The Use of Image Analysis to Detect Seed Contamination—A Case Study of Triticale
Samples of triticale seeds of various qualities were assessed in the study. The seeds were obtained during experiments, reflecting the actual sowing conditions. The experiments were conducted on an original test facility designed by the authors of this study. The speed of the air (15, 20, 25 m/s) tr...
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doaj-6a53f7ef33694bbea2539a038d864d1f2020-12-30T00:01:23ZengMDPI AGSensors1424-82202021-12-012115115110.3390/s21010151The Use of Image Analysis to Detect Seed Contamination—A Case Study of TriticaleŁukasz Gierz0Krzysztof Przybył1Krzysztof Koszela2Adamina Duda3Witold Ostrowicz4Institute of Machine Design, Faculty of Mechanical Engineering, Poznań University of Technology, Piotrowo 3, 60-965 Poznan, PolandDepartment of Food Technology of Plant Origin, Faculty of Food Sciences and Nutrition, Poznań University of Life Sciences, Wojska Polskiego 31, 60-624 Poznan, PolandDepartment of Biosystems Engineering, Poznan University of Life Sciences, Wojska Polskiego 50, 60-625 Poznan, PolandDepartment of Food Technology of Plant Origin, Faculty of Food Sciences and Nutrition, Poznań University of Life Sciences, Wojska Polskiego 31, 60-624 Poznan, PolandInstitute of Machine Design, Faculty of Mechanical Engineering, Poznań University of Technology, Piotrowo 3, 60-965 Poznan, PolandSamples of triticale seeds of various qualities were assessed in the study. The seeds were obtained during experiments, reflecting the actual sowing conditions. The experiments were conducted on an original test facility designed by the authors of this study. The speed of the air (15, 20, 25 m/s) transporting seeds in the pneumatic conduit was adjusted to sowing. The resulting graphic database enabled the distinction of six classes of seeds according to their quality and sowing speed. The database was prepared to build training, validation and test sets. The neural model generation process was based on multi-layer perceptron networks (MLPN) and statistical (machine training). When the MLPN was used to identify contaminants in seeds sown at a speed of 15 m/s, the lowest RMS error of 0.052 was noted, whereas the classification correctness coefficient amounted to 0.99.https://www.mdpi.com/1424-8220/21/1/151triticaleentropyimage analysis and processingartificial neural networks |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Łukasz Gierz Krzysztof Przybył Krzysztof Koszela Adamina Duda Witold Ostrowicz |
spellingShingle |
Łukasz Gierz Krzysztof Przybył Krzysztof Koszela Adamina Duda Witold Ostrowicz The Use of Image Analysis to Detect Seed Contamination—A Case Study of Triticale Sensors triticale entropy image analysis and processing artificial neural networks |
author_facet |
Łukasz Gierz Krzysztof Przybył Krzysztof Koszela Adamina Duda Witold Ostrowicz |
author_sort |
Łukasz Gierz |
title |
The Use of Image Analysis to Detect Seed Contamination—A Case Study of Triticale |
title_short |
The Use of Image Analysis to Detect Seed Contamination—A Case Study of Triticale |
title_full |
The Use of Image Analysis to Detect Seed Contamination—A Case Study of Triticale |
title_fullStr |
The Use of Image Analysis to Detect Seed Contamination—A Case Study of Triticale |
title_full_unstemmed |
The Use of Image Analysis to Detect Seed Contamination—A Case Study of Triticale |
title_sort |
use of image analysis to detect seed contamination—a case study of triticale |
publisher |
MDPI AG |
series |
Sensors |
issn |
1424-8220 |
publishDate |
2021-12-01 |
description |
Samples of triticale seeds of various qualities were assessed in the study. The seeds were obtained during experiments, reflecting the actual sowing conditions. The experiments were conducted on an original test facility designed by the authors of this study. The speed of the air (15, 20, 25 m/s) transporting seeds in the pneumatic conduit was adjusted to sowing. The resulting graphic database enabled the distinction of six classes of seeds according to their quality and sowing speed. The database was prepared to build training, validation and test sets. The neural model generation process was based on multi-layer perceptron networks (MLPN) and statistical (machine training). When the MLPN was used to identify contaminants in seeds sown at a speed of 15 m/s, the lowest RMS error of 0.052 was noted, whereas the classification correctness coefficient amounted to 0.99. |
topic |
triticale entropy image analysis and processing artificial neural networks |
url |
https://www.mdpi.com/1424-8220/21/1/151 |
work_keys_str_mv |
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