Görüntü İşleme ve ANFIS ile Emperor Elmasının Otomatik Sınıflandırılması

Abstract

Ağırlık tabanlı meyve sınıflandırma, paketleme ve pazarlanmanın iyileştirilmesi açısından önemli bir terimdir. Ağırlıklarına göre sınıflandırma doğrudan veya dolaylı yöntemlerle gerçekleştirilebilir. Bu çalışma için, Kirmanşah, İran’da (Boylam: 7.03 °E; Enlem: 4.22 °N). bir meyve bahçesinden rastgele 100 Emperor Elma örneği seçilmiştir. Tüm testler Ziraat Mühendisliği Fakültesi, Razi Üniversitesi, Kirmanşah, İran Fizik Laboratuarında yapılmıştır. Her elma için görüntü işleme ile ondört parametre elde edilmiştir. ANFIS ve doğrusal regresyon yöntemleri kullanılarak çeşitli ağırlık modelleri geliştirilmiştir. En iyi model sırasıyla ANFIS, doğrusal ve doğrusal olmayan regresyon için, R2, SSE, ve MSE için 0.990, 276.58, 13.17, 0.856, 15980.96, 166.47 ve 0.791, 24512.16, 255.35 şeklindedir. Yani, makine görme sistemi ile meyve ile temas etmeden ağırlık tabanlı elma sınıflandırması sağlanabilir. Mekanik ve elektrik sistemleri üzerinden bu sistemin faydaları şunlardır: 1- Farklı boyutlarda gruplar için makinenin tekrar kalibrasyon kolaylığı, 2- Dolaylı sınıflandırma kullanılarak daha doğru ağırlık ölçümü ve yüksek çalışma hızına ulaşma.

Keywords

• SPSS
• Paketleme
• Pazarlama
• Yapay görme
• Bulanık çıkarım sistemi
• Ayırma

Automatic Grading of Emperor Apples Based on Image Processing and

Abstract

Mass-based fruit classification is important in terms of improving packaging and marketing. Mass sizing can be

accomplished by direct or indirect methods. In this study, 100 samples of Emperor Apples were randomly selected from

an orchard in Kermanshah, Iran (longitude: 7.03 °E; latitude: 4.22 °N). All tests were carried out in Physical Laboratory,

Faculty of Agriculture Engineering, Razi University, and Kermanshah, Iran. Fourteen parameters were obtained by

image processing for each apple. Several mass modeling were made using ANFIS and linear regression methods. In the

best model for ANFIS, linear and nonlinear regression, R2, SSE, and MSE were 0.990, 276.58, 13.17, 0.856, 15980.96,

166.47 and 0.791, 24512.16, 255.35, respectively. So, a mass-based sorting system was proposed with machine vision

system and using ANFIS method that could obtain apple mass without contact with the fruit. Benefits of this system over

mechanical and electrical systems were: 1- Easier recalibration of the machine to the groups with different sizes, and

2- Reaching more accurate mass measurement and higher operating speed using indirect grading.

Keywords

• SPSS; Packaging; Marketing; Machine vision; Fuzzy inference system; Sorting
• SPSS
• fUZZY

References

1. Gonzalez R C, Woods R E & Eddins S L (2004). Digital Image Processing Using MATLAB. Prentice Hall
2. Khojastehnazhand M, Omid M & Tabatabaeefar A (2009). Determination of orange volume and surface area using image processing technique. International Agrophysics 23: 237-224
3. Koc A B (2007). Determination of watermelon volume using ellipsoid approximation and image processing. Journal of Postharvest Biology and Technology 45: 366-371
4. Leemans V, Magein H & Destain M F (2004). On-line fruit grading according to their external quality using machine vision. Biosystems Engineering 83: 397-404
5. Lu R (2003). Detection of bruises on apples using near- infrared hyperspectral imaging. Transactions of ASAE 46(2): 523-530
6. Mizushima A & Lu R (2013). An image segmentation method for apple sorting and grading using support vector machine and Otsu’s method. Computers and Electronics in Agriculture 94: 29-37
7. Naderloo L, Alimardani R, Omid M, Sarmadian F, Javadikia P, Torabi M Y & Alimardani F (2012). Application of ANFIS to predict crop yield based on different energy inputs. Measurement 45: 1406-1413
8. Rashidi M, Gholami M & Abbassi S (2009). Cantaloupe volume determination through Image Processing. Journal of Agricultural Science and Technology 11: 623-631 (2012). Modeling the free convection heat transfer in a partitioned cavity using ANFIS. International Communications in Heat and Mass Transfer 39: 470- 475

9. Sabliov C M, Boldor D, Keener K M & Farkas B E (2002). Image processing method to determine surface area and volume of axisymmetric agricultural products. International Journal of Food Properties 5: 641-653
10. Shin J S, Lee W S & Ehsani R (2012). Postharvest citrus mass and size estimation using a logistic classification model and a watershed algorithm. Biosystems Engineering 113: 42-53
11. Sabzi S, Javadikia P, Rabani H & Adelkhani A (2013). Mass modeling of Bam orange with ANFIS and SPSS methods for using in machine vision. Measurement 46: 3333-3341
12. Sabzi S, Javadikia P, Rabani H, Adelkhani A & Naderloo L (2013). Exploring the best model for sorting Blood orange using ANFIS method. Agricultural Engineering International: CIGR Journal 15(4): 213- 219
13. Sivasankaran S, Sivaprasad K, Narayanasamy R & Iyer V K (2011). Evaluation of compaction equations and prediction using adaptive neuro-fuzzy inference system on compressibility behavior of AA 6061100−x–x wt. % TiO 2 nanocomposites prepared by mechanical alloying. Powder Technology 209: 124-137
14. Taylan O & Karagozoglu B (2009). An adaptive neuro- fuzzy model for prediction of student’s academic performance. Computers & Industrial Engineering 57: 732-741
15. Tong J H, Li J B & Jiang H Y (2013). Machine vision techniques for the evaluation of seedling quality based on leaf area. Biosystems Engineering 115: 369-379
16. Xing J, Bravo C, Jancso K P T, Ramon H & Baerdemaeker J D (2005). Detecting bruises on ‘Golden Delicious’ apples using hyperspectral imaging with multiple wavebands. Biosystems Engineering 90(1): 27–36
17. Zheng H, Lu H, Zheng YLou H & Chen C (2010). Automatic sorting of Chinese jujube (Zizyphus jujuba Mill. cv. ‘hongxing’) using chlorophyll fluorescence and support vector machine. Journal of Food Engineering 101: 402-408