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Volume 9
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Proceeding Paper

An Easily Installed Method of the Estimation of Soybean Yield Based on Meteorological Environments with Regression Analysis †

by
Akihiro Nitta
1,*,
Yuya Chonan
2,
Satoshi Hayashi
2,
Takuji Nakamura
2,
Hiroyuki Tsuji
2,
Noriyuki Murakami
2,
Ryo Nishide
3,
Takenao Ohkawa
1 and
Seiichi Ozawa
4
1
Graduate School of System Informatics, Kobe University, Kobe 657-0013, Japan
2
Hokkaido Agricultural Research Center, NARO, Sapporo 062-0045, Japan
3
The Center for Data Science Education and Research, Shiga University, Hikone 522-8522, Japan
4
Graduate School of Engineering, Kobe University, Kobe 657-08501, Japan
*
Author to whom correspondence should be addressed.
†
Presented at the 13th EFITA International Conference, online, 25–26 May 2021.
Eng. Proc. 2021, 9(1), 26; https://doi.org/10.3390/engproc2021009026
Published: 30 November 2021
(This article belongs to the Proceedings of The 13th EFITA International Conference)
Versions Notes

Abstract

A simple method for estimating soybean yield under ideal environments in Japan is proposed. Several models that simulate soybean yield have been proposed in other countries; however, direct adaptation to Japanese species is difficult in terms of climatic and regional characteristics. In addition, they often require variety-specific information or various environmental information, which is sometimes hard to simulate. Therefore, we attempted to create a simple simulation model with meteorological data as the main input to the model. The proposed model ignores the features that need setting for each cultivated field and is composed of a statistical model instead of a physiological analysis for the sake of brevity. Although the prediction accuracy of the model needs to be improved, we can use it as a decision support system for soybean cultivation because it requires only location information and can be easily introduced by many farmers.
Keywords: regression analysis; data mining; smart farming regression analysis; data mining; smart farming

Share and Cite

MDPI and ACS Style

Nitta, A.; Chonan, Y.; Hayashi, S.; Nakamura, T.; Tsuji, H.; Murakami, N.; Nishide, R.; Ohkawa, T.; Ozawa, S. An Easily Installed Method of the Estimation of Soybean Yield Based on Meteorological Environments with Regression Analysis. Eng. Proc. 2021, 9, 26. https://doi.org/10.3390/engproc2021009026

AMA Style

Nitta A, Chonan Y, Hayashi S, Nakamura T, Tsuji H, Murakami N, Nishide R, Ohkawa T, Ozawa S. An Easily Installed Method of the Estimation of Soybean Yield Based on Meteorological Environments with Regression Analysis. Engineering Proceedings. 2021; 9(1):26. https://doi.org/10.3390/engproc2021009026

Chicago/Turabian Style

Nitta, Akihiro, Yuya Chonan, Satoshi Hayashi, Takuji Nakamura, Hiroyuki Tsuji, Noriyuki Murakami, Ryo Nishide, Takenao Ohkawa, and Seiichi Ozawa. 2021. "An Easily Installed Method of the Estimation of Soybean Yield Based on Meteorological Environments with Regression Analysis" Engineering Proceedings 9, no. 1: 26. https://doi.org/10.3390/engproc2021009026

APA Style

Nitta, A., Chonan, Y., Hayashi, S., Nakamura, T., Tsuji, H., Murakami, N., Nishide, R., Ohkawa, T., & Ozawa, S. (2021). An Easily Installed Method of the Estimation of Soybean Yield Based on Meteorological Environments with Regression Analysis. Engineering Proceedings, 9(1), 26. https://doi.org/10.3390/engproc2021009026

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