Estudio de sistemas de recomendación para la agricultura y su aplicación en Colombia

En el documento se explicara el estudio de tres contextos diferentes sobre sistemas de recomendación para la agricultura, a nivel mundial, latinoamericano y local en donde se entregan un conjunto de buenas prácticas para el desarrollo de sistemas de recomendación para la agricultura colombiana, por...

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Autores:
Campo Martínez, José Edgar
Echeverry Camayo, Juan Camilo
Tipo de recurso:
Trabajo de grado de pregrado
Fecha de publicación:
2021
Institución:
Universidad Cooperativa de Colombia
Repositorio:
Repositorio UCC
Idioma:
OAI Identifier:
oai:repository.ucc.edu.co:20.500.12494/32799
Acceso en línea:
https://hdl.handle.net/20.500.12494/32799
Palabra clave:
Sistemas de recomendación
Revisión sistemática
Agricultura
Agricultor
Recommendation systems
Systematic review
Agriculture
Farmer
Rights
openAccess
License
Atribución – No comercial – Compartir igual
id COOPER2_10e9fe8e3fbb0b905fa1ee2ddc3b66a3
oai_identifier_str oai:repository.ucc.edu.co:20.500.12494/32799
network_acronym_str COOPER2
network_name_str Repositorio UCC
repository_id_str
dc.title.spa.fl_str_mv Estudio de sistemas de recomendación para la agricultura y su aplicación en Colombia
title Estudio de sistemas de recomendación para la agricultura y su aplicación en Colombia
spellingShingle Estudio de sistemas de recomendación para la agricultura y su aplicación en Colombia
Sistemas de recomendación
Revisión sistemática
Agricultura
Agricultor
Recommendation systems
Systematic review
Agriculture
Farmer
title_short Estudio de sistemas de recomendación para la agricultura y su aplicación en Colombia
title_full Estudio de sistemas de recomendación para la agricultura y su aplicación en Colombia
title_fullStr Estudio de sistemas de recomendación para la agricultura y su aplicación en Colombia
title_full_unstemmed Estudio de sistemas de recomendación para la agricultura y su aplicación en Colombia
title_sort Estudio de sistemas de recomendación para la agricultura y su aplicación en Colombia
dc.creator.fl_str_mv Campo Martínez, José Edgar
Echeverry Camayo, Juan Camilo
dc.contributor.advisor.none.fl_str_mv Figueroa Martínez, Cristhian Nicolas
Mera Paz, Julián Andrés
dc.contributor.author.none.fl_str_mv Campo Martínez, José Edgar
Echeverry Camayo, Juan Camilo
dc.subject.spa.fl_str_mv Sistemas de recomendación
Revisión sistemática
Agricultura
Agricultor
topic Sistemas de recomendación
Revisión sistemática
Agricultura
Agricultor
Recommendation systems
Systematic review
Agriculture
Farmer
dc.subject.other.spa.fl_str_mv Recommendation systems
Systematic review
Agriculture
Farmer
description En el documento se explicara el estudio de tres contextos diferentes sobre sistemas de recomendación para la agricultura, a nivel mundial, latinoamericano y local en donde se entregan un conjunto de buenas prácticas para el desarrollo de sistemas de recomendación para la agricultura colombiana, por el cual a través de una revisión sistemática de literatura se logra identificar que el tipo de sistema de recomendación adecuado para el contexto colombiano es el tipo hibrido, ya que este por su gran robustez y combinación de múltiples sistemas de recomendación permite el estudio de diferentes características de los suelos, precipitación, clima, entre otras que permiten realizar recomendaciones acertadas, entregando datos claves para la mejora en la producción y tratamiento de los cultivos por los agricultores.
publishDate 2021
dc.date.accessioned.none.fl_str_mv 2021-01-23T08:10:56Z
dc.date.available.none.fl_str_mv 2021-01-23T08:10:56Z
dc.date.issued.none.fl_str_mv 2021-01-21
dc.type.none.fl_str_mv Trabajo de grado - Pregrado
dc.type.coar.none.fl_str_mv http://purl.org/coar/resource_type/c_7a1f
dc.type.driver.none.fl_str_mv info:eu-repo/semantics/bachelorThesis
dc.type.version.none.fl_str_mv info:eu-repo/semantics/acceptedVersion
format http://purl.org/coar/resource_type/c_7a1f
status_str acceptedVersion
dc.identifier.uri.none.fl_str_mv https://hdl.handle.net/20.500.12494/32799
dc.identifier.bibliographicCitation.spa.fl_str_mv Campo Martinez J. E. y Echeverry Camayo J. C. (2020). Estudio de sistemas de recomendación para la agricultura y su aplicación en Colombia [Tesis de pregrado, Universidad Cooperativa de Colombia].Repositorio Institucional UCC. https://repository.ucc.edu.co/handle/20.500.12494/32799
url https://hdl.handle.net/20.500.12494/32799
identifier_str_mv Campo Martinez J. E. y Echeverry Camayo J. C. (2020). Estudio de sistemas de recomendación para la agricultura y su aplicación en Colombia [Tesis de pregrado, Universidad Cooperativa de Colombia].Repositorio Institucional UCC. https://repository.ucc.edu.co/handle/20.500.12494/32799
dc.relation.conferenceplace.spa.fl_str_mv Popayán
dc.relation.references.spa.fl_str_mv Cardona, V. (27 de 02 de 2015). eltiempo.com. Obtenido de eltiempo.com: eltiempo.com/archivo/documento/CMS-15313755
Cenicaña. (23 de 09 de 2019). Centro de investigacio de la caña de azucar . Obtenido de https://www.cenicana.org
Colciencias. (11 de 09 de 2016). Colciencias. Obtenido de https://www.colciencias.gov.co/sala_de_prensa/colombia-el-segundo-pais-mas-biodiverso-del-mundo
Colciencias. (09 de 11 de 2016). www.colciencias.gov.co. Obtenido de www.colciencias.gov.co: https://www.colciencias.gov.co/sala_de_prensa/colombia-el-segundo-pais-mas-biodiverso-del-mundo
Dane. (12 de agosto de 2015). Informe de contexto del 3er Censo. Obtenido de https://www.dane.gov.co/files/CensoAgropecuario/avanceCNA/CNA_Contexto_2015.pdf
Kanpo. (2016). Kanpo. Obtenido de kanpo: http://www.kanpo.com.co/ MinAgricultura, A. (26 de 12 de 2018). Agronet. Obtenido de https://www.agronet.gov.co/Paginas/inicio.aspx
Plagapp. (2017). plagapp. Obtenido de https://plagapp.cl/home/
Rural, M. d. (s.f.). https://www.minagricultura.gov.co. Obtenido de https://www.minagricultura.gov.co: https://www.minagricultura.gov.co/Normatividad/Paginas/Leyes.aspx
S.A.S, E. L. (27 de enero de 2018). Editorial La República . Obtenido de https://www.larepublica.co/internet-economy/conozca-las-aplicaciones-que-estan-ayudando-al-desarrollo-del-sector-agropecuario-2593192
Alex, S. A., & Kanavalli, A. (2019a). Assessment framework modeling using location aware computing for fertilizer management and crop recommendation. International Journal of Recent Technology and Engineering, 8(3), 1315–1319. https://doi.org/10.35940/ijrte.B3245.098319
Alex, S. A., & Kanavalli, A. (2019b). Assessment framework modeling using location aware computing for fertilizer management and crop recommendation. International Journal of Recent Technology and Engineering, 8(3), 1315–1319. https://doi.org/10.35940/ijrte.B3245.098319
Antle, J. M., Jones, J. W., & Rosenzweig, C. E. (2017). Next generation agricultural system data, models and knowledge products: Introduction. Agricultural Systems, 155, 186–190. https://doi.org/10.1016/j.agsy.2016.09.003
Avey, D., Givens, W. A., Heimbaugh, R. L., Mitchell, S. B., & Wei, J. (2014). Targeted agricultural recommendation system. Google Patents.
