The Detection of Spoiled Fruits on a Conveyor Belt Using Image Processing Techniques and OPC Server Software

Öz

Quality plays a key role in the marketing of vegetables and fruits. There is a reason that the agricultural products you see in the store usually aren’t rotten. Someone at a sorting facility has already looked over all the products coming in from the fields, and taken out the spoiled ones. This method does not exactly meet the standards of quality and it causes time and labour force loss. Moreover, it has seen that the use of conveyor belt systems are increasing rapidly in today’s industry. These systems, designed to meet the developing needs of the industry, are used to categorize objects with different characteristics. This research was conducted to develop a system for detecting the rottenness on apples on a conveyor belt using image processing techniques, PLC and OPC server. Morphological image processing techniques were used for detecting spots or rottenness emerged on apples and Matlab software was used for image processing. Image processing algorithm developed to detect the characteristics of the apples. Overall conveyor belt system was controlled by PLC. OPC server was used for communication between image processing part and controller part. In this study; image processing algorithm, OPC server and PLC algorithm were tested in real time and results were studied.

Anahtar Kelimeler

• Image processing,Spot detection,Quality control,Fruit defect detection

Kaynakça

1. Reference1 Dhanabal, T., and Debabrata Samanta. "Computerized spoiled tomato detection." International Journal of Research in Engineering and Technology 2 (2013): 38-41.
2. Reference2 SABANCI, Kadir, Cevat AYDIN, and Muhammed Fahri ÜNLERŞEN. "Görüntü İşleme ve Yapay Sinir Ağları Yardımıyla Patates Sınıflandırma Parametrelerinin Belirlenmesi." Iğdır Üniversitesi Fen Bilimleri Enstitüsü Dergisi 2.10 (2012).
3. Reference3 Cho, Byoung-Kwan, et al. "Detection of cuticle defects on cherry tomatoes using hyperspectral fluorescence imagery." Postharvest biology and technology 76 (2013): 40-49.
4. Reference4 Sofu, Mehmet Mahir, et al. "Elmaların görüntü işleme yöntemi ile sınıflandırılması ve leke tespiti." Gıda Teknolojileri Elektronik Dergisi 8.1 (2013): 12-25.
5. Reference5 Karhan, M., et al. "Morfolojik görüntü işleme yöntemleri ile kayısılarda yaprak delen (çil) hastalığı sonucu oluşan lekelerin tespiti." 6 th International Advanced Technologies Symposium (IATS’11), Elazığ, Türkiye. 2011.
6. Reference6 Shieh, J., et al. "The selection of sensors." Progress in materials science 46.3-4 (2001): 461-504.
7. Reference7 Lipták, Béla G., ed. Process Control: Instrument Engineers' Handbook. Butterworth-Heinemann, 2013.
8. Reference8 Chitra, S., and V. Raghavan. "Conveyor Control Using Programmable Logic Controller." International Journal of Advancements in Research and Technology 3.8 (2014): 1-7.

9. Reference9 Mishra, Alok, Pallavi Asthana, and Pooja Khanna. "The quality identification of fruits in image processing using Matlab." International Journal of Research in Engineering and Technology 3.10 (2014): 92-95.
10. Reference10 Panli, H. E. "Fruit surface defects detection and classification based on attention model." Journal of Computational Information Systems 8.10 (2012): 4233-4240.
11. Reference11 Van Tan, Vu, Dae-Seung Yoo, and Myeong-Jae Yi. "Security in automation and control systems based on OPC techniques." Strategic Technology, 2007. IFOST 2007. International Forum on. IEEE, 2007.
12. Reference12 Ćwikła, Grzegorz, et al. "Case study of application of OPC technology in integration of laboratory equipment and software." Selected Engineering Problems 6 (2015): 9-14.