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Journals / European Journal of Engineering and Natural Sciences / 2019 / Cilt: 3 Sayı: 2

Shape Features Based Conic Arcs for Unclassified Wheat Identification

Kemal Özkan(Osmangazi Univercity,Eskişehir)
Ahmet Okan Onarcan(ANADOLU ÜNİVERSİTESİ)
Erol Seke(Eskişehir Osmangazi Univercity)
Murat Olgun(Eskişehir Osmangazi Univercity)
Pages
152–156
Publication Date
2019-10-10
DOI
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AI Summary & Key Takeaways

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

Performance is improved with cross validation for each class.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Wheat is one of the main nutrients used in the world.
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Key Findings & Data:It is necessary to separate the high and low quality wheat.
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Conclusion & Impact:Performance is improved with cross validation for each class.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:feature extraction · unclassified wheat · shape descriptor

Abstract

Wheat is one of the main nutrients used in the world. Consumption of foodstuff produced from quality wheat is of great importance for healthy generations. It is necessary to separate the high and low quality wheat. In this paper, a new recognition method for quality wheat and unclassified wheat is presented. The most distinctive feature for determination of wheat quality is its shape. In this study, objects are first represented by a few descriptive points on their contours obtained from their images. Neighboring points are connected by linear or conical curve fitting. The objects are then represented by an attribute vector constructed from parameters of the curves. Finally, these vectors are used to classify objects (wheat) using support vector machines (svm). Performance is improved with cross validation for each class.

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Cite This Article

Özkan, K., Onarcan, A. O., Seke, E., Olgun, M. (2019). Shape Features Based Conic Arcs for Unclassified Wheat Identification. European Journal of Engineering and Natural Sciences, Cilt: 3 Sayı: 2, 152–156. https://ajindex.com/articles/987417

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