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4/2018 vol. 63
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ORIGINAL PAPER
Modeling methods of predicting potato yield - examples and possibilities of application
M. Piekutowska 1
,
 
G. Niedbała 2
,
 
M. Adamski 2
,
 
M. Czechlowski 2
,
 
T. Wojciechowski 2
,
 
A. Czechowska-Kosacka 3
,
 
K. Wójcik Oliveira 3
 
 
 
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1
Koszalin University of Technology, Department of Agrobiotechnology, Faculty of Mechanical Engineering ul. Racławicka 15-17, 75-620 Koszalin, Poland
 
2
Poznań University of Life Sciences, Institute of Biosystems Engineering, Faculty of Agronomy and Bioengineering ul. Wojska Polskiego 50, 60-627 Poznań, Poland
 
3
Lublin University of Technology Institute of Environmental Protection Engineering, Faculty of Environmental Engineering ul. Nadbystrzycka 40B, 20-618 Lublin, Poland
 
 
Journal of Research and Applications in Agricultural Engineering 2018;63(4):176-180
 
Article (PDF)
References (35)
 
KEYWORDS
yield prediction
potato
artificial neural networks
regression
 
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: groundbased 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.
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