Mostrar el registro sencillo del ítem
dc.contributor.author | Lezoche, Mario | es_ES |
dc.contributor.author | Hernández, Jorge E. | es_ES |
dc.contributor.author | Alemany Díaz, María Del Mar | es_ES |
dc.contributor.author | Panetto, Hervé | es_ES |
dc.contributor.author | Kacprzyk, Janusz | es_ES |
dc.date.accessioned | 2021-04-23T03:31:33Z | |
dc.date.available | 2021-04-23T03:31:33Z | |
dc.date.issued | 2020-05 | es_ES |
dc.identifier.issn | 0166-3615 | es_ES |
dc.identifier.uri | http://hdl.handle.net/10251/165518 | |
dc.description.abstract | [EN] The term "Agri-Food 4.0" is an analogy to the term Industry 4.0; coming from the concept "agriculture 4.0". Since the origins of the industrial revolution, where the steam engines started the concept of Industry 1.0 and later the use of electricity upgraded the concept to Industry 2.0, the use of technologies generated a milestone in the industry revolution by addressing the Industry 3.0 concept. Hence, Industry 4.0, it is about including and integrating the latest developments based on digital technologies as well as the interoperability process across them. This allows enterprises to transmit real-time information in terms behaviour and performance. Therefore, the challenge is to maintain these complex networked structures efficiently linked and organised within the use of such technologies, especially to identify and satisfy supply chain stakeholders dynamic requirements. In this context, the agriculture domain is not an exception although it possesses some specialities depending from the domain. In fact, all agricultural machinery incorporates electronic controls and has entered to the digital age, enhancing their current performance. In addition, electronics, using sensors and drones, support the data collection of several agriculture key aspects, such as weather, geographical spatialization, animals and crops behaviours, as well as the entire farm life cycle. However, the use of the right methods and methodologies for enhancing agriculture supply chains performance is still a challenge, thus the concept of Industry 4.0 has evolved and adapted to agriculture 4.0 in order analyse the behaviours and performance in this specific domain. Thus, the question mark on how agriculture 4.0 support a better supply chain decision-making process, or how can help to save time to farmer to make effective decision based on objective data, remains open. Therefore, in this survey, a review of more than hundred papers on new technologies and the new available supply chains methods are analysed and contrasted to understand the future paths of the Agri-Food domain. | es_ES |
dc.description.sponsorship | Authors of this publication acknowledge the contribution of the Project 691249, RUC-APS "Enhancing and implementing Knowledge based ICT solutions within high Risk and Uncertain Conditions for Agriculture Production Systems" (www.ruc-aps.eu), funded by the European Union under their funding scheme H2020-MSCARISE-2015. | es_ES |
dc.language | Inglés | es_ES |
dc.publisher | Elsevier | es_ES |
dc.relation.ispartof | Computers in Industry | es_ES |
dc.rights | Reconocimiento - No comercial - Sin obra derivada (by-nc-nd) | es_ES |
dc.subject | Agri-Food 4.0 | es_ES |
dc.subject | Agriculture 4.0 | es_ES |
dc.subject | Supply chains | es_ES |
dc.subject | Internet of things | es_ES |
dc.subject | Big data | es_ES |
dc.subject | Blockchain | es_ES |
dc.subject | Artificial intelligence | es_ES |
dc.subject.classification | ORGANIZACION DE EMPRESAS | es_ES |
dc.title | Agri-food 4.0: A survey of the supply chains and technologies for the future agriculture | es_ES |
dc.type | Artículo | es_ES |
