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Texture-based fruit detection

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Abstract

In this paper, a technique based on texture analysis is proposed for detecting green fruits on plants. The method involves interest point feature extraction and descriptor computation, interest point classification using support vector machines, candidate fruit point mapping, morphological closing and fruit region extraction. In an empirical study using low-cost web camera sensors suitable for use in mechanized systems, 24 combinations of interest point features and interest point descriptors were evaluated on two fruit types (pineapple and bitter melon). The method is highly accurate, with single-image detection rates of 85 % for pineapples and 100 % for bitter melons. The method is thus sufficiently accurate for precise location and monitoring of textured fruit in the field. Future work will explore combination of detection and tracking for further improved results.

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References

  • 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.

    Article  Google Scholar 

  • Bansal, R., Lee, W., & Satish, S. (2012). Green citrus detection using fast Fourier transform (FFT) leakage. Precision Agriculture, 14(1), 59–70.

    Article  Google Scholar 

  • Bay, H., Ess, A., Tuytelaars, T., & Van Gool, L. (2008). Speeded-up robust features (SURF). Computer Vision and Image Understanding, 110(3), 346–359.

    Article  Google Scholar 

  • 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.

  • 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.

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Harris, C., & Stephens, M. (1988). A combined corner and edge detector. In Proceedings of the Fourth Alvey Vision Conference (pp. 147–151).

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Kaewapichai, W., Kaewtrakulpong, P., & Prateepasen, A. (2006). A real-time automatic inspection system for Pattavia pineapples. Key Engineering Materials, 321–322, 1186–1191.

    Article  Google Scholar 

  • 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.

  • 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.

  • Lee, W. S., Slaughter, D. C., & Giles, D. K. (1999). Robotic weed control system for tomatoes. Precision Agriculture, 1(1), 95–113.

    Article  Google Scholar 

  • 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

  • Lowe, D. (2004). Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60(2), 91–110.

    Article  Google Scholar 

  • 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.

    Article  CAS  Google Scholar 

  • Nascimento, J. C., & Marques, J. S. (2006). Performance evaluation of object detection algorithms for video surveillance. IEEE Transactions on Multimedia, 8(4), 761–774.

    Article  Google Scholar 

  • 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.

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

  • 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.

  • 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.

    Article  Google Scholar 

  • Szeliski, R. (2011). Computer vision: Algorithms and applications. London: Springer.

    Book  Google Scholar 

  • 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.

  • 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.

    Article  Google Scholar 

  • Vapnik, V. N. (1995). The nature of statistical learning theory. New York: Springer.

    Book  Google Scholar 

  • Zhang, Y., & Wu, L. (2012). Classification of fruits using computer vision and a multiclass support vector machine. Sensors, 12(9), 12489–12505.

    Article  PubMed  PubMed Central  Google Scholar 

  • 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.

    Article  CAS  Google Scholar 

  • 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.

    Article  Google Scholar 

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Acknowledgments

SC was supported by the Thailand National Science and Technology Development Agency (NSTDA). The authors thank the members of the AIT Vision and Graphics Lab (VGL) for suggestions and help with data collection.

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Authors and Affiliations

  1. Computer Science and Information Management, Asian Institute of Technology, P.O.Box 4, Klong Luang, Pathumthani, 12120, Thailand

    Supawadee Chaivivatrakul & Matthew N. Dailey

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  1. Supawadee Chaivivatrakul
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  2. Matthew N. Dailey
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Correspondence to Supawadee Chaivivatrakul.

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Chaivivatrakul, S., Dailey, M.N. Texture-based fruit detection. Precision Agric 15, 662–683 (2014). https://doi.org/10.1007/s11119-014-9361-x

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