Intelligent system for weeds management in pineapple crop with precision agriculture concepts

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Autores

ANDRÉS FERNANDO JIMÉNEZ LÓPEZ https://orcid.org/0000-0001-8308-7815
DIANA ANDREA CAMARGO PICO
Dayra Yisel García Ramírez https://orcid.org/0000-0002-2501-4842

Abstract

The aim of precision agriculture is to apply agricultural inputs in the right place, time and amount. The site-specific weed management is a precision agriculture strategy that allows reducing the herbicide application, minimizing inputs costs, with positive effects for the environment. The objective of this paper is to show the advances in the development of weed detection and herbicide application system for a pineapple crop, using precision agriculture concepts. The prototype uses an artificial vision system for acquisition of reflectance in plants in the visible spectrum and an embedded system that allows the image processing in real-time as a weed detection mechanism. The prototype has an automatic fumigation system, that emulates the selective herbicide application, which together is implemented above a terrestrial vehicle that travels into the pineapple crop rows. The weed detection algorithm for pineapple had an efficiency major than 80%, obtaining satisfactory outcomes and the fulfilment of the requirements for the weed detection and input application only in the places where it is necessary.


 

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This work is licensed under a Creative Commons Attribution 4.0 International License.

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All papers included in the Revista Ciencia y Agricultura are published under  Creative Commons Attribution 4.0 International

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Utstumo, T., Urdal, F., Brevik, A., Dørum, J., Netland, J., Overskeid, Ø. & Gravdahl, J. T. (2018). Robotic In-Row Weed Control in Vegetables. Computers and Electronics in Agriculture, 154, 36-45. https://doi.org/10.1016/j.compag.2018.08.043

Wang, A., Zhang, W. & Wei, X. (2019). A review on weed detection using ground-based machine vision and image processing techniques. Computers and Electronics in Agriculture, 158, 226-240. https://doi.org/10.1016/j.compag.2019.02.005

Wagstaff, K. L. & Liu, G. Z. (2018). Automated Classification to Improve the Efficiency of Weeding Library Collections. The Journal of Academic Librarianship, 44(2), 238-247. https://doi.org/10.1016/j.acalib.2018.02.001

Weatherspark.com (2019). [online] https://weatherspark.com/y/24273/Average-Weather-in-Villavicencio-Colombia-Year-Round

Yang, C. C., Prasher, S. O., Landry, J. A. & Ramaswamy, H. S. (2003). Development of a Herbicide Application Map Using Artificial Neural Networks and Fuzzy Logic. Agricultural Systems, 76(2), 561-574. https://doi.org/10.1016/S0308-521X(01)00106-8

Zhang, W. & Wei, X. (2019). A Review on Weed Detection Using Ground-Based Machine Vision and Image Processing Techniques. Computers and Electronics in Agriculture, 158, 226-240. https://doi.org/10.1016/j.compag.2019.02.005

Zheng, Y., Zhu, Q., Huang, M., Guo, Y. & Qin, J. (2017). Maize and Weed Classification Using Color Indices with Support Vector Data Description in Outdoor Fields. Computers and Electronics in Agriculture, 141, 215-222. https://doi.org/10.1016/j.compag.2017.07.028

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