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NTIS 바로가기한국농림기상학회지 = Korean Journal of Agricultural and Forest Meteorology, v.23 no.4, 2021년, pp.306 - 315
김태양 (경상국립대학교 바이오시스템공학과 (농업생명과학연구원)) , 유찬석 (경상국립대학교 바이오시스템공학과 (농업생명과학연구원)) , 강예성 (경상국립대학교 바이오시스템공학과 (농업생명과학연구원)) , 장시형 (경상국립대학교 바이오시스템공학과 (농업생명과학연구원)) , 박준우 (경상국립대학교 바이오시스템공학과 (농업생명과학연구원)) , 강경석 (경상국립대학교 바이오시스템공학과 (농업생명과학연구원)) , 백현찬 (경상국립대학교 바이오시스템공학과 (농업생명과학연구원)) , 박민준 (경상국립대학교 바이오시스템공학과 (농업생명과학연구원)) , 박진기 (국립식량과학원 남부작물부 생산기술개발과)
This study analyzed the effects of light-shielding curtains and halogens on spectrum when acquiring hyperspectral images in a greenhouse. The image data of tarp (1.4*1.4 m, 12%) with 30 degrees of angles was achieved three times with four conditions depending on 14 heights using the automatic image ...
Bruning, B., B. Berger, M. Lewis, H. Liu, and T. Garnett, 2020: Approaches, applications, and future directions for hyperspectral vegetation studies: An emphasis on yield-limiting factors in wheat. The Plant Phenome Journal 3(1), e20007.
Choi, K. I., H. M. Noh, S. H. Jeong, and C. J. Yoo, 2019: Classification of growth conditions in crops using hyperspectral images and deep neural network: Case study of paprika leaf. The Journal of Korean Institute of Information Technology 17(12), 1-12. (in Korean with English abstract)
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Kim, Y. J., J. Y. Park, and Y. G. Park, 2016: An analysis of the current status and success factors of smart farms. Korea Rural Economic Institute, 1-74. (in Korean with English abstract)
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Ma, D., H. Maki, S. Neeno, L. Zhang, L. Wang, and J. Jin, 2020: Application of non-linear partial least squares analysis on prediction of biomass of maize plants using hyperspectral images. Biosystems Engineering 200, 40-54.
Moghadam, P., D. Ward, E. Goan, S. Jayawardena, P. Sikka, and E. Hernandez, 2017: Plant disease detection using hyperspectral imaging. In 2017 International Conference on Digital Image Computing: Techniques and Applications (DICTA), 1-8. IEEE.
Pinter Jr, P. J., J. L. Hatfield, J. S. Schepers, E. M. Barnes, M. S. Moran, C. S. Daughtry, and D. R. Upchurch, 2003: Remote sensing for crop management. Photogrammetric Engineering & Remote Sensing 69(6), 647-664.
Yang, W., C. Yang, Z. Hao, C. Xie, and M. Li, 2019: Diagnosis of plant cold damage based on hyperspectral imaging and convolutional neural network. Ieee Access 7, 118239-118248.
Yoosefzadeh-Najafabadi, M., H. J. Earl, D. Tulpan, J. Sulik, and M. Eskandari, 2021: Application of machine learning algorithms in plant breeding: predicting yield from hyperspectral reflectance in soybean. Frontiers in plant science 11, 2169.
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