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.
Keywords
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
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)
Gée, C., Bossu, J., Jones, G., Truchetet, F.: Crop/weed discrimination in perspective agronomic images. Comput. Electron. Agric. 60(1), 49–59 (2008)
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
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)
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)
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)
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
Vidovic, I., Cupec, R., Hocenski, Z.: Crop row detection by global energy minimization. Pattern Recogn. 55, 68–86 (2016)
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
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)
MathWorks, Inc. (2015). http://www.mathworks.com/products/new_products/release2015a.html
Sogaard, H.T., Olsen, H.J.: Determination of crop rows by image analysis without segmentation. Comput. Electron. Agric. 38(2), 141–158 (2003)
Otsu, N.: Threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9(1), 62–66 (1979)
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)
Hough, P.: Method and means for recognizing complex patterns. Patente 3069654 (1962)
Gonzalez, R., Woods, R.: Digital Image Processing, 3rd edn. Pearson/Prentice Hall, Upper Saddle River (2010)
Maltsev, A.I.: Weed Vegetation of the USSR and Measures of its Control. Selkhozizdat, Leningrad-Moscow (1962). (in Russian)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-319-73450-7_34
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-73449-1
Online ISBN: 978-3-319-73450-7
eBook Packages: EngineeringEngineering (R0)