Browse > Article
http://dx.doi.org/10.5532/KJAFM.2021.23.4.329

Estimation of Fresh Weight and Leaf Area Index of Soybean (Glycine max) Using Multi-year Spectral Data  

Jang, Si-Hyeong (Department of Bio-system Engineering, GyeongSang National University (Institute of Agriculture & Life Science))
Ryu, Chan-Seok (Department of Bio-system Engineering, GyeongSang National University (Institute of Agriculture & Life Science))
Kang, Ye-Seong (Department of Bio-system Engineering, GyeongSang National University (Institute of Agriculture & Life Science))
Park, Jun-Woo (Department of Bio-system Engineering, GyeongSang National University (Institute of Agriculture & Life Science))
Kim, Tae-Yang (Department of Bio-system Engineering, GyeongSang National University (Institute of Agriculture & Life Science))
Kang, Kyung-Suk (Department of Bio-system Engineering, GyeongSang National University (Institute of Agriculture & Life Science))
Park, Min-Jun (Department of Bio-system Engineering, GyeongSang National University (Institute of Agriculture & Life Science))
Baek, Hyun-Chan (Department of Bio-system Engineering, GyeongSang National University (Institute of Agriculture & Life Science))
Park, Yu-hyeon (Department of Plant Bioscience, Pusan National University (Natural Resources & 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))
Kim, Min-Cheol (Department of Plant Bioscience, Pusan National University (Natural Resources & Life Science))
Kwon, Yeon-Ju (Department of Plant Bioscience, Pusan National University (Natural Resources & Life Science))
Han, Seung-ah (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.23, no.4, 2021 , pp. 329-339 More about this Journal
Abstract
Soybeans (Glycine max), one of major upland crops, require precise management of environmental conditions, such as temperature, water, and soil, during cultivation since they are sensitive to environmental changes. Application of spectral technologies that measure the physiological state of crops remotely has great potential for improving quality and productivity of the soybean by estimating yields, physiological stresses, and diseases. In this study, we developed and validated a soybean growth prediction model using multispectral imagery. We conducted a linear regression analysis between vegetation indices and soybean growth data (fresh weight and LAI) obtained at Miryang fields. The linear regression model was validated at Goesan fields. It was found that the model based on green ratio vegetation index (GRVI) had the greatest performance in prediction of fresh weight at the calibration stage (R2=0.74, RMSE=246 g/m2, RE=34.2%). In the validation stage, RMSE and RE of the model were 392 g/m2 and 32%, respectively. The errors of the model differed by cropping system, For example, RMSE and RE of model in single crop fields were 315 g/m2 and 26%, respectively. On the other hand, the model had greater values of RMSE (381 g/m2) and RE (31%) in double crop fields. As a result of developing models for predicting a fresh weight into two years (2018+2020) with similar accumulated temperature (AT) in three years and a single year (2019) that was different from that AT, the prediction performance of a single year model was better than a two years model. Consequently, compared with those models divided by AT and a three years model, RMSE of a single crop fields were improved by about 29.1%. However, those of double crop fields decreased by about 19.6%. When environmental factors are used along with, spectral data, the reliability of soybean growth prediction can be achieved various environmental conditions.
Keywords
Soybean; Remote sensing; Simple linear regression; Multispectral imagery; Unmanned aerial vehicle;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Jang, S. H., C. S. Ryu, Y. S. Kang, S. R. Jun, J. W. Park, H. Y. Song, K. S. Kang, D. W. Kang, K. Zou, and T. H. Jun, 2019: Estimation of fresh weight, dry weight, and leaf area index of soybean plant using multispectral camera mounted on rotor-wing UAV. Korean Journal of Agricultural and Forest Meteorology 21(4), 327-336.   DOI
2 Kang, Y. S., S. H. Kim, J. G. Kang, Y. K. Hong, T. K. Sarkar, and C. S. Ryu, 2016: Estimation of leaf dry mass and nitrogen content for soybean using multi-spectral camera mounted on unmanned aerial vehicle. Journal of Agriculture & Life Science 50(6), 183-190.
3 Kurbanov, R. K., and N. I. Zakharova, 2020: Application of vegetation indices to assess the condition of crops. Agricultural Machinery and Technologies 14(4), 4-11.   DOI
4 Lee, K., H. An, C. Park, K. So, S. Na, and S. Jang, 2019: Estimation of rice grain yield distribution using UAV imagery. Journal of the Korean Society of Agricultural Engineers 61(4), 1-10.   DOI
5 Lee, Y. H., W. G. Sang, J. I. Cho, and M. C. Seo, 2019: Duration of drought stress effects on soybean growth characteristic and seed yield distribution patterns. Korean Journal of Agricultural and Forest Meteorology 21(4), 269-276.   DOI
6 Scott, H. D., J. DeAngulo, M. B. Daniels, and L. S. Wood, 1989: Flood duration effects on soybean growth and yield. Agronomy Journal 81(4), 631-636.   DOI
7 Mutanga, O., and A. K. Skidmore, 2004: Narrow band vegetation indices overcome the saturation problem in biomass estimation. International Journal of Remote Sensing 25(19), 3999-4014.   DOI
