Seoul National University · Agricultural and Biological Sciences
Professor Hak-Jin Kim's research lab specializes in precision agriculture and smart farming technologies, focusing on the development of advanced sensing, imaging, and signal processing techniques for real-time monitoring of crop growth and soil conditions. The lab integrates unmanned aerial vehicles (UAVs), RGB-D cameras, ion-selective electrodes (ISEs), and multisensor fusion systems to enable non-destructive, high-resolution assessment of biophysical and nutritional parameters in crops. Key research directions include automated plant phenotyping, hydroponic nutrient monitoring, and robust positioning for agricultural robots, with an emphasis on data-driven models such as convolutional neural networks and artificial neural networks for improved accuracy and efficiency.
Figures are computed from collected data and may differ slightly.
Conventional crop-monitoring methods are time-consuming and labor-intensive, necessitating new techniques to provide faster measurements and higher sampling intensity. This study reports on mathematical modeling and testing of growth status for Chinese cabbage and white radish using unmanned aerial vehicle-red, green and blue (UAV-RGB) imagery for measurement of their biophysical properties. Chinese cabbage seedlings and white radish seeds were planted at 7–10-day intervals to provide a wide ran
Automated sensing of soil macronutrients would be useful in mapping soil nutrient variability for variable‐rate nutrient management. Ion‐selective electrodes (ISEs) are a promising approach because of their small size, rapid response, and ability to directly measure the analyte. This study reports on the laboratory evaluation of a sensor array including three different ISEs, based on TDDA–NPOE and valinomycin–DOS membranes, and Co rod, for the simultaneous determination of NO 3 –N, available K,
Growth indices can quantify crop productivity and establish optimal environmental, nutritional, and irrigation control strategies. A convolutional neural network (CNN)-based model is presented for estimating various growth indices (i.e., fresh weight, dry weight, height, leaf area, and diameter) of four varieties of greenhouse lettuce using red, green, blue, and depth (RGB-D) data obtained using a stereo camera. Data from an online autonomous greenhouse challenge (Wageningen University, June 202
In closed hydroponics, fast and continuous measurement of individual nutrient concentrations is necessary to improve water- and nutrient-use efficiencies and crop production. Ion-selective electrodes (ISEs) could be one of the most attractive tools for hydroponic applications. However, signal drifts over time and interferences from other ions present in hydroponic solutions make it difficult to use the ISEs in hydroponic solutions. In this study, hybrid signal processing combining a two-point no
High-precision position estimations of agricultural mobile robots (AMRs) are crucial for implementing control instructions. Although the global navigation satellite system (GNSS) and real-time kinematic GNSS (RTK-GNSS) provide high-precision positioning, the AMR accuracy decreases when the signals interfere with buildings or trees. An improved position estimation algorithm based on multisensor fusion and autoencoder neural network is proposed. The multisensor, RTK-GNSS, inertial-measurement-unit
Real-time monitoring of crop growth parameters in plant factory can provide useful information about accurate assessment of their growth status for precision crop management. Plant weight is one of the most important biophysical properties used to determine the optimum time for harvesting. Conventional plant weight measurements are destructive and laborious. An on-the-go image processing system consisting of image acquisition and weight estimation was developed to generate a fresh weight map of
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