dc.identifier.doi | 10.1016/j.compind.2020.103187 | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/EC/H2020/691249/EU/Enhancing and implementing Knowledge based ICT solutions within high Risk and Uncertain Conditions for Agriculture Production Systems/ | es_ES |
dc.rights.accessRights | Abierto | es_ES |
dc.contributor.affiliation | Universitat Politècnica de València. Departamento de Organización de Empresas - Departament d'Organització d'Empreses | es_ES |
dc.description.bibliographicCitation | Lezoche, M.; Hernández, JE.; Alemany Díaz, MDM.; Panetto, H.; Kacprzyk, J. (2020). Agri-food 4.0: A survey of the supply chains and technologies for the future agriculture. Computers in Industry. 117:1-15. https://doi.org/10.1016/j.compind.2020.103187 | es_ES |
dc.description.accrualMethod | S | es_ES |
dc.relation.publisherversion | https://doi.org/10.1016/j.compind.2020.103187 | es_ES |
dc.description.upvformatpinicio | 1 | es_ES |
dc.description.upvformatpfin | 15 | es_ES |
dc.type.version | info:eu-repo/semantics/publishedVersion | es_ES |
dc.description.volume | 117 | es_ES |
dc.relation.pasarela | S\405776 | es_ES |
dc.contributor.funder | European Commission | es_ES |
dc.description.references | Ahumada, O., & Villalobos, J. R. (2009). Application of planning models in the agri-food supply chain: A review. European Journal of Operational Research, 196(1), 1-20. doi:10.1016/j.ejor.2008.02.014 | es_ES |
dc.description.references | Ait-Mouheb, N., Bahri, A., Thayer, B. B., Benyahia, B., Bourrié, G., Cherki, B., … Harmand, J. (2018). The reuse of reclaimed water for irrigation around the Mediterranean Rim: a step towards a more virtuous cycle? Regional Environmental Change, 18(3), 693-705. doi:10.1007/s10113-018-1292-z | es_ES |
dc.description.references | Ali, J., & Kumar, S. (2011). Information and communication technologies (ICTs) and farmers’ decision-making across the agricultural supply chain. International Journal of Information Management, 31(2), 149-159. doi:10.1016/j.ijinfomgt.2010.07.008 | es_ES |
dc.description.references | Alzahrani, S. M. (2018). Development of IoT mining machine for Twitter sentiment analysis: Mining in the cloud and results on the mirror. 2018 15th Learning and Technology Conference (L&T). doi:10.1109/lt.2018.8368490 | es_ES |
dc.description.references | Amandeep, Bhattacharjee, A., Das, P., Basu, D., Roy, S., Ghosh, S., … Rana, T. K. (2017). Smart farming using IOT. 2017 8th IEEE Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON). doi:10.1109/iemcon.2017.8117219 | es_ES |
dc.description.references | Annosi, M. C., Brunetta, F., Monti, A., & Nati, F. (2019). Is the trend your friend? An analysis of technology 4.0 investment decisions in agricultural SMEs. Computers in Industry, 109, 59-71. doi:10.1016/j.compind.2019.04.003 | es_ES |
dc.description.references | Baio, F. H. R. (2011). Evaluation of an auto-guidance system operating on a sugar cane harvester. Precision Agriculture, 13(1), 141-147. doi:10.1007/s11119-011-9241-6 | es_ES |
dc.description.references | Belaud, J.-P., Prioux, N., Vialle, C., & Sablayrolles, C. (2019). Big data for agri-food 4.0: Application to sustainability management for by-products supply chain. Computers in Industry, 111, 41-50. doi:10.1016/j.compind.2019.06.006 | es_ES |
dc.description.references | Nicolaas Bezuidenhout, C., Bodhanya, S., & Brenchley, L. (2012). An analysis of collaboration in a sugarcane production and processing supply chain. British Food Journal, 114(6), 880-895. doi:10.1108/00070701211234390 | es_ES |
dc.description.references | Bhatt, M. R., & Buch, S. (2015). Prediction of formability for sheet metal component using artificial intelligent technique. 2015 2nd International Conference on Signal Processing and Integrated Networks (SPIN). doi:10.1109/spin.2015.7095356 | es_ES |
