Understanding greenhouse microclimatic variability is essential for precise environmental monitoring and control. This study evaluated temperature, relative humidity, CO2 concentration, and light intensity variability in Korean greenhouses during summer and winter, and developed artificial neural network (ANN) models to predict indoor temperature and relative humidity at different layers. A glass greenhouse and an arched-frame double-layer plastic greenhouse were monitored during summer and winter, respectively. A wireless sensor network was deployed at multiple spatial positions and vertical layers, and layer-specific artificial neural network (ANN) models were developed to predict indoor temperature and relative humidity at the top, middle, and bottom layers. The measured results revealed clear temperature and humidity stratification, with the top layer generally showing a higher temperature and lower humidity than the middle and bottom layers. In summer, temperatures reached 36.4 degrees C, while relative humidity ranged from 55% to 92%, while in winter, temperature varied from 3.4 degrees C to 35.0 degrees C and relative humidity ranged from 73% to 91%. Spatial contour mapping showed clear microclimatic gradients, and ANOVA with Tukey's HSD tests confirmed significant differences among sensor locations (p < 0.05). The ANN models predicted indoor temperature with high accuracy, with R-2 values generally above 0.95, while humidity prediction showed larger errors.

Interest in light detection and ranging (LiDAR) for the precise monitoring of vegetative growth of grain crops has increased. The study was conducted to estimate wheat size and plant distance using LiDAR and the convex hull method (CHM) compared to the voxel grid method (VGM). A commercial LiDAR system was used for data collection in the middle and late growth stages using static and dynamic scanning. A small number (ten) of data frames, consisting of a region of interest (ROI) of 1 m × 0.9 m for each frame, were selected as data samples. The data processing workflow consisted of data conversion, targeted data frame selection, visualization, region of interest (ROI) segmentation, outlier and untargeted point removal, downsampling, denoising, voxelization, preparation of the convex hull, and 3D PCD density map. To estimate the plant size and distance of wheat, the results obtained using CHM and VGM were compared with measured data results, and both methods were applied for the middle and late growth stages of wheat. The relative accuracy of LiDAR-estimated plant height, canopy volume, plant spacing, and row distances with respect to the measured results were 94%, 87%, 94%, and 87%, respectively, using CHM, and 76%, 72%, 62%, and 71% by VGM for static data scanning; for dynamic scanning, the estimated relative accuracy percentages were 87%, 91%, 94%, and 93%, respectively, using CHM, and 77%, 74%, 75%, and 74%, respectively, using VGM. The same methods were applied to the late growth stage data sets. Between the two methods, CHM provided higher accuracy for static and dynamic data-scanning approaches in the middle and late growth stages because the complex geometry of plants, thin and sparse leaf area, and structure complicated voxelization. Despite several challenges in PCD collection and processing, this study supports size and distance estimation for wheat and similar grains as non-destructive methods.

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.

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.

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.

Ultrasonic sensing technology can contribute significantly to improving smart agricultural practices by recognizing plants and land features. Accurate detection of these field features is essential for the development of unmanned vehicles, which require precision navigation, obstacle avoidance, and successful field operation. Therefore, the objectives of the study were to employ ultrasonic sensors to detect key parameters of pepper plants and land features, specifically plant height, canopy volume, row spacing, and ridge spacing. Row spacing is the space between rows of plants, and ridge features are the raised soil beds that are often made for planting in upland farming systems. A data collection device was developed and tested in both laboratory and open-field environments. Initially, laboratory tests were conducted to evaluate the sensor accuracy of pepper plant height and canopy volume detection. Following successful validation, field trials were carried out in a pepper cultivation area using a remote-controlled vehicle platform to measure plant height, canopy volume, and row and ridge spacing. An open-source application was used to collect data and visualize the outcomes in real-time. The algorithm presented in the study effectively estimated the height, canopy volume, row spacing, and ridge spacing for pepper plants and associated land features. The results showed plant height of 61.34 and 61.49 cm, canopy volume of 0.29 and 0.31 m³, ridge spacing of 28.88 and 28.94 cm, and row spacing of 44.42 and 43.88 cm, respectively. No significant differences (p>0.05) were found between the measured and estimated plant and land features. Estimation values were strongly correlated with the measured values, with simple linear coefficients of determination (r2) of 0.95, 0.93, 0.88, and 0.81 for height, canopy volume, row spacing, and ridge spacing, respectively. The RMSE of these measurements ranged from 0.93 to 2.08 cm, highlighting relatively high accuracy of the proposed methods. The developed system shows the potential of ultrasonic sensors to develop automatic crop monitoring systems and support smart crop production and be adaptable to greenhouses, open fields or on-farm vehicles to identify different types of plants and land features.

