최소 단어 이상 선택하여야 합니다.
최대 10 단어까지만 선택 가능합니다.
다음과 같은 기능을 한번의 로그인으로 사용 할 수 있습니다.
NTIS 바로가기한국산림과학회지 = Journal of korean society of forest science, v.112 no.2, 2023년, pp.145 - 156
김용율 (국립산림과학원 생명정보연구과) , 구자정 (국립산림과학원 생명정보연구과) , 구다은 (국립산림과학원 생명정보연구과) , 한심희 (국립산림과학원 생명정보연구과) , 강규석 (서울대학교 농림생물자원학부)
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, N...
Chen, Q., Lin, H. and Zhao, J. 2021. Advanced nondestructive detection technologies in food. Springer Nature Singapore. Gateway East, Singapore. pp. 333.
Chen, T. and Guestrin, C. 2016. XGBoost: a scalable tree boosting system. pp. 785-794. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining(KDD '16). Association for Computing Machinery. New York, U.S.A.
Daneshvar, A., Tigabu, M., Karimidoost, A. and Oden, P.C. 2015. Single seed near infrared spectroscopy discriminates viable and non-viable seeds of Juniperus polycarpos. Silva Fennica 49(5): 1-14.
Food and Agriculture Organization of the United Nations. 2014. Genebank standards for plant genetic resources for food and agriculture. FAO Working Group. Rome, Italy. pp. 181.
JMP Statistical Discovery LLC. 2022. JMP® 17 Documentation Library. JMP Statistical Discovery LLC. North Carolina, U.S.A.
Kim, D.H., Han, S.H., Song, J.H. and Jang, K.H. 2012. Seed storage and longevity in woody plant. Korea Forest Research Institute, Seoul, Republic of Korea. pp. 159.
Kim, J.H., Ku, J.J., Lim, H.I. and Kim, Y.Y. 2020. Characteristics and conservation status of useful forest genetic resources seeds. National Institute of Forest Science, Seoul, Republic of Korea. pp. 178.
Kumari, R. and Srivastava, S. 2017. Machine learning: a review on binary classification. International Journal of Computer Applications 160(7): 11-15.
Lestander, T. and Oden, P.C. 2002. Separation of viable and nonviable filled scots pine seeds by differentiating between drying rates using single seed near infrared transmittance spectroscopy. Seed Science and Technology 30(2): 383-392.
Liu, W., Liu, J., Jiang, J. and Li, Y. 2021. Comparison of partial least squares-discriminant analysis, support vector machines and deep neural networks for spectrometric classification of seed vigour in a broad range of tree species. Journal of Near Infrared Spectroscopy 29(1): 33-41.
Mo, L., Chen, H., Chen, W., Feng, Q. and Xu, L. 2020. Study on evolution methods for the optimization of machine learning models based on FT-NIR spectroscopy. Infrared Physics and Technology 108: 103366.
Mukasa, P., Cho, B.K., Joo, H.J. and Kwon, Y.R. 2018. Determination of viability of Japanese larch seeds using hyperspectral imaging. Proceedings of the Korean Society for Agricultural Machinery Conference 23(1): 195.
Mukasa, P., Wakholi, C., Mo, C.Y., Oh, M.R., Joo, H.J., Suh, H.K. and Cho, B.K. 2019. Determination of viability of retinispora (Hinoki cypress) seeds using FT-NIR spectroscopy. Infrared Physics and Technology 98: 62-68.
Narassiguin, A., Bibimoune, M., Elghazel, H. and Aussem, A. 2016. An extensive empirical comparison of ensemble learning methods for binary classification. Pattern Analysis and Application 19(4): 1093-1128.
Qiu, G., Lu, E., Lu, H., Xu, S., Zeng, F. and Shui, Q. 2018. Single-kernel FT-NIR spectroscopy for detecting supersweet corn (Zea mays L. Saccharata Sturt) seed viability with multivariate data analysis. Sensors 18(4): 1010.
