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Postprint
This is the accepted version of a paper presented at The 5th Israeli Conference on Robotics 2016, Air Force Conference Center Hertzilya, Israel, 13-14 April, 2016.
Citation for the original published paper:
Kurtser, P., Arad, B., Ben-Shahar, O., van Bree, M., Moonen, J. et al. (2016) Robotic data acquisition of sweet pepper images for research and development In:
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Robotic Data Acquisition of Sweet Pepper Images for Research and Development
Polina Kurtser1*, Boaz Arad2, Ohad Ben Shahar2, Milan van Bree3, Joep Moonen3 , Bart van Tujil4, Yael Edan1
1Department of Industrial Engineering and Management, Ben-Gurion University of the Negev, Beer Sheva, Israel * kurtser@post.bgu.ac.il
2 Department of Computer Science, Ben-Gurion University of the Negev, Beer Sheva, Israel 3 Irmato Industrial Solutions Veghel B.V., Veghel, The Netherlands
4Wageningen UR Greenhouse Horticulture, P.O. Box 644, 6700 AP Wageningen, The Netherlands
A main problem limiting the development of robotic harvesters is robust fruit detection [5]. Despite intensive research conducted in identifying the fruits and their location [2,3], current fruit detection algorithms have a limited detection rate of 0.87 which is unfeasible from an economic perspective [5]. The complexity of the fruit detection task is due to the unstructured and dynamic nature of both the objects and the environment [4-6]: the fruit have inherent high variability in size, shape, texture, and location; occlusion and variable illumination conditions significantly influence the detection performance[3].
A common practice for image processing R&D for complicated problems is the acquisition of a large database (e.g., Labelme open source labeling database [1], Oxford building dataset [2]). These datasets enable to advance vision algorithms development [7] and provide a benchmark for evaluating new algorithms. To the best of our knowledge, to date there is no open dataset available for R&D in image processing of agricultural objects. Evaluation of previously reported algorithms was based on limited data [5]. Previous research indicated the importance of evaluating algorithms for a wide range of sensory, crop, and environmental conditions [5].
A robotic acquisition system and procedure was developed using a 6 degree of freedom manipulator, equipped with 3 different sensors to automatically acquire images from several viewpoints with different sensors and illumination conditions. Measurements were conducted along the day and at night in a commercial greenhouse and resulted in a total of 1764 images from 14 viewpoints for each scene. Additionally, drawbacks and advantages of the proposed approach as compared to other approaches previously utilized will be discussed along with recommendations for future acquisitions.
References:
[1] Russell, B. C., Torralba, A., Murphy, K. P., & Freeman, W. T. (2008). LabelMe: a database and web-based tool for image annotation. International Journal of Computer Vision, 77(1-3), 157-173.
[2] Philbin, J., Chum, O. , Isard, M. , Sivic, J. and Zisserman, A. Object retrieval with large vocabularies and fast spatial matching. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2007 [3] Hemming, J., Ruizendaal, J., Hofstee, J. W., & van Henten, E. J. (2014). Fruit detectability analysis for different
camera positions in sweet-pepper. Sensors,14(4), 6032-6044.
[4] Bac C.W., Hemming J., van Henten E.J., Stem localization of sweet-pepper plants using the support wire as a visual cue, Computers and Electronics in Agriculture, Volume 105, July 2014, Pages 111-120, ISSN 0168-1699 [5] Bac, C.W., Henten, E.J. van, Hemming, J., Edan, Y. 2014. Harvesting robots for high-value crops:
state-of-the-art review and challenges ahead. Journal of Field Robotics 31(6): 888–911.
[6] Kapach K., Barnea E., Mairon R., Edan Y., BenShahar O. 2012. Computer Vision for Fruit Harvesting Robots -State of the Art and Challenges Ahead. Int. J. of Computational Vision and Robotics 3(1/2): 4-34.
[7] Szeliski, R. Computer vision: algorithms and applications. (2010). Springer Science & Business Media Acknowledgements
This research was supported by the European Commission (SWEEPER GA no 644313) and partially supported by the Helmsley Charitable Trust through the Agricultural, Biological and Cognitive Robotics Center and by the Rabbi W. Gunther Plaut Chair in Manufacturing Engineering, both at Ben-Gurion University of the Negev.
