Predicting Starch Content in Early Potato Varieties Using Neural Networks and Regression Models: A Comparative Study
Starch content serves as a crucial indicator of the quality and palatability of potato tubers. It has become a common practice to evaluate the polysaccharide content directly in tubers freshly harvested from the field. This study aims to develop models that can predict starch content prior to the ha...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Agronomy |
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| Online Access: | https://www.mdpi.com/2073-4395/14/12/3010 |
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| author | Magdalena Piekutowska Patryk Hara Katarzyna Pentoś Tomasz Lenartowicz Tomasz Wojciechowski Sebastian Kujawa Gniewko Niedbała |
| author_facet | Magdalena Piekutowska Patryk Hara Katarzyna Pentoś Tomasz Lenartowicz Tomasz Wojciechowski Sebastian Kujawa Gniewko Niedbała |
| author_sort | Magdalena Piekutowska |
| collection | DOAJ |
| description | Starch content serves as a crucial indicator of the quality and palatability of potato tubers. It has become a common practice to evaluate the polysaccharide content directly in tubers freshly harvested from the field. This study aims to develop models that can predict starch content prior to the harvesting of potato tubers. Very early potato varieties were cultivated in the northern and northwestern regions of Poland. The research involved constructing multiple linear regression (MLR) and artificial neural network (ANN-MLP) models, drawing on data from eight years of field trials. The independent variables included factors such as sunshine duration, average daily air temperatures, precipitation, soil nutrient levels, and phytophenological data. The NSM demonstrated a higher accuracy in predicting the dependent variable compared to the RSM, with MAPE errors of 7.258% and 9.825%, respectively. This study confirms that artificial neural networks are an effective tool for predicting starch content in very early potato varieties, making them valuable for monitoring potato quality. |
| format | Article |
| id | doaj-art-865d757fb8ba445ab0920031326f5115 |
| institution | Kabale University |
| issn | 2073-4395 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agronomy |
| spelling | doaj-art-865d757fb8ba445ab0920031326f51152024-12-27T14:04:42ZengMDPI AGAgronomy2073-43952024-12-011412301010.3390/agronomy14123010Predicting Starch Content in Early Potato Varieties Using Neural Networks and Regression Models: A Comparative StudyMagdalena Piekutowska0Patryk Hara1Katarzyna Pentoś2Tomasz Lenartowicz3Tomasz Wojciechowski4Sebastian Kujawa5Gniewko Niedbała6Department of Botany and Nature Protection, Institute if Biology, Pomeranian University in Słupsk, 22b Arciszewskiego St., 76-200 Słupsk, PolandAgrotechnology, 4 Jagiellonów St., 73-150 Łobez, PolandInstitute of Agricultural Engineering, Wrocław University of Environmental and Life Sciences, 37b Chełmowskiego St., 51-630 Wrocław, PolandResearch Centre for Cultivar Testing, Słupia Wielka 34, 63-022 Słupia Wielka, PolandDepartment of Biosystems Engineering, Faculty of Environmental and Mechanical Engineering, Poznań University of Life Sciences, Wojska Polskiego 50, 60-627 Poznań, PolandDepartment of Biosystems Engineering, Faculty of Environmental and Mechanical Engineering, Poznań University of Life Sciences, Wojska Polskiego 50, 60-627 Poznań, PolandDepartment of Biosystems Engineering, Faculty of Environmental and Mechanical Engineering, Poznań University of Life Sciences, Wojska Polskiego 50, 60-627 Poznań, PolandStarch content serves as a crucial indicator of the quality and palatability of potato tubers. It has become a common practice to evaluate the polysaccharide content directly in tubers freshly harvested from the field. This study aims to develop models that can predict starch content prior to the harvesting of potato tubers. Very early potato varieties were cultivated in the northern and northwestern regions of Poland. The research involved constructing multiple linear regression (MLR) and artificial neural network (ANN-MLP) models, drawing on data from eight years of field trials. The independent variables included factors such as sunshine duration, average daily air temperatures, precipitation, soil nutrient levels, and phytophenological data. The NSM demonstrated a higher accuracy in predicting the dependent variable compared to the RSM, with MAPE errors of 7.258% and 9.825%, respectively. This study confirms that artificial neural networks are an effective tool for predicting starch content in very early potato varieties, making them valuable for monitoring potato quality.https://www.mdpi.com/2073-4395/14/12/3010starch contentpotatopredictionartificial neural networksmultiple linear regression |
| spellingShingle | Magdalena Piekutowska Patryk Hara Katarzyna Pentoś Tomasz Lenartowicz Tomasz Wojciechowski Sebastian Kujawa Gniewko Niedbała Predicting Starch Content in Early Potato Varieties Using Neural Networks and Regression Models: A Comparative Study Agronomy starch content potato prediction artificial neural networks multiple linear regression |
| title | Predicting Starch Content in Early Potato Varieties Using Neural Networks and Regression Models: A Comparative Study |
| title_full | Predicting Starch Content in Early Potato Varieties Using Neural Networks and Regression Models: A Comparative Study |
| title_fullStr | Predicting Starch Content in Early Potato Varieties Using Neural Networks and Regression Models: A Comparative Study |
| title_full_unstemmed | Predicting Starch Content in Early Potato Varieties Using Neural Networks and Regression Models: A Comparative Study |
| title_short | Predicting Starch Content in Early Potato Varieties Using Neural Networks and Regression Models: A Comparative Study |
| title_sort | predicting starch content in early potato varieties using neural networks and regression models a comparative study |
| topic | starch content potato prediction artificial neural networks multiple linear regression |
| url | https://www.mdpi.com/2073-4395/14/12/3010 |
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