Efficient management of soil nutrients is essential for optimizing crop production, ensuring sustainable agricultural practices, and addressing the challenges posed by population growth and environmental degradation. Smart agriculture, using advanced technologies, plays an important role in achieving these goals by enabling real-time monitoring and precision management of nutrients. In open-field soil cultivation, spatial variability in soil properties demands site-specific nutrient management and integration with variable-rate technology (VRT) to optimize fertilizer application, reduce nutrient losses, and enhance crop yields. Hydroponic solution cultivation, on the other hand, requires precise monitoring and control of nutrient solutions to maintain optimal conditions for plant growth, ensuring efficient use of water and fertilizers. This review aims to explore recent trends in soil and solution nutrient sensing technologies for open-field soil and facilitated hydroponic cultivation, highlighting advancements that promote efficiency and sustainability. Key technologies include electrochemical and optical sensors, Internet of Things (IoT)-enabled monitoring, and the integration of machine learning (ML) and artificial intelligence (AI) for predictive modeling. Blockchain technology is also emerging as a tool to enhance transparency and traceability in nutrient management, promoting compliance with environmental standards and sustainable practices. In open-field soil cultivation, real-time sensing technologies support targeted nutrient application by accounting for spatial variability, minimizing environmental risks such as runoff and eutrophication. In hydroponic solution cultivation, precise solution sensing ensures nutrient balance, optimizing plant health and productivity. By advancing these technologies, smart agriculture can achieve sustainable crop production, improved resource efficiency, and environmental protection, fostering a resilient food system.

Management of smart greenhouses represents a transformative advancement in precision agriculture, enabling sustainable intensification of food production through the integration of multi-sensor networks, intelligent control, and sophisticated data filtering techniques. Unlike conventional greenhouses that rely on manual monitoring, smart greenhouses combine environmental sensors, Internet of Things (IoT) platforms, and artificial intelligence (AI)-driven decision making to optimize microclimates, improve yields, and enhance resource efficiency. This review systematically investigates three key technological pillars, multi-sensor monitoring, intelligent control, and data filtering techniques, for smart greenhouse environment management. A structured literature screening of 114 peer-reviewed studies was conducted across major databases to ensure methodological rigor. The analysis compared sensor technologies such as temperature, humidity, carbon dioxide (CO₂), light, and energy to evaluate the control strategies such as IoT-based automation, fuzzy logic, model predictive control, and reinforcement learning, along with filtering methods like time- and frequency-domain, Kalman, AI-based, and hybrid models. Major findings revealed that multi-sensor integration enhanced precision and resilience but faced changes in calibration and interoperability. Intelligent control improved energy and water efficiency yet required robust datasets and computational resources. Advanced filtering strengthens data integrity but raises concerns of scalability and computational cost. The distinct contribution of this review was an integrated synthesis by linking technical performance to implementation feasibility, highlighting pathways towards affordable, scalable, and resilient smart greenhouse systems.

Advancement of precision agriculture increasingly relies on cost-effective and scalable technologies for real-time environmental management, particularly in greenhouse environments where vertical and spatial microclimate heterogeneity influences crop performance. This study presents the design, implementation, and experimental validation of an Android-based smartphone application edge supervisory monitoring system integrated with multi-layer wireless sensing and control nodes for real-time monitoring in a smart greenhouse. The system combined multi-layer wireless sensor nodes, wireless control nodes, a Long-Range Wide Area Network (LoRaWAN) gateway, Message Queuing Telemetry Transport (MQTT) communication, and a cloud-synchronized smartphone-based supervisory interface for visualizing environmental data, detecting defined abnormal events, and controlling actuators remotely. For feasibility tests, 54 sensing nodes and 12 actuator nodes were deployed across three vertical layers in two sections, measuring temperature, humidity, CO₂ concentration, and light intensity. Abnormality was defined as environmental threshold violations, statistical signal deviations, actuator power inconsistencies, and communication timeout events. Experimental results revealed vertical and spatial environmental variability across greenhouse sections, while real-time time-series and 3D spatial maps enabled the rapid detection of abnormal conditions. The rule-based abnormality detection engine identified out-of-range environmental values and sensor-related inconsistencies and generated immediate notifications. Smartphone profiling revealed that display and system-level processes accounted for energy consumption, with battery power reaching a peak of 3.5 W and application CPU utilization ranging from 40% to 70% during active monitoring. The results demonstrate system-level feasibility, responsiveness, and scalability under commercial greenhouse workloads, supporting future integration of predictive control and energy-efficient operation.

