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dc.contributor.authorCadavid, Héctor
dc.contributor.authorGarzón, Wilmer
dc.contributor.authorPérez, Alexander
dc.contributor.authorLópez, Germán
dc.contributor.authorMendivelso, Cristian
dc.contributor.authorRamírez, Carlos
dc.description.abstractColombia 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.eng
dc.format.extent15 pá
dc.publisherSpringer Naturespa
dc.relation.ispartofseriesCCIS;Vol. 885
dc.rights© Springer Nature Switzerland AG 2018eng
dc.titleTowards a Smart Farming Platform: From IoT-Based Crop Sensing to Data Analyticseng
dc.typeCapítulo - Parte de Librospa
dc.relation.ispartofbookCommunications in Computer and Information Scienceeng
dc.relation.referencesAhmed, E., et al.: The role of big data analytics in internet of things. Comput. Netw. 129, 459–471 (2017)spa
dc.relation.referencesAlvarez 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)spa
dc.relation.referencesBashir, 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)spa
dc.relation.referencesBonér, J., Klang, V., Kuhn, R., et
dc.relation.referencesBruinsma, J.: World Agriculture: Towards 2015/2030: An FAO Study. Routledge, London (2017)spa
dc.relation.referencesCadavid, 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).spa
dc.relation.referencesEspana, 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)spa
dc.relation.referencesFry, W., et al.: Five reasons to consider Phytophthora infestans a reemerging pathogen. Phytopathology 105(7), 966–981 (2015)spa
dc.relation.referencesHewitt, 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)spa
dc.relation.referencesIglesias, I., Escuredo, O., Seijo, C., Méndez, J.: Phytophthora infestans prediction for a potato crop. Am. J. Potato Res. 87(1), 32–40 (2010)spa
dc.relation.referencesawad, 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)spa
dc.relation.referencesPoole, 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 2014spa
dc.relation.referencesLasso, 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).spa
dc.relation.referencesLasso, 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).spa
dc.relation.referencesNuthall, P.: Farm Business Management: Analysis of Farming Systems. Lincoln University, CABI (2011)spa
dc.relation.referencesInternational 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)spa
dc.relation.referencesPeisker, A., Dalai, S.: Data analytics for rural development. Indian J. Sci. Technol. 8(S4), 50–60 (2015)spa
dc.relation.referencesSarangi, 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)spa
dc.relation.referencesThingsBoard. Thingsboard - open-source IoT platform (2018).spa
dc.relation.referencesVasisht, D., et al.: Farmbeats: an IoT platform for data-driven agriculture. In: NSDI, pp. 515–529 (2017)spa
dc.relation.referencesBeulens, 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)spa
dc.rights.creativecommonsAtribución 4.0 Internacional (CC BY 4.0)spa
dc.subject.proposalSmart farmingeng
dc.subject.proposalData analyticseng
dc.subject.proposalPrecision agricultureeng

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