Object detection is crucial for smart apple orchard management using agricultural machinery to avoid obstacles. The objective of this study was to detect apple trees and other objects in an apple orchard using LiDAR and the YOLOv5 algorithm. A commercial LiDAR was attached to a tripod to collect apple tree trunk data, which were then pre-processed and converted into PNG images. A pre-processed set of 1500 images was manually annotated with bounding boxes and class labels (trees, water tanks, and others) to train and validate the YOLOv5 object detection algorithm. The model, trained over 100 epochs, resulted in 90% precision, 87% recall, mAP@0.5 of 0.89, and mAP@0.5:0.95 of 0.48. The accuracy reached 89% with a low classification loss of 0.001. Class-wise accuracy was high for water tanks (96%) and trees (95%), while the “others” category had lower accuracy (82%) due to inter-class similarity. Accurate object detection is challenging since the apple orchard environment is complex and unstructured. Background misclassifications highlight the need for improved dataset balance, better feature discrimination, and refinement in detecting ambiguous objects.

Pakchoi and Tatsoi are agriculturally and commercially important subspecies of Brassica rapa. Dacheongchae is a new crop generated via the hybridization of Tatsoi and Pakchoi. Metabolite profiles of carbohydrates, sugar alcohols, amines, amino acids, carotenoids, phenolics, organic acids, and glucosinolates were carried out in the three B. rapa cultivars. The majority of amino acids were higher in Dacheongchae than in Pakchoi and Tatsoi. In addition to the amino acid content, higher contents of phenolic compounds and carotenoids were obtained in Dacheongchae. Similarly, Dacheongchae and Pakchoi contained higher amounts of glucosinolates compared with Tatsoi. Pakchoi, Tatsoi, and Dacheongchae showed marked antimicrobial activity against Bacillus cereus, Escherichia coli, Candida albicans, Pseudomonas aeruginosa, Proteus mirabilis, and methicillin-resistant P. aeruginosa. Furthermore, Dacheongchae extracts exhibited only the inhibition activity of Salmonella paratyphi. Consistent with these higher amounts of bioactive compounds, Dacheongchae exhibited higher antimicrobial activities, suggesting synergistic antimicrobial properties from these bioactive compounds in Dacheongchae.

Pig farming is experiencing significant transformations, driven by technological advancements, which have greatly improved management practices and overall productivity. Sound-based technologies are emerging as a valuable tool in enhancing precision pig farming. This review explores the advancements in sound-based technologies and their role in improving precision pig farming through enhanced monitoring of health, behavior, and environmental conditions. When strategically placed on farms, non-invasive technologies such as microphones and sound sensors can continuously collect data without disturbing the animals, making them highly efficient. Farmers using sound data, can monitor key factors such as respiratory conditions, stress levels, and social behaviors, leading to improved animal welfare and optimized production. Advancements in sensor technology and data analytics have enhanced the capabilities of sound-based precision systems in pig farming. The integration of machine learning and artificial intelligence (AI) is further enhancing the capacity to interpret complex sound patterns, enabling the automated detection of abnormal behaviors or health issues. Moreover, sound-based precision technologies offer solutions for improving environmental sustainability and resource management in pig farming. By continuously monitoring ventilation, feed distribution, and other key factors, these systems optimize resource use, reduce energy consumption, and detect stressors such as heat and poor air quality. The integration of sound technologies with other precision farming tools, such as physiological monitoring sensors and automated feeding systems, further enhances farm management and productivity. However, despite the advantages, challenges remain in terms of low accuracy and high initial costs, and further research is needed to improve specificity across different pig breeds and environmental conditions. Nonetheless, acoustic technologies hold immense promise for pig farming, offering enhanced management, an optimized performance, and improved animal welfare. Continued research can refine these tools and address the challenges, paving the way for a more efficient, profitable, and sustainable future for the industry.