Cenicaña. (2018). Agricultura de Precisión. In Cenicaña. http://www.cenicana.org/web/programas-de-investigacion/agronomia/geomatica/agricultura-de-precision
Chen, Q., Zhang, H., Li, X., Christie, P., Horlacher, D., & Liebig, H.-P. (2005). Use of a modified N-expert system for vegetable production in the Beijing region. Journal of Plant Nutrition, 28(3), 475–487. https://doi.org/10.1081/PLN-200049184
Clermont-Dauphin, C., Meynard, J. M., & Cabidoche, Y. M. (2003). Devising fertiliser recommendations for diverse cropping systems in a region: The case of low-input bean/maize intercropping in a tropical highland of Haïti. Agronomie, 23(7), 673–681. https://doi.org/10.1051/agro:2003046
Csatho, P., Arendas, T., Fodor, N., Nemeth, T., Csathó, P., Árendás, T., Fodor, N., & Németh, T. (2009). Evaluation of different fertilizer recommendation systems on various soils and crops in Hungary. COMMUNICATIONS IN SOIL SCIENCE AND PLANT ANALYSIS, 40(11–12), 1689–1711. https://doi.org/10.1080/00103620902895797
De Oliveira, F H T, Novais, R. F., Alvarez, V. H., & Cantarutti, R. B. (2005). Development of a fertilization recommendation system for banana plantations. Revista Brasileira de Ciencia Do Solo, 29(1), 131–143. https://www.scopus.com/inward/record.uri?eid=2-s2.0-33748966296&partnerID=40&md5=9190db3abee40ede65544f2073f511fe
De Oliveira, Fábio Henrique Tavares, Novais, R. F., Alvarez, V. H., & Cantarutti, R. B. (2005). Development of a fertilization recommendation system for banana plantations. Revista Brasileira de Ciencia Do Solo, 29(1), 131–143. 60 https://doi.org/10.1590/s0100-06832005000100015
Dezordi, L. R., de Aquino, L. A., Novais, R. F., de Aquino, P. M., & dos Santos, L. P. (2015). NUTRIENT RECOMMENDATION MODEL FOR CARROT CROP - FERTICALC CARROT. REVISTA BRASILEIRA DE CIENCIA DO SOLO, 39(6), 1714–1722. https://doi.org/10.1590/01000683rbcs20150065
Doshi, Z., Nadkarni, S., Agrawal, R., & Shah, N. (2018). AgroConsultant: Intelligent Crop Recommendation System Using Machine Learning Algorithms. 2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA), 1–6. https://doi.org/10.1109/ICCUBEA.2018.8697349
Dubos, B., Baron, V., Bonneau, X., Dassou, O., Flori, A., Impens, R., Ollivier, J., & Pardon, L. (2019). Precision agriculture in oil palm plantations: Diagnostic tools for sustainable N and K nutrient supply. OCL - Oilseeds and Fats, Crops and Lipids, 26. https://doi.org/10.1051/ocl/2019001
Dupré, R. L. C., Khiari, L., Gallichand, J., & Joseph, C. A. (2019). Multi-factor diagnostic and recommendation system for boron in neutral and acidic soils. Agronomy, 9(8). https://doi.org/10.3390/agronomy9080410
E+E Elektronik Ges.m.b.H. (n.d.). Optimisation of FRUIT TRANSPORT. 43(0), 44043101. http://sensovant.com/productos-aplicaciones/agricultura/instrumentos-tecnologicos-agricultura.html
El futuro de la Tendencias alimentación y la agricultura. (2017).
Elektronik. (n.d.). Climate control for fruit storage. 43(0), 44043101. http://sensovant.com/productos-aplicaciones/agricultura/instrumentos-tecnologicos-agricultura.html
Estado, E. L., De, M., Agricultura, L. A., & Alimentación, L. A. (2016). Nota de prensa. In Revista Iberoamericana de Fertilidad y Reproduccion Humana (Vol. 33, Issue 1, p. 49). www.fao.org/publications/es/
FAO | Organización de las Naciones Unidas para la Alimentación y la Agricultura. (2018). http://www.fao.org/colombia/fao-en-colombia/colombia-en-una-mirada/es/
Geypens, M., & Vandendriessche, H. (1996). Advisory systems for nitrogen fertilizer recommendations. Plant and Soil, 181(1), 31–38. https://doi.org/10.1007/BF00011289
Gott, R M, Aquino, L. A., Clemente, J. M., Santos, L. P. D. D., Carvalho, A. M. X., & Xavier, F. O. (2017). Foliar Diagnosis Indexes for Corn by the Methods Diagnosis and Recommendation Integrated System (DRIS) and Nutritional Composition (CND). Communications in Soil Science and Plant Analysis, 48(1), 11–19. https://doi.org/10.1080/00103624.2016.1253714
Gott, Roney Mendes, Aquino, L. A., Clemente, J. M., Santos, L. P. D. Dos, Carvalho, A. M. X., & Xavier, F. O. (2017). Foliar Diagnosis Indexes for Corn by the Methods Diagnosis and Recommendation Integrated System (DRIS) and Nutritional Composition (CND). Communications in Soil Science and Plant Analysis, 48(1), 11–19. https://doi.org/10.1080/00103624.2016.1253714
Hadfi, I. H., & Yusoh, Z. I. M. (2018). Banana ripeness detection and servings recommendation system using artificial intelligence techniques. Journal of 62 Telecommunication, Electronic and Computer Engineering, 10(2–8), 83–87. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85052017060&partnerID=40&md5=c20b12f9f13e3b1738dfbcf0e8af4069
Henrique, L. (2013). Desenvolvimento e validação de um sistema de recomendação de informações tecnológicas sobre. 387–395.
