Görüntü İşleme Tabanlı Akıllı Algoritma Geliştirerek Şekilsiz Patateslerin Belirlenmesi

Abstract

Bu çalışmanın amacı görüntü işleme tabanlı akıllı algoritma geliştirerek şekilsiz patateslerin belirlenmesi ve homojen şekilli patates elde edilmesidir. Materyal olarak İran’ın kuzeybatısında bulunan Ardabil bölgesinin Agria patates çeşidinin farklı boyut ve farklı görüntüleri kullanılmıştır. Şekilsiz patateslerin belirlenmesinde uzunluk, genişlik, yuvarlaklık gibi farklı özellikler göz önüne alınmış ve uzama ile Fourier tanımlayıcılarından yararlanılmıştır. İstatistik analize PCA dayalı olarak sınıflandırmada çok önemli olan 7 özellik seçilmiştir. Araştırma sonucunda önerilen 7 yöntemin yüksek bir doğruluğa sahip olduğu, sınıflandırmada ortalama % 98 doğruluk oranına ulaştığı görülmüştür. Ayrıca patatesler % 100 oranında küçük, orta ve büyük gruplara ayrılabilmiştir. Elde edilen sonuçlara göre geliştirilen görüntü işleme tabanlı algoritma şekilsiz ürünlerin sınıflandırılmasında kullanılabilir.

Keywords

• Sınıflandırma
• Görüntü işleme
• Şekil bozukluğu
• Patates
• Biçim

Identifying Irregular Potatoes by Developing an Intelligent Algorithm Based on Image Processing

Abstract

The objective of this study was to develop an algorithm based on image processing for detecting misshapen potatoes from the mass of potatoes and obtaining homogeneous products. The database used in this research included the digital images acquired from Agria variety of Ardabil Iranian northern-west potato with different sizes and shapes. A combination of morphological features including geometrical features like length, width and features related to shape such as roundness were taken into consideration in identifying irregular potatoes from others employing elongation and Fourier descriptors. Using statistical principal component analysis PCA , seven features were selected as the most prominent for classification. The experimental results showed that the proposed method achieves a high level of accuracy with merely seven selected discriminative features, obtaining an average correct classification rate of 98% for training set. Additionally, regular potatoes were separated into small, medium and large categories with 100% accuracy. According to the results, the developed algorithm based on image processing can be used in classifying products with no proper shape

Keywords

• Classification
• Image processing
• Irregularity
• Potato
• Shape

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