Rocha, W.F.D.C., Prado, C.B.D. and Blonder, N. 2020. Comparison of chemometric problems in food analysis using non-linear methods. Molecules 25(13): 3025. https://doi.org/10.3390/molecules25133025.
Ruiz-Perez, D., Guan, H., Madhivanan, P., Mathee, K. and Narasimhan, G. 2020. So you think you can PLS-DA?. BMC Bioinformatics 21(Suppl 1): 2.
Sampaio, P.S. and Brites, C.M. 2021. Near-Infrared spectroscopy and machine learning: analysis and classification methods of rice. pp. 257-288. In: Huang, M. (Ed.). Integrative Advances in Rice Research. IntechOpen. London, UK.
Sato, T., Kawano, S. and Iwamoto, M. 1991. Near-infrared spectral patterns of fatty acid analysis from fats and oils. Journal of the Americal Oil Chemists Society 68(11): 827-833.
Savitzky, A. and Golay, M.J.E. 1964. Smoothing and differentiation of data by simplified least squares procedures. Analytical Chemistry 36(8): 1627-1639.
Schwanninger, M., Rodrigues, J.C. and Fackler, K. 2011. A review of band assignments in near infrared spectra of wood and wood components. Journal of Near Infrared Spectroscopy 19(5): 287-308.
Shenk, J.S., Workman, J.J. and Westerhaus, M.O. 2007. Application of NIR spectroscopy to agricultural products. pp. 419-474. In: Burns D.A. and Ciurczak E.W. (eds), Handbook of Near-Infrared Spectroscopy. CRC Press. New York, U.S.A.
Shetty, N, Min T.G., Gislum, R., Olesen M.H. and Boelt B. 2011. Optimal sample size for predicting viability of cabbage and radish seeds based on near infrared spectra of single seeds. Journal of Near Infrared Spectroscopy 19(6): 451-461.
Tian, W., Zang, L., Nie, L., Li, L., Zhong, L., Guo, X., Huang, S. and Zang, H. 2023. Structural analysis and classification of low-molecular-weight hyaluronic acid by near-infrared spectroscopy: a comparison between traditional machine learning and deep learning. Molecules 28(2): 809.
Tigabu, M. 2003. Characterization of forest tree seed quality with near infrared spectroscopy and multivariate analysis. (Dissertation). Umea, Sweden. Acta Universitatis Agriculturae Sueciae.
Tigabu, M. and Oden, P.C. 2003. Discrimination of viable and empty seeds of Pinus patula Schiede & Deppe with near-infrared spectroscopy. New Forest. 25(3): 163-176.
Tigabu, M., Daneshvar, A., Jingjing, R., Wu, P., Ma, X. and Oden, P.C. 2019. Multivariate discriminant analysis of single seed near infrared spectra for sorting dead-filled and viable seeds of three pine Species: Does one model fit all species? Forests 10(6): 469-482.
Tigabu, M., Daneshvar, A., Wu, P., Ma, X. and Oden, P.C. 2020. Rapid and non-destructive evaluation of seed quality of Chinese fir by near infrared spectroscopy and multivariate discriminant analysis. New Forests 51(3): 395-408.
Wang, L, Huang, Z. and Wang, R. 2021. Discrimination of cracked soybean seeds by near-infrared spectroscopy and random variable selection. Infrared Physics and Technology 115: 103731.
Workman, J. and Weyer, L. 2012. Practical guide and spectral atlas for interpretative near-infrared spectroscopy. 2nd Edition. CRC Press. New York, U.S.A. pp. 326.
Xia, Y., Xu, Y., Li, J., Zhang, C. and Fan, S. 2019. Recent advances in emerging techniques for non-destructive detection of seed viability: A review. Artificial Intelligence in Agriculture 1: 35-47.
*원문 PDF 파일 및 링크정보가 존재하지 않을 경우 KISTI DDS 시스템에서 제공하는 원문복사서비스를 사용할 수 있습니다.
Free Access. 출판사/학술단체 등이 허락한 무료 공개 사이트를 통해 자유로운 이용이 가능한 논문
※ AI-Helper는 부적절한 답변을 할 수 있습니다.