Ivanyi, I., & Izsaki, Z. (2009). Effect of Nitrogen, Phosphorus, and Potassium Fertilization on Nutrional Status of Fiber Hemp. COMMUNICATIONS IN SOIL SCIENCE AND PLANT ANALYSIS, 40(1–6), 974–986. https://doi.org/10.1080/00103620802693466
Iványi, I., & Izsáki, Z. (2009). Effect of nitrogen, phosphorus, and potassium fertilization on nutrional status of fiber hemp. Communications in Soil Science and Plant Analysis, 40(1–6), 974–986. https://doi.org/10.1080/00103620802693466
Jannach, D., & Dortmund, T. U. (2014). Recommender Systems An introduction Recommender Systems. https://doi.org/10.1109/QELS.2001.961932
Janssen, S. J. C., Porter, C. H., Moore, A. D., Athanasiadis, I. N., Foster, I., Jones, J. W., & Antle, J. M. (2017). Towards a new generation of agricultural system data, models and knowledge products: Information and communication technology. Agricultural Systems, 155, 200–212. https://doi.org/10.1016/j.agsy.2016.09.017
Jat, R. A., Wani, S. P., Sahrawat, K. L., Singh, P., Dhaka, S. R., & Dhaka, B. L. (2012). Recent approaches in nitrogen management for sustainable agricultural production and eco-safety. Archives of Agronomy and Soil Science, 58(9), 1033–1060. https://doi.org/10.1080/03650340.2011.557368
Jearanaiwongkul, W., Anutariya, C., & Andres, F. (2019). A formal model for managing multiple observation data in agriculture. International Journal of Intelligent Information Technologies, 15(3), 79–100. https://doi.org/10.4018/IJIIT.2019070105
Ju, X., & Christie, P. (2011). Calculation of theoretical nitrogen rate for simple nitrogen recommendations in intensive cropping systems: A case study on the North China Plain. Field Crops Research, 124(3), 450–458. https://doi.org/10.1016/j.fcr.2011.08.002
Kay, B. D., Mahboubi, A. A., Beauchamp, E. G., & Dharmakeerthi, R. S. (2006). Integrating soil and weather data to describe variability in plant available nitrogen. Soil Science Society of America Journal, 70(4), 1210–1221. https://doi.org/10.2136/sssaj2005.0039
Khedr, A. E., Kadry, M., & Walid, G. (2015). Proposed Framework for Implementing Data Mining Techniques to Enhance Decisions in Agriculture Sector Applied Case on Food Security Information Center Ministry of Agriculture, Egypt. Procedia Computer Science. https://doi.org/10.1016/j.procs.2015.09.007
Kim, J. Y., Lee, C. G., Baek, S. H., & Rhee, J.-Y. (2015). Open farm information system data-exchange platform for interaction with agricultural information systems. Agricultural Engineering International: CIGR Journal, 17(2), 296–309. https://www.scopus.com/inward/record.uri?eid=2-s2.0-84936884359&partnerID=40&md5=34d587334fc90f1e940e500ef50bd226
Kim, T.-H., Solanki, V. S., Baraiya, H. J., Mitra, A., Shah, H., & Roy, S. (2020). A smart, sensible agriculture system using the exponential moving average model. Symmetry, 12(3). https://doi.org/10.3390/sym12030457
Kitchenham, B., Pretorius, R., Budgen, D., Brereton, O. P., Turner, M., Niazi, M., & Linkman, S. (2010). Systematic literature reviews in software engineering-A tertiary study. In Information and Software Technology (Vol. 52, Issue 8, pp. 792–805). https://doi.org/10.1016/j.infsof.2010.03.006
Kulkarni, N. H., Srinivasan, G. N., Sagar, B. M., & Cauvery, N. K. (2018). Improving Crop Productivity Through A Crop Recommendation System Using Ensembling Technique. 2018 3rd International Conference on Computational Systems and Information Technology for Sustainable Solutions (CSITSS), 114–119. https://doi.org/10.1109/CSITSS.2018.8768790
Kumar, A, Kumar, A., De, A., Shekhar, S., & Singh, R. K. (2019). IoT based farming recommendation system using soil nutrient and environmental condition detection. International Journal of Innovative Technology and Exploring Engineering, 8(11), 3055–3060. https://doi.org/10.35940/ijitee.K2335.0981119
Kumar, Avinash, Sarkar, S., & Pradhan, C. (2019). Recommendation system for crop identification and pest control technique in agriculture. Proceedings of the 2019 IEEE International Conference on Communication and Signal Processing, ICCSP 2019, 185–189. https://doi.org/10.1109/ICCSP.2019.8698099
Lacasta, J., Javier Lopez-Pellicer, F., Espejo-Garcia, B., Nogueras-Iso, J., & Javier Zarazaga-Soria, F. (2018). Agricultural recommendation system for crop protection. COMPUTERS AND ELECTRONICS IN AGRICULTURE, 152, 82–89. https://doi.org/10.1016/j.compag.2018.06.049
Lagos-Ortiz, K., del Pilar Salas-Zárate, M., Paredes-Valverde, M. A., García-Díaz, J. A., & Valencia-García, R. (2020a). Agrient: A knowledge-based web platform for managing insect pests of field crops. Applied Sciences (Switzerland), 10(3). https://doi.org/10.3390/app10031040
Lagos-Ortiz, K., del Pilar Salas-Zárate, M., Paredes-Valverde, M. A., García-Díaz, J. A., & Valencia-García, R. (2020b). Agrient: A knowledge-based web platform for managing insect pests of field crops. Applied Sciences (Switzerland), 10(3). https://doi.org/10.3390/app10031040
Laliwala, Z., Sorathia, V., & Chaudhary, S. (2006). Semantic and Rule Based Event-driven Services-Oriented Agricultural Recommendation System. 26th IEEE International Conference on Distributed Computing Systems Workshops (ICDCSW’06), 24. https://doi.org/10.1109/ICDCSW.2006.95
Lian, J. W., & Ke, C. K. (2016). Using a modified ELECTRE method for an agricultural product recommendation service on a mobile device. Computers and Electrical Engineering, 56, 277–288. https://doi.org/10.1016/j.compeleceng.2015.11.014
Logan, J., Mueller, M. A., & Graves, C. R. (1998). Oilseeds: A comparison of early and recommended soybean production systems in Tennessee. Journal of Production Agriculture, 11(3), 319–325. https://doi.org/10.2134/jpa1998.0319
Majumdar, J., Naraseeyappa, S., & Ankalaki, S. (2017). Analysis of agriculture data using data mining techniques: application of big data. Journal of Big Data. https://doi.org/10.1186/s40537-017-0077-4
Malnou, C. S., Jaggard, K. W., & Sparkes, D. L. (2006). A canopy approach to nitrogen fertilizer recommendations for the sugar beet crop. European Journal of Agronomy, 25(3), 254–263. https://doi.org/10.1016/j.eja.2006.06.002
Manoj Athreya, A., Hrithik Gowda, S., Madhu, S., & Ravikumar, V. (2019a). Agriculture based recommender system using IoT - A research. International Journal of Recent Technology and Engineering, 8(2 Special Issue 8), 1817–1821. https://doi.org/10.35940/ijrte.B1161.0882S819
Manoj Athreya, A., Hrithik Gowda, S., Madhu, S., & Ravikumar, V. (2019b). Agriculture based recommender system using IoT - A research. International Journal of Recent Technology and Engineering, 8(2 Special Issue 8), 1817–1821. https://doi.org/10.35940/ijrte.B1161.0882S819
Mantovani, E. C., & Magdalena, C. (n.d.). Programa Cooperativo para el Desarrollo Tecnológico Agroalimentario y Agroindustrial del Cono Sur. http://www.iica.int
Mantovani, E. C., & Magdalena, C. (2014). Manual de agricultura de precisión. Embrapa Milho e Sorgo-Livro Científico (ALICE).
