Application of artificial neural networks for the prediction of quality characteristics of potato tubers - innovator variety
Journal Title: Journal of Research and Applications in Agricultural Engineering (ISSN 1642-686X) - Year 2018, Vol 63, Issue 4
Abstract
The aim of the research was to create a model for prediction of tuber dry matter on the basis of underwater weight of tubers (UWW), with the use of neural modelling methods. In order to achieve the aim of the study, data from the years 2011-2017 were collected from the production fields of an individual farm located at the border of Pomeranian and West Pomeranian Voivodeships in Słupski and Sławieński districts. The subject of the research concerned potatoes of the Innovator variety, which were grown for processing purposes - production of French fries. To build a neural model, data from September sampling as well as meteorological and fertilizer data were used. A total of 82 learning cases from the fields covered by the analyses were used, which were divided into two sets. Set 1, for the construction of the neural model consisted of 75 samples. Set 2, which consisted of 7 randomly selected samples, had a validation function and did not participate in the construction of the neural model. For proper model validation, four forecast error measures were used, i.e. relative approximation error (RAE), root mean square error (RMS), mean absolute error (MAE), mean absolute percentage error (MAPE). The model MLP 8:8-12-5-1:1 (BP100,CG31b) was based on eight inputs (meteorological data, fertilization levels) and one output (dry matter of tubers under water). The analysis resulted in a forecast error of 2.81% of MAPE. Moreover, the sensitivity analysis of the neural network showed that the mean air temperature in the period from April to September (T4-9) had the greatest influence on the dry matter of tubers.
Authors and Affiliations
Gniewko Niedbała, Magdalena Piekutowska
Keywords
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