Stochastic Analysis and Neural Network-Based Yield Prediction with Precision Agriculture

Humayra Shoshi, Erik Hanson, William Nganje and Indranil SenGupta
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Humayra Shoshi: Department of Mathematics, North Dakota State University, Fargo, ND 58108-6050, USA
Erik Hanson: Department of Agribusiness and Applied Economics, North Dakota State University, Fargo, ND 58108-6050, USA
Indranil SenGupta: Department of Mathematics, North Dakota State University, Fargo, ND 58108-6050, USA

JRFM, 2021, vol. 14, issue 9, 1-17

Abstract: 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.

Keywords: neural networks; precision agriculture; statistical moments; yield; categorical data (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2021
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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