• Research Article

    Detection and removal of metallic foreign substances in powdered foods using electric fields
    Jae Kyun Kwak, Seong Yong Joe, Jun-Hwi So, Sang Hui Kim, Solim Lee, Seung Hyun Lee
    Powdered agricultural products have gained popularity due to their convenience and extended shelf life. However, the presence of metallic contaminants, often introduced … + READ MORE
    Powdered agricultural products have gained popularity due to their convenience and extended shelf life. However, the presence of metallic contaminants, often introduced during milling or processing, has raised significant safety concerns. Conventional detection systems, such as magnetic separators and X-ray devices, face challenges including high costs, limited applicability in small-scale operations, and difficulties in detecting non-ferrous metals like aluminum and copper. To address these issues, this study explores the use of electric fields for metallic contaminant detection and removal. Stainless steel, aluminum, and copper particles were tested to determine their responsiveness in electric fields. The optimal electric field strength for each metal was identified using an IGBT-based power supply, with stainless steel showing the highest sensitivity at 1,000 V. A mesh-shaped electrode design was developed to enhance detection accuracy while minimizing particle aggregation and system malfunctions. Results demonstrated that higher voltages improved detection performance, and the system effectively differentiated between metal types. This research provides a foundation for an innovative and cost-effective detection method suitable for small-scale processing facilities. Future work will focus on refining system designs and validating their performance in real-world food processing environments to ensure broader industry adoption. - COLLAPSE
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    30 June 2025

  • Research Article

    Effect of soil texture and water content on the soil-material adhesion force
    Dae-Wi Jeong, Bo-Min Bae, Min-Seung Kim, Se-O Choi, Shin-Young Noh, Yeon-Soo Kim, Yong-Joo Kim
    This study experimentally analyzed the soil adhesion characteristics of three different soil types (sandy loam, sandy clay loam, and loam) and three … + READ MORE
    This study experimentally analyzed the soil adhesion characteristics of three different soil types (sandy loam, sandy clay loam, and loam) and three surface materials (stainless steel, engineering plastic, and urethane) under six water content conditions ranging from 5% to 30%. Soil physical transitions were quantified using Atterberg limits: Plastic Limit (PL), Liquid Limit (LL), and Plasticity Index (PI) to interpret soil adhesion behavior with respect to water content. The results showed that adhesion generally increased with water content and peaked near each soil's LL. Urethane exhibited the highest adhesion across all soil types due to its high surface friction and deformability, while stainless steel showed consistently lower adhesion, making it suitable for low-adhesion applications. Furthermore, soils with higher PI values exhibited more stable and predictable adhesion increases, while lower-PI soils showed more abrupt, nonlinear changes. These findings suggest that both soil properties and environmental water conditions should be considered when selecting surface materials for agricultural machinery. Practical guidelines are proposed for selecting material types and operation timing based on soil texture and water content to reduce adhesion in actual farming operations. - COLLAPSE
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    30 June 2025

  • Review Article

    Remote management and variability assessment of environmental conditions for smart vertical farms: A review
    Ezatullah Zakir, Md Nasim Reza, Md Rejaul Karim, Gusti Ayu Putri Mei Ulainti, Md Razob Ali, Hongbin Jin, Sun-Ok Chung, Md Shaha Nur Kabir
    Vertical farming (VF) has emerged as a transformative approach to urban agriculture, enabling year-round crop production in compact, multilayered indoor environments. By … + READ MORE
    Vertical farming (VF) has emerged as a transformative approach to urban agriculture, enabling year-round crop production in compact, multilayered indoor environments. By employing soilless cultivation systems and climate-controlled conditions, VF decouples food production from environmental constraints such as limited arable land and changing climate. However, the vertical stratification of crops introduces significant microclimatic variability in temperature, humidity, light intensity, and CO2 concentration, which can adversely affect crop uniformity and resource efficiency. This review aimed to explore remote management technologies and variability assessment of environmental conditions for smart vertical farms. The integration of wireless sensor network (WSNs), Internet of Thing (IoT) technologies, and low-power wide-area network (LPWAN) protocols like long range wide area network (LoRaWAN) for environmental data acquisition and control were evaluated. Key sensing modules for monitoring critical variables such as pH, electrical conductivity (EC), temperature, and CO2 are discussed, alongside recent developments in real-time communication, edge computing, and machine learning (ML)-driven control systems. A particular focus is given to microclimatic variability assessment using geostatistical and ML-based methods for spatial-temporal mapping and predictive decision-making. The review also analyzes power supply strategies, including energy harvesting, node placement optimization, and signal preprocessing techniques for noise reduction and multi-sensor fusion. Findings reveal that wireless systems offer considerable advantages over wired setups in terms of flexibility, scalability, and operational efficiency in VF environments. The successful deployment of smart VF systems depends on the precise alignment of sensor placement, communication protocols, data processing, and visualization interfaces. Despite advancements, challenges remain, including signal attenuation in enclosed layers, energy limitations, and sensor drift under harsh microclimates. - COLLAPSE
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    30 June 2025

