Ngày nhận bài: 22-07-2015
Ngày duyệt đăng: 03-09-2015
DOI:
Lượt xem
Download
Cách trích dẫn:
IDENTIFICATION OF SEEDS OF DIFFERENT RICE VARIETIES USING IMAGE PROCESSING AND COMPUTER VISION TECHNIQUES
,
Tran Thi Thanh Hai
2
,
Le Thi Lan
2
,
Vo Ta Hoang
2
,
Nguyen Thi Thuy
3
Tóm tắt
This paper presents a system for automated classification of rice varieties for seed production using computer vision and image processing techniques. Rice seeds of different varieties are visually similar in color, shape and texture that make the classification of seeds of different varieties at high accuracy for evaluation of genetic purity challenging. We investigated various feature extraction techniques for efficient rice seed image representation. We analyzed the performance of powerful classifiers on the extracted features for finding the robust one. 1026 to 2229 images each of six different rice varieties in northern Viet Nam were performed. Our experiments have demonstrated that the average accuracy of our classification system can reach 90.54% by using Random Forest method with a basic feature extraction technique. This result can be used for developing a computer-aided machine vision system for automated assessment of varietal purity of rice seeds.
Tài liệu tham khảo
Breiman L. (2001). "Random forests", Machine Learning, 45(1): 5 - 32.
Brosnan Tadhg and Da-Wen Sun (2002). "Inspection and grading of agricultural and food products by computer vision systems - a review", Computers and Electronics in Agriculture, 36(2-3): 193 - 213.
Du Cheng-Jin and Da-Wen Sun (2006). "Learning techniques used in computer vision for food quality evaluation: a review", Food Engineering, 72(1): 39-55.
van Dalen Gerard (2006). Characterisation of rice using flatbed scanning and image analysis, Arthur P. Riley (Ed.).
Guzman D Jose and Peralta K. Engelbert (2008). "Classification of Philippine Rice Grains Using Machine Vision and Artificial Neural Networks" in World conference on Agricultural information and IT, p. 41 - 48.
Goodman D.E. and R.M. Rao (1984). "A new, rapid, interactive image analysis method for determining physical dimensions of milled rice kernels," Journal of Food Science, 49(2): 648 - 649.
Kong W., C. Zhang, F. Liu, P Nie, and Y. He (2013). "Rice seed cultivar identification using Near-Infrared hyperspectral imaging and multivariate data analysis", Sensors, 13: 8916 - 8927.
Lai F.S., I. Zayas, and Y Pomeranz (1982). "Application of pattern recognition techniques in the analysis of cereal grains", Cereal Chemistry, 63(2): 168 - 172.
Luo X, D. S. Jayas, and S. J. Symons (1999). "Identification of damaged kernels in wheat using a color machine vision," Cereal Science, 30: 45 - 59.
Mousavirad S.J., F. A. Tab, and K. Mollazade (2012). "Design of an Expert System for Rice Kernel Identification using Optimal Morphological Features and Back Propagation Neural Network", International Journal of Applied information systems, 3: 33 - 37.
Oliva A. and A. Torralba (2001). "Modeling the shape of the scene: A holistic representation of the spatial envelope", Int. J. Comput. Vision, 42: 145 - 175.
Szeliski Richard (2010). "Computer Vision: Algorithms and Applications", Springer.
Sakai N., S. Yonekawa, A. Matsuzaki , and H. Morishima (1996). "Two-dimensional image analysis of the shape of rice and its application to separating varieties", Journal of Food Engineering, 27: 397 - 407.
Sun Da-Wen (2008). Computer Vision Technology for Food Quality Evaluation.
Vapnik V. (1995). "The Nature of Statistical Learning Theory", Springer-Verlag.
Zayas I., F. S. Lai , and L. Y. Pomeranz (1986). "Discrimination between wheat classes and varieties by image analysis," Cereal Chemistry, 63: 52 - 56.
Zayas I., Y. Pomeranz, and F. S. Lai (1989). "Discrimination of wheat and nonwheat components in grain samples by image analysis", Cereal Chemistry, 66: 233 - 237.
Zhao-yan L., C. Fang, Y. Yi-bin, and R-Xiu-qin (2005). "Identification of rice seed varieties using neural networks", Journal of Zhejiang University Science, 11: 1095 - 1100.