Sensing distance and speed have crucial effects on the data of active and passive sensors, providing valuable information relevant to crop growth monitoring and environmental conditions. The objective of this study was to evaluate the effects of sensing speed and sensor height on the variation in proximal canopy reflectance data to improve rice vegetation monitoring. Data were collected from a rice field using active and passive sensors with calibration procedures including downwelling light sensor (DLS) calibration, field of view (FOV) alignment, and radiometric calibration, which were conducted per official guidelines. The data were collected at six sensor heights (30–130 cm) and speeds (0–0.5 ms–1). Analyses, including peak signal-to-noise ratio (PSNR) and normalized difference vegetation index (NDVI) calculations and statistical assessments, were conducted to explore the impacts of these parameters on reflectance data variation. PSNR analysis was performed on passive sensor image data to evaluate image data variation under varying data collection conditions. Statistical analysis was conducted to assess the effects of sensor speed and height on the NDVI derived from active and passive sensor data. The PSNR analysis confirmed that there were significant impacts on data variation for passive sensors, with the NIR and G bands showing higher noise sensitivity at increased speeds. The NDVI analysis showed consistent patterns at sensor heights of 70–110 cm and sensing speeds of 0–0.3 ms–1. Increased sensing speeds (0.4–0.5 ms–1) introduced motion-related variability, while lower heights (30–50 cm) heightened ground interference. An analysis of variance (ANOVA) indicated significant individual effects of speed and height on four spectral bands, red (R), green (G), blue (B), and near-infrared (NIR), in the passive sensor images, with non-significant interaction effects observed on the red edge (RE) band. The analysis revealed that sensing speed and sensor height influence NDVI reliability, with the configurations of 70–110 cm height and 0.1–0.3 ms–1 speed ensuring the stability of NDVI measurements. This study notes the importance of optimizing sensor height and sensing speed for precise vegetation index calculations during field data acquisition for agricultural crop monitoring.

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.

Changes in posture and movement during the growing period can often indicate abnormal development or health in pigs, making it possible to monitor and detect early morphological symptoms and health risks, potentially helping to limit the spread of infections. Largescale pig farming requires extensive visual monitoring by workers, which is time-consuming and laborious. However, a potential solution is computer vision-based monitoring of posture and movement. The objective of this study was to recognize and detect pig posture using a masked-based instance segmentation for automated pig monitoring in a closed pig farm environment. Two automatic video acquisition systems were installed from the top and side views. RGB images were extracted from the RGB video files and used for annotation work. Manual annotation of 600 images was used to prepare a training dataset, including the four postures: standing, sitting, lying, and eating from the food bin. An instance segmentation framework was employed to recognize and detect pig posture. A region proposal network was used in the Mask R-CNN-generated candidate boxes and the features from these boxes were extracted using RoIPool, followed by classification and bounding-box regression. The model effectively identified standard postures, achieving a mean average precision of 0.937 for piglets and 0.935 for adults. The proposed model showed strong potential for real-time posture monitoring and early welfare issue detection in pigs, aiding in the optimization of farm management practices. Additionally, the study explored body weight estimation using 2D image pixel areas, which showed a high correlation with actual weight, although limitations in capturing 3D volume could affect precision. Future work should integrate 3D imaging or depth sensors and expand the use of the model across diverse farm conditions to enhance real-world applicability.

