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Prediction of Pea ( Pisum sativum L.) Seeds Yield Using Artificial Neural Networks

Author

Listed:
  • Patryk Hara

    (Agrotechnology, Jagiellonów 4, 73-150 Łobez, Poland)

  • Magdalena Piekutowska

    (Department of Geoecology and Geoinformation, Institute of Biology and Earth Sciences, Pomeranian University in Słupsk, 27 Partyzantów St., 76-200 Słupsk, Poland)

  • Gniewko Niedbała

    (Department of Biosystems Engineering, Faculty of Environmental and Mechanical Engineering, Poznań University of Life Sciences, Wojska Polskiego 50, 60-627 Poznań, Poland)

Abstract

A sufficiently early and accurate prediction can help to steer crop yields more consciously, resulting in food security, especially with an expanding world population. Additionally, prediction related to the possibility of reducing agricultural chemistry is very important in an era of climate change. This study analyzes the performance of pea ( Pisum sativum L.) seed yield prediction by a linear (MLR) and non-linear (ANN) model. The study used meteorological, agronomic and phytophysical data from 2016–2020. The neural model (N2) generated highly accurate predictions of pea seed yield—the correlation coefficient was 0.936, and the RMS and MAPE errors were 0.443 and 7.976, respectively. The model significantly outperformed the multiple linear regression model (RS2), which had an RMS error of 6.401 and an MAPE error of 148.585. The sensitivity analysis carried out for the neural network showed that the characteristics with the greatest influence on the yield of pea seeds were the date of onset of maturity, the date of harvest, the total amount of rainfall and the mean air temperature.

Suggested Citation

  • Patryk Hara & Magdalena Piekutowska & Gniewko Niedbała, 2023. "Prediction of Pea ( Pisum sativum L.) Seeds Yield Using Artificial Neural Networks," Agriculture, MDPI, vol. 13(3), pages 1-19, March.
    • Handle: RePEc:gam:jagris:v:13:y:2023:i:3:p:661-:d:1095075
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    References listed on IDEAS

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    10. Gniewko Niedbała & Danuta Kurasiak-Popowska & Kinga Stuper-Szablewska & Jerzy Nawracała, 2020. "Application of Artificial Neural Networks to Analyze the Concentration of Ferulic Acid, Deoxynivalenol, and Nivalenol in Winter Wheat Grain," Agriculture, MDPI, vol. 10(4), pages 1-12, April.
    11. Abid Nazir & Saleem Ullah & Zulfiqar Ahmad Saqib & Azhar Abbas & Asad Ali & Muhammad Shahid Iqbal & Khalid Hussain & Muhammad Shakir & Munawar Shah & Muhammad Usman Butt, 2021. "Estimation and Forecasting of Rice Yield Using Phenology-Based Algorithm and Linear Regression Model on Sentinel-II Satellite Data," Agriculture, MDPI, vol. 11(10), pages 1-14, October.
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    13. Yanxi Zhao & Dengpan Xiao & Huizi Bai & Jianzhao Tang & De Li Liu & Yongqing Qi & Yanjun Shen, 2022. "The Prediction of Wheat Yield in the North China Plain by Coupling Crop Model with Machine Learning Algorithms," Agriculture, MDPI, vol. 13(1), pages 1-19, December.
    14. Agnieszka Szparaga & Maciej Kuboń & Sławomir Kocira & Ewa Czerwińska & Anna Pawłowska & Patryk Hara & Zbigniew Kobus & Dariusz Kwaśniewski, 2019. "Towards Sustainable Agriculture—Agronomic and Economic Effects of Biostimulant Use in Common Bean Cultivation," Sustainability, MDPI, vol. 11(17), pages 1-21, August.
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    Cited by:

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    2. Gniewko Niedbała & Sebastian Kujawa, 2023. "Digital Innovations in Agriculture," Agriculture, MDPI, vol. 13(9), pages 1-10, August.
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    4. Aliakbar Mohammadi Mirik & Mahdieh Parsaeian & Abbas Rohani & Shaneka Lawson, 2023. "Optimizing Linseed ( Linum usitatissimum L.) Seed Yield through Agronomic Parameter Modeling via Artificial Neural Networks," Agriculture, MDPI, vol. 14(1), pages 1-21, December.

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