dc.description.references | Birkel, H. S., & Hartmann, E. (2019). Impact of IoT challenges and risks for SCM. Supply Chain Management: An International Journal, 24(1), 39-61. doi:10.1108/scm-03-2018-0142 | es_ES |
dc.description.references | Boehlje, M. (1999). Structural Changes in the Agricultural Industries: How Do We Measure, Analyze and Understand Them? American Journal of Agricultural Economics, 81(5), 1028-1041. doi:10.2307/1244080 | es_ES |
dc.description.references | Bonney, L., Clark, R., Collins, R., & Fearne, A. (2007). From serendipity to sustainable competitive advantage: insights from Houston’s Farm and their journey of co‐innovation. Supply Chain Management: An International Journal, 12(6), 395-399. doi:10.1108/13598540710826326 | es_ES |
dc.description.references | Boshkoska, B. M., Liu, S., Zhao, G., Fernandez, A., Gamboa, S., del Pino, M., … Chen, H. (2019). A decision support system for evaluation of the knowledge sharing crossing boundaries in agri-food value chains. Computers in Industry, 110, 64-80. doi:10.1016/j.compind.2019.04.012 | es_ES |
dc.description.references | Brewster, C., Roussaki, I., Kalatzis, N., Doolin, K., & Ellis, K. (2017). IoT in Agriculture: Designing a Europe-Wide Large-Scale Pilot. IEEE Communications Magazine, 55(9), 26-33. doi:10.1109/mcom.2017.1600528 | es_ES |
dc.description.references | Bronson, K., & Knezevic, I. (2016). Big Data in food and agriculture. Big Data & Society, 3(1), 205395171664817. doi:10.1177/2053951716648174 | es_ES |
dc.description.references | Brown, K. (2013). Global environmental change I. Progress in Human Geography, 38(1), 107-117. doi:10.1177/0309132513498837 | es_ES |
dc.description.references | Chilcanan, D., Navas, P., & Escobar, S. M. (2017). Expert system for remote process automation in multiplatform servers, through human machine conversation. 2017 12th Iberian Conference on Information Systems and Technologies (CISTI). doi:10.23919/cisti.2017.7975913 | es_ES |
dc.description.references | Choi, J., In, Y., Park, C., Seok, S., Seo, H., & Kim, H. (2016). Secure IoT framework and 2D architecture for End-To-End security. The Journal of Supercomputing, 74(8), 3521-3535. doi:10.1007/s11227-016-1684-0 | es_ES |
dc.description.references | Cohen, W. M., & Levinthal, D. A. (1990). Absorptive Capacity: A New Perspective on Learning and Innovation. Administrative Science Quarterly, 35(1), 128. doi:10.2307/2393553 | es_ES |
dc.description.references | Dabbene, F., Gay, P., & Tortia, C. (2014). Traceability issues in food supply chain management: A review. Biosystems Engineering, 120, 65-80. doi:10.1016/j.biosystemseng.2013.09.006 | es_ES |
dc.description.references | Del Borghi, A., Gallo, M., Strazza, C., & Del Borghi, M. (2014). An evaluation of environmental sustainability in the food industry through Life Cycle Assessment: the case study of tomato products supply chain. Journal of Cleaner Production, 78, 121-130. doi:10.1016/j.jclepro.2014.04.083 | es_ES |
dc.description.references | Devarakonda, R., Shrestha, B., Palanisamy, G., Hook, L., Killeffer, T., Krassovski, M., … Lazer, K. (2014). OME: Tool for generating and managing metadata to handle BigData. 2014 IEEE International Conference on Big Data (Big Data). doi:10.1109/bigdata.2014.7004476 | es_ES |
dc.description.references | Nascimento, A. F. do, Mendonça, E. de S., Leite, L. F. C., Scholberg, J., & Neves, J. C. L. (2012). Calibration and validation of models for short-term decomposition and N mineralization of plant residues in the tropics. Scientia Agricola, 69(6), 393-401. doi:10.1590/s0103-90162012000600008 | es_ES |
