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Towards a Smart Farming Platform: From IoT-Based Crop Sensing to Data Analytics

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Cadavid, Héctor
Garzón, Wilmer
López, Germán
Mandivelso, Cristian
Ramírez, Carlos
Pérez Ruiz, Alexander
Abstract (Spanish)
Colombia es un país con un enorme potencial agrícola, gracias a su tamaño y diversidad geográfica. Desgraciadamente, está lejos de utilizarla de manera eficiente: el 65% de sus tierras agrícolas no se utilizan o están infrautilizadas debido a problemas políticos. Además, gran parte de la agricultura colombiana se caracteriza -en comparación con otros países- por bajos niveles de productividad, debido a la falta de buenas prácticas y tecnologías agrícolas. El nuevo marco político creado por el acuerdo de paz recientemente firmado en este país abre nuevas oportunidades para incrementar su vocación agrícola. Sin embargo, todavía queda mucho trabajo por hacer en este país para mejorar la sinergia entre la academia, la industria, los expertos agrícolas y los agricultores para mejorar la productividad en este campo. Los avances en tecnologías de agricultura inteligente, como la teledetección (RS), el Internet de las cosas (IoT), los macrodatos/análisis de datos y los sistemas de información geográfica (SIG), brindan una gran oportunidad para contribuir a dicha sinergia. Estas tecnologías permiten no sólo recopilar y analizar datos directamente de los cultivos en tiempo real, sino extraer nuevos conocimientos de ellos. Además, este nuevo conocimiento, combinado con el conocimiento de expertos locales, podría convertirse en el núcleo de futuras herramientas de asistencia técnica y sistemas de apoyo a la toma de decisiones para países con una gran variedad de suelos y fondos tropicales como Colombia. Motivado por estos problemas, este artículo propone una extensión de Thingsboard, una popular plataforma de IoT de código abierto. Esta versión ampliada pretende ser el núcleo de una plataforma de Smart Farming basada en la nube que concentrará sensores, un sistema de soporte de decisiones y una configuración de dispositivos autónomos y controlados remotamente (por ejemplo, dispensadores de agua, rovers o drones). La arquitectura de la plataforma se describe en detalle y luego se muestra en un escenario con sensores simulados. En tal escenario, las alertas tempranas de un patógeno vegetal importante en Colombia se generan mediante análisis de datos y, en consecuencia, se envían acciones en dispositivos de terceros.
Abstract (English)
Colombia is a country with a huge agricultural potential, thanks to its size and geography diversity. Unfortunately, it is far from using it efficiently: 65% of its farmland is either unused or underused due to political problems. Furthermore, vast of Colombian agriculture is characterized- when compared with other countries- by low levels of productivity, due to the lack of good farming practices and technologies. The new political framework created by the recently signed peace agreement in this country opens new opportunities to increase its agricultural vocation. However, a lot of work is still required in this country to improve the synergy between academia, industry, agricultural experts, and farmers towards improving productivity in this field. Advances in smart-farming technologies such as Remote Sensing (RS), Internet of Things (IoT), Big Data/Data Analytics and Geographic Information Systems (GIS), bring a great opportunity to contribute to such synergy. These technologies allow not only to collect and analyze data directly from the crops in real time, but to extract new knowledge from it. Furthermore, this new knowledge, combined with the knowledge of local experts, could become the core of future technical assistance and decision support systems tools for countries with a great variety of soils and tropical floors such as Colombia. Motivated by these issues, this paper proposes an extension to Thingsboard, a popular open-source IoT platform. This extended version aims to be the core of a cloud-based Smart Farming platform that will concentrate sensors, a decision support system, and a configuration of remotely controlled and autonomous devices (e.g. water dispensers, rovers or drones). The architecture of the platform is described in detail and then showcased in a scenario with simulated sensors. In such scenario early warnings of an important plant pathogen in Colombia are generated by data analytics, and actions on third-party devices are dispatched in consequence.
Extent
15 páginas
URI: https://repositorio.escuelaing.edu.co/handle/001/3174
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