Cabbage (Brassica rapa L. ssp. Pekinensis) is an important vegetable crop in the Republic of Korea, due to its essential role in kimchi production. However, labor shortages and an aging population necessitate mechanization to sustain productivity. This study aimed to evaluate the field performance of a cabbage transplanter under development with a commercial transplanter and a cabbage collector under greenhouse conditions. This study evaluated transplanting efficiency, planting performance, and yield of cabbage using seedlings at three distinct age groups (30, 35, and 43 days). A cabbage transplanter (Transplanter A) under development, a commercial model (Transplanter B), and manual transplanting were used for comparative analysis. At harvest, a tractor-mounted cabbage collector was used to collect and pack all the cabbages. Transplanter A demonstrated a forward speed of 1.27 km/h and an average planting rate of 2365 seedlings/h, significantly higher than manual transplanting (513 seedlings/h). The effective field capacity (EFC) ranged from 0.11 to 0.13 ha/h, compared to 0.019–0.028 ha/h for manual planting. While Transplanter A showed a higher missing transplant rate (18.17%) than Transplanter B (7.67%), it maintained consistently lower bad planting rates (2.5–4.5%) compared to Transplanter B (3.3–8.8%). In addition, it produced significantly higher cabbage weights (6070 g/plant) and better root metrics than manual transplanting. The cabbage collector achieved 100% efficiency with no crop damage or contamination. The transplanter under development proved effective for greenhouse use, offering faster operation, better planting accuracy, and higher yields, supporting broader mechanization in Korean agriculture.

IoT-based smart greenhouse sensing, real-time signal conditioning and abnormality detection are still predominantly executed at gateway or cloud levels, limiting responsiveness and increasing vulnerability to noise-induced false alarms. This study proposes and experimentally validates a mobile-edge signal processing and abnormality detection framework executed entirely within an Android-based smartphone application, eliminating dependence on continuous cloud-side analytics. Environmental data from 27 wireless sensor nodes measuring temperature, relative humidity, CO₂ concentration, and light intensity were processed in real time using a sliding-window moving-average filter (N = 6) implemented with O(1) computational complexity. Abnormal conditions were determined via thresholding combined with temporal majority voting validation to suppress transient violations. Performance was also evaluated with direct threshold-based detection on raw signals to assess the effect of mobile-side filtering and temporal majority validation on abnormal sample counts, event fragmentation, and detection consistency. Mobile application side signal conditioning reduced short-term variance by 35–55% while maintaining an effective delay below two sampling intervals. Event-level analysis demonstrated substantial consolidation of noise-induced detections, reducing abnormal event frequency by up to 69% and increasing median event duration from 5 to 38 min for temperature, with negligible detection bias (±1.1%). End-to-end processing latency remained bounded under sustained multi-node streaming, with median delays of 1.0–1.6 s and 95th-percentile delays below 4.0 s. These results demonstrate that lightweight mobile-edge signal conditioning can significantly enhance detection robust-ness, reduce false alarms, and achieve low-latency environmental monitoring in green-houses. The proposed framework provides scalable and computationally efficient architecture for real-time abnormality detection in precision agriculture systems.

Environmental factors like light, photoperiod, temperature, and water are vital for crop growth, and even slight deviations from their optimal ranges can cause seedling stress and reduce yield. Therefore, this study aimed to quantify seedling stress symptoms using image features analysis under varying light, photoperiod, temperature, and water conditions. Seedlings were grown under controlled low, normal, and high environmental conditions. Light intensity at 50 µmol m⁻² s⁻¹ (low), 250 µmol m⁻² s⁻¹ (normal), and 450 µmol m⁻² s⁻¹ (high), photoperiod cycles, 8/16 h (day/night) (low), 10/14 h (day/night) (normal), and 16/8 h (day/night) (high) day/night, temperature at 20 °C (low), 25 °C (normal), and 30 °C (high), and water availability at 1 L per day (optimal), 1 L every two days (moderate stress), and 1 L every three days (severe stress) were applied for 15 days. Commercial low-cost RGB, thermal, and depth sensors were used to collect data every day. A total of 1080 RGB images, which were pre-processed with histogram equalization and filters (Median and Gaussian), were used for noise reduction to minimize illumination effects. Morphological, color, and texture features were then analyzed using ANOVA (p < 0.05) to assess treatment effects. The result shows that the maximum canopy area for tomato was 115,226 pixels, while lettuce’s maximum plant height was 9.28 cm. However, 450 µmol m⁻² s⁻¹ light intensity caused increased surface roughness, indicating stress-induced morphological alteration. The analysis of Combined Stress Index (CSI) values indicated that the highest stress levels were 50% for pepper, 55% for tomato, 62% for cucumber, 55% for watermelon, 50% for lettuce, and 50% for pak choi. The findings showed that image-based stress detection enables precise environmental control and improves early-stage crop management.