8 Scott, W. O., and S. A. Aldrich, 1983: Modern soybean production. S&A Publication. Inc., Champaign, Illinois.
9 Perry, E. M., and J. R. Davenport, 2007: Spectral and spatial differences in response of vegetation indices to nitrogen treatments on apple. Computers and Electronics in Agriculture 59(1-2), 56-65.   DOI
10 Stehr, N. J., 2015: Drones: The newest technology for precision agriculture. Natural Sciences Education 44(1), 89-91.   DOI
11 Wang, J., B. Gong, Y. Wang, Y. Wen, J. Zhou, and Q. He, 2017: The potential multiple mechanisms and microbial communities in simultaneous nitrification and denitrification process treating high carbon and nitrogen concentration saline wastewater. Bioresource Technology 243, 708-715.   DOI
12 Ashley, D. A., and W. J. Ethridge, 1978: Irrigation effects on vegetative and reproductive development of three soybean cultivars 1. Agronomy Journal 70(3), 467-471.   DOI
13 Lee, J. E., G. H. Jung, S. K. Kim, M. T. Kim, S. H. Shin, and W. T. Jeon, 2019: Effects of growth period and cumulative temperature on flowering, ripening and yield of soybean by sowing times. Korean Journal of Crop Science 64(4), 406-413.
14 Lee, K. D., S. I. Na, C. W. Park, S. Y. Hong, K. H. So, and H. Y. Ahn, 2020: Diurnal change of reflectance and vegetation index from UAV image in clear day condition. Korean Journal of Remote Sensing 36(5-1), 735-747.   DOI
15 Lee, Y. H., H. S. Cho, J. H. Kim, W. G. Sang, P. Shin, J. K. Baek, and M. C. Seo, 2018: The effects of increased temperature on seed nutrition, protein, and oil contents of soybean [Glycine max (L.)]. Korean Journal of Crop Science 63(4), 331-337.   DOI
16 Rouse, J. W., R. H. Haas, J. A. Schell, and D. W. Deering, 1974: Monitoring vegetation systems in the Great Plains with ERTS. NASA Special Publication 351, 309pp.
17 Souza, G. M., T. A. Catuchi, S. C. Bertolli, and R. P. Soratto, 2013: Soybean under water deficit: physiological and yield responses. A Comprehensive Survey of International Soybean Research: Genetics, Physiology Agronomy and Nitrogen Relationships, 273-298.
18 Ahn, H. Y., S. I. Na, C. W. Park, S. Y. Hong, K. H. So, and K. D. Lee, 2020: Analysis of UAV-based multispectral reflectance variability for agriculture monitoring. Korean Journal of Remote Sensing 36(6-1), 1379-1391.   DOI
19 Kim, I. J., S. Y. Son, S. Y. Nam, I. M. Ryu, T. J. Kim, C. H. Lee, and T. S. Kim, 2004: Effect of alternative row pinching on growth and yield in soybean. Korean Journal of Crop Science 49(6), 457-462.
20 Morellos, A., X. E. Pantazi, D. Moshou, T. Alexandridis, R. Whetton, G. Tziotzios, J. Wiebensohn, R. Bill, and A. M. Mouazen, 2016: Machine learning based prediction of soil total nitrogen, organic carbon and moisture content by using VIS-NIR spectroscopy. Biosystems Engineering 152, 104-116.   DOI
21 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 Sensing of Environment 58(3), 289-298.   DOI
22 Kang, Y. S., C. S. Ryu, S. H. Kim, S. R. Jun, S. H. Jang, J. W. Park, and T. K. Sarkar, 2018: Yield prediction of Chinese cabbage (Brassicaceae) using broadband multispectral imagery mounted unmanned aerial system in the air and narrowband hyperspectral imagery on the ground. Journal of Biosystems Engineering 43(2), 138-147.   DOI
23 Candiago, S., F. Remondino, M. De Giglio, M. Dubbini, and M. Gattelli, 2015: Evaluating multispectral images and vegetation indices for precision farming applications from UAV images. Remote sensing 7(4), 4026-4047.   DOI
24 Jung, K. Y., E. Y. Yun, C. Y. Park, J. B. Hwang, Y. D. Choi, S. H. Jeon, and H. A. Lee, 2012: Effect of soil compaction levels and textures on soybean (Glycine max L.) root elongation and yield. Korean Journal of Soil Science and Fertilizer 45(3), 332-338.   DOI
25 Kang, Y. S., J. W. Nam, Y. Kim, S. T. Lee, D. G. Seong, S. H. Jang, and C. S. Ryu, 2021: Assessment of regression models for predicting rice yield and protein content using unmanned aerial vehicle-based multispectral imagery. Remote Sensing 13(8), 1508.   DOI