Browse > Article
http://dx.doi.org/10.5532/KJAFM.2019.21.4.327

Estimation of Fresh Weight, Dry Weight, and Leaf Area Index of Soybean Plant using Multispectral Camera Mounted on Rotor-wing UAV  

Jang, Si-Hyeong (Department of Agro-System Engineering, GyeongSang National University (Institute of Agriculture & Life Science))
Ryu, Chan-Seok (Department of Agro-System Engineering, GyeongSang National University (Institute of Agriculture & Life Science))
Kang, Ye-Seong (Department of Agro-System Engineering, GyeongSang National University (Institute of Agriculture & Life Science))
Jun, Sae-Rom (Department of Agro-System Engineering, GyeongSang National University (Institute of Agriculture & Life Science))
Park, Jun-Woo (Department of Agro-System Engineering, GyeongSang National University (Institute of Agriculture & Life Science))
Song, Hye-Young (Department of Agro-System Engineering, GyeongSang National University (Institute of Agriculture & Life Science))
Kang, Kyeong-Suk (Department of Agro-System Engineering, GyeongSang National University (Institute of Agriculture & Life Science))
Kang, Dong-Woo (Department of Plant Bioscience, Pusan National University (Natural Resources & Life Science))
Zou, Kunyan (Department of Plant Bioscience, Pusan National University (Natural Resources & Life Science))
Jun, Tae-Hwan (Department of Plant Bioscience, Pusan National University (Natural Resources & Life Science))
Publication Information
Korean Journal of Agricultural and Forest Meteorology / v.21, no.4, 2019 , pp. 327-336 More about this Journal
Abstract
Soybean is one of the most important crops of which the grains contain high protein content and has been consumed in various forms of food. Soybean plants are generally cultivated on the field and their yield and quality are strongly affected by climate change. Recently, the abnormal climate conditions, including heat wave and heavy rainfall, frequently occurs which would increase the risk of the farm management. The real-time assessment techniques for quality and growth of soybean would reduce the losses of the crop in terms of quantity and quality. The objective of this work was to develop a simple model to estimate the growth of soybean plant using a multispectral sensor mounted on a rotor-wing unmanned aerial vehicle(UAV). The soybean growth model was developed by using simple linear regression analysis with three phenotypic data (fresh weight, dry weight, leaf area index) and two types of vegetation indices (VIs). It was found that the accuracy and precision of LAI model using GNDVI (R2= 0.789, RMSE=0.73 ㎡/㎡, RE=34.91%) was greater than those of the model using NDVI (R2= 0.587, RMSE=1.01 ㎡/㎡, RE=48.98%). The accuracy and precision based on the simple ratio indices were better than those based on the normalized vegetation indices, such as RRVI (R2= 0.760, RMSE=0.78 ㎡/㎡, RE=37.26%) and GRVI (R2= 0.828, RMSE=0.66 ㎡/㎡, RE=31.59%). The outcome of this study could aid the production of soybeans with high and uniform quality when a variable rate fertilization system is introduced to cope with the adverse climate conditions.
Keywords
Remote sensing; Unmanned Aerial Vehicle; Vegetation Indices; Soybean; Simple linear regression;
Citations & Related Records
Times Cited By KSCI : 5  (Citation Analysis)
연도 인용수 순위
1 Chung, U., H. S. Cho, J. H. Kim, W. G. Sang, P. Shin, M. C. Seo, and W. S. Jung, 2016 : Responses of soybean yield to high temperature stress during growing season: A case study of the Korean soybean. Korean Journal of Agricultural and Forest Meteorology 18(4), 188-198. (in Korean with English abstract)   DOI
2 Dammer, K. H., B. Moller, B. Rodemann, and D. Heppner, 2011 : Detection of head blight (Fusarium ssp.) in winter wheat by color and multispectral image analyses. Crop Protection 30(4), 420-428.   DOI
3 Gitelson, A. A., Y. J. Kaufman, and M. N. Merzlyak, 1996 : Use of a green channel in remote sensing of global vegetation from EOSMODIS. Remote sensing of Environment 58(3), 289-298.   DOI
4 Han-ya, I., K. Ishii, and N. Noguchi, 2011 : Rice yields and protein content estimation by lowaltitude remote sensing using spectroradiometer. IFAC Proceedings Volumes 44(1), 1796-1801.   DOI
5 Hong, S. Y., J. T. Lee, S. K. Rim, W. K. Jung, and I. S. Jo, 1998 : Estimation of paddy rice growth increment by using spectral reflectance signature. Journal of the Korea Society of Remote Sensing 14(1), 83-94. (in Korean with English abstract)
6 Kim, J., C. K. Lee, W. Sang, P. Shin, H. Cho, and M. Seo, 2017 : Introduction to empirical approach to estimate rice yield and comparison with remote sensing approach. Korean Journal of Remote Sensing 33(5_2), 733-740. (in Korean with English abstract)   DOI
7 Jin, X., S. Liu, F. Baret, M. Hemerle, and A. Comar, 2017 : Estimates of plant density of wheat crops at emergence from very low altitude UAV imagery. Remote Sensing of Environment 198, 105-114.   DOI
