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A template-free machine vision-based crop row detection algorithm

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Abstract

Due to the increase in the use of precision agriculture, field trials have increased in size to allow for genomic selection tool development by linking quantitative phenotypic traits to sequence variations in the DNA of various crops. Crop row detection is an important step to enable the development of an efficient downstream analysis pipeline for genomic selection. In this paper, an efficient crop row detection algorithm was proposed that detected crop rows in colour images without the use of templates and most other pre-information such as number of rows and spacing between rows. The method only requires input on field weed intensity. The algorithm was robust in challenging field trial conditions such as variable light, sudden shadows, poor illumination, presence of weeds and noise and irregular crop shape. The algorithm can be applied to crop images taken from the top and side views. The algorithm was tested on a public dataset with side view images of crop rows and on Genomic Sub-Selection dataset in which images were taken from the top view. Different analyses were performed to check the robustness of the algorithm and to the best of authors’ knowledge, the Receiver Operating Characteristic graph has been applied for the first time in crop row detection algorithm testing. Lastly, comparing this algorithm with several state-of-the-art methods, it exhibited superior performance.

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References

  • Asif, M., Amir, S., Israr, A., & Faraz, M. (2010). A vision system for autonomous weed detection robot. International Journal of Computer and Electrical Engineering, 2(3), 486–491.

    Article  Google Scholar 

  • Bah, M. D., Hafiane, A., & Canals, R. (2019). CRowNet: Deep network for crop row detection in UAV images. IEEE Access, 8, 5189–5200.

    Article  Google Scholar 

  • Bakker, T., Wouters, H., Asselt, K. V., Bontsema, J., Tang, L., Müller, J., et al. (2008). A vision-based row detection system for sugar beet. Computers and Electronics in Agriculture, 60(1), 87–95.

    Article  Google Scholar 

  • Basso, M., & de Freitas, E. P. (2019). A UAV guidance system using crop row detection and line follower algorithms. Journal of Intelligent & Robotic Systems, 97, 605–621.

    Article  Google Scholar 

  • Billingsley, J., & Schoenfisch, M. (1997). The successful development of a vision guidance system for agriculture. Computers and Electronics in Agriculture, 16(2), 147–163.

    Article  Google Scholar 

  • Burgos-Artizzu, X. P., Ribeiro, A., Guijarro, M., & Pajares, G. (2011). Real-time image processing for crop/weed discrimination in maize fields. Computers and Electronics in Agriculture, 75(2), 337–346.

    Article  Google Scholar 

  • Caldwell, D. (2012). Robotics and automation in the food industry. Current and future technologies (pp. 1–528). Cambridge, UK: Woodhead Publishing.

    Google Scholar 

  • Cervantes-Godoy, D., & Dewbre, J. (2010). Economic importance of agriculture for poverty reduction. OECD Food, Agriculture and Fisheries Working Papers, 23, 1–27.

    Google Scholar 

  • Cupec, R. (2018). Crop row benchmark dataset. http://www.etfos.unios.hr/r3dvgroup/index.php?id=crd_dataset/.

  • Fontaine, V., & Crowe, T. G. (2006). Development of line-detection algorithms for local positioning in densely seeded crops. Canadian Biosystems Engineering, 48(7), 19–29.

    Google Scholar 

  • García-Santillán, I., Guerrero, J. M., Montalvo, M., & Pajares, G. (2018). Curved and straight crop row detection by accumulation of green pixels from images in maize fields. Precision Agriculture, 19(1), 18–41.

    Article  Google Scholar 

  • Gonzalez, R. C., & Woods, R. E. (2018). Digital image processing, global edition. New York City, USA: Pearson, ISBN-10: 9780133356724$4.

  • Hague, T., Tillett, N. D., & Wheeler, H. (2006). Automated crop and weed monitoring in widely spaced cereals. Precision Agriculture, 7(1), 21–32.

    Article  Google Scholar 

  • Hough, P. V. C. (1960). A method and means for recognizing complex patterns. US Patent, Office No. 3069654.

  • Ji, R., & Qi, L. (2011). Crop-row detection algorithm based on Random Hough Transformation. Mathematical and Computer Modelling, 54(3–4), 1016–1020.

    Article  Google Scholar 

  • Jiang, G., Wang, Z., & Liu, H. (2015). Automatic detection of crop rows based on multi-ROIs. Expert Systems with Applications, 42(5), 2429–2441.

    Article  Google Scholar 

  • Jiang, G., Wang, X., Wang, Z., & Liu, H. (2016). Wheat rows detection at the early growth stage based on Hough transform and vanishing point. Computers and Electronics in Agriculture, 123, 211–223.

