Sistema Inteligente para el manejo de Malezas en el cultivo de Piña con Conceptos de Agricultura de Precisión

Resumen

La agricultura de precisión busca la aplicación de insumos en cultivos agrícolas en el lugar, el momento y la cantidad adecuados. El manejo de malezas específico del sitio es una estrategia de agricultura de precisión que permite la reducción en la aplicación de herbicidas, minimizando costos de insumos, con efectos positivos para el medioambiente. El objetivo de este artículo es mostrar los avances en el desarrollo de un sistema inteligente para la detección de malezas y aplicación de herbicida en un cultivo de piña con conceptos de agricultura de precisión. El prototipo utiliza un sistema de visión artificial para la adquisición de la reflectancia en las plantas en el espectro del visible y un sistema embebido que permite el procesamiento de las imágenes en tiempo real como mecanismo de detección de maleza. El prototipo cuenta con un sistema de fumigación automático, el cual emula la aplicación del herbicida selectivo; lo que en conjunto es implementado sobre un vehículo terrestre que realiza su recorrido entre los surcos de un cultivo de piña. El algoritmo de detección de malezas para el cultivo de piña tuvo una eficiencia de más del 80 %, obteniendo así resultados satisfactorios y el cumplimiento de requerimiento para la detección y aplicación de insumo solo en los lugares en donde se necesita.

Palabras clave

Agricultura de Precisión, cultivo agrícola, malezas, sistema de detección, piña

Biografía del autor/a

ANDRÉS FERNANDO JIMÉNEZ LÓPEZ

Ingeniero electrónico de la Universidad Pedagógica y Tecnológica de Colombia. Magister en Ciencias Básicas - Física de la Universidad Nacional de Colombia. Actualmente soy docente de planta de la Universidad de los Llanos, del Departamento de Matemáticas y Física de la Facultad de Ciencias Básicas e Ingeniería.

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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