Mokarrama, M J, & Arefin, M. S. (2017). RSF: A recommendation system for farmers. 2017 IEEE Region 10 Humanitarian Technology Conference (R10-HTC), 843–850. https://doi.org/10.1109/R10-HTC.2017.8289086
Mokarrama, Miftahul Jannat, & Arefin, M. S. (2018). RSF: A recommendation system for farmers. 5th IEEE Region 10 Humanitarian Technology Conference 2017, R10-HTC 2017, 2018-Janua, 843–850. https://doi.org/10.1109/R10-HTC.2017.8289086
Montesinos-López, O. A., Montesinos-López, A., Crossa, J., Montesinos-López, J. C., Mota-Sanchez, D., Estrada-González, F., Gillberg, J., Singh, R., Mondal, S., & Juliana, P. (2018). Prediction of multiple-trait and multiple-environment genomic data using recommender systems. G3: Genes, Genomes, Genetics, 8(1), 131–147. https://doi.org/10.1534/g3.117.300309
Neela, R., & Nithya, P. (2019). Fertilizers recommendation system for disease prediction in tree leave. International Journal of Scientific and Technology Research, 8(11), 3343–3346. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85075556373&partnerID=40&md5=133e09f3729c2a42d1b491e90a8b51d4
Nguyen, V. G. N., Drogoul, A., & Huynh, H. X. (2012). Toward an agent-based multi-scale recommendation system for Brown plant hopper control. Proceedings - UKSim-AMSS 6th European Modelling Symposium, EMS 2012, 9–14. https://doi.org/10.1109/EMS.2012.18
Nimirthi, P., Venkata Krishna, P., Obaidat, M. S., & Saritha, V. (2019). A framework for sentiment analysis based recommender system for agriculture using deep learning approach. SpringerBriefs in Applied Sciences and Technology, 59–66. https://doi.org/10.1007/978-981-13-1456-8_5
Orozco, Ó. A., & Llano Ramírez, G. (2016). Sistemas de Información enfocados en tecnologías de agricultura de precisión y aplicables a la caña de azúcar, una revisión. Revista Ingenierías Universidad de Medellín, 15(28), 103–124. https://doi.org/10.22395/rium.v15n28a6
OTIC MinAgricultura Colombia. (2017). Plan Estratégico de Tecnologías de Información y Comunicación Sectorial.
Patil, N. N., & Saiyyad, M. A. M. (2019). Machine learning technique for crop recommendation in agriculture sector. International Journal of Engineering and Advanced Technology, 9(1), 1359–1363. https://doi.org/10.35940/ijeat.A1171.109119
Pawar, M., & Chillarge, G. (2018). Soil Toxicity Prediction and Recommendation System Using Data Mining In Precision Agriculture. 2018 3rd International Conference for Convergence in Technology (I2CT), 1–5. https://doi.org/10.1109/I2CT.2018.8529754
Prithvi Ram, V., Rajeshwari, S. B., & Kalliman, J. S. (2019). Smart yield accuracy prediction using linear regression and collaborative filtration. International Journal of Recent Technology and Engineering, 8(3), 664–670. https://doi.org/10.35940/ijrte.B2657.098319
Pudumalar, S., Ramanujam, E., Rajashree, R. H., Kavya, C., Kiruthika, T., & Nisha, J. (2017a). Crop recommendation system for precision agriculture. 2016 8th International Conference on Advanced Computing, ICoAC 2016, 32–36. https://doi.org/10.1109/ICoAC.2017.7951740
Pudumalar, S., Ramanujam, E., Rajashree, R. H., Kavya, C., Kiruthika, T., & Nisha, J. (2017b). Crop recommendation system for precision agriculture. 2016 Eighth International Conference on Advanced Computing (ICoAC), October, 32–36. https://doi.org/10.1109/ICoAC.2017.7951740
Pudumalar, S., Ramanujam, E., Rajashree, R. H., Kavya, C., Kiruthika, T., & Nisha, J. (2017c). Crop recommendation system for precision agriculture. 2016 Eighth International Conference on Advanced Computing (ICoAC), October, 32–36. https://doi.org/10.1109/ICoAC.2017.7951740
Rahman, K. M. A., & Zhang, D. (2018). Effects of fertilizer broadcasting on the excessive use of inorganic fertilizers and environmental sustainability. Sustainability (Switzerland), 10(3). https://doi.org/10.3390/su10030759
Rahn, C. R. (2018). Challenges of devising nitrogen recommendation systems for open field vegetables. Acta Horticulturae, 1192, 11–20. https://doi.org/10.17660/ActaHortic.2018.1192.2
Raja, S. K. S., Rishi, R., Sundaresan, E., & Srijit, V. (2017). Demand based crop recommender system for farmers. 2017 IEEE Technological Innovations in ICT for Agriculture and Rural Development (TIAR), 2018-Janua(Tiar), 194–199. https://doi.org/10.1109/TIAR.2017.8273714
Ren, Z., & Lu, X. (2012). Design of fertilization recommendation knowledge base and appllication. 2012 1st International Conference on Agro-Geoinformatics, Agro-Geoinformatics 2012, 203–207. https://doi.org/10.1109/Agro-Geoinformatics.2012.6311640
Revathi, P., Revathi, R., & Hemalatha, M. (2011). Comparative Study of Knowledge in Crop Diseases Using Machine Learning Techniques. Inter-National Journal of Computer Science and Information Technologies (IJCSIT), 2(5), 2180–2182.
Ricci, F., Rokach, L., Shapira, B., Kantor, P. B., & Ricci, F. (2011). Recommender Systems Handbook. In Recommender Systems Handbook. https://doi.org/10.1007/978-0-387-85820-3
Santos, F. C. dos, Neves, J. C. L., Novais, R. F., Alvarez V., V. H., & Sediyama, C. S. (2008). Modelagem da recomendação de corretivos e fertilizantes para a cultura da soja. Revista Brasileira de Ciência Do Solo, 32(4), 1661–1674. https://doi.org/10.1590/s0100-06832008000400031
SENSOVANT. (2014). Sensores / Instrumentos para Agricultura y Ganadería. http://sensovant.com/productos-aplicaciones/agricultura/instrumentos-tecnologicos-agricultura.html
Silva, A. P. da, Alvarez V, V. H., Souza, A. P. de, Neves, J. C. L., Novais, R. F., & Dantas, J. P. (2009). Sistema de recomendação de fertilizantes e corretivos para a cultura do abacaxi - fertcalc-abacaxi. Revista Brasileira de Ciência Do Solo, 33(5), 1269–1280. https://doi.org/10.1590/s0100-06832009000500020
Sujithra, T., Thanjaivadivel, M., & Durai, S. (2017a). Fertilizer recommendation system for coconut cultivation. International Journal of Civil Engineering and Technology, 8(9), 813–819. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85029895779&partnerID=40&md5=1c6e972f7cf611f24f4aa08e7fbd6bf0
Sujithra, T., Thanjaivadivel, M., & Durai, S. (2017b). Fertilizer recommendation system for coconut cultivation. International Journal of Civil Engineering and Technology, 8(9), 813–819. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85029895779&partnerID=40&md5=1c6e972f7cf611f24f4aa08e7fbd6bf0
Suma, V., Shetty, R. A., Tated, R. F., Rohan, S., & Pujar, T. S. (2019). CNN based Leaf Disease Identification and Remedy Recommendation System. 2019 3rd International Conference on Electronics, Communication and Aerospace Technology (ICECA), 395–399. https://doi.org/10.1109/ICECA.2019.8821872
