Towards a Smart Farming Platform: From IoT-Based Crop Sensing to Data Analytics
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 co...
- Autores:
-
Cadavid, Héctor
Garzón, Wilmer
Pérez, Alexander
López, Germán
Mendivelso, Cristian
Ramírez, Carlos
- Tipo de recurso:
- Article of investigation
- Fecha de publicación:
- 2018
- Institución:
- Escuela Colombiana de Ingeniería Julio Garavito
- Repositorio:
- Repositorio Institucional ECI
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.escuelaing.edu.co:001/1802
- Acceso en línea:
- https://repositorio.escuelaing.edu.co/handle/001/1802
- Palabra clave:
- Agricultura - Colombia
Agricultura inteligente
Análisis de datos
Agricultura - aspectos tecnológicos
Smart farming
Data analytics
Precision agriculture
IoT
- Rights
- closedAccess
- License
- © Springer Nature Switzerland AG 2018
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Towards a Smart Farming Platform: From IoT-Based Crop Sensing to Data Analytics |
| title |
Towards a Smart Farming Platform: From IoT-Based Crop Sensing to Data Analytics |
| spellingShingle |
Towards a Smart Farming Platform: From IoT-Based Crop Sensing to Data Analytics Agricultura - Colombia Agricultura inteligente Análisis de datos Agricultura - aspectos tecnológicos Smart farming Data analytics Precision agriculture IoT |
| title_short |
Towards a Smart Farming Platform: From IoT-Based Crop Sensing to Data Analytics |
| title_full |
Towards a Smart Farming Platform: From IoT-Based Crop Sensing to Data Analytics |
| title_fullStr |
Towards a Smart Farming Platform: From IoT-Based Crop Sensing to Data Analytics |
| title_full_unstemmed |
Towards a Smart Farming Platform: From IoT-Based Crop Sensing to Data Analytics |
| title_sort |
Towards a Smart Farming Platform: From IoT-Based Crop Sensing to Data Analytics |
| dc.creator.fl_str_mv |
Cadavid, Héctor Garzón, Wilmer Pérez, Alexander López, Germán Mendivelso, Cristian Ramírez, Carlos |
| dc.contributor.author.none.fl_str_mv |
Cadavid, Héctor Garzón, Wilmer Pérez, Alexander López, Germán Mendivelso, Cristian Ramírez, Carlos |
| dc.contributor.researchgroup.spa.fl_str_mv |
Informática |
| dc.subject.armarc.spa.fl_str_mv |
Agricultura - Colombia Agricultura inteligente Análisis de datos Agricultura - aspectos tecnológicos |
| topic |
Agricultura - Colombia Agricultura inteligente Análisis de datos Agricultura - aspectos tecnológicos Smart farming Data analytics Precision agriculture IoT |
| dc.subject.proposal.eng.fl_str_mv |
Smart farming Data analytics Precision agriculture IoT |
| description |
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. |
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2018 |
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2018 |
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2021-11-04T22:01:01Z |
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2021-11-04T22:01:01Z |
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Artículo de revista |
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CCIS;Vol. 885 |
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Ahmed, E., et al.: The role of big data analytics in internet of things. Comput. Netw. 129, 459–471 (2017) Alvarez Villada, D.M., Estrada Iza, M., Cock, J.H.: Rasta rapid soil and terrain assessment: Guía práctica para la caracterización del suelo y del terreno (2010) Bashir, M.R., Gill, A.Q.: Towards an IoT big data analytics framework: smart buildings systems. In: 2016 IEEE 18th International Conference on IEEE 2nd International Conference on Data Science and Systems (HPCC/SmartCity/DSS), pp. 