Comparison of object classification methods in seed stream separation; Advances in Computer Science Research; Vol. 72 : Information technologies in Science, Management, Social sphere and Medicine (ITSMSSM 2017)
| Parent link: | Advances in Computer Science Research Vol. 72 : Information technologies in Science, Management, Social sphere and Medicine (ITSMSSM 2017).— 2017.— [P. 179-181] |
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| Tác giả của công ty: | |
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| Tóm tắt: | Title screen The paper presents a study of machine learning approaches to detect and classify seeds of a grain crop in order to enhance agricultural seed purification line. The main features of seeds that are hard to recognize during a separation with mechanical methods are resolved with the help of machine learning approach. The main machine learning methods used in research was traditional machine learning and deep learning based on neural networks. A special training image database was retrieved in order to check if the stated approaches are reasonable to use and develop. A set of tests is provided to show the effectiveness of the machine learning applied to solve the stated problem. |
| Ngôn ngữ: | Tiếng Anh |
| Được phát hành: |
2017
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| Những chủ đề: | |
| Truy cập trực tuyến: | http://dx.doi.org/10.2991/itsmssm-17.2017.38 |
| Định dạng: | Điện tử Chương của sách |
| KOHA link: | https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=657526 |
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| 200 | 1 | |a Comparison of object classification methods in seed stream separation |f A. V. Vlasov, A. S. Fadeev | |
| 203 | |a Text |c electronic | ||
| 300 | |a Title screen | ||
| 320 | |a [References: p. 181 (15 tit.)] | ||
| 330 | |a The paper presents a study of machine learning approaches to detect and classify seeds of a grain crop in order to enhance agricultural seed purification line. The main features of seeds that are hard to recognize during a separation with mechanical methods are resolved with the help of machine learning approach. The main machine learning methods used in research was traditional machine learning and deep learning based on neural networks. A special training image database was retrieved in order to check if the stated approaches are reasonable to use and develop. A set of tests is provided to show the effectiveness of the machine learning applied to solve the stated problem. | ||
| 461 | 1 | |0 (RuTPU)RU\TPU\network\18167 |t Advances in Computer Science Research | |
| 463 | 0 | |0 (RuTPU)RU\TPU\network\24029 |t Vol. 72 : Information technologies in Science, Management, Social sphere and Medicine (ITSMSSM 2017) |o IV International Scientific Conference, 5-8 December 2017, Tomsk, Russia |o [proceedings] |f National Research Tomsk Polytechnic University (TPU) ; eds. O. G. Berestneva [et al.] |v [P. 179-181] |d 2017 | |
| 610 | 1 | |a электронный ресурс | |
| 610 | 1 | |a труды учёных ТПУ | |
| 610 | 1 | |a image processing | |
| 610 | 1 | |a seeds sorting | |
| 610 | 1 | |a classification | |
| 610 | 1 | |a feature extraction | |
| 610 | 1 | |a convolutional neural network | |
| 610 | 1 | |a automatic detection | |
| 610 | 1 | |a grains | |
| 610 | 1 | |a agriculture | |
| 610 | 1 | |a обработка изображений | |
| 610 | 1 | |a классификация | |
| 610 | 1 | |a нейронные сети | |
| 610 | 1 | |a автоматическое обнаружение | |
| 610 | 1 | |a зерна | |
| 610 | 1 | |a сельское хозяйство | |
| 610 | 1 | |a машинное обучение | |
| 610 | 1 | |a зерновые культуры | |
| 700 | 1 | |a Vlasov |b A. V. |c specialist in the field of informatics and computer technology |c postgraduate of Tomsk Polytechnic University |f 1991- |g Andrey Vladimirovich |3 (RuTPU)RU\TPU\pers\38686 | |
| 701 | 1 | |a Fadeev |b A. S. |c specialist in the field of informatics and computer technology |c Vice-Rector for Digitalization - Director of the Digital Educational Technologies Center, Associate Professor of Tomsk Polytechnic University, Candidate of technical sciences |f 1981- |g Aleksandr Sergeevich |3 (RuTPU)RU\TPU\pers\35328 |9 18588 | |
| 712 | 0 | 2 | |a Национальный исследовательский Томский политехнический университет |b Инженерная школа информационных технологий и робототехники |b Отделение информационных технологий |3 (RuTPU)RU\TPU\col\23515 |
| 801 | 2 | |a RU |b 63413507 |c 20180221 |g RCR | |
| 856 | 4 | |u http://dx.doi.org/10.2991/itsmssm-17.2017.38 | |
| 942 | |c CF | ||