Papers by Author: Kang Jin Lee

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: This study was aimed at developing a new quality evaluation system for classifying the cucumber based on its length and curvature, and removing the taper and dumbbell shaped cucumbers using the thickness changes. Especially machine vision technique was used in carrying out field application. The cucumber image was obtained from a frame grabber, and the image was improved by minimizing the nonuniform illumination and image blurring due to line movement. From the obtained image, background was separated from the original image, and cucumber length and curvature was calculated after thinning and post-processing operation. After thinning operation, cucumber region was sliced and the thickness was calculated. From the thickness calculation, cucumber can be classified as straight, cudgel and dumbbell shape. The classification rate for bowing was close to 100%. The overall average recognition rate for good, dumbbell and cudgel cucumber fruits was 90.7%

Abstract: This study was conducted to develop an accurate quality evaluation system based on optimized factors such as light source array and light power, which are used in non-destructive fruit sorter to obtain the internal quality information of fruits using the near infrared transmittance spectra. It is necessary to provide the proper design guide for the light source part in the existing non-destructive fruit sorters for apples and pears, and to measure the real-time near infrared transmittance spectrum without the leakage of light. The near infrared transmittance spectrum detection system was developed with the light source part which has the power-controllable 12 halogen lamps (100W/12V) with gold coating, light detection part, and transfer line. By using the accurate control of the voltage and current (maximum power is 1.2kW) in light power control part, it is concluded that the minimum power for the internal quality evaluation of apples and pears was over 0.5 kW. To prevent the leakage of light, the array of light source was rearranged and tested. Without changing the tray structure, it is concluded that the leakage of light can be prevented by the proper array of light source and power. For the irradiation for the moving apples and pears, 2 upper lamps and 4 lower lamps combination did not have leakage of light and the correlation coefficient of this combination shows the 0.90 for apples and 0.96 for pears.