1325–1332. IEEE (2016) Bonér, J., Klang, V., Kuhn, R., et al. Bruinsma, J.: World Agriculture: Towards 2015/2030: An FAO Study. Routledge, London (2017) Cadavid, H., Pérez, A., Rocha, C.: Reliable control architecture with PLEXIL and ROS for autonomous wheeled robots. In: Solano, A., Ordoñez, H. (eds.) CCC 2017. CCIS, vol. 735, pp. 611–626. Springer, Cham (2017). Espana, V.A.A., Pinilla, A.R.R., Bardos, P., Naidu, R.: Contaminated land in colombia: a critical review of current status and future approach for the management of contaminated sites. Sci. Total Environ. 618, 199–209 (2018) Fry, W., et al.: Five reasons to consider Phytophthora infestans a reemerging pathogen. Phytopathology 105(7), 966–981 (2015) Hewitt, C., Bishop, P., Steiger, R.: A universal modular actor formalism for artificial intelligence. In: Proceedings of the 3rd International Joint Conference on Artificial Intelligence, pp. 235–245. Morgan Kaufmann Publishers Inc. (1973) Iglesias, I., Escuredo, O., Seijo, C., Méndez, J.: Phytophthora infestans prediction for a potato crop. Am. J. Potato Res. 87(1), 32–40 (2010) awad, H.M., Nordin, R., Gharghan, S.K., Jawad, A.M., Ismail, M.: Energy-efficient wireless sensor networks for precision agriculture: a review. Sensors 17(8), 1781 (2017) Poole, J., Rae, B., González, L., Hsu, Y., Rutherford, I.: A world that counts: mobilising the data revolution for sustainable development. Technical report, Independent Expert Advisory Group on a Data Revolution for Sustainable Development, November 2014 Lasso, E., Corrales, J.C.: Towards an alert system for coffee diseases and pests in a smart farming approach based on semi-supervised learning and graph similarity. In: Angelov, P., Iglesias, J.A., Corrales, J.C. (eds.) AACC’17 2017. AISC, vol. 687, pp. 111–123. Springer, Cham (2018). Lasso, E., Valencia, O., Corrales, D.C., López, I.D., Figueroa, A., Corrales, J.C.: A cloud-based platform for decision making support in Colombian agriculture: a study case in coffee rust. In: Angelov, P., Iglesias, J.A., Corrales, J.C. (eds.) AACC’17 2017. AISC, vol. 687, pp. 182–196. Springer, Cham (2018). Nuthall, P.: Farm Business Management: Analysis of Farming Systems. Lincoln University, CABI (2011) International Federation of Organic Agriculture Movements (IFOAM): Best Practice Guideline for Agriculture and Value Chains. Sustainable Organic Agriculture Action Network/International Federation of Organic Agriculture Movements (IFOAM) (2013) Peisker, A., Dalai, S.: Data analytics for rural development. Indian J. Sci. Technol. 8(S4), 50–60 (2015) Sarangi, S., Umadikar, J., Kar, S.: Automation of agriculture support systems using wisekar: case study of a crop-disease advisory service. Comput. Electron. Agric. 122, 200–210 (2016) ThingsBoard. Thingsboard - open-source IoT platform (2018). Vasisht, D., et al.: Farmbeats: an IoT platform for data-driven agriculture. In: NSDI, pp. 515–529 (2017) Beulens, A.J., Reijers, H.A., van der Vorst, J.G., Verdouw, C.N.: A control model for object virtualization in supply chain management. Comput. Ind. 68, 116–131 (2015) |
