Automated seed sorting is widely used in the agricultural industry. Deep learning is a new field of study in agricultural seed sorting applications. In this study, a classification of buckwheat seeds and foreign materials, such as sticks, chaff, stones was performed using deep learning. The main purpose of the study was to show the effect of scaling the images on the classification results, while creating a dataset. An industrial experimental setup was used to generate the datasets of buckwheat seeds and foreign materials to be sorted by deep learning. The images in the created dataset were rescaled with two different techniques, precision scaling and direct scaling, which were labelled as Type1 dataset and Type2 dataset, respectively. To classify buckwheat seeds and foreign materials, AlexNet architecture was used. The classification accuracy was calculated as 98.57% for Type1 Dataset and 97.34% for Type2 Dataset. As a result, it was concluded that the Type1 dataset had a higher accuracy and the use of precision scaling can be used to improve the classification results in industrial applications.
Image processing Dataset generation Seed sorting Convolutional neural networks
FDK-2019-4879 and TUBITAK/BIDEB/2211-C/1649B031900774
Automated seed sorting is widely used in the agricultural industry. Deep learning is a new field of study in agricultural seed sorting applications. In this study, a classification of buckwheat seeds and foreign materials, such as sticks, chaff, stones was performed using deep learning. The main purpose of the study was to show the effect of scaling the images on the classification results, while creating a dataset. An industrial experimental setup was used to generate the datasets of buckwheat seeds and foreign materials to be sorted by deep learning. The images in the created dataset were rescaled with two different techniques, precision scaling and direct scaling, which were labelled as Type1 dataset and Type2 dataset, respectively. To classify buckwheat seeds and foreign materials, AlexNet architecture was used. The classification accuracy was calculated as 98.57% for Type1 Dataset and 97.34% for Type2 Dataset. As a result, it was concluded that the Type1 dataset had a higher accuracy and the use of precision scaling can be used to improve the classification results in industrial applications.
Image processing Dataset generation Seed sorting Convolutional neural networks
Akdeniz University BAP Coordinate and TUBITAK
FDK-2019-4879 and TUBITAK/BIDEB/2211-C/1649B031900774
This research was supported by The Scientific and Technological Research Council of Turkey (TUBITAK) (grant number BIDEB/2211-C/1649B031900774) and also supported by Akdeniz University BAP Coordinate (grant number FDK-2019-4879). This study was produced from a doctoral thesis.
Birincil Dil | İngilizce |
---|---|
Konular | Ziraat Mühendisliği |
Bölüm | Makaleler |
Yazarlar | |
Proje Numarası | FDK-2019-4879 and TUBITAK/BIDEB/2211-C/1649B031900774 |
Yayımlanma Tarihi | 4 Aralık 2023 |
Gönderilme Tarihi | 5 Mayıs 2023 |
Yayımlandığı Sayı | Yıl 2023 Cilt: 36 Sayı: 3 |
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