Accurate Fruits Fault Detection in Agricultural Goods using an Efficient Algorithm
محورهای موضوعی : Information and communication technology in agriculture
1 - گروه مهندسی برق، واحد رشت، دانشگاه آزاد اسلامی، رشت، ایران
کلید واژه: image processing, fruit, fault, DCT transform, noise,
چکیده مقاله :
The main purpose of this paper was to introduce an efficient algorithm for fault identification in fruits images. First, input image was de-noised using the combination of Block Matching and 3D filtering (BM3D) and Principle Component Analysis (PCA) model. Afterward, in order to reduce the size of images and increase the execution speed, refined Discrete Cosine Transform (DCT) algorithm was utilized. Finally, for segmentation, fuzzy clustering algorithm with spatial information was applied on the compressed image. Implementation results in MATLAB environment and based on the gathered data by the author showed that the proposed algorithm contains a good capability in de-noising. Also, in the proposed method, identification accuracy of faulty regions in fruit was higher than other methods. The major advantage of the proposed method was its high speed which makes it appropriate for real time applications.
هدف اصلی این مقاله معرفی یک الگوریتم بهینه برای تشخیص خرابی میوهها در محصولات کشاورزی است. ابتدا، تصویر ورودی با استفاده از مدل ترکیبی فیلرینگ تطبیق بلوک سهبعدی و آنالیز مولفههای اصلی نویززدایی میشود. سپس به منظور کاهش ابعاد تصویر و در نتیجه بهبود سرعت پردازش، الگوریتم بهبودیافته تبدیل کسینوسی گسسته مورد استفاده قرار میگیرد. نهایتا الگوریتم خوشهبندی فازی با اطلاعات مکانی بر روی تصویر فشرده شده اعمال میشود. نتایج پیادهسازی در محیط نرمافزار متلب و بر اساس تصاویر جمعآوری شده توسط نویسنده نشان میدهد که الگوریتم پیشنهادی از قابلیت بالایی در حذف نویز برخوردار است. همچنین در این الگوریتم تشخیص نواحی خراب در میوه در مقایسه با سایر روشهای متداول با دقت بالاتری صورت میپذیرد. مزیت عمده الگوریتم پیشنهادی سرعت بالای آن بوده بطوریکه استفاده از آنرا برای کاربردهای بلادرنگ امکانپذیر میسازد.
1- Abbot, J.A. (1999). Quality Measurement of fruits and vegetables. Postharvest Biology and Technology, 15, 207-225.
2- Ahmed, M.N., Yamany, S.M., Mohamed, N., & Farag, A. (2002). A modified fuzzy c-means algorithm for bias field estimation and segmentation of MRI data. IEEE Transactions on Medical Imaging, 21, 193–199.
3- Arumugam, N., Mohamed Arshad, F., Chiew, E., & Mohamed, Z. (2011). Determinates of fresh fruits and vegetables (FFV) farmers participation in contrast farming in Peninsular Malaysia. International Jornal of Agricultural Management and Development, 1(2), 65-71.
4- Bezdek, J.C. (1987). Pattern recognition with fuzzy objective function algorithms. New York/ London, UK, Plenum.
5- Brosnan, T., & Sun, D.W. (2002). Inspection and grading of agriculture and food products by computer vision system-A reviews, Computer and Electronic in Agriculture. 36, 193-213.
6- Charles, D., Salmon, J., & Dalalyam, A. (2011). Image denoising with patch based PCA: local versus global. In Jesse Hoey, Stephen McKenna and Emanuele Trucco, Proceedings of the British Machine Vision Conference, pages 25.1-25.10. BMVA Press.
7- Dabov, K., Foi, A., Katkovnik, V., & Egiazarian, K. (2009). BM3D image denoising with shapeadaptive principle component analysis. PROC. Workshop on signal processing with adaptive sparse structured presentations (SPARS’09).
8- Foi, A., Katkovnik, V., & Egiazarian, K. (2007). Pointwise shape-adaptive DCT for high-quality denoising and deblocking of grayscale and color images. IEEE Transactions on Image Processing. 16, 1395.1411.
9- Graves, M., & Batchelor, B. (2003). Machine vision for the inspection of Natural products. London, Springer.
10- Halls, L., Evans, S., & Nott, K. (1998). Measurement of textural changes of food by MRI relaxometry. Magnetic Resonance Imaging, 16, 485-492.
11- Hills, B. (1995). Food processing and MRI perspective. Trends in Food Science and Technology. 6, 111-117.
12- Izadbakhshi, M., & Javadikia, H. (2014). Application of hybrid feed-forward neural network (ffnn)- genetic algorithm for predicting evaporation in storage dam reservoirs. Agricultural Communications, 2, 57-62.
13- Knaus, C., & Zwicker, M. (2014). Progressive image denoising. IEEE Transactions on Image Processing, 23, 3114-3125.
14- Lehmussola, A., Ruusuvuori, P., & Yli-Harja, O. (2006). Evaluating the performance of microarray segmentation algorithms. Bioinformatics, 22(23), 2910-2917.
15- Linker, R., Cohen, O., & Naor, A. (2012). Determination of the number of green apples in RGB images recorded in orchards. Computers and Electronics in Agriculture, 81, 45–57.
16- Malamas, E.N., Petrakis, E.G.M., Zervakis, M.,Petit, L., & Legat, J.D. (2003). A survey on industrial vision systems, applications and tools. Image and Vision Computing, 21,171–188.
17- Mizushima, A., & R. Lu. (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.
18- Moradi, G., Shamsi, M., Sedaghi, M. H., & Alsharif, M.R. (2011, April). Fruit defect detection from color images using ACM and MFCM algorithms. International Conference on Electronic Devices, Systems and Applications (lCEDSA), 2011 International Conference on (pp. 182- 186). IEEE.
19- Moradi. G., Shamsi, M., Sedaaghi, M.H. Moradi, S., & Alsharif, M. R. (2012). Apple defect detection using statistical histogram based EM algorithm. 19th Iranian Conference on Electrical Engineering (ICEE).
20- Payne, A.B. Walsh, K.B., Subedi, P.P., & Jarvis, D. (2013). Estimation of mango crop yield using image analysis– Segmentation method. Computers and Electronics in Agriculture, 91, 57–64.
21- Rakun, J., Stajnko, D., & Zazula, D. (2011). Detecting fruits in natural scenes by using spatialfrequency based texture analysis and multiview geometry. Computers and Electronics in Agriculture, 76, 80-88.
22- Romano, G. Argyropoulos, D., Nagle, M., Khan, M.T., & Muller, J. (2012). Combination of digital images and laser light to predict moisture content and color of bell pepper simultaneously during drying. Journal of Food Engineering, 119, 438–448.
23- Saberkari, H., & Shamsi, M. (2012). Comparison of different algorithms for ECG signal compression based on transfer coding. IEEE Symposium on Industrial Electronics and Applications (ISIEA 2012), 23-26.
24- Saberkari, H., Bahrami, S., Shamsi, M., Amoshahy, M.J., Badri Ghavifekr, H., & Sedaaghi, M.H. (2015). Fully automated complementary DNA microarray segmentation using a novel fuzzy-based algorithm. Journal of Medical Signals and Sensors, 5(3), 182-191.
25- Huynh-Thu, Q., & Ghanbari, M. (2008). Scope of validity of PSNR in image/video quality assessment. Electronics Letters, 44(13), 800-801.