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Cadavid, Héctor1419fa48babb695dddb738176b5abcb4600Garzón, Wilmer6b04a33a7db33dd5cc3491063fc48a95600Pérez, Alexander9993c6a7080229dbf901830e1398a2ff600López, Germáne4f4c662ab3a2f88a1d3ba97c5a053f8600Mendivelso, Cristian3fedf0d1fdee52268f3b762debbb808f600Ramírez, Carlos31ef7038cca40f1ae62b650e704561bc600Informática2021-11-04T22:01:01Z2021-11-04T22:01:01Z201897833199899839783319989976https://repositorio.escuelaing.edu.co/handle/001/1802Colombia 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.Colombia es un país con un enorme potencial agrícola, gracias a su extensión y diversidad geográfica. Desafortunadamente, está lejos de utilizarlo de manera eficiente: el 65% de sus tierras de cultivo 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 firmado recientemente en este país abre nuevas oportunidades para incrementar su vocación agrícola. Sin embargo, todavía se requiere mucho trabajo 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 detección remota (RS), Internet de las cosas (IoT), Big Data/Análisis de datos y sistemas de información geográfica (GIS), brindan una gran oportunidad para contribuir a dicha sinergia. Estas tecnologías permiten no solo 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 decisión para países con una gran variedad de suelos y pisos tropicales como Colombia. Motivado por estos problemas, este documento propone una extensión de Thingsboard, una popular plataforma IoT de código abierto. Esta versión extendida pretende ser el núcleo de una plataforma de agricultura inteligente basada en la nube que concentrará sensores, un sistema de soporte de decisiones y una configuración de dispositivos autónomos y controlados de forma remota (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, se generan alertas tempranas de un patógeno vegetal importante en Colombia mediante el análisis de datos y, en consecuencia, se envían acciones en dispositivos de terceros.15 páginas.application/pdfengSpringer NatureSwitzerland.CCIS;Vol. 885851237N/ACommunications in Computer and Information ScienceAhmed, E., et al.: The role of big data analytics in internet of things. Comput. Netw. 129, 459–471 (2017)Alvarez Villada, D.M., Estrada Iza, M., Cock, J.H.: Rasta rapid soil and terrain assessment: Guía práctica para la caracterización del suelo y del terreno (2010)Bashir, M.R., Gill, A.Q.: Towards an IoT big data analytics framework: smart buildings systems. In: 2016 IEEE 18th International Conference on IEEE 2nd International Conference on Data Science and Systems (HPCC/SmartCity/DSS), pp. 1325–1332. IEEE (2016)Bonér, J., Klang, V., Kuhn, R., et al.Bruinsma, J.: World Agriculture: Towards 2015/2030: An FAO Study. Routledge, London (2017)Cadavid, H., Pérez, A., Rocha, C.: Reliable control architecture with PLEXIL and ROS for autonomous wheeled robots. In: Solano, A., Ordoñez, H. (eds.) CCC 2017. CCIS, vol. 735, pp. 611–626. Springer, Cham (2017).Espana, V.A.A., Pinilla, A.R.R., Bardos, P., Naidu, R.: Contaminated land in colombia: a critical review of current status and future approach for the management of contaminated sites. Sci. Total Environ. 618, 199–209 (2018)Fry, W., et al.: Five reasons to consider Phytophthora infestans a reemerging pathogen. Phytopathology 105(7), 966–981 (2015)Hewitt, C., Bishop, P., Steiger, R.: A universal modular actor formalism for artificial intelligence. In: Proceedings of the 3rd International Joint Conference on Artificial Intelligence, pp. 235–245. Morgan Kaufmann Publishers Inc. (1973)Iglesias, I., Escuredo, O., Seijo, C., Méndez, J.: Phytophthora infestans prediction for a potato crop. Am. J. Potato Res. 87(1), 32–40 (2010)awad, H.M., Nordin, R., Gharghan, S.K., Jawad, A.M., Ismail, M.: Energy-efficient wireless sensor networks for precision agriculture: a review. Sensors 17(8), 1781 (2017)Poole, J., Rae, B., González, L., Hsu, Y., Rutherford, I.: A world that counts: mobilising the data revolution for sustainable development. Technical report, Independent Expert Advisory Group on a Data Revolution for Sustainable