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 com...

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Autores:
Cadavid, Héctor
Garzón, Wilmer
Pérez, Alexánder
López, Germán
Mandivelso, Cristian
Ramírez, Carlos
Tipo de recurso:
Part of book
Fecha de publicación:
2018
Institución:
Escuela Colombiana de Ingeniería Julio Garavito
Repositorio:
Repositorio Institucional ECI
Idioma:
eng
OAI Identifier:
oai:repositorio.escuelaing.edu.co:001/3174
Acceso en línea:
https://repositorio.escuelaing.edu.co/handle/001/3174
https://repositorio.escuelaing.edu.co/
Palabra clave:
Tecnología agrícola - Colombia
Agricultural technology - Colombia
Agricultura - Procesamiento de datos
Agriculture - Data processing
Desarrollo de software
Computer software - Development
LOT
Smart farming
Data analytics
Precision agriculture
Agricultura inteligente
Análisis de datos
Agricultura de precisión
Rights
closedAccess
License
http://purl.org/coar/access_right/c_14cb
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network_acronym_str ESCUELAIG2
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repository_id_str
dc.title.eng.fl_str_mv 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
Tecnología agrícola - Colombia
Agricultural technology - Colombia
Agricultura - Procesamiento de datos
Agriculture - Data processing
Desarrollo de software
Computer software - Development
LOT
Smart farming
Data analytics
Precision agriculture
Agricultura inteligente
Análisis de datos
Agricultura de precisión
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, Alexánder
López, Germán
Mandivelso, Cristian
Ramírez, Carlos
dc.contributor.author.none.fl_str_mv Cadavid, Héctor
Garzón, Wilmer
Pérez, Alexánder
López, Germán
Mandivelso, Cristian
Ramírez, Carlos
dc.contributor.researchgroup.spa.fl_str_mv CTG - Informática
dc.subject.armarc.none.fl_str_mv Tecnología agrícola - Colombia
Agricultural technology - Colombia
Agricultura - Procesamiento de datos
Agriculture - Data processing
Desarrollo de software
Computer software - Development
topic Tecnología agrícola - Colombia
Agricultural technology - Colombia
Agricultura - Procesamiento de datos
Agriculture - Data processing
Desarrollo de software
Computer software - Development
LOT
Smart farming
Data analytics
Precision agriculture
Agricultura inteligente
Análisis de datos
Agricultura de precisión
dc.subject.proposal.eng.fl_str_mv LOT
Smart farming
Data analytics
Precision agriculture
dc.subject.proposal.spa.fl_str_mv Agricultura inteligente
Análisis de datos
Agricultura de precisión
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.
publishDate 2018
dc.date.issued.none.fl_str_mv 2018
dc.date.accessioned.none.fl_str_mv 2024-07-16T16:21:31Z
dc.date.available.none.fl_str_mv 2024-07-16T16:21:31Z
dc.type.spa.fl_str_mv Capítulo - Parte de Libro
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dc.identifier.instname.spa.fl_str_mv Universidad Escuela Colombiana de Ingeniería Julio Garavito
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https://repositorio.escuelaing.edu.co/
identifier_str_mv 9783319989976
Universidad Escuela Colombiana de Ingeniería Julio Garavito
Repositorio Digital
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language eng
dc.relation.ispartofseries.none.fl_str_mv Colombian Conference;13th
dc.relation.citationedition.spa.fl_str_mv 13th Colombian Conference, CCC 2018, Cartagena, Colombia, September 26-28, 2018, Proceedings
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dc.relation.ispartofbook.eng.fl_str_mv Advances in Computing
dc.relation.references.spa.fl_str_mv 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: Gu´ıa pr´actica para la caracterizaci´on 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)
Bon´ er, J., Klang, V., Kuhn, R., et al.: Akka library. http://akka.io/
Bruinsma, J.: World Agriculture: Towards 2015/2030: An FAO Study. Routledge, London (2017)
Cadavid, H., P´erez, A., Rocha, C.: Reliable control architecture with PLEXIL and ROSfor autonomous wheeled robots. In: Solano, A., Ordo˜nez, H. (eds.) CCC 2017. CCIS, vol. 735, pp. 611–626. Springer, Cham (2017). https://doi.org/10.1007/9783-319-66562-7 44
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 artif icial 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., M´endez, J.: Phytophthora infestans prediction for a potato crop. Am. J. Potato Res. 87(1), 32–40 (2010)
Jawad, H.M., Nordin, R., Gharghan, S.K., Jawad, A.M., Ismail, M.: Energyefficient wireless sensor networks for precision agriculture: a review. Sensors 17(8), 1781 (2017)
Poole, J., Rae, B., Gonz´alez, 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). https://doi.org/10.1007/978-3-319-70187-5 9