Ward, B. P., Brown-Guedira, G., Tyagi, P., Kolb, F. L., van Sanford, D. A., Sneller, C. H., & Griffey, C. A. (2019). Multienvironment and multitrait genomic selection models in unbalanced early-generation wheat yield trials. Crop Science, 59(2), 491–507. https://doi.org/10.2135/cropsci2018.03.0189
Wong, M. T. F., Corner, R. J., & Cook, S. E. (2001). A decision support system for mapping the site-specific potassium requirement of wheat in the field. Australian Journal of Experimental Agriculture, 41(5), 655. https://doi.org/10.1071/EA00191
Xu, M., Zhang, J., Wu, F., & Wang, X. (2015). Preliminary the diagnosis and Recommendation Integrated System (DRIS) norms for evaluating the nutrient status of apple. Advance Journal of Food Science and Technology, 7(2), 74–80. https://doi.org/10.19026/ajfst.7.1270
Yu, F., Zhang, Q., Luan, R., Zhang, J., & Liu, X. (2013). Application and improvement of intelligent recommendation for Agricultural Information. 2013 Ninth International Conference on Natural Computation (ICNC), 1077–1081. https://doi.org/10.1109/ICNC.2013.6818137
Zhao, Y., & Bai, S. H. (2012). Research on optimizing recommend system for agriculture information personalization based on user clustering. Proceedings of the 2012 International Conference on Industrial Control and Electronics Engineering, ICICEE 2012, 1477–1480. https://doi.org/10.1109/ICICEE.2012.388
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spelling Figueroa Martínez, Cristhian NicolasMera Paz, Julián AndrésCampo Martínez, José EdgarEcheverry Camayo, Juan Camilo2021-01-23T08:10:56Z2021-01-23T08:10:56Z2021-01-21https://hdl.handle.net/20.500.12494/32799Campo Martinez J. E. y Echeverry Camayo J. C. (2020). Estudio de sistemas de recomendación para la agricultura y su aplicación en Colombia [Tesis de pregrado, Universidad Cooperativa de Colombia].Repositorio Institucional UCC. https://repository.ucc.edu.co/handle/20.500.12494/32799En el documento se explicara el estudio de tres contextos diferentes sobre sistemas de recomendación para la agricultura, a nivel mundial, latinoamericano y local en donde se entregan un conjunto de buenas prácticas para el desarrollo de sistemas de recomendación para la agricultura colombiana, por el cual a través de una revisión sistemática de literatura se logra identificar que el tipo de sistema de recomendación adecuado para el contexto colombiano es el tipo hibrido, ya que este por su gran robustez y combinación de múltiples sistemas de recomendación permite el estudio de diferentes características de los suelos, precipitación, clima, entre otras que permiten realizar recomendaciones acertadas, entregando datos claves para la mejora en la producción y tratamiento de los cultivos por los agricultores.The document will explain the study of three different contexts on recommendation systems for agriculture, at the global, Latin American and local levels, where a set of good practices for the development of recommendation systems for Colombian agriculture is delivered, by which Through a systematic literature review, it is possible to identify that the type of recommendation system suitable for the Colombian context is the hybrid type, since this, due to its great robustness and combination of multiple recommendation systems, allows the study of different characteristics of the soils, precipitation, climate, among others that allow making accurate recommendations, providing key data for improving the production and treatment of crops by farmers.Resumen. -- Abstract. -- Palabras clave. -- key words. -- Introducción. -- Planteamiento del problema. -- Objetivos. -- Objetivo general. -- Objetivo específico. -- Justificación. -- Marco de referencia. -- Metodología. -- Cronograma. -- Recursos, presupuestos y fuentes de financiación. -- Resultados y discusiones. -- Conclusiones. -- Recomendaciones. -- Apéndice. -- Bibliografía.jose.campoma@campusucc.edu.cojuan.echeverryca@campusucc.edu.co81 p.Universidad Cooperativa de Colombia, Facultad de Ingenierías, Ingeniería de Sistemas, PopayánIngeniería de SistemasPopayánSistemas de recomendaciónRevisión sistemáticaAgriculturaAgricultorRecommendation systemsSystematic reviewAgricultureFarmerEstudio de sistemas de recomendación para la agricultura y su aplicación en ColombiaTrabajo de grado - Pregradohttp://purl.org/coar/resource_type/c_7a1finfo:eu-repo/semantics/bachelorThesisinfo:eu-repo/semantics/acceptedVersionAtribución – No comercial – Compartir igualinfo:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2PopayánCardona, V. (27 de 02 de 2015). eltiempo.com. Obtenido de eltiempo.com: eltiempo.com/archivo/documento/CMS-15313755Cenicaña. (23 de 09 de 2019). Centro de investigacio de la caña de azucar . Obtenido de https://www.cenicana.orgColciencias. (11 de 09 de 2016). Colciencias. Obtenido de https://www.colciencias.gov.co/sala_de_prensa/colombia-el-segundo-pais-mas-biodiverso-del-mundoColciencias. (09 de 11 de 2016). www.colciencias.gov.co. Obtenido de www.colciencias.gov.co: https://www.colciencias.gov.co/sala_de_prensa/colombia-el-segundo-pais-mas-biodiverso-del-mundoDane. (12 de agosto de 2015). Informe de contexto del 3er Censo. Obtenido de https://www.dane.gov.co/files/CensoAgropecuario/avanceCNA/CNA_Contexto_2015.pdfKanpo. (2016). Kanpo. Obtenido de kanpo: http://www.kanpo.com.co/ MinAgricultura, A. (26 de 12 de 2018). Agronet. Obtenido de https://www.agronet.gov.co/Paginas/inicio.aspxPlagapp. (2017). plagapp. Obtenido de https://plagapp.cl/home/Rural, M. d. (s.f.). https://www.minagricultura.gov.co. Obtenido de https://www.minagricultura.gov.co: https://www.minagricultura.gov.co/Normatividad/Paginas/Leyes.aspxS.A.S, E. L. (27 de enero de 2018). Editorial La República . Obtenido de https://www.larepublica.co/internet-economy/conozca-las-aplicaciones-que-estan-ayudando-al-desarrollo-del-sector-agropecuario-2593192Alex, S. A., & Kanavalli, A. (2019a). Assessment framework modeling using location aware computing for fertilizer management and crop recommendation. International Journal of Recent Technology and Engineering, 8(3), 1315–1319. https://doi.org/10.35940/ijrte.B3245.098319Alex, S. A., & Kanavalli, A. (2019b). Assessment framework modeling using location aware computing for fertilizer management and crop recommendation. International Journal of Recent Technology and Engineering, 8(3), 1315–1319. https://doi.org/10.35940/ijrte.B3245.098319Antle, J. M., Jones, J. W., & Rosenzweig, C. E. (2017). Next generation agricultural system data, models and knowledge products: Introduction. Agricultural Systems, 155, 186–190. https://doi.org/10.1016/j.agsy.2016.09.003Avey, D., Givens, W. A., Heimbaugh, R. L., Mitchell, S. B., & Wei, J. (2014). Targeted agricultural recommendation system. Google Patents.Cenicaña. (2018). Agricultura de Precisión. In Cenicaña. http://www.cenicana.org/web/programas-de-investigacion/agronomia/geomatica/agricultura-de-precisionChen, Q., Zhang, H., Li, X., Christie, P., Horlacher, D., & Liebig, H.