dc.description.references | Dolan, C., & Humphrey, J. (2000). Governance and Trade in Fresh Vegetables: The Impact of UK Supermarkets on the African Horticulture Industry. Journal of Development Studies, 37(2), 147-176. doi:10.1080/713600072 | es_ES |
dc.description.references | Dragincic, J., Korac, N., & Blagojevic, B. (2015). Group multi-criteria decision making (GMCDM) approach for selecting the most suitable table grape variety intended for organic viticulture. Computers and Electronics in Agriculture, 111, 194-202. doi:10.1016/j.compag.2014.12.023 | es_ES |
dc.description.references | Dworak, V., Selbeck, J., Dammer, K.-H., Hoffmann, M., Zarezadeh, A., & Bobda, C. (2013). Strategy for the Development of a Smart NDVI Camera System for Outdoor Plant Detection and Agricultural Embedded Systems. Sensors, 13(2), 1523-1538. doi:10.3390/s130201523 | es_ES |
dc.description.references | Eisele, M., Kiese, R., Krämer, A., & Leibundgut, C. (2001). Application of a catchment water quality model for assessment and prediction of nitrogen budgets. Physics and Chemistry of the Earth, Part B: Hydrology, Oceans and Atmosphere, 26(7-8), 547-551. doi:10.1016/s1464-1909(01)00048-x | es_ES |
dc.description.references | Elsayed, K. M. F., Ismail, T., & S. Ouf, N. (2018). A Review on the Relevant Applications of Machine Learning in Agriculture. IJIREEICE, 6(8), 1-17. doi:10.17148/ijireeice.2018.681 | es_ES |
dc.description.references | Esteso, A., Alemany, M. M. E., & Ortiz, A. (2017). Métodos y Modelos Deterministas e Inciertos para la Gestión de Cadenas de Suministro Agroalimentarias. Dirección y Organización, 41-46. doi:10.37610/dyo.v0i0.509 | es_ES |
dc.description.references | Esteso, A., Alemany, M. M. E., & Ortiz, A. (2018). Conceptual framework for designing agri-food supply chains under uncertainty by mathematical programming models. International Journal of Production Research, 56(13), 4418-4446. doi:10.1080/00207543.2018.1447706 | es_ES |
dc.description.references | GERHARDS, R., GUTJAHR, C., WEIS, M., KELLER, M., SÖKEFELD, M., MÖHRING, J., & PIEPHO, H. P. (2011). Using precision farming technology to quantify yield effects attributed to weed competition and herbicide application. Weed Research, 52(1), 6-15. doi:10.1111/j.1365-3180.2011.00893.x | es_ES |
dc.description.references | Govindan, K., Jafarian, A., Khodaverdi, R., & Devika, K. (2014). Two-echelon multiple-vehicle location–routing problem with time windows for optimization of sustainable supply chain network of perishable food. International Journal of Production Economics, 152, 9-28. doi:10.1016/j.ijpe.2013.12.028 | es_ES |
dc.description.references | Gumaste, S. S., & Kadam, A. J. (2016). Future weather prediction using genetic algorithm and FFT for smart farming. 2016 International Conference on Computing Communication Control and automation (ICCUBEA). doi:10.1109/iccubea.2016.7860028 | es_ES |
dc.description.references | Hashem, H., & Ranc, D. (2016). A review of modeling toolbox for BigData. 2016 International Conference on Military Communications and Information Systems (ICMCIS). doi:10.1109/icmcis.2016.7496565 | es_ES |
dc.description.references | Hefnawy, A., Elhariri, T., Cherifi, C., Robert, J., Bouras, A., Kubler, S., & Framling, K. (2017). Combined use of lifecycle management and IoT in smart cities. 2017 11th International Conference on Software, Knowledge, Information Management and Applications (SKIMA). doi:10.1109/skima.2017.8294112 | es_ES |
dc.description.references | Hosseini, S. H., Tang, C. Y., & Jiang, J. N. (2014). Calibration of a Wind Farm Wind Speed Model With Incomplete Wind Data. IEEE Transactions on Sustainable Energy, 5(1), 343-350. doi:10.1109/tste.2013.2284490 | es_ES |