Precise monitoring of vegetative growth is essential for assessing crop responses to environmental changes. Conventional methods of geometric characterization of plants such as RGB imaging, multispectral sensing, and manual measurements often lack precision or scalability for growth monitoring of rice. LiDAR offers high-resolution, non-destructive 3D canopy characterization, yet applications in rice cultivation across different growth stages remain underexplored, while LiDAR has shown success in other crops such as vineyards. This study addresses that gap by using LiDAR for geometric characterization of rice plants at early, middle, and late growth stages. The objective of this study was to characterize rice plant geometry such as plant height, canopy volume, row distance, and plant spacing using the proximal LiDAR sensing technique at three different growth stages. A commercial LiDAR sensor (model: VPL−16, Velodyne Lidar, San Jose, CA, USA) mounted on a wheeled aluminum frame for data collection, preprocessing, visualization, and geometric feature characterization using a commercial software solution, Python (version 3.11.5), and a custom algorithm. Manual measurements compared with the LiDAR 3D point cloud data measurements, demonstrating high precision in estimating plant geometric characteristics. LiDAR-estimated plant height, canopy volume, row distance, and spacing were 0.5 ± 0.1 m, 0.7 ± 0.05 m3, 0.3 ± 0.00 m, and 0.2 ± 0.001 m at the early stage; 0.93 ± 0.13 m, 1.30 ± 0.12 m3, 0.32 ± 0.01 m, and 0.19 ± 0.01 m at the middle stage; and 0.99 ± 0.06 m, 1.25 ± 0.13 m3, 0.38 ± 0.03 m, and 0.10 ± 0.01 m at the late growth stage. These measurements closely matched manual observations across three stages. RMSE values ranged from 0.01 to 0.06 m and r2 values ranged from 0.86 to 0.98 across parameters, confirming the high accuracy and reliability of proximal LiDAR sensing under field conditions. Although precision was achieved across growth stages, complex canopy structures under field conditions posed segmentation challenges. Further advances in point cloud filtering and classification are required to reliably capture such variability.

To ensure a high crop profit in Chinese solar greenhouses (CSGs), it is crucial to effectively manage major environmental variables such as temperature, humidity, and CO2 concentrations, among others, to mitigate harmful effects on crop growth. The objectives of this study were to assess the spatial, vertical, and temporal variability of major environmental variables in CSGs during summer, and to provide fundamental information that could facilitate the monitoring and control of environmental factors in CSGs. The experiments were conducted in two CSGs: one with crops and another without crops. The measured environmental variables included air temperature, humidity, CO2 concentration, light intensity, and wind conditions. Significant variations in the spatial, vertical, and temporal distribution of environmental factors were observed in both greenhouses. The results revealed significant diurnal patterns in temperature and humidity, with higher daytime temperatures and lower humidity levels. The greenhouse with crops exhibited warmer bottom layers due to restricted air mobility. CO2 concentrations peaked at night, aligning with plants’ respiration and photosynthesis cycles, whereas light intensity showed substantial daytime peaks, slightly affected by the presence of crops. The study emphasized the necessity of stratified control of the environment and dynamic management of CO2. The deployment of a wireless sensor network (WSN) and placement of an error-based sensor ensured precise monitoring, highlighting the importance of continuous data collection and adaptive management for optimal greenhouse conditions.

Accurate information on the spatial distribution of crop yields is essential for the successful implementation of precision agriculture. Achieving high accuracy in yield monitoring necessitates the development of systems that effectively minimize errors caused by uneven field terrain and dynamic harvesting. This study aimed to estimate the real-time volume of radishes using computer vision techniques, applied to images captured under simulated vibration and sloped harvesting field conditions using a laboratory test bench. An image acquisition system was developed and installed on a conveyor, which was mounted on a vibration table and a slope platform. Top-view RGB images of radishes were captured under slope levels of 3, 6, and 9°; vibration levels of 0.43, 0.78, and 0.98 m/s2; and combinations of the slope and vibration conditions. The captured images were processed to remove noise induced by the slope and vibration conditions, extract size-related features, and estimate radish volume. Two approaches were used to estimate radish volume: ellipsoidal and multiple linear regression (MLR) models. The volume estimated was compared to the volume measured by the water displacement method (WDM). The performance of the noise filtering algorithm was evaluated using peak signal-to-noise ratio (PSNR) and the structural similarity index measurement (SSIM), while the accuracy of the radish volume estimates was assessed through the coefficient of determination (R2). Analysis of variance (ANOVA) was also conducted to determine the statistical significance of the volume estimation results. Results indicated that radish volume was underestimated across all slope and vibration levels, compared to the values by the WDM. The MLR approach performed better than the ellipsoidal approximation approach. Average width demonstrated better performance compared to other geometrical attributes for volume estimation. No significant difference was observed between the mean estimated volumes by the MLR and the WDM method for all the slope and vibration conditions. The highest R2 values were achieved at 0.94, 0.96, and 0.92 for slope levels of 3, 6, and 9°, respectively, when slope was tested independently using the MLR model. This approach demonstrated potential for providing accurate volume estimates under uneven field conditions and dynamic harvesting.