-P. (2005). Use of a modified N-expert system for vegetable production in the Beijing region. Journal of Plant Nutrition, 28(3), 475–487. https://doi.org/10.1081/PLN-200049184Clermont-Dauphin, C., Meynard, J. M., & Cabidoche, Y. M. (2003). Devising fertiliser recommendations for diverse cropping systems in a region: The case of low-input bean/maize intercropping in a tropical highland of Haïti. Agronomie, 23(7), 673–681. https://doi.org/10.1051/agro:2003046Csatho, P., Arendas, T., Fodor, N., Nemeth, T., Csathó, P., Árendás, T., Fodor, N., & Németh, T. (2009). Evaluation of different fertilizer recommendation systems on various soils and crops in Hungary. COMMUNICATIONS IN SOIL SCIENCE AND PLANT ANALYSIS, 40(11–12), 1689–1711. https://doi.org/10.1080/00103620902895797De Oliveira, F H T, Novais, R. F., Alvarez, V. H., & Cantarutti, R. B. (2005). Development of a fertilization recommendation system for banana plantations. Revista Brasileira de Ciencia Do Solo, 29(1), 131–143. https://www.scopus.com/inward/record.uri?eid=2-s2.0-33748966296&partnerID=40&md5=9190db3abee40ede65544f2073f511feDe Oliveira, Fábio Henrique Tavares, Novais, R. F., Alvarez, V. H., & Cantarutti, R. B. (2005). Development of a fertilization recommendation system for banana plantations. Revista Brasileira de Ciencia Do Solo, 29(1), 131–143. 60 https://doi.org/10.1590/s0100-06832005000100015Dezordi, L. R., de Aquino, L. A., Novais, R. F., de Aquino, P. M., & dos Santos, L. P. (2015). NUTRIENT RECOMMENDATION MODEL FOR CARROT CROP - FERTICALC CARROT. REVISTA BRASILEIRA DE CIENCIA DO SOLO, 39(6), 1714–1722. https://doi.org/10.1590/01000683rbcs20150065Doshi, Z., Nadkarni, S., Agrawal, R., & Shah, N. (2018). AgroConsultant: Intelligent Crop Recommendation System Using Machine Learning Algorithms. 2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA), 1–6. https://doi.org/10.1109/ICCUBEA.2018.8697349Dubos, B., Baron, V., Bonneau, X., Dassou, O., Flori, A., Impens, R., Ollivier, J., & Pardon, L. (2019). Precision agriculture in oil palm plantations: Diagnostic tools for sustainable N and K nutrient supply. OCL - Oilseeds and Fats, Crops and Lipids, 26. https://doi.org/10.1051/ocl/2019001Dupré, R. L. C., Khiari, L., Gallichand, J., & Joseph, C. A. (2019). Multi-factor diagnostic and recommendation system for boron in neutral and acidic soils. Agronomy, 9(8). https://doi.org/10.3390/agronomy9080410E+E Elektronik Ges.m.b.H. (n.d.). Optimisation of FRUIT TRANSPORT. 43(0), 44043101. http://sensovant.com/productos-aplicaciones/agricultura/instrumentos-tecnologicos-agricultura.htmlEl futuro de la Tendencias alimentación y la agricultura. (2017).Elektronik. (n.d.). Climate control for fruit storage. 43(0), 44043101. http://sensovant.com/productos-aplicaciones/agricultura/instrumentos-tecnologicos-agricultura.htmlEstado, E. L., De, M., Agricultura, L. A., & Alimentación, L. A. (2016). Nota de prensa. In Revista Iberoamericana de Fertilidad y Reproduccion Humana (Vol. 33, Issue 1, p. 49). www.fao.org/publications/es/FAO | Organización de las Naciones Unidas para la Alimentación y la Agricultura. (2018). http://www.fao.org/colombia/fao-en-colombia/colombia-en-una-mirada/es/Geypens, M., & Vandendriessche, H. (1996). Advisory systems for nitrogen fertilizer recommendations. Plant and Soil, 181(1), 31–38. https://doi.org/10.1007/BF00011289Gott, R M, Aquino, L. A., Clemente, J. M., Santos, L. P. D. D., Carvalho, A. M. X., & Xavier, F. O. (2017). Foliar Diagnosis Indexes for Corn by the Methods Diagnosis and Recommendation Integrated System (DRIS) and Nutritional Composition (CND). Communications in Soil Science and Plant Analysis, 48(1), 11–19. https://doi.org/10.1080/00103624.2016.1253714Gott, Roney Mendes, Aquino, L. A., Clemente, J. M., Santos, L. P. D. Dos, Carvalho, A. M. X., & Xavier, F. O. (2017). Foliar Diagnosis Indexes for Corn by the Methods Diagnosis and Recommendation Integrated System (DRIS) and Nutritional Composition (CND). Communications in Soil Science and Plant Analysis, 48(1), 11–19. https://doi.org/10.1080/00103624.2016.1253714Hadfi, I. H., & Yusoh, Z. I. M. (2018). Banana ripeness detection and servings recommendation system using artificial intelligence techniques. Journal of 62 Telecommunication, Electronic and Computer Engineering, 10(2–8), 83–87. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85052017060&partnerID=40&md5=c20b12f9f13e3b1738dfbcf0e8af4069Henrique, L. (2013). Desenvolvimento e validação de um sistema de recomendação de informações tecnológicas sobre. 387–395.Ivanyi, I., & Izsaki, Z. (2009). Effect of Nitrogen, Phosphorus, and Potassium Fertilization on Nutrional Status of Fiber Hemp. COMMUNICATIONS IN SOIL SCIENCE AND PLANT ANALYSIS, 40(1–6), 974–986. https://doi.org/10.1080/00103620802693466Iványi, I., & Izsáki, Z. (2009). Effect of nitrogen, phosphorus, and potassium fertilization on nutrional status of fiber hemp. Communications in Soil Science and Plant Analysis, 40(1–6), 974–986. https://doi.org/10.1080/00103620802693466Jannach, D., & Dortmund, T. U. (2014). Recommender Systems An introduction Recommender Systems. https://doi.org/10.1109/QELS.2001.961932Janssen, S. J. C., Porter, C. H., Moore, A. D., Athanasiadis, I. N., Foster, I., Jones, J. W., & Antle, J. M. (2017). Towards a new generation of agricultural system data, models and knowledge products: Information and communication technology. Agricultural Systems, 155, 200–212. https://doi.org/10.1016/j.agsy.2016.09.017Jat, R. A., Wani, S. P., Sahrawat, K. L., Singh, P., Dhaka, S. R., & Dhaka, B. L. (2012). Recent approaches in nitrogen management for sustainable agricultural production and eco-safety. Archives of Agronomy and Soil Science, 58(9), 1033–1060. https://doi.org/10.1080/03650340.2011.557368Jearanaiwongkul, W., Anutariya, C., & Andres, F. (2019). A formal model for managing multiple observation data in agriculture. International Journal of Intelligent Information Technologies, 15(3), 79–100. https://doi.org/10.4018/IJIIT.2019070105Ju, X., & Christie, P. (2011). Calculation of theoretical nitrogen rate for simple nitrogen recommendations in intensive cropping systems: A case study on the North China Plain. Field Crops Research, 124(3), 450–458. https://doi.org/10.1016/j.fcr.2011.08.002Kay, B. D., Mahboubi, A. A., Beauchamp, E. G., & Dharmakeerthi, R. S. (2006). Integrating soil and weather data to describe variability in plant available nitrogen. Soil Science Society of America Journal, 70(4), 1210–1221. https://doi.org/10.2136/sssaj2005.0039Khedr, A. E., Kadry, M., & Walid, G. (2015). Proposed Framework for Implementing Data Mining Techniques to Enhance Decisions in Agriculture Sector Applied Case on Food Security Information Center Ministry of Agriculture, Egypt. Procedia Computer Science. https://doi.org/10.1016/j.procs.2015.09.007Kim, J. Y., Lee, C. G., Baek, S. H., & Rhee, J.