무인비행체 영상 기반 연차 간 벼 생육 및 흰잎마름병 병해 추정
Yearly Estimation of Rice Growth and Bacterial Leaf Blight Inoculation Effect Using UAV Imagery
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참고문헌
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초록
The purpose of this study is to develop a technology for estimating rice growth and damage effect according to bacterial leaf blight using UAVmulti-spectral imagery. For this purpose, we analyzed the change of aerial images, rice growth factors (plant height, dry weight, LAI) and disease effectsaccording to disease occurrence by using UAV images for 3 rice varieties (Milyang23, Sindongjin-byeo, Saenuri-byeo) from 2017 to 2018. Thecorrelation between vegetation index and rice growth factor during vegetative growth period showed a high value of 0.9 or higher each year. As aresult of applying the growth estimation model built in 2017 to 2018, the plant height of Milyang23 showed good error withing 10%. However, itis considered that studies to improve the accuracy of other items are needed. Fixed wing unmanned aerial photographs were also possible to estimatethe damage area after 2 to 4 weeks from inoculation. Although sensing data in the multi-spectral (Blue, Green, Red, NIR) band have limitations inearly diagnosis of rice disease, for rice varieties such as Milyang23 and Sindongjin-byeo, it was possible to construct the equation of infected leaf arearatio and rice yield estimation using UAV imagery in early and mid-September with high correlation coefficient of 0.8 to 0.9. The results of this studyare expected to be useful for farming and policy support related to estimating rice growth, rice plant disease and yield change based on UAV images.
Abstract
The purpose of this study is to develop a technology for estimating rice growth and damage effect according to bacterial leaf blight using UAVmulti-spectral imagery. For this purpose, we analyzed the change of aerial images, rice growth factors (plant height, dry weight, LAI) and disease effectsaccording to disease occurrence by using UAV images for 3 rice varieties (Milyang23, Sindongjin-byeo, Saenuri-byeo) from 2017 to 2018. Thecorrelation between vegetation index and rice growth factor during vegetative growth period showed a high value of 0.9 or higher each year. As aresult of applying the growth estimation model built in 2017 to 2018, the plant height of Milyang23 showed good error withing 10%. However, itis considered that studies to improve the accuracy of other items are needed. Fixed wing unmanned aerial photographs were also possible to estimatethe damage area after 2 to 4 weeks from inoculation. Although sensing data in the multi-spectral (Blue, Green, Red, NIR) band have limitations inearly diagnosis of rice disease, for rice varieties such as Milyang23 and Sindongjin-byeo, it was possible to construct the equation of infected leaf arearatio and rice yield estimation using UAV imagery in early and mid-September with high correlation coefficient of 0.8 to 0.9. The results of this studyare expected to be useful for farming and policy support related to estimating rice growth, rice plant disease and yield change based on UAV images.
참고문헌 (22)
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