MLP artificial neural networks in predicting the yield if spring barley
Monika Janaszek-Mańkowska
monika_janaszek@sggw.plWydział Inżynierii Produkcji, Szkoła Główna Gospodarstwa Wiejskiego w Warszawie (Poland)
https://orcid.org/0000-0003-0855-9098
Dariusz R. Mańkowski
Zakład Nasiennictwa i Nasionoznawstwa, IHAR — PIB Radzików (Poland)
https://orcid.org/0000-0002-7499-8016
Janusz Kozdój
Zakład Biotechnologii i Cytogenetyki Roślin, IHAR — PIB Radzików (Poland)
Abstract
In cereal plants, individual yielding characteristics and the morphological structure of the spike are formed in certain phases of development, which occur in varying environmental conditions. The interaction of genotype with the biotic and abiotic environmental factors during the period of vegetation also affects the formation of yield. In this paper we approximate the yield on the basis of traits that characterize not only the vegetation period, but also the phase of full maturity. For the approximation, an MLP network with a very simple topology, resulting from both the number and structure of available data, was used. The effect of network training was positive. The results obtained show clearly that the MLP network may be used as a support tool for the prediction of the yield of spring barley.
Keywords:
artificial neural networks, DH lines, MLP, spring barley, yield predictionReferences
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Authors
Monika Janaszek-Mańkowskamonika_janaszek@sggw.pl
Wydział Inżynierii Produkcji, Szkoła Główna Gospodarstwa Wiejskiego w Warszawie Poland
https://orcid.org/0000-0003-0855-9098
Authors
Dariusz R. MańkowskiZakład Nasiennictwa i Nasionoznawstwa, IHAR — PIB Radzików Poland
https://orcid.org/0000-0002-7499-8016
Authors
Janusz KozdójZakład Biotechnologii i Cytogenetyki Roślin, IHAR — PIB Radzików Poland
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