-Y. (2015). Open farm information system data-exchange platform for interaction with agricultural information systems. Agricultural Engineering International: CIGR Journal, 17(2), 296–309. https://www.scopus.com/inward/record.uri?eid=2-s2.0-84936884359&partnerID=40&md5=34d587334fc90f1e940e500ef50bd226Kim, T.-H., Solanki, V. S., Baraiya, H. J., Mitra, A., Shah, H., & Roy, S. (2020). A smart, sensible agriculture system using the exponential moving average model. Symmetry, 12(3). https://doi.org/10.3390/sym12030457Kitchenham, B., Pretorius, R., Budgen, D., Brereton, O. P., Turner, M., Niazi, M., & Linkman, S. (2010). Systematic literature reviews in software engineering-A tertiary study. In Information and Software Technology (Vol. 52, Issue 8, pp. 792–805). https://doi.org/10.1016/j.infsof.2010.03.006Kulkarni, N. H., Srinivasan, G. N., Sagar, B. M., & Cauvery, N. K. (2018). Improving Crop Productivity Through A Crop Recommendation System Using Ensembling Technique. 2018 3rd International Conference on Computational Systems and Information Technology for Sustainable Solutions (CSITSS), 114–119. https://doi.org/10.1109/CSITSS.2018.8768790Kumar, A, Kumar, A., De, A., Shekhar, S., & Singh, R. K. (2019). IoT based farming recommendation system using soil nutrient and environmental condition detection. International Journal of Innovative Technology and Exploring Engineering, 8(11), 3055–3060. https://doi.org/10.35940/ijitee.K2335.0981119Kumar, Avinash, Sarkar, S., & Pradhan, C. (2019). Recommendation system for crop identification and pest control technique in agriculture. Proceedings of the 2019 IEEE International Conference on Communication and Signal Processing, ICCSP 2019, 185–189. https://doi.org/10.1109/ICCSP.2019.8698099Lacasta, J., Javier Lopez-Pellicer, F., Espejo-Garcia, B., Nogueras-Iso, J., & Javier Zarazaga-Soria, F. (2018). Agricultural recommendation system for crop protection. COMPUTERS AND ELECTRONICS IN AGRICULTURE, 152, 82–89. https://doi.org/10.1016/j.compag.2018.06.049Lagos-Ortiz, K., del Pilar Salas-Zárate, M., Paredes-Valverde, M. A., García-Díaz, J. A., & Valencia-García, R. (2020a). Agrient: A knowledge-based web platform for managing insect pests of field crops. Applied Sciences (Switzerland), 10(3). https://doi.org/10.3390/app10031040Lagos-Ortiz, K., del Pilar Salas-Zárate, M., Paredes-Valverde, M. A., García-Díaz, J. A., & Valencia-García, R. (2020b). Agrient: A knowledge-based web platform for managing insect pests of field crops. Applied Sciences (Switzerland), 10(3). https://doi.org/10.3390/app10031040Laliwala, Z., Sorathia, V., & Chaudhary, S. (2006). Semantic and Rule Based Event-driven Services-Oriented Agricultural Recommendation System. 26th IEEE International Conference on Distributed Computing Systems Workshops (ICDCSW’06), 24. https://doi.org/10.1109/ICDCSW.2006.95Lian, J. W., & Ke, C. K. (2016). Using a modified ELECTRE method for an agricultural product recommendation service on a mobile device. Computers and Electrical Engineering, 56, 277–288. https://doi.org/10.1016/j.compeleceng.2015.11.014Logan, J., Mueller, M. A., & Graves, C. R. (1998). Oilseeds: A comparison of early and recommended soybean production systems in Tennessee. Journal of Production Agriculture, 11(3), 319–325. https://doi.org/10.2134/jpa1998.0319Majumdar, J., Naraseeyappa, S., & Ankalaki, S. (2017). Analysis of agriculture data using data mining techniques: application of big data. Journal of Big Data. https://doi.org/10.1186/s40537-017-0077-4Malnou, C. S., Jaggard, K. W., & Sparkes, D. L. (2006). A canopy approach to nitrogen fertilizer recommendations for the sugar beet crop. European Journal of Agronomy, 25(3), 254–263. https://doi.org/10.1016/j.eja.2006.06.002Manoj Athreya, A., Hrithik Gowda, S., Madhu, S., & Ravikumar, V. (2019a). Agriculture based recommender system using IoT - A research. International Journal of Recent Technology and Engineering, 8(2 Special Issue 8), 1817–1821. https://doi.org/10.35940/ijrte.B1161.0882S819Manoj Athreya, A., Hrithik Gowda, S., Madhu, S., & Ravikumar, V. (2019b). Agriculture based recommender system using IoT - A research. International Journal of Recent Technology and Engineering, 8(2 Special Issue 8), 1817–1821. https://doi.org/10.35940/ijrte.B1161.0882S819Mantovani, E. C., & Magdalena, C. (n.d.). Programa Cooperativo para el Desarrollo Tecnológico Agroalimentario y Agroindustrial del Cono Sur. http://www.iica.intMantovani, E. C., & Magdalena, C. (2014). Manual de agricultura de precisión. Embrapa Milho e Sorgo-Livro Científico (ALICE).Mokarrama, M J, & Arefin, M. S. (2017). RSF: A recommendation system for farmers. 2017 IEEE Region 10 Humanitarian Technology Conference (R10-HTC), 843–850. https://doi.org/10.1109/R10-HTC.2017.8289086Mokarrama, Miftahul Jannat, & Arefin, M. S. (2018). RSF: A recommendation system for farmers. 5th IEEE Region 10 Humanitarian Technology Conference 2017, R10-HTC 2017, 2018-Janua, 843–850. https://doi.org/10.1109/R10-HTC.2017.8289086Montesinos-López, O. A., Montesinos-López, A., Crossa, J., Montesinos-López, J. C., Mota-Sanchez, D., Estrada-González, F., Gillberg, J., Singh, R., Mondal, S., & Juliana, P. (2018). Prediction of multiple-trait and multiple-environment genomic data using recommender systems. G3: Genes, Genomes, Genetics, 8(1), 131–147. https://doi.org/10.1534/g3.117.300309Neela, R., & Nithya, P. (2019). Fertilizers recommendation system for disease prediction in tree leave. International Journal of Scientific and Technology Research, 8(11), 3343–3346. