Artikel
Stochastic analysis and neural network-based yield prediction with precision agriculture
In this paper, we propose a general mathematical model for analyzing yield data. The data analyzed in this paper come from a characteristic corn field in the upper midwestern United States. We derive expressions for statistical moments from the underlying stochastic model. Consequently, we illustrate how a particular feature variable contributes to the statistical moments (and in effect, the characteristic function) of the target variable (i.e., yield). We also analyze the data with neural network techniques and provide two methods of data analysis. This mathematical model and neural network-based data analysis allow for better understanding of the variability within the data set, which is useful to farm managers attempting to make current and future decisions using the yield data. Lenders and risk management consultants may benefit from the insights of this mathematical model and neural network-based data analysis regarding yield expectations.
- Sprache
-
Englisch
- Erschienen in
-
Journal: Journal of Risk and Financial Management ; ISSN: 1911-8074 ; Volume: 14 ; Year: 2021 ; Issue: 9 ; Pages: 1-17 ; Basel: MDPI
- Klassifikation
-
Wirtschaft
- Thema
-
categorical data
neural networks
precision agriculture
statistical moments
yield
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Shoshi, Humayra
Hanson, Erik
Nganje, William
SenGupta, Indranil
- Ereignis
-
Veröffentlichung
- (wer)
-
MDPI
- (wo)
-
Basel
- (wann)
-
2021
- DOI
-
doi:10.3390/jrfm14090397
- Handle
- Letzte Aktualisierung
-
20.09.2024, 08:25 MESZ
Datenpartner
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Artikel
Beteiligte
- Shoshi, Humayra
- Hanson, Erik
- Nganje, William
- SenGupta, Indranil
- MDPI
Entstanden
- 2021
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