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Machine Learning for Prediction of Energy in Wheat Production

Author

Listed:
  • Ali Mostafaeipour

    (Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam
    The Faculty of Civil Engineering, Duy Tan University, Da Nang 550000, Vietnam
    Department of Sustainable Energy, Faculty of Environmental Management, Prince of Songkla University, Songkhla 90110, Thailand)

  • 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
    School of Economics and Business, Norwegian University of Life Sciences, 1430 Ås, Norway
    Kando Kalman Faculty of Electrical Engineering, Obuda University, 1034 Budapest, Hungary
    Thuringian Institute of Sustainability and Climate Protection, 07743 Jena, Germany)

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.

Suggested Citation

  • Ali Mostafaeipour & Mohammad Bagher Fakhrzad & Sajad Gharaat & Mehdi Jahangiri & Joshuva Arockia Dhanraj & Shahab S. Band & Alibek Issakhov & Amir Mosavi, 2020. "Machine Learning for Prediction of Energy in Wheat Production," Agriculture, MDPI, vol. 10(11), pages 1-19, October.
    • Handle: RePEc:gam:jagris:v:10:y:2020:i:11:p:517-:d:438056
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    References listed on IDEAS

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    Cited by:

    1. Tan Wang & Xianbao Xu & Cong Wang & Zhen Li & Daoliang Li, 2021. "From Smart Farming towards Unmanned Farms: A New Mode of Agricultural Production," Agriculture, MDPI, vol. 11(2), pages 1-26, February.
    2. Alicia Ramírez-Orellana & Daniel Ruiz-Palomo & Alfonso Rojo-Ramírez & John E. Burgos-Burgos, 2021. "The Ecuadorian Banana Farms Managers’ Perceptions: Innovation as a Driver of Environmental Sustainability Practices," Agriculture, MDPI, vol. 11(3), pages 1-18, March.
    3. Igor Atamanyuk & Valerii Havrysh & Vitalii Nitsenko & Oleksii Diachenko & Mariia Tepliuk & Tetiana Chebakova & Hanna Trofimova, 2022. "Forecasting of Winter Wheat Yield: A Mathematical Model and Field Experiments," Agriculture, MDPI, vol. 13(1), pages 1-22, December.
    4. Aslam, Sheraz & Herodotou, Herodotos & Mohsin, Syed Muhammad & Javaid, Nadeem & Ashraf, Nouman & Aslam, Shahzad, 2021. "A survey on deep learning methods for power load and renewable energy forecasting in smart microgrids," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
    5. Shahram Rezapour & Erfan Jooyandeh & Mohsen Ramezanzade & Ali Mostafaeipour & Mehdi Jahangiri & Alibek Issakhov & Shahariar Chowdhury & Kuaanan Techato, 2021. "Forecasting Rainfed Agricultural Production in Arid and Semi-Arid Lands Using Learning Machine Methods: A Case Study," Sustainability, MDPI, vol. 13(9), pages 1-28, April.

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