All Issue

2022 Vol.55, Issue 4 Preview Page

Original research article

Estimation of Rice Yield in Paddy Damaged by Rice Blast Using UAV Imagery
Kyung-Do Lee1
,
Ho-Yong An1
,
Jae-Hyun Ryu1
,
Byung-Mo Lee2
,
Sang-Il Na1*
1Researcher, National Institute of Agricultural Sciences, RDA, Wanju 56365, Korea
2Senior Researcher, National Institute of Agricultural Sciences, RDA, Wanju 56365, Korea
*Corresponding Author
30 November 2022. pp. 361-368
PDF XML Citation
Abstract

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.

Vegetation index (NDVI) and yield distribution map using UAV imagery on the paddy field damaged by rice blast.
Keywords
Aerial photos
Remote sensing
Rice disease
Vegetation index
References
1
Choi, S.J. and C.M. Heo. 2022. A study on the determinants of business performance of agricultural firms with the digital agriculture -Focused on moderating effect of finance support program-. J. Digital Convergence 20(2):225-239.
2
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.
3
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. 10.1016/j.rse.2021.112763
4
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. 10.3389/fpls.2019.0020430873194PMC6400984
5
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. 10.1016/S0034-4257(96)00072-7
6
Gong, H.Y., S.S. Kang, and S.D. Hong. 2011. Estimation of nitrogen uptake and biomass of rice (Oryza sativa L.) using ground-based remote sensing techniques. Korean J. Soil Sci. Fert. 44(5):779-787. 10.7745/KJSSF.2011.44.5.779
7
Jordan, C.F. 1969. Derivation of leaf-area index from quality of light on the forest floor. Ecology 50:663-666. 10.2307/1936256
8
Kim, H.N. and J.H. Park. 2021. Research trends using soil sensors for precise nutrient and water management in soil for smart farm. Korean J. Soil Sci. Fert. 54(3):366-382. 10.7745/KJSSF.2021.54.3.366
9
Kim, S.H. 2016. A study on the diffusion of Korean agricultural ICT and role of the agricultural cooperative federation using the theory of technology adoption life cycle and chasm. Coop. Manage. Rev. 45:1-27.
10
Lee, K.D., H.Y. Ahn, J.H. Ryu, K.H. So, and S.I. Na. 2021. Comparison of reflectance and vegetation index changes by type of UAV-mounted multi-spectral sensors. Korean J. Remote Sens. 37(5-1):947-958.
11
Lee, K.D., H.Y. An, C.W. Park, K.H. So, S.I. Na, and S.Y. Jang. 2019. Estimation of rice grain yield distribution using UAV imagery. J. Korean Soc. Agric. Eng. 61(4):1-10.
12
Lee, K.D., H.Y. An, S.Y. Hong, C.W. Park, K.H. So, S.I. Shim, G.E. Song, and S.I. Na. 2020a. Estimation of winter wheat nitrogen content, biomass and yield using UAV images in South Korea. Korean J. Soil Sci. Fert. 53(4):589-599. 10.7745/KJSSF.2020.53.4.589
13
Lee, K.D., S.I. Na, S.C. Baek, K.D. Park, J.S. Choi, S.J. Kim, H.J. Kim, H.S. Choi, and S.Y. Hong. 2015. Estimating the amount of nitrogen in hairy vetch on paddy fields using unmaned aerial vehicle imagery. Korean J. Soil Sci. Fert. 48(5):384-390. 10.7745/KJSSF.2015.48.5.384
14
Lee, K.D., S.M. Kim, H.Y. An, C.W. Park, S.Y. Hong, K.H. So, and S.I. Na. 2020b. Yearly estimation of rice growth and bacterial leaf blight inoculation effect using UAV imagery. J. Korean Soc. Agric. Eng. 62(4):75-86.
15
Lee, K.D., Y.E. Lee, C.W. Park, S.Y. Hong, and S.I. Na. 2016. Study on reflectance and NDVI of aerial images using a fixed-wing UAV “Ebee”. Korean J. Soil Sci. Fert. 49(6):731-742. 10.7745/KJSSF.2016.49.6.731
16
Min, B.R., J.T. Hong, D.W. Lee, and J.Y. Kim. 2013. The performance analysis for amounts of sprayed quantity by using the small and medium-sized sprayer. J. Anim. Environ. Sci. 19(1):19-24. 10.11109/JAES.2013.19.1.019
17
Rouse, J.W., R.H. Haas, J.A. Schell, and D.W. Deering. 1973. Monitoring vegetation systems in the great plains with ERTS. pp. 309-317. In Proceedings of the Third Earth Resources Technology Satellite-1 Symposium, NASA SP-351, December 10-14, Washington, D.C., USA.
18
Sripada, R.P., R.W. Heiniger, J.G. White, and A.D. Meijer. 2006. Aerial color infrared photography for determining early in-season nitrogen requirements in corn. Agron. J. 98:968-977. 10.2134/agronj2005.0200
19
Zhou, X., H.B. Zheng, X.Q. Xu, J.Y. He, X.K. Ge, X. Yao, T. Cheng, Y. Zhe, W.X. Cao, and Y.C. Tian. 2017. Predicting grain yield in rice using multi-temporal vegetation indices from UAV-based multispectral and digital imagery. ISPRS J. Photogramm. Remote Sens. 130:246-255. 10.1016/j.isprsjprs.2017.05.003
Information
  • Publisher :Korean Society of Soil Science and Fertilizer
  • Publisher(Ko) :한국토양비료학회
  • Journal Title :Korean Journal of Soil Science and Fertilizer
  • Journal Title(Ko) :한국토양비료학회 학회지
  • Volume : 55
  • No :4
  • Pages :361-368
  • Received Date : 2022-10-04
  • Revised Date : 2022-10-27
  • Accepted Date : 2022-10-31
  • DOI :https://doi.org/10.7745/KJSSF.2022.55.4.361
Journal Informaiton Korean Journal of Soil Science and Fertilizer Korean Journal of Soil Science and Fertilizer
KJSSF Online Submission
  • NRF
  • KOFST
  • crossref crossmark
  • crossref cited-by
  • crosscheck
  • orcid
  • open access
  • ccl
Journal Informaiton Journal Informaiton - close