• Journal of Korea Society of Industrial Information Systems
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• v.25 no.5
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• pp.83-90
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• 2020

In this paper, a web-based seed phenotype visualization support system is proposed to extract and visualize data such as the surface color, length, area, perimeter and compactness of seed, which is phenotype information from the image of soybean/rice seeds. This system systematically stores data extracted from seeds in databases, and provides a web-based user interface that facilitates the analysis of data by researchers using data tables and charts. Conventional seed characteristic studies have been manually measured by humans, but the system developed in this paper allows researchers to simply upload seed images for analysis and obtain seed's numerical data after image processing. It is expected that the proposed system will be able to obtain time efficiency and remove spatial restriction, if it is used in seed characterization research, and it will be easy to analyze through systematic management of research results and visualization of the phenotype characteristics.

• Journal of Korea Society of Industrial Information Systems
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• v.25 no.6
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• pp.25-32
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• 2020

The seed, the most basic component, is an important factor in increasing production and efficiency in agriculture. Seeds with superior genes can be expected to improve agricultural productivity, crop survival, and reproduction. Currently, however, screening of superior seeds depends mostly on manual work, which requires a lot of time and manpower. In this paper, we propose a system that can extract the characteristics of seed phenotypes by using computer image processing technology, so that even a small number of people and a short period of time are needed to extract the characteristics of seeds. The proposed system detects individual seeds from images containing large quantities of seeds, and extracts and stores various characteristics such as representative colors, area, perimeter and roundness for each individual seed. Due to the regularity of input images, the accuracy of individual seed extraction in the proposed system is 99.12% for soybean seeds and 99.76% for rice seeds. The extracted data will be used as basic data for various data analyses that reflect the opinions of experts in the future, and will be used as basic data to determine the expressive nature of each seed.

• Journal of the Korean Society for Nondestructive Testing
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• v.36 no.1
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• pp.35-44
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• 2016

Seed viability is one of the most important parameters that is directly related with seed germination performance and seedling emergence. In this study, a hyperspectral imaging (HSI) system having a range of 1000-2500 nm was used to classify viable watermelon seeds from nonviable seeds. In order to obtain nonviable watermelon seeds, a total of 96 seeds were artificially aged by immersing the seeds in hot water ($25^{\circ}C$) for 15 days. Further, hyperspectral images for 192 seeds (96 normal and 96 aged) were acquired using the developed HSI system. A germination test was performed for all the 192 seeds in order to confirm their viability. Spectral data from the hyperspectral images of the seeds were extracted by selecting pixels from the region of interest. Each seed spectrum was averaged and preprocessed to develop a classification model of partial least square discriminant analysis (PLS-DA). The developed PLS-DA model showed a classification accuracy of 94.7% for the calibration set, and 84.2% for the validation set. The results demonstrate that the proposed technique can classify viable and nonviable watermelon seeds with a reasonable accuracy, and can be further converted into an online sorting system for rapid and nondestructive classification of watermelon seeds with regard to viability.

• Journal of Korea Society of Industrial Information Systems
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• v.25 no.5
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• pp.23-29
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• 2020

The National Institute of Agricultural Sciences of the Rural Developement Administration (NAS, RDA) is conducting various studies on various crops, such as monitoring the cultivation environment and analyzing harvested seeds for high-throughput phenotyping. In this paper, we propose a deep learning-based rice seed segmentation method to analyze the seeds of various crops owned by the NAS. Using Mask-RCNN deep learning model, we perform the rice seed segmentation from manually taken images under specific environment (constant lighting, white background) for analyzing the seed characteristics. For this purpose, we perform the parameter tuning process of the Mask-RCNN model. By the proposed method, the results of the test on seed object detection showed that the accuracy was 82% for rice stem image and 97% for rice grain image, respectively. As a future study, we are planning to researches of more reliable seeds extraction from cluttered seed images by a deep learning-based approach and selection of high-throughput phenotype through precise data analysis such as length, width, and thickness from the detected seed objects.

• Journal of the Korean Society for Nondestructive Testing
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• v.32 no.5
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• pp.518-525
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• 2012

In this study, the feasibility of hyperspectral reflectance imaging technique was investigated for the discrimination of viable and non-viable lettuce seeds. The spectral data of hyperspectral reflectance images with the spectral range between 750 nm and 1000 nm were used to develop PLS-DA model for the classification of viable and non-viable lettuce seeds. The discrimination accuracy of the calibration set was 81.6% and that of the test set was 81.2%. The image analysis method was developed to construct the discriminant images of non-viable seeds with the developed PLS-DA model. The discrimination accuracy obtained from the resultant image were 91%, which showed the feasibility of hyperspectral reflectance imaging technique for the mass discrimination of non-viable lettuce seeds from viable ones.

