• Journal of Biosystems Engineering
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• 제20권1호
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• pp.22-35
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• 1995

Pepper is the most important horticultural plant in Korean farm. Pepper harvesting has been known to be the most difficult process in pepper cultivation so that demand for mechanization is strong. In a research to develop a pepper harvesting machine performance and capacity of the harvester should be determined based on both economical feasibility and machine design concept. In order to accomplish an economical analysis of the pepper harvester, a mathematical model for comparing manual harvesting cost to machine harvest cost was developed. Validity of the model depends on the data used in the model. Economical information for the model variables was acquired from the result of farm survey on pepper cultivation technique and economics of pepper farmer. Technical information on pepper harvester were also collected through literature review and analyzed. Based on the economical analysis and synthesis of the technical information on pepper harvesters, its performance and capacity were determined. The operating performances of the harvester such as cutting, conveying, flipping, pepper removing and post-processing (sorting) were determined. Daisy capacity of the machine was determined to be 0.41 ha. A pepper harvester with the suggested capacity was economically feasible if the price of pepper harvester, pepper recovery ratio and service life of harvester were about 6 million won, 80%, and 4 years, respectively.

• Journal of Biosystems Engineering
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• 제22권3호
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• pp.289-294
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• 1997

The chestnut is a well-known and important forest product in Korea. The annual production of chestnut is about 100, 000tons and its cultivating area is 80, 000ha. However, the peeling process of outer and inner skins of chestnut is very difficult due to hardness and adhesiveness of chestnut skin. The peeling process of chestnut was operated by manual work and the performance of chestnut peeling machine is very low. The purpose of this study is to develope the prototype of new chestnut peeling system. The hardness of chestnuts was tested with six different drying conditions and its range was from 949$g/mm^2$ to 2, 149$g/mm^2$. The hardness of chestnuts was decresed gradually during the drying process. The chestnut peeling Process includes sorting, storage, drying, outer skin cutting, flame peeling, continuous frictional skin peeling, and inner skin cutting operation. The developed chestnut peeling system consists of outer skin cutter, flame peeler, continuous frictional skin peeler and inner skin cutter. The system can peel domestic chestnuts at 150$kg/hr$ with peeling rate of 78%.

• 한국산학기술학회논문지
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• 제13권3호
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• pp.1319-1324
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• 2012

토마토는 여러 가지 다른 숙성 단계에서 수확될 수 있다. 토마토의 숙성 상태를 판단하기 위해 토마토 과육을 HPLC로 분석한 여러 가지 화합물과 토마토 RGB 이미지를 ICA로 분석한 독립성분간의 관계를 분석하였다. 여러 토마토 화합물중 품질에 가장 영향을 많이 미치는 라이코펜과 토마토 RGB 이미지의 독립성분간의 부분최소제곱 $Q^2$ 값이 0.92로 매우 높음을 알 수 있었다. 그리고 라이코펜에 대응되는 독립성분을 토마토 RGB 이미지에 적용하여 픽셀 면적을 구한 것과 단순이진 이미지로 구해진 이미지의 픽셀 면적간의 비교를 제시하여 독립성분의 유효성을 제시하였다. 독립성분을 반영한 토마토 이미지를 통해 토마토의 숙성 상태를 보여주는 것이 가능하며, ICA 독립성분을 이용한 농축이미지 생성을 통해 토마토의 색상이 좋지 않거나 라이코펜과 같은 주요 성분이 없게 된 토마토를 분류해 내는 것이 가능해진다.

• Agricultural and Biosystems Engineering
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• 제1권2호
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• pp.106-110
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• 2000

An integrated on-line inspection system was constructed with seven cameras, half mirrors to split images. 720 nm and 970 nm band pass filters, illumination chamber having several tungsten-halogen lamps, one main computer, one color frame grabber, two 4-channel multiplexors, and flat plate conveyer, etc. A total of seven images, that is, one color image form the top of an apple and two B/W images from each side (top, right and left) could be captured and displayed on a computer monitor through the multiplexor. One of the two B/W images captured from each side is 720nm filtered image and the other is 970 nm. With this system an on-line grading software was developed to evaluate appearance quality. On-line test results with Fuji apples that were manually fed on the conveyer showed that grading accuracies of the color, defect and shape were 95.3%, 86% and 88.6%, respectively. Grading time was 0.35 second per apple on an average. Therefore, this on-line grading system could be used for inspection of the final products produced from an apple sorting system.

• 한국농업기계학회:학술대회논문집
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• 한국농업기계학회 1996년도 International Conference on Agricultural Machinery Engineering Proceedings
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• pp.834-841
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• 1996

Egg production in Korea is becoming automated with a large scale farm. Although many operations in egg production have been and cracks are regraded as a critical problem. A computer vision system was built to generate images of a single , stationary egg. This system includes a CCD camera, a frame grabber board, a personal computer (IBM PC AT 486) and an incandescent back lighting system. Image processing algorithms were developed to inspect egg shell and to sort eggs. Those values of both gray level and area of dark spots in the egg image were used as criteria to detect holes in egg and those values of both area and roundness of dark spots in the egg and those values of both area and roundness of dark spots in the egg image were used to detect cracks in egg. Fro a sample of 300 eggs. this system was able to correctly analyze an egg for the presence of a defect 97.5% of the time. The weights of eggs were found to be linear to both the projected area and the perimeter of eggs v ewed from above. Those two values were used as criteria to sort eggs. Accuracy in grading was found to be 96.7% as compared with results from weight by electronic scale.