dc.description.references | Hu, Y., Zhang, L., Li, J., & Mehrotra, S. (2016). ICME 2016 Image Recognition Grand Challenge. 2016 IEEE International Conference on Multimedia & Expo Workshops (ICMEW). doi:10.1109/icmew.2016.7574663 | es_ES |
dc.description.references | A. Irmak, J. W. Jones, W. D. Batchelor, S. Irmak, K. J. Boote, & J. O. Paz. (2006). Artificial Neural Network Model as a Data Analysis Tool in Precision Farming. Transactions of the ASABE, 49(6), 2027-2037. doi:10.13031/2013.22264 | es_ES |
dc.description.references | Jeon, S., Kim, B., & Huh, J. (2017). Study on methods to determine rotor equivalent wind speed to increase prediction accuracy of wind turbine performance under wake condition. Energy for Sustainable Development, 40, 41-49. doi:10.1016/j.esd.2017.06.001 | es_ES |
dc.description.references | Joly, P.-B. (2005). Resilient farming systems in a complex world — new issues for the governance of science and innovation. Australian Journal of Experimental Agriculture, 45(6), 617. doi:10.1071/ea03252 | es_ES |
dc.description.references | Joshi, R., Banwet, D. K., & Shankar, R. (2009). Indian cold chain: modeling the inhibitors. British Food Journal, 111(11), 1260-1283. doi:10.1108/00070700911001077 | es_ES |
dc.description.references | Kamata, T., Roshanianfard, A., & Noguchi, N. (2018). Heavy-weight Crop Harvesting Robot - Controlling Algorithm. IFAC-PapersOnLine, 51(17), 244-249. doi:10.1016/j.ifacol.2018.08.165 | es_ES |
dc.description.references | Kamble, S. S., Gunasekaran, A., & Gawankar, S. A. (2020). Achieving sustainable performance in a data-driven agriculture supply chain: A review for research and applications. International Journal of Production Economics, 219, 179-194. doi:10.1016/j.ijpe.2019.05.022 | es_ES |
dc.description.references | Kamilaris, A., Kartakoullis, A., & Prenafeta-Boldú, F. X. (2017). A review on the practice of big data analysis in agriculture. Computers and Electronics in Agriculture, 143, 23-37. doi:10.1016/j.compag.2017.09.037 | es_ES |
dc.description.references | Kelepouris, T., Pramatari, K., & Doukidis, G. (2007). RFID‐enabled traceability in the food supply chain. Industrial Management & Data Systems, 107(2), 183-200. doi:10.1108/02635570710723804 | es_ES |
dc.description.references | Khan, S. F., & Ismail, M. Y. (2018). An Investigation into the Challenges and Opportunities Associated with the Application of Internet of Things (IoT) in the Agricultural Sector-A Review. Journal of Computer Science, 14(2), 132-143. doi:10.3844/jcssp.2018.132.143 | es_ES |
dc.description.references | Kladivko, E. J., Helmers, M. J., Abendroth, L. J., Herzmann, D., Lal, R., Castellano, M. J., … Villamil, M. B. (2014). Standardized research protocols enable transdisciplinary research of climate variation impacts in corn production systems. Journal of Soil and Water Conservation, 69(6), 532-542. doi:10.2489/jswc.69.6.532 | es_ES |
dc.description.references | Ko, T., Lee, J., & Ryu, D. (2018). Blockchain Technology and Manufacturing Industry: Real-Time Transparency and Cost Savings. Sustainability, 10(11), 4274. doi:10.3390/su10114274 | es_ES |
dc.description.references | KÖK, M. S. (2009). Application of Food Safety Management Systems (ISO 22000/HACCP) in the Turkish Poultry Industry: A Comparison Based on Enterprise Size. Journal of Food Protection, 72(10), 2221-2225. doi:10.4315/0362-028x-72.10.2221 | es_ES |
dc.description.references | Kvíz, Z., Kroulik, M., & Chyba, J. (2014). Machinery guidance systems analysis concerning pass-to-pass accuracy as a tool for efficient plant production in fields and for soil damage reduction. Plant, Soil and Environment, 60(No. 1), 36-42. doi:10.17221/622/2012-pse | es_ES |
