Machine Learning for Prediction of Energy in Wheat Production

Ali Mostafaeipour, Mohammad Bagher Fakhrzad, Sajad Gharaat, Mehdi Jahangiri, Joshuva Arockia Dhanraj, Shahab S. Band, Alibek Issakhov and Amir Mosavi
Additional contact information
Ali Mostafaeipour: Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam
Mohammad Bagher Fakhrzad: Industrial Engineering Department, Yazd University, Yazd 89195-741, Iran
Sajad Gharaat: Industrial Engineering Department, Yazd University, Yazd 89195-741, Iran
Mehdi Jahangiri: Department of Mechanical Engineering, Shahrekord Branch, Islamic Azad University, Shahrekord 8813733395, Iran
Joshuva Arockia Dhanraj: Centre for Automation & Robotics (ANRO), Department of Mechanical Engineering, Hindustan Institute of Technology and Science, Chennai 603103, India
Shahab S. Band: Future Technology Research Center, College of Future, National Yunlin University of Science and Technology 123 University Road, Section 3, Douliou, Yunlin 64002, Taiwan
Alibek Issakhov: Faculty of Mechanics and Mathematics, Department of Mathematical and Computer Modelling, Al-Farabi Kazakh National University, Almaty 050040, Kazakhstan
Amir Mosavi: Faculty of Civil Engineering, Technische Universität Dresden, 01069 Dresden, Germany

Agriculture, 2020, vol. 10, issue 11, 1-19

Abstract: The global population growth has led to a considerable rise in demand for wheat. Today, the amount of energy consumption in agriculture has also increased due to the need for sufficient food for the growing population. Thus, agricultural policymakers in most countries rely on prediction models to influence food security policies. This research aims to predict and reduce the amount of energy consumption in wheat production. Data were collected from the farms of Estahban city in Fars province of Iran by the Jihad Agricultural Department’s experts for 20 years from 1994 to 2013. In this study, a novel prediction method based on consumed energy in the production period is proposed. The model is developed based on artificial intelligence to forecast the output energy in wheat production and uses extreme learning machine (ELM) and support vector regression (SVR). In the experimental stage, the value of elevation metrics for the EVM and ELM was reported to be equal to 0.000000409 and 0.9531, respectively. Total input energy (consumed) is found to be 1,460,503.1 Mega Joules (MJ), and output energy (produced wheat) is 1,401,011.945 MJ for the Estahban. The result indicates the superiority of the ELM model to enhance the decisions of the agricultural policymakers.

Keywords: wheat production; extreme learning machine (ELM); machine learning; support vector regression (SVR); food science; data science; big data; network science; artificial intelligence; artificial neural network (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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