Modeling methods of predicting potato yield - examples and possibilities of application

Abstract

The purpose of the following work is to review the methods used in predicting plant yields, with particular emphasis on potato production. The article refers to the histological methods of estimating plant yields and prevailing trends: ground-based remote sensing, which is often associated with regression calculus, multiple regression, artificial intelligence and image analysis. There are also two popular models SUBSTOR and LINTUL-POTATO, which are the foundation for developing more and more accurate tools of potato yield estimation. There are many methods that allow to predict yields before the end of the growing season. The most important element in creating prediction models is choosing the appropriate number of independent variables that actually shape the yielding of potatoes. Timely and accurate prediction of crop yields improve the management of agricultural production as well as limit financial, quantitative and qualitative losses of crops.

Authors and Affiliations

Magdalena Piekutowska, Gniewko Niedbała, Mariusz Adamski, Mirosław Czechlowski, Tomasz Wojciechowski, Aneta Czechowska-Kosacka, Katarzyna Wójcik-Oliveira

Keywords

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  • EP ID EP515137
  • DOI -
  • Views 45
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How To Cite

Magdalena Piekutowska, Gniewko Niedbała, Mariusz Adamski, Mirosław Czechlowski, Tomasz Wojciechowski, Aneta Czechowska-Kosacka, Katarzyna Wójcik-Oliveira (2018). Modeling methods of predicting potato yield - examples and possibilities of application. Journal of Research and Applications in Agricultural Engineering (ISSN 1642-686X), 63(4), 176-180. https://europub.co.uk/articles/-A-515137