Achieving high buckwheat sorting accuracy in a deep learning based model by applying fine scaling method

Hakan Aktaş; Övünç Polat

Makale detayları

Achieving high buckwheat sorting accuracy in a deep learning based model by applying fine scaling method

Yazarlar:

Hakan Aktaş | Övünç Polat

Yayıncı:

Akdeniz University

Ana Başlıklar:

Agricultural Engineering

Dergi:

Mediterranean Agricultural Sciences Vol.36 Issue 3, pp.135-141

Belge Tipi:

Makale

Yayın Tarihi:

4/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001304570

pISSN:

2528-9675

Anahtar kelimeler:

Özet:

 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.

 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.

 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.

 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.

Makale detayları

Achieving high buckwheat sorting accuracy in a deep learning based model by applying fine scaling method

Yazarlar:

Hakan Aktaş | Övünç Polat

Yayıncı:

Akdeniz University

Ana Başlıklar:

Agricultural Engineering

Dergi:

Mediterranean Agricultural Sciences Vol.36 Issue 3, pp.135-141

Belge Tipi:

Makale

Yayın Tarihi:

4/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001304570

pISSN:

2528-9675

Image processing

Dataset generation

Seed sorting

Convolutional neural networksImage processing

Dataset generation

Seed sorting

Convolutional neural networks

Sayı

Makale detayları

Yazarlar: Hakan Aktaş | Övünç Polat

Yayıncı: Akdeniz University

Ana Başlıklar: Agricultural Engineering

Dergi: Mediterranean Agricultural Sciences Vol.36 Issue 3, pp.135-141

Belge Tipi: Makale

Yayın Tarihi: 4/2023

Belge Dili: İngilizce

Document ID (Org No.): 2001304570

pISSN: 2528-9675

Sayı

Yazarlar:

Hakan Aktaş | Övünç Polat

Yayıncı:

Akdeniz University

Ana Başlıklar:

Agricultural Engineering

Dergi:

Mediterranean Agricultural Sciences Vol.36 Issue 3, pp.135-141

Belge Tipi:

Makale

Yayın Tarihi:

4/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001304570

pISSN:

2528-9675