Papers by Author: Gi Young Kim

Abstract: Precision agriculture, also called as site-specific crop (or field) management, is a recent trend in crop production that uses field information collected at different within-field locations to optimize amount, timing, and location of agricultural inputs according to the site-specific requirements. Recent development of soil property sensors has facilitated sensor-based data collection for SSCM in many countries around the world. In this study, commercial soil strength, electrical conductivity, and water content and temperature sensors were applied to a Korean rice (Oriza Sativa L) field and spatial and non-spatial statistical techniques were used to assess soil conditions and the variability, and investigate optimum sampling intensity. Results of the study would be useful for establishment of data collection schemes and better application of soil property sensors to Korean paddy fields for successful precision agriculture.

Abstract: Watermelons are usually sorted by theirs weight and internal quality. Some automated watermelon weight sorters have been developed and operated in watermelon production areas. However, inspection of internal quality of watermelon is still performed by manually. Principal method of identifying internal defect of watermelon is analyzing the percussion sound of watermelon by human experts. Development of non-destructive evaluation technique for internal quality of watermelon is required to reduce human decision errors. The objective of this study was to develop a non-destructive sorting system which can detect internal defect of watermelons. The internal defect evaluation system has a constant-force hitting hammer to generate the acoustic sound, a multi-point sound signal acquiring system, a noise removal circuit, and a signal processing and quality evaluation program. An internal quality prediction model by PLSR (Partial Least Square Regression) was developed by analyzing the percussion sound of watermelons. Using the developed model, the prediction result shows that the overall prediction accuracy was 90.1%, and severely defected watermelons were identified perfectly.

Abstract: Fiber-optic biosensor uses light transmittable tapered fiber to send excitation laser light and receive emitted fluorescent light. The fluorescent light excited by an evanescent wave generated by the laser is quantitatively related to biomolecules immobilized on the fiber surface [1]. An automated fiber-optic biosensor based detection method for Listeria monocytogenes was developed in this research. Detections of Listeria monocytogenes in hotdog sample were performed to evaluate the method. By using the detection method with automated fiber-optic biosensor, 5.4×107 cfu/ml of Listeria monocytogenes was able to detect.

Abstract: Frequent outbreaks of foodborne illness have been increasing the need for simple, rapid and sensitive methods to detect foodborne pathogens. Conventional methods for pathogen detection and identification are labor-intensive and take days to complete. Some immunological rapid assays are developed, but these assays still require prolonged enrichment steps. Biosensors have shown great potential for the rapid detection of foodborne pathogens. Among the biosensors, fiber-optic methods have much potential because they can be very sensitive and simple to operate. Fiber-optic biosensors typically use a light transmittable, tapered fiber to send excitation laser light to the detection surface and receive emitted fluorescent light. The fluorescent light excited by an evanescent wave generated by the laser is quantitatively related to fluorophor-labeled biomolecules immobilized on the fiber surface. A portable and automated fiber-optic biosensor, RAPTOR (Research International, Monroe, WA), was used to detect Salmonella enteritidis in food samples. A binding inhibition assay based on the biosensor was developed to detect the bacteria in hot dog samples. The biosensor and the binding inhibition assay could detect 104 cfu/ml of bacteria in less than 10 min of assay time.