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Prediction of Protein Content in Pea ( Pisum sativum L.) Seeds 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

Pea ( Pisum sativum L.) is a legume valued mainly for its high seed protein content. The protein content of pea is characterized by a high lysine content and low allergenicity. This has made consumers appreciate peas increasingly in recent years, not only for their taste, but also for their nutritional value. An important element of pea cultivation is the ability to predict protein content, even before harvest. The aim of this research was to develop a linear and a non-linear model for predicting the percentage of protein content in pea seeds and to perform a comparative analysis of the effectiveness of these models. The analysis also focused on identifying the variables with the greatest impact on protein content. The research included the method of machine learning (artificial neural networks) and multiple linear regression (MLR). The input parameters of the models were weather, agronomic and phytophenological data from 2016–2020. The predictive properties of the models were verified using six ex-post forecast measures. The neural model (N1) outperformed the multiple regression (RS) model. The N1 model had an RMS error magnitude of 0.838, while the RS model obtained an average error value of 2.696. The MAPE error for the N1 and RS models was 2.721 and 8.852, respectively. The sensitivity analysis performed for the best neural network showed that the independent variables most influencing the protein content of pea seeds were the soil abundance of magnesium, potassium and phosphorus. The results presented in this work can be useful for the study of pea crop management. In addition, they can help preserve the country’s protein security.

Suggested Citation

  • Patryk Hara & Magdalena Piekutowska & Gniewko Niedbała, 2022. "Prediction of Protein Content in Pea ( Pisum sativum L.) Seeds Using Artificial Neural Networks," Agriculture, MDPI, vol. 13(1), pages 1-21, December.
    • Handle: RePEc:gam:jagris:v:13:y:2022:i:1:p:29-:d:1011685
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    References listed on IDEAS

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    1. Emerson Rodolfo Abraham & João Gilberto Mendes dos Reis & Oduvaldo Vendrametto & Pedro Luiz de Oliveira Costa Neto & Rodrigo Carlo Toloi & Aguinaldo Eduardo de Souza & Marcos de Oliveira Morais, 2020. "Time Series Prediction with Artificial Neural Networks: An Analysis Using Brazilian Soybean Production," Agriculture, MDPI, vol. 10(10), pages 1-18, October.
    2. Mohsen Sabzi-Nojadeh & Gniewko Niedbała & Mehdi Younessi-Hamzekhanlu & Saeid Aharizad & Mohammad Esmaeilpour & Moslem Abdipour & Sebastian Kujawa & Mohsen Niazian, 2021. "Modeling the Essential Oil and Trans -Anethole Yield of Fennel ( Foeniculum vulgare Mill. var. vulgare ) by Application Artificial Neural Network and Multiple Linear Regression Methods," Agriculture, MDPI, vol. 11(12), pages 1-17, November.
    3. Saeed Nosratabadi & Sina Ardabili & Zoltan Lakner & Csaba Mako & Amir Mosavi, 2021. "Prediction of Food Production Using Machine Learning Algorithms of Multilayer Perceptron and ANFIS," Agriculture, MDPI, vol. 11(5), pages 1-13, May.
    4. Ahmed, Moiz Uddin & Hussain, Iqbal, 2022. "Prediction of Wheat Production Using Machine Learning Algorithms in northern areas of Pakistan," Telecommunications Policy, Elsevier, vol. 46(6).
    5. 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.
    6. Saeed Nosratabadi & Sina Ardabili & Zoltan Lakner & Csaba Mako & Amir Mosavi, 2021. "Prediction of Food Production Using Machine Learning Algorithms of Multilayer Perceptron and ANFIS," Papers 2104.14286, arXiv.org.
    7. 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.
    8. Gniewko Niedbała & Danuta Kurasiak-Popowska & Magdalena Piekutowska & Tomasz Wojciechowski & Michał Kwiatek & Jerzy Nawracała, 2022. "Application of Artificial Neural Network Sensitivity Analysis to Identify Key Determinants of Harvesting Date and Yield of Soybean ( Glycine max [L.] Merrill) Cultivar Augusta," Agriculture, MDPI, vol. 12(6), pages 1-17, May.
    9. Patryk Hara & Magdalena Piekutowska & Gniewko Niedbała, 2021. "Selection of Independent Variables for Crop Yield Prediction Using Artificial Neural Network Models with Remote Sensing Data," Land, MDPI, vol. 10(6), pages 1-21, June.
    10. Katarzyna Szwedziak & Ewa Polańczyk & Żaneta Grzywacz & Gniewko Niedbała & Wiktoria Wojtkiewicz, 2020. "Neural Modeling of the Distribution of Protein, Water and Gluten in Wheat Grains during Storage," Sustainability, MDPI, vol. 12(12), pages 1-14, June.
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    Cited by:

    1. Gniewko Niedbała & Sebastian Kujawa, 2023. "Digital Innovations in Agriculture," Agriculture, MDPI, vol. 13(9), pages 1-10, August.
    2. Jarosław Kurek & Gniewko Niedbała & Tomasz Wojciechowski & Bartosz Świderski & Izabella Antoniuk & Magdalena Piekutowska & Michał Kruk & Krzysztof Bobran, 2023. "Prediction of Potato ( Solanum tuberosum L.) Yield Based on Machine Learning Methods," Agriculture, MDPI, vol. 13(12), pages 1-25, December.
    3. 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.

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