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Computer Vision-Based Method for Automatic Detection of Crop Rows in Potato Fields

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Proceedings of the International Conference on Information Technology & Systems (ICITS 2018) (ICITS 2018)

Abstract

This work presents an adaptation and validation of a method for automatic crop row detection from images captured in potato fields (Solanum tuberosum) for initial growth stages based on the micro-ROI concept. The crop row detection is a crucial aspect for autonomous guidance of agricultural vehicles and site-specific treatments application. The images were obtained using a color camera installed in the front of a tractor under perspective projection. There are some issues that can affect the quality of the images and the detection procedure, among them: uncontrolled illumination in outdoor agricultural environments, different plant densities, presence of weeds and gaps in the crop rows. The adapted approach was designed to address these adverse situations and it consists of three linked phases. The main contribution is the ability to detect straight and curved crop rows in potato crops. The performance was quantitatively compared against two existing methods, achieving acceptable results in terms of accuracy and processing time.

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References

  1. Gonzalez-de-Santos, P., Ribeiro, A. (eds.): Proceedings of the Second International Conference on Robotics and Associated High-Technologies And Equipment for Agriculture and Forestry: New Trends in Mobile Robotics, Perception and Actuation for Agriculture and Forestry, RHEA (2014)

    Google Scholar 

  2. Gée, C., Bossu, J., Jones, G., Truchetet, F.: Crop/weed discrimination in perspective agronomic images. Comput. Electron. Agric. 60(1), 49–59 (2008)

    Article  Google Scholar 

  3. Emmi, L., Gonzalez-de-Soto, M., Pajares, G., Gonzalez-de-Santos, P.: New trends in robotics for agriculture: integration and assessment of a real fleet of robots. Sci. World J. 2014, 21 pages (2014). Article ID 404059

    Google Scholar 

  4. Rovira-Más, F., Zhang, Q., Reid, J.F., Will, J.D.: Machine vision based automated tractor guidance. Int. J. Smart Eng. Syst. Des. 5(4), 467–480 (2003)

    Article  Google Scholar 

  5. Montalvo, M., Pajares, G., Guerrero, J.M., Romeo, J., Guijarro, M., Ribeiro, A., Cruz, J.M.: Automatic detection of crop rows in maize fields with high weeds pressure. Expert Syst. Appl. 39(15), 11889–11897 (2012)

    Article  Google Scholar 

  6. Guerrero, J.M., Guijarro, M., Montalvo, M., Romeo, J., Emmi, L., Ribeiro, A., Pajares, G.: Automatic expert system based on images for accuracy crop row detection in maize fields. Expert Syst. Appl. 40(2), 656–664 (2013)

    Article  Google Scholar 

  7. García-Santillán, I., Guerrero, J., Montalvo, M., Pajares, G.: Curved and straight crop row detection by accumulation of green pixels from images in maize fields. Precision Agriculture (2017). https://doi.org/10.1007/s11119-016-9494-1

  8. Vidovic, I., Cupec, R., Hocenski, Z.: Crop row detection by global energy minimization. Pattern Recogn. 55, 68–86 (2016)

    Article  Google Scholar 

  9. García-Santillán, I., Montalvo, M., Guerrero, M., Pajares, G.: Automatic detection of curved and straight crop rows from images in maize fields. Biosyst. Eng. 156, 61–79 (2017). https://doi.org/10.1016/j.biosystemseng.2017.01.013

    Article  Google Scholar 

  10. Pajares, G., García-Santillán, I., Campos, Y., Montalvo, M., Guerrero, J.M., Emmi, L., et al.: Machine-vision systems selection for agricultural vehicles: a guide. J. Imag. 2, 34 (2016)

    Article  Google Scholar 

  11. MathWorks, Inc. (2015). http://www.mathworks.com/products/new_products/release2015a.html

  12. Sogaard, H.T., Olsen, H.J.: Determination of crop rows by image analysis without segmentation. Comput. Electron. Agric. 38(2), 141–158 (2003)

    Article  Google Scholar 

  13. Otsu, N.: Threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9(1), 62–66 (1979)

    Article  Google Scholar 

  14. Onyango, C.M., Marchant, J.A.: Segmentation of row crop plants from weeds using colour and morphology. Comput. Electron. Agric. 39(3), 141–155 (2003)

    Article  Google Scholar 

  15. Hough, P.: Method and means for recognizing complex patterns. Patente 3069654 (1962)

    Google Scholar 

  16. Gonzalez, R., Woods, R.: Digital Image Processing, 3rd edn. Pearson/Prentice Hall, Upper Saddle River (2010)

    Google Scholar 

  17. Maltsev, A.I.: Weed Vegetation of the USSR and Measures of its Control. Selkhozizdat, Leningrad-Moscow (1962). (in Russian)

    Google Scholar 

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Correspondence to Iván García-Santillán .

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García-Santillán, I., Peluffo-Ordoñez, D., Caranqui, V., Pusdá, M., Garrido, F., Granda, P. (2018). Computer Vision-Based Method for Automatic Detection of Crop Rows in Potato Fields. In: Rocha, Á., Guarda, T. (eds) Proceedings of the International Conference on Information Technology & Systems (ICITS 2018). ICITS 2018. Advances in Intelligent Systems and Computing, vol 721. Springer, Cham. https://doi.org/10.1007/978-3-319-73450-7_34

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  • DOI: https://doi.org/10.1007/978-3-319-73450-7_34

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-73449-1

  • Online ISBN: 978-3-319-73450-7

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