Shape-based Segmentation of Tomatoes for Agriculture Monitoring

Ujjwal Verma, Florence Rossant, Isabelle Bloch, Julien Orensanz, Denis Boisgontier

Abstract

In this paper, we present a segmentation procedure based on a parametric active contour with shape constraint, in order to follow the growth of the tomatoes from the images acquired in the field. This is a challenging task because of the poor contrast in the images and the occlusions by the vegetation. In our sequential approach, considering one image per day, we assume that a segmentation of the tomatoes is available for the image acquired the previous day. An initial curve for the active contour model is computed by combining gradient information and region information. Then, an active contour with shape constraint is applied to provide an elliptic approximation of the tomato boundary. We performed a quantitative evaluation of our approach by comparing the results with the manual segmentation. Given the varying degree of occlusion in the images, the image data set was divided into three categories, based on the occlusion degree of the tomato in the processed image. For the cases with low occlusion, good results were obtained, with an average relative distance between the manual segmentation and the automatic segmentation of 2.73% (expressed as percentage of the size of tomato). For the images with significant amount of occlusion, a good segmentation was obtained on 44% of the images, where the average error was less than 10%.

References

  1. Aggelopoulou, A., Bochtis, D., Fountas, S., Swain, K., Gemtos, T., and Nanos, G. (2011). Yield prediction in apple orchards based on image processing. Journal of Precision Agriculture, 12:448-456.
  2. Aitkenhead, M., Dalgetty, I., Mullins, C., McDonald, A., and Strachan, N. (2003). Weed and crop discrimination using image analysis and artificial intelligence methods. Computers and Electronics in Agriculture, 39(3):157 - 171.
  3. Charmi, M., Ghorbel, F., and Derrode, S. (2009). Using Fourier-based shape alignment to add geometric prior to snakes. In ICASSP, pages 1209-1212.
  4. Du, C. and Sun, D. (2006). Learning techniques used in computer vision for food quality evaluation: a review. Journal of Food Engineering, 72(1):39 - 55.
  5. Fischler, A. and Bolles, C. (1981). Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM, 24(6):381-395.
  6. Foulonneau, A., Charbonnier, P., and Heitz, F. (2006). Affine-invariant geometric shape priors for regionbased active contours. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(8):1352-1357.
  7. Jayas, D., Paliwal, J., and Visen, N. (2000). Review paper (automation and emerging technologies): Multilayer neural networks for image analysis of agricultural products. Journal of Agricultural Engineering Research, 77(2):119 - 128.
  8. Kass, M., Witkin, A., and Terzopoulos, D. (1988). Snakes: Active Contour Models. International Journal of Computer Vision, 1(4):321-331.
  9. Lee, W. S., Slaughter, D. C., and Giles, D. K. (1999). Robotic weed control system for tomatoes. Precision Agriculture, 1:95-113.
  10. Mkhabela, M., Bullock, P., Raj, S., Wang, S., and Yang, Y. (2011). Crop yield forecasting on the Canadian prairies using MODIS NDVI data. Agricultural and Forest Meteorology, 151(3):385 - 393.
  11. Narendra, V. G. and Hareesh, K. S. (2010). Prospects of computer vision automated grading and sorting systems in agricultural and food products for quality evaluation. International Journal of Computer Applications, 1(4):1-9.
  12. Otsu, N. (1975). A threshold selection method from graylevel histograms. Automatica, 11(285-296):23-27.
  13. Prasad, A., Chai, L., Singh, R., and Kafatos, M. (2006). Crop yield estimation model for Iowa using remote sensing and surface parameters. International Journal of Applied Earth Observation and Geoinformation, 8(1):26 - 33.
  14. Stajnko, D. and Cmelik, Z. (2005). Modelling of apple fruit growth by application of image analysis. Agriculturae Conspectus Scientificus, 70:59-64.
  15. Xu, C. and Prince, J. (1998). Snakes, shapes, and gradient vector flow. IEEE Transactions on Image Processing, 7(3):359-369.
  16. Yang, C., Prasher, S., Landry, J., Ramaswamy, H., and Ditommaso, A. (2000). Application of artificial neural networks in image recognition and classification of crop and weeds. Canadian Agricultural Engineering, 42(3):147 - 152.
  17. Zhao, H. and Pei, Z. (2013). Crop growth monitoring by integration of time series remote sensing imagery and the WOFOST model. In 2013 Second International Conference on Agro-Geoinformatics (AgroGeoinformatics), pages 568-571.
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Paper Citation


in Harvard Style

Verma U., Rossant F., Bloch I., Orensanz J. and Boisgontier D. (2014). Shape-based Segmentation of Tomatoes for Agriculture Monitoring . In Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-018-5, pages 402-411. DOI: 10.5220/0004818804020411


in Bibtex Style

@conference{icpram14,
author={Ujjwal Verma and Florence Rossant and Isabelle Bloch and Julien Orensanz and Denis Boisgontier},
title={Shape-based Segmentation of Tomatoes for Agriculture Monitoring},
booktitle={Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2014},
pages={402-411},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004818804020411},
isbn={978-989-758-018-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Shape-based Segmentation of Tomatoes for Agriculture Monitoring
SN - 978-989-758-018-5
AU - Verma U.
AU - Rossant F.
AU - Bloch I.
AU - Orensanz J.
AU - Boisgontier D.
PY - 2014
SP - 402
EP - 411
DO - 10.5220/0004818804020411