  • Research Article

    Radish mass estimation using simulated harvester dynamics using GA-ELM modeling
    Md Sazzadul Kabir, Md Nasim Reza, Kyu-Ho Lee, Md Ashrafuzzaman Gulandaz, Milon Chowdhury
    Achieving precise yield mapping is a pivotal requirement for precision agriculture, but terrain-induced disturbances often complicate accurate infield measurements. To address this … + READ MORE
    Achieving precise yield mapping is a pivotal requirement for precision agriculture, but terrain-induced disturbances often complicate accurate infield measurements. To address this challenge, a computervisionbased approach was developed to estimate the massume of radishes under varying slopes and vibrations similar to field harvesting conditions using extreme learning machine (ELM), optimized via a genetic algorithm (GA) modeling. A laboratory test bench was constructed to simulate realistic harvester dynamics by incorporating adjustable slope angles (3°, 6°, and 9°) and vibration intensities (0.43, 0.78, and 0.98 m/s2). An overhead RGB camera captured images of the radishes as they passed along a conveyor exposed to these conditions. The images were then analyzed to extract relevant size parameters, and an extreme learning machine (ELM), optimized via a genetic algorithm (GA), was applied to predict the radish masses. These results were compared with the actual mass measured by the weight measuring method. Although the imagebased estimates tended to slightly underpredict across all experimental conditions, statistical analyses revealed no significant differences from the actual mass measurements. The proposed method achieved a coefficient of determination (R2) of 0.94 at 9° slope, 0.97 at 0.43 m/s2 vibration, and 0.98 when both 6° slope and 0.43 m/s2 vibration were combined. This approach demonstrates the feasibility of automated yield estimation for radishes and real-time field applicaiton for similarly shaped crops under challenging field conditions. - COLLAPSE
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    30 June 2025

  • Review Article

    Remote monitoring and components abnormality detection in smart vertical farming systems: A review
    Gusti Ayu Putri Mei Ulianti, Md Nasim Reza, Hongbin Jin, Bicamumakuba Emmanuel, Kyu-Ho Lee, Sun-Ok Chung
    The challenges of global food security have been increased by rapid urbanization, climate change, and the decrease of land for agriculture, particularly … + READ MORE
    The challenges of global food security have been increased by rapid urbanization, climate change, and the decrease of land for agriculture, particularly in urban areas with high populations. Smart vertical farming systems have emerged as a promising solution to address food security challenges associated with rapid urbanization, climate change, and land scarcity. These systems support controlled environment agriculture (CEA) technologies and automation to maximize crop yield in limited urban spaces. However, the complexity of their operation requires efficient remote monitoring and rapid detection of component abnormalities to ensure reliability, reduce operational downtime, and optimize resource use. This review survey the current status in remote sensing, data acquisition, and monitoring solution using internet of thing (IoT) deployed in smart vertical farms. Advanced technologies allow for precise control over the microclimate within vertical farms. Moreover, sensor data are processed to ensure accuracy and then analyzed using machine learning techniques for both data classification and anomaly detection. The integration of IoT and machine learning is further facilitating predictive maintenance and early detection of abnormalities, including equipment failures or pest infestations. The used of remote monitoring to control farm conditions via mobile apps, can enhances growing conditions and increase productivity and crop quality. Despite the effectiveness of the smart farm technology in monitoring environmental conditions, it does not explicitly address potential challenges such as the initial setup costs, power consumption, and maintenance of the IoT systems. Nonetheless, the applications of remote monitoring and abnormal detection in smart vertical farming systems offering enhanced productivity and sustainability of future farming operations. Further studies can be conducted to enhance the methods and deal with these limitations. - COLLAPSE
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    30 June 2025
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