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Article

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

1
Department of Agrobiotechnology, Koszalin University of Technology, Racławicka 15–17, 75-620 Koszalin, Poland
2
Department of Geoecology and Geoinformation, Institute of Biology and Earth Sciences, Pomeranian University in Słupsk, 27 Partyzantów St., 76-200 Słupsk, Poland
3
Department of Biosystems Engineering, Faculty of Environmental and Mechanical Engineering, Poznań University of Life Sciences, Wojska Polskiego 50, 60-627 Poznań, Poland
*
Author to whom correspondence should be addressed.
Academic Editors: Bin Chen, Yufang Jin and Le Yu
Received: 28 April 2021 / Revised: 5 June 2021 / Accepted: 7 June 2021 / Published: 7 June 2021
(This article belongs to the Special Issue Advances in Remote Sensing for Crop Monitoring and Yield Estimation)
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. View Full-Text
Keywords: crop yield prediction; independent variables; ANN; remote sensing crop yield prediction; independent variables; ANN; remote sensing
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MDPI and ACS Style

Hara, P.; Piekutowska, M.; Niedbała, G. Selection of Independent Variables for Crop Yield Prediction Using Artificial Neural Network Models with Remote Sensing Data. Land 2021, 10, 609. https://0-doi-org.brum.beds.ac.uk/10.3390/land10060609

AMA Style

Hara P, Piekutowska M, Niedbała G. Selection of Independent Variables for Crop Yield Prediction Using Artificial Neural Network Models with Remote Sensing Data. Land. 2021; 10(6):609. https://0-doi-org.brum.beds.ac.uk/10.3390/land10060609

Chicago/Turabian Style

Hara, Patryk, Magdalena Piekutowska, and Gniewko Niedbała. 2021. "Selection of Independent Variables for Crop Yield Prediction Using Artificial Neural Network Models with Remote Sensing Data" Land 10, no. 6: 609. https://0-doi-org.brum.beds.ac.uk/10.3390/land10060609

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