Development, November 2014Lasso, E., Corrales, J.C.: Towards an alert system for coffee diseases and pests in a smart farming approach based on semi-supervised learning and graph similarity. In: Angelov, P., Iglesias, J.A., Corrales, J.C. (eds.) AACC’17 2017. AISC, vol. 687, pp. 111–123. Springer, Cham (2018).Lasso, E., Valencia, O., Corrales, D.C., López, I.D., Figueroa, A., Corrales, J.C.: A cloud-based platform for decision making support in Colombian agriculture: a study case in coffee rust. In: Angelov, P., Iglesias, J.A., Corrales, J.C. (eds.) AACC’17 2017. AISC, vol. 687, pp. 182–196. Springer, Cham (2018).Nuthall, P.: Farm Business Management: Analysis of Farming Systems. Lincoln University, CABI (2011)International Federation of Organic Agriculture Movements (IFOAM): Best Practice Guideline for Agriculture and Value Chains. Sustainable Organic Agriculture Action Network/International Federation of Organic Agriculture Movements (IFOAM) (2013)Peisker, A., Dalai, S.: Data analytics for rural development. Indian J. Sci. Technol. 8(S4), 50–60 (2015)Sarangi, S., Umadikar, J., Kar, S.: Automation of agriculture support systems using wisekar: case study of a crop-disease advisory service. Comput. Electron. Agric. 122, 200–210 (2016)ThingsBoard. Thingsboard - open-source IoT platform (2018).Vasisht, D., et al.: Farmbeats: an IoT platform for data-driven agriculture. In: NSDI, pp. 515–529 (2017)Beulens, A.J., Reijers, H.A., van der Vorst, J.G., Verdouw, C.N.: A control model for object virtualization in supply chain management. Comput. Ind. 68, 116–131 (2015)© Springer Nature Switzerland AG 2018https://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/closedAccessAtribución 4.0 Internacional (CC BY 4.0)http://purl.org/coar/access_right/c_14cbTowards a Smart Farming Platform: From IoT-Based Crop Sensing to Data AnalyticsArtículo de revistainfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_2df8fbb1http://purl.org/coar/resource_type/c_3248Textinfo:eu-repo/semantics/bookParthttp://purl.org/redcol/resource_type/ARThttp://purl.org/coar/version/c_970fb48d4fbd8a85Agricultura - ColombiaAgricultura inteligenteAnálisis de datosAgricultura - aspectos tecnológicosSmart farmingData analyticsPrecision agricultureIoTTHUMBNAILTowards a Smart Farming Platform From IoT-Based Crop Sensing to Data Analytics.pngTowards a Smart Farming Platform From IoT-Based Crop Sensing to Data Analytics.pngimage/png94133https://repositorio.escuelaing.edu.co/bitstream/001/1802/7/Towards%20a%20Smart%20Farming%20Platform%20From%20IoT-Based%20Crop%20Sensing%20to%20Data%20Analytics.pngcbc9f50ae440eddef55601d5de6469acMD57open accessTowards a Smart Farming Platform From IoT-Based Crop Sensing to Data Analytics.pdf.jpgTowards a Smart Farming Platform From IoT-Based Crop Sensing to Data Analytics.pdf.jpgGenerated Thumbnailimage/jpeg12030https://repositorio.escuelaing.edu.co/bitstream/001/1802/9/Towards%20a%20Smart%20Farming%20Platform%20From%20IoT-Based%20Crop%20Sensing%20to%20Data%20Analytics.pdf.jpgb1c69277284bc07bf78913f1c960a4f0MD59metadata only accessORIGINALTowards a Smart Farming Platform From IoT-Based Crop Sensing to Data Analytics.pdfTowards a Smart Farming Platform From IoT-Based Crop Sensing to Data Analytics.pdfArtículo de revistaapplication/pdf4217113https://repositorio.escuelaing.edu.co/bitstream/001/1802/6/Towards%20a%20Smart%20Farming%20Platform%20From%20IoT-Based%20Crop%20Sensing%20to%20Data%20Analytics.pdf5ba88a44af5010a0f85fdb201c4b93acMD56metadata only accessLICENSElicense.txtlicense.txttext/plain; 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