    Article  Google Scholar 

  • Jones, G., Gée, C., & Truchetet, F. (2009). Modelling agronomic images for weed detection and comparison of crop/weed discrimination algorithm performance. Precision Agriculture, 10(1), 1–15.

    Article  Google Scholar 

  • Katariya, S. S., Gundal, S. S., Kanawade, M. T., & Mazhar, K. (2015). Automation in agriculture. International Journal of Recent Scientific Research, 6(6), 4453–4456.

    Google Scholar 

  • Kise, M., Zhang, Q., & Más, F. R. (2005). A stereovision-based crop row detection method for tractor-automated guidance. Biosystems Engineering, 90(4), 357–367.

    Article  Google Scholar 

  • Kise, M., & Zhang, Q. (2008). Development of a stereovision sensing system for 3D crop row structure mapping and tractor guidance. Biosystems Engineering, 101(2), 191–198.

    Article  Google Scholar 

  • Meuwissen, T. H., Hayes, B. J., & Goddard, M. E. (2001). Prediction of total genetic value using genome-wide dense marker maps. Genetics, 157(4), 1819–1847.

    CAS  PubMed  PubMed Central  Google Scholar 

  • Montalvo, M., Pajares, G., Guerrero, J. M., Romeo, J., Guijarro, M., Ribeiro, A., et al. (2012). Automatic detection of crop rows in maize fields with high weeds pressure. Expert Systems with Applications, 39(15), 11889–11897.

    Article  Google Scholar 

  • Otsu, N. (1979). A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man, and Cybernetics, 9(1), 62–66.

    Article  Google Scholar 

  • Pajares, G., Santillán, I. G., Campos, Y., Montalvo, M., Guerrero, J. M., Emmi, L., et al. (2016). Machine-vision systems selection for agricultural vehicles: A guide. Journal of Imaging, 2(4), 1–31.

    Article  Google Scholar 

  • Ramesh, K. N., Chandrika, N., Omkar, S. N., Meenavathi, M. B., & Rekha, V. (2016). Detection of rows in agricultural crop images acquired by remote sensing from a UAV. International Journal of Image, Graphics and Signal Processing, 8(11), 25–31.

    Article  Google Scholar 

  • Romeo, J., Pajares, G., Montalvo, M., Guerrero, J. M., Guijarro, M., & Ribeiro, A. (2012). Crop row detection in maize fields inspired on the human visual perception. The Scientific World Journal, 2012, 1–10.

    Article  Google Scholar 

  • Rovira-Más, F., Zhang, Q., & Reid, J. F. (2008). Stereo vision three-dimensional terrain maps for precision agriculture. Computers and Electronics in Agriculture, 60(2), 133–143.

    Article  Google Scholar 

  • Sogaard, H. T., & Olsen, H. J. (2003). Determination of crop rows by image analysis without segmentation. Computers and Electronics in Agriculture, 38, 141–158.

    Article  Google Scholar 

  • Vidović, I., Cupec, R., & Hocenski, Ž. (2016). Crop row detection by global energy minimization. Pattern Recognition, 55, 68–86.

    Article  Google Scholar 

  • Visscher, P. M., Wray, N. R., Zhang, Q., Sklar, P., McCarthy, M. I., Brown, M. A., et al. (2017). 10 years of GWAS discovery: Biology, function, and translation. The American Journal of Human Genetics, 101(1), 5–22.

    Article  CAS  PubMed  Google Scholar 

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Authors and Affiliations

  1. School of Applied Systems Biology, La Trobe University, Bundoora, Australia

    Saba Rabab & Hans D. Daetwyler

  2. Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, Australia

    Saba Rabab & Hans D. Daetwyler

  3. Agriculture Victoria, Hamilton Centre, Hamilton, Australia

    Pieter Badenhorst

  4. Department of Computer Science and Information Technology, La Trobe University, Bundoora, Australia

    Yi-Ping Phoebe Chen

Authors
  1. Saba Rabab
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  2. Pieter Badenhorst
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  3. Yi-Ping Phoebe Chen
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  4. Hans D. Daetwyler
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Correspondence to Saba Rabab.

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Rabab, S., Badenhorst, P., Chen, YP.P. et al. A template-free machine vision-based crop row detection algorithm. Precision Agric 22, 124–153 (2021). https://doi.org/10.1007/s11119-020-09732-4

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  • DOI: https://doi.org/10.1007/s11119-020-09732-4

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