• 한국농업기계학회:학술대회논문집
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• 한국농업기계학회 2000년도 THE THIRD INTERNATIONAL CONFERENCE ON AGRICULTURAL MACHINERY ENGINEERING. V.III
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• pp.513-520
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• 2000

An integrated grader which measures soluble solid content, color and weight of fresh apples was developed by NAMRI. The prototype grader consists of the near infrared spectroscopy and machine vision system. Image processing system and an algorithm to evaluate color were developed to speed up the color evaluation of apples. To avoid the light glare and specular reflection, an half-spherical illumination chamber was designed and fabricated to detect the color images of spherical-shaped apples more precisely. A color revision model based on neural network was developed. Near-infrared(NIR) spectroscopy system using NIR reflectance method developed by Lee et al(1998) of NAMRI was used to evaluate soluble solid content. In order to observe the performance of the grader, tests were conducted on conditions that there are 3 classes in weight sorting, 4 classes in combination of color and soluble solid content, and thus 12 classes in combined sorting. The average accuracy in weight, color and soluble solid content is more than about 90 % with the capacity of 3 fruits per second.

• 한국농업기계학회:학술대회논문집
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• 한국농업기계학회 2000년도 THE THIRD INTERNATIONAL CONFERENCE ON AGRICULTURAL MACHINERY ENGINEERING. V.III
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• pp.551-559
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• 2000

An integrated on-line inspection system was constructed with seven cameras, half mirrors to split images, 720 nm and 970 nm band pass filters, illumination chamber having several tungsten-halogen lamps, one main computer, one color frame grabber, two 4-channel multiplexors, and flat plate conveyer, etc., so that a total of seven images, that is, one color image from the top side of an apple and two B/W images from each side (top, right and left) could be captured and displayed on a computer monitor through the multiplexor. One of the two B/W images captured from each side is 720nm filter image and the other is 970nm. With this system an on-line grading software was developed to evaluate appearance quality. On-line test results to the Fuji apples that were manually fed on the conveyer showed that grading accuracies of the color, defective and shape were 95.3%, 86% and 91%, respectively. Grading time was 0.35 sec per apple on an average. Therefore, this on-line grading system could be used for inspection of the final products produced from an apple sorting system.

• Journal of Biosystems Engineering
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• 제36권1호
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• pp.40-47
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• 2011

Image analysis algorithm for the quality evaluation of ginseng seedling was investigated. The images of ginseng seedling were acquired with a color CCD camera and processed with the image analysis methods, such as binary conversion, labeling, and thinning. The processed images were used to calculate the length and weight of ginseng seedlings. The length and weight of the samples could be predicted with standard errors of 0.343 mm, and 0.0214 g respectively, $R^2$ values of 0.8738 and 0.9835 respectively. For the evaluation of the three quality grades of Gab, Eul, and abnormal ginseng seedlings, features from the processed images were extracted. The features combined with the ratio of the lengths and areas of the ginseng seedlings efficiently differentiate the abnormal shapes from the normal ones of the samples. The grade levels were evaluated with an efficient pattern recognition method of support vector machine analysis. The quality grade of ginseng seedling could be evaluated with an accuracy of 95% and 97% for training and validation, respectively. The result indicates that color image analysis with support vector machine algorithm has good potential to be used for the development of an automatic sorting system for ginseng seedling.

• 한국정보통신학회논문지
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• 제10권4호
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• pp.645-651
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• 2006

본 논문은 컴퓨터 시스템의 성능평가와 분석을 대칭구조의 멀티프로세서를 실행할 수 있는 시뮬레이터를 사용하여 살펴보았으며 또한 시스템 분석을 하는데 있어서 멀티프로세서를 위한 멀티프로그램 벤치마크의 집합체인 SPLASH-2를 이행하여 대칭구조의 운영체제 IRIX5.3 탑재한 멀티프로세서의 행위범위의 연구를 수행하기 위하여 멀티프로세서의 시스템 분석을 실시 하였다. 또한 대칭구조의 멀티프로세서의 구조와 평가방법을 보다 유효하게 하기 위해서 멀티프로세서의 확장성을 functionality-based 소프트웨어인 SimOS를 가지고 증명하였으며 본 논문을 통하여 멀티프로그램 벤치마크인 RADIX 정렬 알고리즘이나 Cholesky 인수분해 알고리즘을 이용하여 로칼 인스트럭션과 로칼 데이터 사이에서의 멀티프로세서의 Cache miss의 수 와 Stall 시간을 동시에 검사하였다.

• International Journal of Computer Science & Network Security
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• 제22권10호
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• pp.271-281
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• 2022

Manual seed classification challenges can be overcome using a reliable and autonomous seed purity identification and classification technique. It is a highly practical and commercially important requirement of the agricultural industry. Researchers can create a new data mining method with improved accuracy using current machine learning and artificial intelligence approaches. Seed classification can help with quality making, seed quality controller, and impurity identification. Seeds have traditionally been classified based on characteristics such as colour, shape, and texture. Generally, this is done by experts by visually examining each model, which is a very time-consuming and tedious task. This approach is simple to automate, making seed sorting far more efficient than manually inspecting them. Computer vision technologies based on machine learning (ML), symmetry, and, more specifically, convolutional neural networks (CNNs) have been widely used in related fields, resulting in greater labour efficiency in many cases. To sort a sample of 3000 seeds, KNN, SVM, CNN and CNN-SVM hybrid classification algorithms were used. A model that uses advanced deep learning techniques to categorise some well-known seeds is included in the proposed hybrid system. In most cases, the CNN-SVM model outperformed the comparable SVM and CNN models, demonstrating the effectiveness of utilising CNN-SVM to evaluate data. The findings of this research revealed that CNN-SVM could be used to analyse data with promising results. Future study should look into more seed kinds to expand the use of CNN-SVMs in data processing.