• Journal of Biosystems Engineering
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• 제32권2호
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• pp.130-135
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• 2007

An on-line real time grading and sorting system for dried oak mushrooms was developed for on-site application. Quality grades of the mushrooms were determined according to an industrial specification. Three dimensional visual quality features were used for the grading. A progressive color computer vision system with white LED illumination was implemented to develop an algorithm to extract external quality patterns of the dried oak mushrooms. Cap (top) and gil (stem) surface images were acquired sequentially and side image was obtained using mirror. Algorithms for extracting size, roundness, pattern and color of the cap, thickness, color of the gil and amount of rolled edge of the dried mushroom were developed. Utilizing those quality factors normal and abnormal ones were classified and normal mushrooms were further classified into 30 different grades. The sorting device was developed using microprocessor controlled electro-pneumatic system with stainless buckets. Grading accuracy was around 97% and processing time was 0.4 s in average.

• Journal of Biosystems Engineering
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• 제26권2호
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• pp.147-154
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• 2001

In Korea and Japan, dried oak mushrooms are classified into 12 to 16 different categories based on its external visual quality. And grading used to be done manually by the human expert and is limited to the randomly sampled oak mushrooms. Visual features of dried oak mushrooms dominate its quality and are distributed over both sides of the gill and the cap. The 2nd prototype computer vision based automatic grading and sorting system for dried oak mushrooms was developed based on the 1st prototype. Sorting function was improved and overall system for grading was simplified to one stage grading instead of two stage grading by inspecting both front and back sides of mushrooms. Neuro-net based side(gill or cap) recognition algorithm of the fed mushroom was adopted. Grading was performed with both images of gill and cap using neural network. A real time simultaneous discharge algorithm, which is good for objects randomly fed individually and for multi-objects located along a series of discharge buckets, was developed and implemented to the controller and the performance was verified. Two hundreds samples chosen from 10 samples per 20 grade categories were used to verify the performance of each unit such as feeding, reversing, grading, and discharging unites. Test results showed that success rates of one-line feeding, reversing, grading, and discharging functions were 93%, 95%, 94%, and 99% respectively. The developed prototype revealed successful performance such as the approximate sorting capability of 3,600 mushrooms/hr per each line i.e. average 1sec/mushroom. Considering processing time of approximate 0.2 sec for grading, it was desired to reduce time to reverse a mushroom to acquire the reversed surface image.

• Journal of Biosystems Engineering
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• 제21권3호
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• pp.325-335
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• 1996

표고의 외관 특징들은 표고의 재배 시 생육상태의 정량적 측정을 위해서, 표고의 건조 시 건조 성능을 나타내는 정량적 지표로서, 그리고 건표고의 품질을 판정하는 요인으로서 중요한 역할을 한다. 본 논문에서는 컴퓨터 시각시스템 및 신경회로망 기술을 적용하여 표고의 갓 및 내피에 고루 분포되어 있는 외관특징을 정량적으로 추출하는 알고리즘을 개발하였다. 기존의 영상 처리 과정에서 유도되는 경험적 판정규칙 또는 명확한 수치적 판정조건에 의한 등급판정은 입력데이타의 결핍 또는 애매모호성에 따른 오차가 발생하기 쉽다. 신경회로망을 이용한 영상인식 기능을 도입함으로써 다양하고 애매모호한 표고의 외관 영상특징들을 효율적으로 처리하여 기존 영상처리 알고리즘에서 발생하는 오차를 개선하였다. 본 논문에서 제안하는 알고리즘은 표고의 갓과 내피면의 인식 및 특징 분할, 꼭지부의 검출, 제거 및 재생 등을 포함한다. 제안한 알고리즘에 의거하여 건표고의 등급판정에 주요한 품질인자들을 추출하고 정량화 하였다. 그리고 알고리즘의 개발은 흑백의 다치입력영상을 이용하여 수행하였다.

• Journal of Biosystems Engineering
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• 제24권2호
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• pp.153-158
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• 1999

As a basic research for the development of the automatic grading and sorting system for dried oak mushrooms, the device to acquire both cap and gill side images of mushroom has been developed and neural network based side recognition and quality grading has been proposed via inputting both side images. 20 quality grades have been selected considering the requirement of grade classifications imposed by the mushroom company. Developed DC motor driven‘V’type reversing device for the image acquisition of both side images of mushroom showed more than 95% success. Most error was caused by very small size mushrooms with a radius of around 1cm. However, it required a further research to reduce the reversing time. Grading and side recognition were performed via inputting normalized size factors and average gray levels of $8{\times}8$ grids converted from the raw images of both surfaces to the multi-layer back propagation(BP) network. Accuracy of the grading showed about 88.5% and the total grading time including reversing operation was around 2 seconds.

• 한국농업기계학회:학술대회논문집
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• 한국농업기계학회 1996년도 International Conference on Agricultural Machinery Engineering Proceedings
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• pp.607-614
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• 1996

A computer vision based automatic intelligent sorting system for dried oak mushrooms has been developed. The developed system was composed of automatic devices for mushroom feeding and handling, two sets of computer vision system for grading , and computer with digital I/O board for PLC interface, and pneumatic actuators for the system control. Considering the efficiency of grading process and the real time on-line system implementation, grading was done sequentially at two consecutive independent stages using the captured image of either side. At the first stage, four grades of high quality categories were determined from the cap surface images and at the second stage 8 grades of medium and low quality categories were determined from the gill side images. The previously developed neuro-net based mushroom grading algorithm which allowed real time on-line processing was implemented and tested. Developed system revealed successful performance of sorting capability of approximate y 5, 000 mushrooms/hr per each line i.e. average 0.75 sec/mushroom with the grading accuracy of more than 88%.

