Görüntü İşleme Tabanlı Akıllı Algoritma Geliştirerek Şekilsiz Patateslerin Belirlenmesi

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.

Anahtar Kelimeler:

Sınıflandırma, Görüntü işleme, Şekil bozukluğu, Patates, Biçim

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

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,

PDF

___

Aleixos N, Blasco J, Navarron F & Molto E (2002). Multispectral inspection of citrus in real-time using machine vision and digital signal processors. Computers and Electronics in Agriculture 33(2): 121- 137
Al-Mallahi A, Kataoka T, Okamoto H & Shibata Y (2010). An image processing algorithm for detecting in-line potato tubers without singulation. Computers and Electronics in Agriculture 70: 239-244
Arribas J I, Sanchez-Ferroro G V, Ruiz-Ruiz G & GomezGil J (2011). Leaf classification in sunflower crops by computer vision and neural networks. Computers and Electronics in Agriculture 78: 9-18
Barnes M, Duckett T, Cielniak G, Stroud G & Harper G (2010). Visual detection of blemishes in potatoes using minimalist boosted classifiers. Journal of Food Engineering 98: 339-346
Bramer M (2007). Principles of data mining. Ecole polytechnique, France and King’s college London, UK
Castleman K (1996). Digital image processing. Englewood Cliffs, NJ: Prentice-Hall, 667 pp
Choudhary R, Paliwal J & Jayas D S (2008). Classification of cereal grains using wavelet morphological, colour and textural features. Biosystems Engineering 99: 330-337
Cubero S, Aleixos N, Moltó E, Gómez-Sanchis J & Blasco J (2011). Advances in machine vision applications for automatic inspection and quality evaluation of fruits and vegetables. Food and Bioprocess Technology 4(4): 487-504
Du C.J & Sun D W (2004). Recent developments in the applications of image processing techniques for food quality evaluation. Trends in Food Science & Technology 15(5): 230-249
FAO (2011). Food and Agriculture Organization Statistics, FAOSTAT
Gonzalez R C & Woods R E (2008). Digital Image Processing. Pearson Education, Inc., Upper Saddle River, NJ, USA
Li Y, Dhakal S & Peng Y (2012). A machine vision system for identification of micro- crack in egg shell. Journal of Food Engineering 109: 127-134
Liming X & Yanchao Z (2010). Automated strawberry grading system based on image processing. Computers and Electronics in Agriculture 715: 532-539
Smith L I (2002). A Tutorial on principal components analysis. Available on the Internet at the following URL:http//www.cs.otago.ac.nz/cosc453/student_ tutorials/principal_components
Moreda G P, Muñoz M A, Ruiz-Altisent M & Perdigones A (2012). Shape determination of horticultural produce using two-dimensional computer vision – A review. Journal of Food Engineering 108(2): 245-261
Nashat S, Abdullah A & Abdullah M Z (2014). Machine vision for crack inspection of biscuits featuring pyramid detection scheme. Journal of Food Engineering 120: 233-247
Razmjooy N, Mousavi B S & Soleymani F (2012). A real-time mathematical computer method for potato inspection using machine vision. Computers and Mathematics with Applications 63(1): 268-279
Shapiro L G & Stockman G (2001). Computer vision. New Jersey, Prentice-Hall, USA
Stehman S V (1997). Selecting and interpreting measures of thematic classification accuracy. Remote Sensing of Environment 62(1): 77-89
Tao Y, Morrow C T, Heinemann P H & Sommer H J (1995). Fourier-based separation technique for shape grading of potatoes using machine vision. Transactions of the ASAE 38(3): 949-957
Ying Y, Jing H, Tao Y & Zhang N (2002). Detecting stem and shape of pears using Fourier transformation and an artificial neural network. Information & Electrical Technologies Division of the ASAE 46(1): 157-162
Zhou L, Chalana V & Kim Y (1998). PC-Based machine vision system for real-time computer-aided potato inspection. International Journal of Imaging Systems and Technology 9(6): 423–433