision horticulture.

Reliable assessment of crop water stress is essential for improving irrigation efficiency for upland crop and orchard fruit production. Thermal sensing combined with the crop water stress index (CWSI) provides a non-destructive approach for characterizing plant water status; however, its performance depends strongly on baseline determination, sensing platform, canopy architecture, environmental conditions, image-processing procedures, and validation strategy. This paper aimed to review thermal imaging and point-based infrared thermometry (IRT) approaches for CWSI measurement and mapping, with particular emphasis on sensing requirements of upland field crops and orchard fruit tree canopies. A PRISMA-aligned literature search was conducted using major scientific databases and complementary citation tracking, resulting in 68 primary studies included in the structured thematic synthesis. Empirical, theoretical, and artificial-reference CWSI formulations were compared together with handheld, ground-based, unmanned aerial vehicle (UAV)-mounted, fixed camera, and point-based IRT sensing configurations. Radiometric calibration, canopy segmentation, temperature extraction, spatial interpolation, and physiological validation methods were critically evaluated. The reviewed evidence indicates that image-based approaches provide valuable spatial information but remain sensitive to radiometric drift, mixed canopy–background pixels, shadowing, and baseline uncertainty, whereas point-based IRT offers high temporal resolution but depends strongly on representative sensor placement and field-of-view geometry. These limitations differed according to canopy architecture, with soil–background contamination being particularly important in incomplete field canopies and radiation-driven within-canopy heterogeneity being more important in orchards. Combining periodic spatial thermal mapping with continuous point-based monitoring provided a promising framework for precision irrigation. Future research should prioritize transferable baseline parameterization, standardized uncertainty reporting, multi-season commercial validation, benchmark datasets, and crop-specific irrigation thresholds.

Hydroponic crop production in smart greenhouses requires accurate nutrient monitoring and reliable control, especially in recirculating systems. This review critically examines sensing principles, data acquisition, control strategies, actuation, feedback, and system integration. Electrical conductivity (EC) and pH are widely used for dosing, but EC cannot quantify individual nutrient concentrations. Emerging technologies include ion-selective electrodes, solid contact and field effect sensors, optical spectroscopy, multi-sensor arrays, and Internet of Things platforms. Their deployment remains constrained by interference, drift, fouling, reference instability, calibration demands, and matrix effects. Control approaches range from threshold-based dosing and EC/pH feedback to proportional–integral–derivative, rule-based, ion-specific, and model-based strategies. Published ion-specific replenishment and hybrid EC–ion studies demonstrate feasibility under tested conditions. However, differences in nutrient formulations, study duration, and error definitions limit conclusions about commercial control accuracy. This review proposes an integrated framework linking sensing, decision making, actuation, and verification, with performance assessed at analytical, temporal, operational, and system/control levels. Future research should prioritize reliable multi-ion sensing, automated data quality checks, delivered dose verification, safe operation during faults, and communication between components. Validation should include complete crop cycles, maintenance, fault recovery, and independent sites. These requirements support evaluation of nutrient use efficiency, operational reliability, and scalability across crops and hydroponic systems under realistic production conditions.

Precision seed metering has evolved from mechanically optimized devices to sensor-based, digitally connected systems. However, research remains fragmented across metering mechanisms, sensing, data processing, communication, and control. This review examined monitoring and closed-loop control throughout the seed-delivery process, from pickup and release to tube passage, furrow placement, and crop emergence. This structured review synthesized 49 primary studies identified through Scopus and the Web of Science Core Collection, with supplementary searching using Google Scholar and literature coverage through 1 September 2026. Optical and photoelectric sensors provide the most direct evidence for seed-passage detection, whereas encoders, pressure, vacuum, motor-current, and vibration measurements primarily indicate machine or actuator conditions. Machine vision provides spatial information and can support independent reference measurements, but requires suitable imaging conditions and computational resources. Capacitive, acoustic, impact-based, and hybrid approaches remain less extensively validated. Reported performance cannot be directly compared across studies due to differences in seed characteristics, mechanisms, operating speeds, event definitions, disturbances, and reference methods. Most studies demonstrate monitoring, alarms, or open-loop optimization rather than complete sensor-to-actuator correction. An engineering framework is therefore proposed to connect measurement targets, evidence maturity, latency, actuator authority, and independent verification. Future research should prioritize standardized testing, disturbance robustness, synchronized multi-row field validation, and measured end-to-end delay. Transparent reporting of correction accuracy, stability, cost, power demand, and interoperability is also required to support deployment and defensible assessment of closed-loop seeding systems.