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학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2022.11
- 수록면
- 361 - 368 (8page)
이용수
초록· 키워드
오류제보하기
Recently, Unmanned Aerial Vehicle (UAV) has been usefully used to produce precise crop information on farmland. The purpose of this study is to develop a technology for estimating rice yield distribution in paddy field damaged by rice blast using UAV imagery. We analyzed the change of sensing information according to disease occurrence by using UAV images for rice paddy in 2021. The correlation between NDVI vegetation index and rice yield showed a high value of 0.8 or higher from booting stage to ripening stage except for heading stage. It means that UAV image reflected the change of rice yield according to rice blast. In addition, it was found that in order to evaluate the effect of rice disease using UAV image, it was necessary to select a sensor and vegetation index considering the change in spectral reflection next to the disease characteristics. This study is expected to be helpful in establishing object damage survey of agricultural disaster insurance and farming plan.
목차
ABSTRACT
Introduction
Materials and Methods
Results and Discussion
Conclusions
References
참고문헌 (19)
참고문헌 신청
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Cohen, W.B. 1991. Response of vegetation indices to changes in three measures of leaf water stress. Photogramm. Eng. Remote Sens. 57(2):195-202.
Dechant, B., Y. Ryu, G. Badgley, P. Kohler, U. Rascher, M. Migiavacca, Y. Zhang, G. Tagliabue, K. Guan, M. Rossini, Y. Goulas, Y. Zeng, C. Frankenberg, and J.A. Berry. 2022. NIRvP: A robust structural proxy for sun-induced chlorophyll fluorescence and photosynthesis across scales. Remote Sens. Environ. 268:112763.
Duan, B., S. Fang, R. Zhu, X. Wu, S. Wang, Y. Gong, and Y. Peng. 2019. Remote estimation of rice yield with unmanned aerial vehicle (UAV) data and spectral mixture analysis. Front. Plant Sci. 10:204.
Gitelson, A.A., Y.J. Kaufman, and M.N. Merzlyak. 1996. Use of a green channel in remote sensing of global vegetation from EOS-MODIS. Remote Sens. Environ. 58:289-298.
Cohen, W.B. 1991. Response of vegetation indices to changes in three measures of leaf water stress. Photogramm. Eng. Remote Sens. 57(2):195-202.
Dechant, B., Y. Ryu, G. Badgley, P. Kohler, U. Rascher, M. Migiavacca, Y. Zhang, G. Tagliabue, K. Guan, M. Rossini, Y. Goulas, Y. Zeng, C. Frankenberg, and J.A. Berry. 2022. NIRvP: A robust structural proxy for sun-induced chlorophyll fluorescence and photosynthesis across scales. Remote Sens. Environ. 268:112763.
Duan, B., S. Fang, R. Zhu, X. Wu, S. Wang, Y. Gong, and Y. Peng. 2019. Remote estimation of rice yield with unmanned aerial vehicle (UAV) data and spectral mixture analysis. Front. Plant Sci. 10:204.
Gitelson, A.A., Y.J. Kaufman, and M.N. Merzlyak. 1996. Use of a green channel in remote sensing of global vegetation from EOS-MODIS. Remote Sens. Environ. 58:289-298.
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