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Journals / Mediterranean Agricultural Sciences / 2023 / Cilt: 36 Sayı: 3

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

Hakan AKTAŞ
Övünç POLAT
Pages
135–141
Publication Date
2023-12-04
DOI
—
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

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.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Automated seed sorting is widely used in the agricultural industry.
📊
Key Findings & Data:An industrial experimental setup was used to generate the datasets of buckwheat seeds and foreign materials to be sorted by deep learning.
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Conclusion & Impact: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.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Image processing · Dataset generation · Seed sorting

Abstract

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.

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.

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Cite This Article

AKTAŞ, H., POLAT, Ö. (2023). Achieving high buckwheat sorting accuracy in a deep learning based model by applying fine scaling method. Mediterranean Agricultural Sciences, Cilt: 36 Sayı: 3, 135–141. https://ajindex.com/articles/1020436

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References (2)

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