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85075556373&partnerID=40&md5=133e09f3729c2a42d1b491e90a8b51d4Nguyen, V. G. N., Drogoul, A., & Huynh, H. X. (2012). Toward an agent-based multi-scale recommendation system for Brown plant hopper control. Proceedings - UKSim-AMSS 6th European Modelling Symposium, EMS 2012, 9–14. https://doi.org/10.1109/EMS.2012.18Nimirthi, P., Venkata Krishna, P., Obaidat, M. S., & Saritha, V. (2019). A framework for sentiment analysis based recommender system for agriculture using deep learning approach. SpringerBriefs in Applied Sciences and Technology, 59–66. https://doi.org/10.1007/978-981-13-1456-8_5Orozco, Ó. A., & Llano Ramírez, G. (2016). Sistemas de Información enfocados en tecnologías de agricultura de precisión y aplicables a la caña de azúcar, una revisión. Revista Ingenierías Universidad de Medellín, 15(28), 103–124. https://doi.org/10.22395/rium.v15n28a6OTIC MinAgricultura Colombia. (2017). Plan Estratégico de Tecnologías de Información y Comunicación Sectorial.Patil, N. N., & Saiyyad, M. A. M. (2019). Machine learning technique for crop recommendation in agriculture sector. International Journal of Engineering and Advanced Technology, 9(1), 1359–1363. https://doi.org/10.35940/ijeat.A1171.109119Pawar, M., & Chillarge, G. (2018). Soil Toxicity Prediction and Recommendation System Using Data Mining In Precision Agriculture. 2018 3rd International Conference for Convergence in Technology (I2CT), 1–5. https://doi.org/10.1109/I2CT.2018.8529754Prithvi Ram, V., Rajeshwari, S. B., & Kalliman, J. S. (2019). Smart yield accuracy prediction using linear regression and collaborative filtration. International Journal of Recent Technology and Engineering, 8(3), 664–670. https://doi.org/10.35940/ijrte.B2657.098319Pudumalar, S., Ramanujam, E., Rajashree, R. H., Kavya, C., Kiruthika, T., & Nisha, J. (2017a). Crop recommendation system for precision agriculture. 2016 8th International Conference on Advanced Computing, ICoAC 2016, 32–36. https://doi.org/10.1109/ICoAC.2017.7951740Pudumalar, S., Ramanujam, E., Rajashree, R. H., Kavya, C., Kiruthika, T., & Nisha, J. (2017b). Crop recommendation system for precision agriculture. 2016 Eighth International Conference on Advanced Computing (ICoAC), October, 32–36. https://doi.org/10.1109/ICoAC.2017.7951740Pudumalar, S., Ramanujam, E., Rajashree, R. H., Kavya, C., Kiruthika, T., & Nisha, J. (2017c). Crop recommendation system for precision agriculture. 2016 Eighth International Conference on Advanced Computing (ICoAC), October, 32–36. https://doi.org/10.1109/ICoAC.2017.7951740Rahman, K. M. A., & Zhang, D. (2018). Effects of fertilizer broadcasting on the excessive use of inorganic fertilizers and environmental sustainability. Sustainability (Switzerland), 10(3). https://doi.org/10.3390/su10030759Rahn, C. R. (2018). Challenges of devising nitrogen recommendation systems for open field vegetables. Acta Horticulturae, 1192, 11–20. https://doi.org/10.17660/ActaHortic.2018.1192.2Raja, S. K. S., Rishi, R., Sundaresan, E., & Srijit, V. (2017). Demand based crop recommender system for farmers. 2017 IEEE Technological Innovations in ICT for Agriculture and Rural Development (TIAR), 2018-Janua(Tiar), 194–199. https://doi.org/10.1109/TIAR.2017.8273714Ren, Z., & Lu, X. (2012). Design of fertilization recommendation knowledge base and appllication. 2012 1st International Conference on Agro-Geoinformatics, Agro-Geoinformatics 2012, 203–207. https://doi.org/10.1109/Agro-Geoinformatics.2012.6311640Revathi, P., Revathi, R., & Hemalatha, M. (2011). Comparative Study of Knowledge in Crop Diseases Using Machine Learning Techniques. Inter-National Journal of Computer Science and Information Technologies (IJCSIT), 2(5), 2180–2182.Ricci, F., Rokach, L., Shapira, B., Kantor, P. B., & Ricci, F. (2011). Recommender Systems Handbook. In Recommender Systems Handbook. https://doi.org/10.1007/978-0-387-85820-3Santos, F. C. dos, Neves, J. C. L., Novais, R. F., Alvarez V., V. H., & Sediyama, C. S. (2008). Modelagem da recomendação de corretivos e fertilizantes para a cultura da soja. Revista Brasileira de Ciência Do Solo, 32(4), 1661–1674. https://doi.org/10.1590/s0100-06832008000400031SENSOVANT. (2014). Sensores / Instrumentos para Agricultura y Ganadería. http://sensovant.com/productos-aplicaciones/agricultura/instrumentos-tecnologicos-agricultura.htmlSilva, A. P. da, Alvarez V, V. H., Souza, A. P. de, Neves, J. C. L., Novais, R. F., & Dantas, J. P. (2009). Sistema de recomendação de fertilizantes e corretivos para a cultura do abacaxi - fertcalc-abacaxi. Revista Brasileira de Ciência Do Solo, 33(5), 1269–1280. https://doi.org/10.1590/s0100-06832009000500020Sujithra, T., Thanjaivadivel, M., & Durai, S. (2017a). Fertilizer recommendation system for coconut cultivation. International Journal of Civil Engineering and Technology, 8(9), 813–819. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85029895779&partnerID=40&md5=1c6e972f7cf611f24f4aa08e7fbd6bf0Sujithra, T., Thanjaivadivel, M., & Durai, S. (2017b). Fertilizer recommendation system for coconut cultivation. International Journal of Civil Engineering and Technology, 8(9), 813–819. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85029895779&partnerID=40&md5=1c6e972f7cf611f24f4aa08e7fbd6bf0Suma, V., Shetty, R. A., Tated, R. F., Rohan, S., & Pujar, T. S. (2019). CNN based Leaf Disease Identification and Remedy Recommendation System. 2019 3rd International Conference on Electronics, Communication and Aerospace Technology (ICECA), 395–399. https://doi.org/10.1109/ICECA.2019.8821872Ward, B. P., Brown-Guedira, G., Tyagi, P., Kolb, F. L., van Sanford, D. A., Sneller, C. H., & Griffey, C. A. (2019). Multienvironment and multitrait genomic selection models in unbalanced early-generation wheat yield trials. Crop Science, 59(2), 491–507. https://doi.org/10.2135/cropsci2018.03.0189Wong, M. T. F., Corner, R. J., & Cook, S. E. (2001). A decision support system for mapping the site-specific potassium requirement of wheat in the field. Australian Journal of Experimental Agriculture, 41(5), 655. https://doi.org/10.1071/EA00191Xu, M., Zhang, J., Wu, F., & Wang, X. (2015). Preliminary the diagnosis and Recommendation Integrated System (DRIS) norms for evaluating the nutrient status of apple. Advance Journal of Food Science and Technology, 7(2), 74–80. https://doi.org/10.19026/ajfst.7.1270Yu, F., Zhang, Q., Luan, R., Zhang, J., & Liu, X. (2013). Application and improvement of intelligent recommendation for Agricultural Information. 2013 Ninth International Conference on Natural Computation (ICNC), 1077–1081. https://doi.org/10.1109/ICNC.2013.6818137Zhao, Y., & Bai, S. H. (2012). Research on optimizing recommend system for agriculture information personalization based on user clustering. 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