8 Kim, S. H., 2015 : Image segmentation and weed detection for soybean. 47pp.
9 Kang, Y. S., S. H. Kim, J. G. Kang, T. K. Sarkar, Y. S. Kwon, S. R. Jun, and C. S. Ryu, 2017 : Model assessment multi-temporal monitoring of Chinese cabbage growth using low altitude remote sensing system. Journal of Agriculture & Life Science 51(4), 149-160. (in Korean with English abstract)   DOI
10 Kim, D. Y., H. K. Oh, N. K. Lee, Y. S. Kim, and B. K. Cho, 2010 : Study on quality factor measurement for cherry tomato using color imagery. Korean Journal of Agricultural Science 37(2), 303-308. (in Korean with English abstract)   DOI
11 Kim, Y. H., and S. Y. Hong, 2007 : Estimation of nondestructive rice leaf nitrogen content using ground optical sensors. Korean Journal of Soil Science and Fertilizer 40(6), 435-441. (in Korean with English abstract)
12 Lee, K. D., S. I. Na, S. Y. Hong, C. W. Park, K. H. So, and J. M. Park, 2017 : Estimating corn and soybean yield using MODIS NDVI and meteorological data in Illinois and Iowa, USA. Korean Journal of Remote Sensing 33(5_2), 741-750. (in Korean with English abstract)   DOI
13 Na, S. I., C. W. Park, Y. K. Cheong, C. S. Kang, I. B. Choi, and K. D. Lee, 2016 : Selection of optimal vegetation indices for estimation of barley & wheat growth based on remote sensing-An application of unmanned aerial vehicle and field investigation data. Korean Journal of Remote Sensing 32(5), 483-497. (in Korean with English abstract)   DOI
14 Shin, Y. H., J. H. Park, and M. S. Park, 2003 : Spectral reflectance characteristics and vegetation indices of field crops. KCID journal 10(2), 43-54. (in Korean with English abstract)
15 Na, S. I., C. W. Park, K. H. So, H. Y. Ahn, and K. D. Lee, 2018 : Application method of unmanned aerial vehicle for crop monitoring in Korea. Korean Journal of Remote Sensing 34(5), 829-846. (in Korean with English abstract)   DOI
16 Park, J. K., and J. H. Park, 2015 : Crops classification using imagery of unmanned aerial vehicle (UAV). Journal of the Korean society of agricultural engineers 57(6), 91-97. (in Korean with English abstract)   DOI
17 Peng, Y., Y. Li, C. Dai, S. Fang, Y. Gong, X. Wu, and K. Liu, 2019 : Remote prediction of yield based on LAI estimation in oilseed rape under different planting methods and nitrogen fertilizer applications. Agricultural and Forest Meteorology 271, 116-125.   DOI
18 Rouse Jr, J., R. H. Haas, J. A. Schell, and D. W. Deering, 1974 : Monitoring vegetation systems in the Great Plains with ERTS.
19 Shin, S. O., W. Y. Han, B. W. Lee, H. J. Park, J. W. Bae, K. Y. Choi, and I. S. Oh, 2015: Major factors for affecting to soybean yield decline in South Korea. The Korean Society of International Agriculture 27(4), 489-496.   DOI
20 Son, N. T., C. F. Chen, C. R. Chen, V. Q. Minh, and N. H. Trung, 2014 : A comparative analysis of multitemporal MODIS EVI and NDVI data for large-scale rice yield estimation. Agricultural and forest meteorology 197, 52-64.   DOI
21 Suh, K. H., S. R. Suh, and J. H. Sung, 2002 : A fundamental study on detection of weeds in paddy field using spectrophotometric analysis. Agricultural Mechanization in Korea 27, 133-142. (in Korean with English abstract)