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Crop Row Detection in Maize for Developing Navigation Algorithms Under Changing Plant Growth Stages

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Abstract

To develop robust algorithms for agricultural navigation, different growth stages of the plants have to be considered. For fast validation and repeatable testing of algorithms, a dataset was recorded by a 4 wheeled robot, equipped with a frame of different sensors and was guided through maize rows. The robot position was simultaneously tracked by a total station, to get precise reference of the sensor data. The plant position and parameters were measured for comparing the sensor values. A horizontal laser scanner and corresponding total station data was recorded for 7 times over a period of 6 weeks. It was used to check the performance of a common RANSAC row algorithm. Results showed the best heading detection at a mean growth height of 0.268 m.

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References

  1. English, A., Ross, P., Ball, D., Corke, P.: Vision based guidance for robot navigation in agriculture. In: IEEE International Conference on Robotics and Automation (ICRA). pp. 1693–1698 (2014)

    Google Scholar 

  2. Marchant, J., Brivot, R.: Real-Time Tracking of Plant Rows Using a Hough Transform. Real-Time Imaging 1, 363–371 (1995)

    Article  Google Scholar 

  3. Jiang, G., Zhao, C., Si, Y.: A machine vision based crop rows detection for agricultural robots. In: Proceedings of the 2010 International Conference on Wavelet Analysis and Pattern Recognition, pp. 11–14 (2010)

    Google Scholar 

  4. Hansen, S., Bayramoglu, E., Andersen, J.C., Ravn, O., Andersen, N.A., Poulsen, N.K.: Derivative free Kalman filtering used for orchard navigation. In: 13th international Conference on Information Fusion (2010)

    Google Scholar 

  5. Barawid, O.C., Mizushima, A., Ishii, K., Noguchi, N.: Development of an Autonomous Navigation System using a Two-dimensional Laser Scanner in an Orchard Application. Biosyst. Eng. 96, 139–149 (2007)

    Article  Google Scholar 

  6. Hiremath, S.A., van der Heijden, G.W.A.M., van Evert, F.K., Stein, A., ter Braak, C.J.F.: Laser range finder model for autonomous navigation of a robot in a maize field using a particle filter. Comput. Electron. Agric. 100, 41–50 (2014)

    Article  Google Scholar 

  7. Papari, G., Petkov, N.: Edge and line oriented contour detection: State of the art. Image Vis. Comput. 29, 79–103 (2011)

    Article  Google Scholar 

  8. Russell, S.J., Norvig, P.: Artificial Intelligence: A modern approach. Ptrentice-Hall, Englewood Cliffs (1995)

    MATH  Google Scholar 

  9. Weiss, U., Biber, P.: Plant detection and mapping for agricultural robots using a 3D LIDAR sensor. Rob. Auton. Syst. 59, 265–273 (2011)

    Article  Google Scholar 

  10. Bochtis, D., Griepentrog, H.W., Vougioukas, S., Busato, P., Berruto, R., Zhou, K.: Route planning for orchard operations. Comput. Electron. Agric. 113, 51–60 (2015)

    Article  Google Scholar 

  11. Fischler, M.A., Bolles, R.C.: Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography. Commun. ACM 24, 381–395 (1981)

    Article  MathSciNet  Google Scholar 

  12. Choi, S., Park, J., Byun, J., Yu, W.: Robust ground plane detection from 3D point clouds. In: 14th International Conference on Control, Automation and Systems (ICCAS 2014), pp. 1076–1081 (2014)

    Google Scholar 

  13. Weiss, U., Biber, P., Laible, S., Bohlmann, K., Zell, A.: Plant species classification using a 3D LIDAR sensor and machine learning. In: Proc. - 9th Int. Conf. Mach. Learn. Appl. ICMLA 2010, pp. 339–345 (2010)

    Google Scholar 

  14. Zhang, J., Maeta, S., Bergerman, M., Singh, S.: Mapping orchards for autonomous navigation. In: ASABE Annual International Meeting, pp. 1–9 (2014)

    Google Scholar 

  15. Marden, S., Whitty, M.: GPS-free localisation and navigation of an unmanned ground vehicle for yield forecasting in a vineyard. In: Proceedings of the 13th International Conference IAS-13 (2014)

    Google Scholar 

  16. Rusu, R.B., Cousins, S.: 3D is here: point cloud library. In: IEEE Int. Conf. Robot. Autom., pp. 1–4 (2011)

    Google Scholar 

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Correspondence to David Reiser .

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Reiser, D., Miguel, G., Arellano, M.V., Griepentrog, H.W., Paraforos, D.S. (2016). Crop Row Detection in Maize for Developing Navigation Algorithms Under Changing Plant Growth Stages. In: Reis, L., Moreira, A., Lima, P., Montano, L., Muñoz-Martinez, V. (eds) Robot 2015: Second Iberian Robotics Conference. Advances in Intelligent Systems and Computing, vol 417. Springer, Cham. https://doi.org/10.1007/978-3-319-27146-0_29

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  • DOI: https://doi.org/10.1007/978-3-319-27146-0_29

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