Texture-based fruit detection

Precision Agriculture

Volumn 15, Issue 6, 2014, Pages 662-683

  Chaivivatrakul, Supawadee ^a   Dailey, Matthew N ^a  

^a ASIAN INSTITUTE OF TECHNOLOGY   (Thailand)

Author keywords

Fruit detection; Keypoint descriptors; Keypoint extraction; Object detection; Support vector machine classification; Texture analysis

Indexed keywords

EMPIRICAL ANALYSIS; FRUIT; FUTURE PROSPECT; PRECISION AGRICULTURE; SENSOR; TEXTURE;

EID: 84911998069     PISSN: 13852256     EISSN: 15731618     Source Type: Journal    
DOI: 10.1007/s11119-014-9361-x     Document Type: Article

References (33)

1. Aggelopoulou, A., Bochtis, D., Fountas, S., Swain, K., Gemtos, T., & Nanos, G. (2011). Yield prediction in apple orchards based on image processing. Precision Agriculture,12(3), 448–456.
2. Bansal, R., Lee, W., & Satish, S. (2012). Green citrus detection using fast Fourier transform (FFT) leakage. Precision Agriculture,14(1), 59–70.
3. Bay, H., Ess, A., Tuytelaars, T., & Van Gool, L. (2008). Speeded-up robust features (SURF). Computer Vision and Image Understanding,110(3), 346–359.
4. Calonder, M., Lepetit, V., Strecha, C., & Fua, P. (2010). BRIEF: Binary robust independent elementary features. In Daniilidis, K., Maragos, P., Paragios, N. (Eds.), Proceedings of the European Conference on Computer Vision (pp. 778–792). Heidelberg: Springer.
5. Chang, C.C., & Lin, C.J. (2001). LIBSVM: A library for support vector machines. Retrieved March 12, 2014, from http://www.csie.ntu.edu.tw/~cjlin/libsvm.
6. 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.
7. Delenne, C., Durrieu, S., Rabatel, G., Deshayes, M., Bailly, J. S., Lelong, C., et al. (2008). Textural approaches for vineyard detection and characterization using very high spatial resolution remote sensing data. International Journal of Remote Sensing,29(4), 1153–1167.
8. Du, C. J., & Sun, D. W. (2006). Learning techniques used in computer vision for food quality evaluation: A review. Journal of Food Engineering,72(1), 39–55.
9. Harris, C., & Stephens, M. (1988). A combined corner and edge detector. In Proceedings of the Fourth Alvey Vision Conference (pp. 147–151).
10. Jiménez, R. A., Ceres, R., & Pons, L. J. (2000a). A vision system based on a laser rangefinder applied to robotic fruit harvesting. Machine Vision and Applications,11(6), 321–329.
11. Jiménez, R. A., Ceres, R., & Pons, L. J. (2000b). A survey of computer vision methods for locating fruit on trees. Transactions of the American Society of Agricultural and Biological Engineers,43(6), 1911–1920.
12. Kaewapichai, W., Kaewtrakulpong, P., & Prateepasen, A. (2006). A real-time automatic inspection system for Pattavia pineapples. Key Engineering Materials,321–322, 1186–1191.
13. Kaewapichai, W., Kaewtrakulpong, P., Prateepasen, A., & Khongkraphan, K. (2007). Fitting a pineapple model for automatic maturity grading. In Proceedings of the IEEE International Conference on Image Processing (pp. I-257–I-260). New York: IEEE.
14. Kitamura, S., & Oka, K. (2005). Recognition and cutting system of sweet pepper for picking robot in greenhouse horticulture. In Proceedings of the IEEE Conference on Mechatronics and Automation (pp. 1807–1812). New York: IEEE.
15. Lee, W. S., Slaughter, D. C., & Giles, D. K. (1999). Robotic weed control system for tomatoes. Precision Agriculture,1(1), 95–113.
16. Li, B., Wang, M., & Wang, N. (2010). Development of a real-time fruit recognition system for pineapple harvesting robots. Paper No. 1009510. ASABE, St Joseph, MI
17. Lowe, D. (2004). Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision,60(2), 91–110.
18. Muller, K. R., Mika, S., Ratsch, G., Tsuda, K., & Scholkopf, B. (2001). An introduction to kernel-based learning algorithms. IEEE Transaction on Neural Networks,12(2), 181–201.
19. Nascimento, J. C., & Marques, J. S. (2006). Performance evaluation of object detection algorithms for video surveillance. IEEE Transactions on Multimedia,8(4), 761–774.
20. OpenCV Community (2013). Open source computer vision library version 2.3.1, [C source code]. Retrieved December 1, 2013, from http://sourceforge.net/projects/opencvlibrary.
21. 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.
22. Pla, F., & Marchant, J. A. (1997). Matching feature points in image sequences through a region-based method. Computer Vision and Image Understanding,66(3), 271–285.
23. Rocha, A., Hauagge, D. C., Wainer, J., & Goldenstein, S. (2010). Automatic fruit and vegetable classification from images. Computers and Electronics in Agriculture,70(1), 96–104.
24. Rosten, E., & Drummond, T. (2006). Machine learning for high-speed corner detection. In Leonardis, A., Bischof, H., Pinz, A. (Eds.), Proceedings of the European Conference on Computer Vision (pp. 430–443). Heidelberg, Germany: Springer.
25. Rublee, E., Rabaud, V., Konolige, K., & Bradski, G. (2011). ORB: An efficient alternative to SIFT or SURF. In Proceedings of the IEEE International Conference on Computer Vision (pp. 2564–2571). New York: IEEE.
26. Slaughter, D. C., Obenland, D. M., Thompson, J. F., Arpaia, M. L., & Margosan, D. A. (2008). Non-destructive freeze damage detection in oranges using machine vision and ultraviolet fluorescence. Postharvest Biology and Technology,48(3), 341–346.
27. Szeliski, R. (2011). Computer vision: Algorithms and applications. London: Springer.
28. Torii, I., Okada, Y., Mizutani, M., & Ishii, N. (2009). A simple method for 3-dimensional modeling and application to complex objects. In Proceedings of the 21st International Conference on Tools with Artificial Intelligence (pp. 41–48). New York: IEEE.
29. Van Henten, E., Hemming, J., Van Tuijl, B., Kornet, J., Meuleman, J., Bontsema, J., et al. (2002). An autonomous robot for harvesting cucumbers in greenhouses. Autonomous Robots,13(3), 241–258.
30. Vapnik, V. N. (1995). The nature of statistical learning theory. New York: Springer.
31. Zhang, Y., & Wu, L. (2012). Classification of fruits using computer vision and a multiclass support vector machine. Sensors,12(9), 12489–12505.
32. Zhang, L., Yang, Q., Xun, Y., Chen, X., Ren, Y., Yuan, T., et al. (2007). Recognition of greenhouse cucumber fruit using computer vision. New Zealand Journal of Agricultural Research,50(5), 1293–1298.
33. Zhou, R., Damerow, L., Sun, Y., & Blanke, M. (2012). Using colour features of cv. ‘Gala’ apple fruits in an orchard in image processing to predict yield. Precision Agriculture,13(5), 568–580.