• Proceedings of the Korean Society for Agricultural Machinery Conference
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• v.10 no.2
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• pp.268-272
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• 2005

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• v.10 no.1
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• pp.289-293
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• 2005

• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
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• 2019.08a
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• pp.292-292
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• 2019

• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
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• 2016.09a
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• pp.82-85
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• 2016

• Journal of Drive and Control
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• v.20 no.4
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• pp.64-70
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• 2023

Garlic is classified as one of the three essential seasoning vegetables in Korea. In 2023, it was reported that the area under garlic cultivation was 24,700 ha, and the production stood at 318,220 tons. Garlic harvesting mechanization currently stands at 43.8%, and garlic is still collected manually after digging out using diggers, so the process is labor intensive. To reduce garlic production costs and enhance competitiveness, it is necessary to develop a high-performance gathering type harvester in place of the digging type harvester. Therefore, in this study, a gathering-type garlic harvester that can dig and collect simultaneously was designed and manufactured, and the harvest performance by factor was analyzed through a harvest performance test. As a result of the performance test, it was analyzed to perform optimally at a driving speed of 0.11m/s and a transfer speed of 85rpm. Work performance was calculated using the results obtained from the factor performance test.

• Agricultural and Biosystems Engineering
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• v.5 no.1
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• pp.5-9
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• 2004

This study was carried out to develop methods of reducing the effect of solar illumination on pixel gray-levels of machine vision for agricultural field use. Two kinds of monochrome CCD cameras with manual and auto-iris lenses were used to take pictures within a range of 15 to 120 klux of solar illumination. A camera having more precise automatic control functions gave much better result. Four kinds of indices using pixel gray-level of the $99\%$ white DRS (diffuse reflectance standard) as a reference were tried to compensate pixel gray-levels of an image for variable illumination. Coefficients of variation of the indices within a range of illumination were used as a criterion for comparison. The study concluded that an index of (A+B)/A, where A is gray-level of the $99\%$ DRS and B is gray-level of the tested material, gave the best consistency in the range of solar illumination.

• Journal of Biosystems Engineering
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• v.33 no.4
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• pp.230-238
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• 2008

Mechanization such as machine harvesting, precleaning, drying and cleaning for rapeseed harvested with high moisture content should be accomplished for biodiesel production. In addition, machine drying and cleaning is inevitable in the mechanization of work, just because rice should be transplanted right after harvesting rapeseed in Korea. Particularly, early harvested rapeseed with the combine have high moisture content and undesirable materials such as stalks and stones which make drying-process difficult and lower the efficiency of drying. Therefore, this study was conducted to develop precleaner and cleaner which could remove foreign substances from harvested rapeseeds. The precleaner consists of throw-in hopper, conveyor, feeding hopper, two precleaning sieves and discharging sections. Precleaning capacity was 1,505 kg/hr in shaking frequency of 370 cpm (cycles per minute) and tilt angles of between $5^{\circ}$ and $7^{\circ}$. The efficiency of precleaning was between 90.9% and 91.5%. The cleaner consists of feeding, shaking, blowing, cleaning and discharging sections. Cleaning performance was 435.4kg/hr in the number of rocking motions of 475 cpm and tilt angle of $10^{\circ}$. The ratios of cleaning, foreign substances and loss were 96.5%, 3.5% and 0.2%, respectively.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.7
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• pp.1175-1180
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• 2008

Recently agriculture has been intelligenced and automatized because the number of agricultural population is reduced, and the various agricultural machine is developed and utilized actively, In these agricultural automation system, the solenoid valve is widely used for the supply of water and fertilizer to the plant and soil. In this solenoid valve system, AC excitation solenoid valve is widely used because of economic merit and simple system scheme. However, the instantaneous chattering vibration and noise of plunger caused by the alternative MMF variation is very important performance characteristics. In order to reduce vibration the DC excitation solenoid valve is sometimes applied for the high-end applications. In this case, the control circuit is essential to control DC excitation current. It may causes the cost increase and system complexity and it is not suitable for the outdoor agricultural machine. In this paper, the electromagnetic structural improvement of AC solenoid valve is studied to reduce the dynamic vibration and noise. As an economical solution, the shading coil is additionally implemented to the conventional solenoid valve. As a result of this study, the vibration and acoustic noise is largely reduced by the compensating MMF of shading coil and it is verified by the test of prototype.

• Korean Journal of Agricultural Science
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• v.50 no.3
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• pp.415-424
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• 2023

The task of sorting and excluding blemished apples and others that lack commercial appeal is currently performed manually by human eye sorting, which not only causes musculoskeletal disorders in workers but also requires a significant amount of time and labor. In this study, an automated apple-sorting machine was developed to prevent musculoskeletal disorders in apple production workers and to streamline the process of sorting blemished and non-marketable apples from the better quality fruit. The apple-sorting machine is composed of an arm-rest, a main body, and a height-adjustable part, and uses object detection through a machine learning technology called 'You Only Look Once (YOLO)' to sort the apples. The machine was initially trained using apple image data, RoboFlow, and Google Colab, and the resulting images were analyzed using Jetson Nano. An algorithm was developed to link the Jetson Nano outputs and the conveyor belt to classify the analyzed apple images. This apple-sorting machine can immediately sort and exclude apples with surface defects, thereby reducing the time needed to sort the fruit and, accordingly, achieving cuts in labor costs. Furthermore, the apple-sorting machine can produce uniform quality sorting with a high level of accuracy compared with the subjective judgment of manual sorting by eye. This is expected to improve the productivity of apple growing operations and increase profitability.

• Journal of Biosystems Engineering
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• v.31 no.6 s.119
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• pp.514-523
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• 2006

Production of green pepper has been increased due to customer's preference and a projected ten-year boom in the industry in Korea. This study was carried out to develop an automatic grading and sorting system for green pepper using machine vision. The system consisted of a feeding mechanism, segregation section, an image inspection chamber, image processing section, system control section, grading section, and discharging section. Green peppers were separated and transported using a bowl feeder with a vibrator and a belt conveyor, respectively. Images were taken using color CCD cameras and a color frame grabber. An on-line grading algorithm was developed using Visual C/C++. The green peppers could be graded into four classes by activating air nozzles located at the discharging section. Length and curvature of each green pepper were measured while removing a stem of it. The first derivative of thickness profile was used to remove a stem area of segmented image of the pepper. While pepper is moving at 0.45 m/s, the accuracy of grading sorting for large, medium and small pepper are 86.0%, 81.3% and 90.6% respectively. Sorting performance was 121 kg/hour, and about five times better than manual sorting. The developed system was also economically feasible to grade and sort green peppers showing the cost about 40% lower than that of manual operations.