• Korean Journal of Environmental Biology
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• v.35 no.2
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• pp.207-214
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• 2017

There is little information about the seed longevity of wild plants, although seed bank storage is an important tool for biodiversity conservation. This study was conducted to predict the seed viability equation of Agastache rugosa. The A. rugosa seeds were stored at moisture contents ranging from 2.7 to 12.5%, and temperatures between 10 and $50^{\circ}C$. Viability data were fitted to the seed viability equation in a one step and two step approach. The A. rugosa seeds showed orthodox seed storage behaviour. The viability constants were $K_E=6.9297$, $C_W=4.2551$ $C_H=0.0329$, and $C_Q=0.00048$. The P85 of A. rugosa seeds was predicted to 152 years under standard seed bank conditions. The P85 predicted by seed viability equation can be used as basic information for optimization of seed storage processes.

• Proceedings of the KIEE Conference
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• 2006.07d
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• pp.1805-1806
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• 2006

ET(Environment Technology) 분야 중 식물의 종자 배양에 관한 연구는 환경 변화에 강하고 생산성을 향상시켜 농가 소득 증대에 중요한 역할을 하는 분야이다. 주로, 온실 또는 청정실에서 연구를 진행하여 민감한 외부환경 변화를 차단하고 정확한 데이터를 산출해야 하는 정밀성이 요구된다. 특히, 온/습도의 변화는 종자의 생장에 지대한 영향을 끼치는 중요한 요소이다. 기존의 연구환경에서는 디지털 온도계 또는 수은 온도계에 의지하는 경우가 대부분이었다. 최근에는 온/습도를 통합 제어하여 일정하게 유지하는 공조시스템을 사용하고 있지만 저렴한 시스템 보급에는 맞지 않다. 본 논문에서는 우선적으로 LabVIEW를 이용하여 온/습도 모니터링 시스템을 설계하고 온/습도 센서를 이용하여 실시간 원격으로 모니터링 할 수 있는 시스템을 개발하고자 하였다.

• Proceedings of the Korean Society for Cognitive Science Conference
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• 2002.05a
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• pp.21-27
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• 2002

본 논문은 문장 안에서 의미 중의성을 갖는 단어가 출현했을 때 그 단어가 어떤 의미로 사용되고 있는지 판별해 주는 방법을 제시하고자 한다. 이를 위해서 먼저 중의적 의미를 가지는 단어의 각 의미 (sense) 마다에 대하여 이 의미를 나타내는 주요단어 즉 종자단어와 연관성이 있는 단어들로 벡터를 구성하여 이 의미를 나타내고자 한다. 종자단어와 말뭉치의 문장을 통하여 연결된 경로를 가진 단어는 이 종자단어에 해당하는 의미를 나타내는 데 기여하는 정보로 본 것이다. 경로는 동일 문장에서 나타나는 두 단어 사이는 링크가 있다고 보고 이러한 링크를 통하여 이루어 질 수 있는 연결 관계를 나타낸다. 이 기법의 장점은 데이터 부족으로 야기되는 문제를 경감시킬 수 있다는 점이다. 실험을 위해 Hantec 품사 부착된 말뭉치를 이용하여 의미정보벡터를 구축하였으며 ETRI 품사 부착된 말뭉치에서 중의적 단어가 포함된 문장을 추출하여 실시하였다. 실험 결과 기존의 방법보다 나은 성능을 보임이 밝혀졌다.

• Proceedings of the KIEE Conference
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• 2004.07d
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• pp.2365-2366
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• 2004

Bio Technology 산업의 활성화에 따라 종자, 백신 등의 체계적인 배양 및 보관기술에 대한 중요성이 높아지고 있다. 종자 및 백신의 배양에 사용되는 시스템들은 연구의 중요성에 비해 낙후된 기술들이 적용되고 있어 정확한 데이터의 계측이 이루어지지 못하고 있다. 본 논문에서는 자동화 및 계측시스템에 다수 보급되어 있는 LabView를 이용하여 배양시스템에 적합한 온습도 계측 시스템을 설계하고 구축하고자 한다.

• Journal of Korean Society of Forest Science
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• v.112 no.2
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• pp.145-156
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• 2023

In this study, Fourier-transform near-infrared (FT-NIR) spectra of Korean red pine seeds stored at -18℃ and 4℃ for 18 years were analyzed. To develop seed-germination prediction models, the performance of seven machine learning methods, namely XGBoost, Boosted Tree, Bootstrap Forest, Neural Networks, Decision Tree, Support Vector Machine, PLS-DA, were compared. The predictive performance, assessed by accuracy, misclassification, and area under the curve (0.9722, 0.0278, and 0.9735 for XGBoost, and 0.9653, 0.0347, and 0.9647 for Boosted Tree), was better for the XGBoost and decision tree models when compared with other models. The 54 wave-number variables of the two models were of high relative importance in seed-germination prediction and were grouped into six spectral ranges (811~1,088 nm, 1,137~1,273 nm, 1,336~1,453 nm, 1,666~1,671 nm, 1,879~2,045 nm, and 2,058~2,409 nm) for aromatic amino acids, cellulose, lignin, starch, fatty acids, and moisture, respectively. Use of the NIR spectral data and two machine learning models developed in this study gave >96% accuracy for the prediction of pine-seed germination after long-term storage, indicating this approach could be useful for non-destructive viability testing of stored seed genetic resources.