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Curved and straight crop row detection by accumulation of green pixels from images in maize fields

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Abstract

This study proposes a new method for detecting curved and straight crop rows in images captured in maize fields during the initial growth stages of crop and weed plants. The images were obtained under perspective projection with a camera installed onboard and conveniently arranged at the front of a tractor. The final goal was the identification of the crop rows which are crucial for precise autonomous guidance and site-specific treatments, including weed removal based on the identification of plants outside the crop rows. Image quality is affected by uncontrolled lighting conditions in outdoor agricultural environments and by gaps in the crop rows (due to lack of germination or defects during planting). Also, different plants heights and volumes occur due to different growth stages affecting the crop row detection process. The proposed method was designed with the required robustness to cope with the above undesirable situations and it consists of three sequentially linked phases: (i) image segmentation, (ii) identification of starting points and (iii) crop row detection. The main contribution is the ability of the method to detect curved crop rows as well as straights rows even with irregular inter-row spaces. The method performance has been tested in terms of accuracy and time processing.

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Acknowledgements

The research leading to these results has been funded by Universidad Politécnica Estatal del Carchi (Ecuador). Also, part of the research has been inspired on the RHEA project funded by the European Union Seventh Framework Programme [FP7/2007-2013] under Grant Agreement nº 245986 in the Theme NMP-2009-3.4-1 (Automation and robotics for sustainable crop and forestry management). Thanks are due to the anonymous referees and editor for their very valuable and detailed comments and suggestions.

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Authors and Affiliations

  1. Department of Software Engineering and Artificial Intelligence, Faculty of Informatics, Complutense University, Madrid, Spain

    Iván García-Santillán, José Miguel Guerrero, Martín Montalvo & Gonzalo Pajares

  2. Department of Computer Science, Universidad Politécnica Estatal del Carchi, Tulcán, Ecuador

    Iván García-Santillán

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  1. Iván García-Santillán
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  2. José Miguel Guerrero
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Correspondence to Iván García-Santillán.

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García-Santillán, I., Guerrero, J.M., Montalvo, M. et al. Curved and straight crop row detection by accumulation of green pixels from images in maize fields. Precision Agric 19, 18–41 (2018). https://doi.org/10.1007/s11119-016-9494-1

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