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Selection of Independent Variables for Crop Yield Prediction Using Artificial Neural Network Models with Remote Sensing Data

Author

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  • Patryk Hara

    (Department of Agrobiotechnology, Koszalin University of Technology, Racławicka 15–17, 75-620 Koszalin, 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

Knowing the expected crop yield in the current growing season provides valuable information for farmers, policy makers, and food processing plants. One of the main benefits of using reliable forecasting tools is generating more income from grown crops. Information on the amount of crop yielding before harvesting helps to guide the adoption of an appropriate strategy for managing agricultural products. The difficulty in creating forecasting models is related to the appropriate selection of independent variables. Their proper selection requires a perfect knowledge of the research object. The following article presents and discusses the most commonly used independent variables in agricultural crop yield prediction modeling based on artificial neural networks (ANNs). Particular attention is paid to environmental variables, such as climatic data, air temperature, total precipitation, insolation, and soil parameters. The possibility of using plant productivity indices and vegetation indices, which are valuable predictors obtained due to the application of remote sensing techniques, are analyzed in detail. The paper emphasizes that the increasingly common use of remote sensing and photogrammetric tools enables the development of precision agriculture. In addition, some limitations in the application of certain input variables are specified, as well as further possibilities for the development of non-linear modeling, using artificial neural networks as a tool supporting the practical use of and improvement in precision farming techniques.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jlands:v:10:y:2021:i:6:p:609-:d:570523
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    References listed on IDEAS

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    1. 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.
    2. Jian Wang & Haiping Si & Zhao Gao & Lei Shi, 2022. "Winter Wheat Yield Prediction Using an LSTM Model from MODIS LAI Products," Agriculture, MDPI, vol. 12(10), pages 1-13, October.
    3. Gaona, Jaime & Benito-Verdugo, Pilar & Martínez-Fernández, José & González-Zamora, Ángel & Almendra-Martín, Laura & Herrero-Jiménez, Carlos Miguel, 2023. "Predictive value of soil moisture and concurrent variables in the multivariate modelling of cereal yields in water-limited environments," Agricultural Water Management, Elsevier, vol. 282(C).
    4. Wang, Ying & Shi, Wenjuan & Wen, Tianyang, 2023. "Prediction of winter wheat yield and dry matter in North China Plain using machine learning algorithms for optimal water and nitrogen application," Agricultural Water Management, Elsevier, vol. 277(C).
    5. Gniewko Niedbała & Jarosław Kurek & Bartosz Świderski & Tomasz Wojciechowski & Izabella Antoniuk & Krzysztof Bobran, 2022. "Prediction of Blueberry ( Vaccinium corymbosum L.) Yield Based on Artificial Intelligence Methods," Agriculture, MDPI, vol. 12(12), pages 1-27, December.
    6. Aqeel Iftikhar Jajja & Assad Abbas & Hasan Ali Khattak & Gniewko Niedbała & Abbas Khalid & Hafiz Tayyab Rauf & Sebastian Kujawa, 2022. "Compact Convolutional Transformer (CCT)-Based Approach for Whitefly Attack Detection in Cotton Crops," Agriculture, MDPI, vol. 12(10), pages 1-17, September.
    7. Christos Vasilakos & George E. Tsekouras & Dimitris Kavroudakis, 2022. "LSTM-Based Prediction of Mediterranean Vegetation Dynamics Using NDVI Time-Series Data," Land, MDPI, vol. 11(6), pages 1-23, June.
    8. Fan Liu & Xiangtao Jiang & Zhenyu Wu, 2023. "Attention Mechanism-Combined LSTM for Grain Yield Prediction in China Using Multi-Source Satellite Imagery," Sustainability, MDPI, vol. 15(12), pages 1-20, June.
    9. Mohammad Rokhafrouz & Hooman Latifi & Ali A. Abkar & Tomasz Wojciechowski & Mirosław Czechlowski & Ali Sadeghi Naieni & Yasser Maghsoudi & Gniewko Niedbała, 2021. "Simplified and Hybrid Remote Sensing-Based Delineation of Management Zones for Nitrogen Variable Rate Application in Wheat," Agriculture, MDPI, vol. 11(11), pages 1-24, November.
    10. 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.
    11. 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.
    12. 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.
    13. 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.

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