Lasso, E., Valencia, O., Corrales, D.C., L´opez, 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). https://doi. org/10.1007/978-3-319-70187-5 14
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). https:// thingsboard.io
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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spelling Cadavid, Héctor1419fa48babb695dddb738176b5abcb4Garzón, Wilmer6b04a33a7db33dd5cc3491063fc48a95Pérez, Alexánder226261b181aa9fcbc0d82ecb380b0787López, Germáne4f4c662ab3a2f88a1d3ba97c5a053f8Mandivelso, Cristian70053c08df8bdc78134bf2dd55bad64bRamírez, Carlos31ef7038cca40f1ae62b650e704561bcCTG - Informática2024-07-16T16:21:31Z2024-07-16T16:21:31Z2018https://repositorio.escuelaing.edu.co/handle/001/31749783319989976Universidad Escuela Colombiana de Ingeniería Julio GaravitoRepositorio Digitalhttps://repositorio.escuelaing.edu.co/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.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.15 páginasapplication/pdfengSpringer NatureSuizaColombian Conference;13th13th Colombian Conference, CCC 2018, Cartagena, Colombia, September 26-28, 2018, Proceedings251237Advances in ComputingAhmed, 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: Gu´ıa pr´actica para la caracterizaci´on 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)Bon´ er, J., Klang, V., Kuhn, R., et al.: Akka library. http://akka.io/Bruinsma, J.: World Agriculture: Towards 2015/2030: An FAO Study. Routledge, London (2017)Cadavid, H., P´erez, A., Rocha, C.: Reliable control architecture with PLEXIL and ROSfor autonomous wheeled robots. In: Solano, A., Ordo˜nez, H. (eds.) CCC 2017. CCIS, vol. 735, pp. 611–626. Springer, Cham (2017). https://doi.org/10.1007/9783-319-66562-7 44Espana, 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 artif icial 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., M´endez, J.: Phytophthora infestans prediction for a potato crop. Am. J. Potato Res. 87(1), 32–40 (2010)Jawad, H.M., Nordin, R., Gharghan, S.K., Jawad, A.M., Ismail, M.: Energyefficient wireless sensor networks for precision agriculture: a review. Sensors 17(8), 1781 (2017)Poole, J., Rae, B., Gonz´alez, 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). https://doi.org/10.1007/978-3-319-70187-5 9Lasso, E., Valencia, O., Corrales, D.C., L´opez, 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). https://doi. org/10.1007/978-3-319-70187-5 14Nuthall, 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). https:// thingsboard.ioVasisht, 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)https://link.springer.com/chapter/10.1007/978-3-319-98998-3_19Towards a Smart Farming Platform: From IoT-Based Crop Sensing to Data AnalyticsCapítulo - Parte de Libroinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_3248Textinfo:eu-repo/semantics/bookParthttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/closedAccesshttp://purl.org/coar/access_right/c_14cbTecnología agrícola - ColombiaAgricultural technology - ColombiaAgricultura - Procesamiento de datosAgriculture - Data processingDesarrollo de softwareComputer software - DevelopmentLOTSmart farmingData analyticsPrecision agricultureAgricultura inteligenteAnálisis de datosAgricultura de precisiónTEXTTowards a smart farming platform.pdf.txtTowards a smart farming platform.pdf.txtExtracted texttext/plain32969https://repositorio.escuelaing.edu.co/bitstream/001/3174/4/Towards%20a%20smart%20farming%20platform.pdf.txt50a512726a4eb7b988995919466aaae3MD54open accessTHUMBNAILPortada Towards a smart farming platform.PNGPortada Towards a smart farming platform.PNGimage/png215203https://repositorio.escuelaing.edu.co/bitstream/001/3174/3/Portada%20Towards%20a%20smart%20farming%20platform.PNGc5f3ba9b0589c880379b0c20b4ff73d2MD53open accessTowards a smart farming platform.pdf.jpgTowards a smart farming platform.pdf.jpgGenerated Thumbnailimage/jpeg11993https://repositorio.escuelaing.edu.co/bitstream/001/3174/5/Towards%20a%20smart%20farming%20platform.pdf.jpg7fe322345b6cccd7843a065e2bb208e7MD55open accessLICENSElicense.txtlicense.txttext/plain; charset=utf-81881https://repositorio.escuelaing.edu.co/bitstream/001/3174/2/license.txt5a7ca94c2e5326ee169f979d71d0f06eMD52open accessORIGINALTowards a smart farming platform.pdfTowards a smart farming platform.pdfapplication/pdf4217113https://repositorio.escuelaing.edu.co/bitstream/001/3174/1/Towards%20a%20smart%20farming%20platform.pdf5ba88a44af5010a0f85fdb201c4b93acMD51metadata only access001/3174oai:repositorio.escuelaing.edu.co:001/31742024-08-06 16:17:36.786metadata only accessRepositorio Escuela Colombiana de Ingeniería Julio Garavitorepositorio.eci@escuelaing.edu.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