• 한국농업기계학회:학술대회논문집
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• 한국농업기계학회 1996년도 International Conference on Agricultural Machinery Engineering Proceedings
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• pp.773-779
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• 1996

Dried oak mushroom have complex and various visual features. Grading and sorting of dried oak mushrooms has been done by the human expert. Though actions involved in human grading looked simple, a decision making underneath the simple action comes from the result of the complex neural processing of the visual image. Through processing details involved in human visual recognition has not been fully investigated yet, it might say human can recognize objects via one of three ways such as extracting specific features or just image itself without extracting those features or in a combined manner. In most cases, extracting some special quantitative features from the camera image requires complex algorithms and processing of the gray level image requires the heavy computing load. This fact can be worse especially in dealing with nonuniform, irregular and fuzzy shaped agricultural products, resulting in poor performance because of the sensitiveness to the crisp criteria or specific ules set up by algorithms. Also restriction of the real time processing often forces to use binary segmentation but in that case some important information of the object can be lost. In this paper, the neuro net based real time recognition algorithm was proposed without extracting any visual feature but using only the directly captured raw gray images. Specially formated adaptable size of grids was proposed for the network input. The compensation of illumination was also done to accomodate the variable lighting environment. The proposed grading scheme showed very successful results.

• 한국농업기계학회:학술대회논문집
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• 한국농업기계학회 1998년도 동계 학술대회 논문집
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• pp.438-443
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• 1998

• 한국식품위생안전성학회지
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• 제7권1호
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• pp.7-14
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• 1992

건조 표고버섯의 고유한 향민관련 성분에 대한 Ethylene Oxide와 감마에너지 (5kGy) 처리의 영향을 검토하였다. 건표고의 주요 휘발성 향기성분으로 확인된 1-octen-3-ol(72.8%), 3-octanone(11.5%), dimethyl disulfide(6.7%) 등의 함량은 살균, 살충목적의 EO 훈증처리에 의해 현저하게 감소되었고, 동일 목적의 감마 에너지에 의해서도 변화가 나타났다. 시료의 유리아미노산과 당(Mannitol, arabitol , trehalose) 성분은 비교적 안정하였지만, 훈증처리의 영향이 안정되었다. 그러나 전반적 향미에 대한 관능 시험에서는 대조군과 각 처리군간에 유의적인 차이가 나타나지 않았다.

• Agricultural and Biosystems Engineering
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• 제4권1호
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• pp.8-15
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• 2003

In Korea, quality evaluation of dried oak mushrooms are done first by classifying them into more than 10 different categories based on the state of opening of the cap, surface pattern, and colors. And mushrooms of each category are further classified into 3 or 4 groups based on its shape and size, resulting into total 30 to 40 different grades. Quality evaluation and sorting based on the external visual features are usually done manually. Since visual features of mushroom affecting quality grades are distributed over the entire surface of the mushroom, both front (cap) and back (stem and gill) surfaces should be inspected thoroughly. In fact, it is almost impossible for human to inspect every mushroom, especially when they are fed continuously via conveyor. In this paper, considering real time on-line system implementation, image processing algorithms utilizing artificial neural network have been developed for the quality grading of a mushroom. The neural network based image processing utilized the raw gray value image of fed mushrooms captured by the camera without any complex image processing such as feature enhancement and extraction to identify the feeding state and to grade the quality of a mushroom. Developed algorithms were implemented to the prototype on-line grading and sorting system. The prototype was developed to simplify the system requirement and the overall mechanism. The system was composed of automatic devices for mushroom feeding and handling, a set of computer vision system with lighting chamber, one chip microprocessor based controller, and pneumatic actuators. The proposed grading scheme was tested using the prototype. Network training for the feeding state recognition and grading was done using static images. 200 samples (20 grade levels and 10 per each grade) were used for training. 300 samples (20 grade levels and 15 per each grade) were used to validate the trained network. By changing orientation of each sample, 600 data sets were made for the test and the trained network showed around 91 % of the grading accuracy. Though image processing itself required approximately less than 0.3 second depending on a mushroom, because of the actuating device and control response, average 0.6 to 0.7 second was required for grading and sorting of a mushroom resulting into the processing capability of 5,000/hr to 6,000/hr.

• 한국식품과학회지
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• 제19권3호
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• pp.273-278
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• 1987

건조 표고버섯에 대한 ethylene oxide 훈증처리와 감마선 조사가 사료의 품질에 관련된 몇가지 이화학적특성에 미치는 영향을 조사한 결과는 다음과 같다. 시료의 아미노산 함량은 감마선 조사구(1-5 kGy)에 비해 ethylene oxide처리구에서는 상당히 감소되었고, 환원당과 유리당(mannitol, arabitol, trehalose)의 함량은 조사선량에 따라 증가되었으나 훈증처리구에서는 감소되었다. 시료의 무기질의 함량은 처리구에 따라 다소 증감하였으나 유의적인 차이는 없었으며, 갈변색소와 산패도는 저장기간의 경과로 점차 증가되었는데 ethylene oxide처리구는 감마산 조사구보다 현저하게 나타났고 저장 상대습도에 따라서는 RH70%에 저장된 시료가 RH50%의 시료보다 이화학적 품질의 변화가 다소 심하였다.