Comparison of object classification methods in seed stream separation / A. V. Vlasov, A. S. Fadeev
Уровень набора: (RuTPU)RU\TPU\network\18167, Advances in Computer Science ResearchЯзык: английский.Страна: France.Резюме или реферат: 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..Примечания о наличии в документе библиографии/указателя: [References: p. 181 (15 tit.)].Тематика: электронный ресурс | труды учёных ТПУ | image processing | seeds sorting | classification | feature extraction | convolutional neural network | automatic detection | grains | agriculture | обработка изображений | классификация | нейронные сети | автоматическое обнаружение | зерна | сельское хозяйство | машинное обучение | зерновые культуры Ресурсы он-лайн:Щелкните здесь для доступа в онлайнTitle screen
[References: p. 181 (15 tit.)]
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.
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