Quality Evaluation of Potato Tubers Using Neural Image Analysis Method

Quality Evaluation of Potato Tubers Using Neural Image Analysis Method

This paper describes the research aimed at developing an effective quality assessment method for potato tubers using neural image analysis techniques. Nowadays, the methods used to identify damage and diseases are time-consuming, require specialized knowledge, and often rely on subjective judgment....

Full description

Bibliographic Details
Main Authors: Andrzej Przybylak, Radosław Kozłowski, Ewa Osuch, Andrzej Osuch, Piotr Rybacki, Przemysław Przygodziński
Format: Article
Language:English
Published: MDPI AG 2020-04-01
Series:Agriculture
Subjects:
artificial neural network
image analysis
potato tubers quality
neural classification
Online Access:https://www.mdpi.com/2077-0472/10/4/112
id doaj-a0b4249536284800a91f75e8edd712a3
record_format Article
spelling doaj-a0b4249536284800a91f75e8edd712a32021-04-02T10:29:11ZengMDPI AGAgriculture2077-04722020-04-011011211210.3390/agriculture10040112Quality Evaluation of Potato Tubers Using Neural Image Analysis MethodAndrzej Przybylak0Radosław Kozłowski1Ewa Osuch2Andrzej Osuch3Piotr Rybacki4Przemysław Przygodziński5Institute of Biosystems Engineering, Faculty of Agronomy and Bioengineering, Poznan University of Life Sciences, ul. Wojska Polskiego 28, 60-637 Poznan, PolandInstitute of Biosystems Engineering, Faculty of Agronomy and Bioengineering, Poznan University of Life Sciences, ul. Wojska Polskiego 28, 60-637 Poznan, PolandInstitute of Biosystems Engineering, Faculty of Agronomy and Bioengineering, Poznan University of Life Sciences, ul. Wojska Polskiego 28, 60-637 Poznan, PolandInstitute of Biosystems Engineering, Faculty of Agronomy and Bioengineering, Poznan University of Life Sciences, ul. Wojska Polskiego 28, 60-637 Poznan, PolandInstitute of Biosystems Engineering, Faculty of Agronomy and Bioengineering, Poznan University of Life Sciences, ul. Wojska Polskiego 28, 60-637 Poznan, PolandInstitute of Biosystems Engineering, Faculty of Agronomy and Bioengineering, Poznan University of Life Sciences, ul. Wojska Polskiego 28, 60-637 Poznan, PolandThis paper describes the research aimed at developing an effective quality assessment method for potato tubers using neural image analysis techniques. Nowadays, the methods used to identify damage and diseases are time-consuming, require specialized knowledge, and often rely on subjective judgment. This study showed the use of the developed neural model as a tool supporting the evaluation of potato tubers during the sorting process in the storage room.https://www.mdpi.com/2077-0472/10/4/112artificial neural networkimage analysispotato tubers qualityneural classification
collection DOAJ
language English
format Article
sources DOAJ
author Andrzej Przybylak
Radosław Kozłowski
Ewa Osuch
Andrzej Osuch
Piotr Rybacki
Przemysław Przygodziński
spellingShingle Andrzej Przybylak
Radosław Kozłowski
Ewa Osuch
Andrzej Osuch
Piotr Rybacki
Przemysław Przygodziński
Quality Evaluation of Potato Tubers Using Neural Image Analysis Method
Agriculture
artificial neural network
image analysis
potato tubers quality
neural classification
author_facet Andrzej Przybylak
Radosław Kozłowski
Ewa Osuch
Andrzej Osuch
Piotr Rybacki
Przemysław Przygodziński
author_sort Andrzej Przybylak
title Quality Evaluation of Potato Tubers Using Neural Image Analysis Method
title_short Quality Evaluation of Potato Tubers Using Neural Image Analysis Method
title_full Quality Evaluation of Potato Tubers Using Neural Image Analysis Method
title_fullStr Quality Evaluation of Potato Tubers Using Neural Image Analysis Method
title_full_unstemmed Quality Evaluation of Potato Tubers Using Neural Image Analysis Method
title_sort quality evaluation of potato tubers using neural image analysis method
publisher MDPI AG
series Agriculture
issn 2077-0472
publishDate 2020-04-01
description This paper describes the research aimed at developing an effective quality assessment method for potato tubers using neural image analysis techniques. Nowadays, the methods used to identify damage and diseases are time-consuming, require specialized knowledge, and often rely on subjective judgment. This study showed the use of the developed neural model as a tool supporting the evaluation of potato tubers during the sorting process in the storage room.
topic artificial neural network
image analysis
potato tubers quality
neural classification
url https://www.mdpi.com/2077-0472/10/4/112
work_keys_str_mv AT andrzejprzybylak qualityevaluationofpotatotubersusingneuralimageanalysismethod
AT radosławkozłowski qualityevaluationofpotatotubersusingneuralimageanalysismethod
AT ewaosuch qualityevaluationofpotatotubersusingneuralimageanalysismethod
AT andrzejosuch qualityevaluationofpotatotubersusingneuralimageanalysismethod
AT piotrrybacki qualityevaluationofpotatotubersusingneuralimageanalysismethod
AT przemysławprzygodzinski qualityevaluationofpotatotubersusingneuralimageanalysismethod
_version_ 1724167272379973632

Similar Items