dc.description.references | Lamsal, K., Jones, P. C., & Thomas, B. W. (2016). Harvest logistics in agricultural systems with multiple, independent producers and no on-farm storage. Computers & Industrial Engineering, 91, 129-138. doi:10.1016/j.cie.2015.10.018 | es_ES |
dc.description.references | Laube, P., Duckham, M., & Palaniswami, M. (2011). Deferred decentralized movement pattern mining for geosensor networks. International Journal of Geographical Information Science, 25(2), 273-292. doi:10.1080/13658810903296630 | es_ES |
dc.description.references | Li, F.-R., Gao, C.-Y., Zhao, H.-L., & Li, X.-Y. (2002). Soil conservation effectiveness and energy efficiency of alternative rotations and continuous wheat cropping in the Loess Plateau of northwest China. Agriculture, Ecosystems & Environment, 91(1-3), 101-111. doi:10.1016/s0167-8809(01)00265-1 | es_ES |
dc.description.references | Liakos, K., Busato, P., Moshou, D., Pearson, S., & Bochtis, D. (2018). Machine Learning in Agriculture: A Review. Sensors, 18(8), 2674. doi:10.3390/s18082674 | es_ES |
dc.description.references | Meichen, L., Jun, C., Xiang, Z., Lu, W., & Yongpeng, T. (2018). Dynamic obstacle detection based on multi-sensor information fusion. IFAC-PapersOnLine, 51(17), 861-865. doi:10.1016/j.ifacol.2018.08.086 | es_ES |
dc.description.references | Louwagie, G., Northey, G., Finn, J. A., & Purvis, G. (2012). Development of indicators for assessment of the environmental impact of livestock farming in Ireland using the Agri-environmental Footprint Index. Ecological Indicators, 18, 149-162. doi:10.1016/j.ecolind.2011.11.003 | es_ES |
dc.description.references | Luque, A., Peralta, M. E., de las Heras, A., & Córdoba, A. (2017). State of the Industry 4.0 in the Andalusian food sector. Procedia Manufacturing, 13, 1199-1205. doi:10.1016/j.promfg.2017.09.195 | es_ES |
dc.description.references | Malhotra, S., Doja, M. ., Alam, B., & Alam, M. (2017). Bigdata analysis and comparison of bigdata analytic approches. 2017 International Conference on Computing, Communication and Automation (ICCCA). doi:10.1109/ccaa.2017.8229821 | es_ES |
dc.description.references | Mayer, J., Gunst, L., Mäder, P., Samson, M.-F., Carcea, M., Narducci, V., … Dubois, D. (2015). «Productivity, quality and sustainability of winter wheat under long-term conventional and organic management in Switzerland». European Journal of Agronomy, 65, 27-39. doi:10.1016/j.eja.2015.01.002 | es_ES |
dc.description.references | McGuire, S., & Sperling, L. (2013). Making seed systems more resilient to stress. Global Environmental Change, 23(3), 644-653. doi:10.1016/j.gloenvcha.2013.02.001 | es_ES |
dc.description.references | Mekala, M. S., & Viswanathan, P. (2017). A Survey: Smart agriculture IoT with cloud computing. 2017 International conference on Microelectronic Devices, Circuits and Systems (ICMDCS). doi:10.1109/icmdcs.2017.8211551 | es_ES |
dc.description.references | Mishra, S., Mishra, D., & Santra, G. H. (2016). Applications of Machine Learning Techniques in Agricultural Crop Production: A Review Paper. Indian Journal of Science and Technology, 9(38). doi:10.17485/ijst/2016/v9i38/95032 | es_ES |
dc.description.references | Mocnej, J., Seah, W. K. G., Pekar, A., & Zolotova, I. (2018). Decentralised IoT Architecture for Efficient Resources Utilisation. IFAC-PapersOnLine, 51(6), 168-173. doi:10.1016/j.ifacol.2018.07.148 | es_ES |
dc.description.references | Mohanraj, I., Gokul, V., Ezhilarasie, R., & Umamakeswari, A. (2017). Intelligent drip irrigation and fertigation using wireless sensor networks. 2017 IEEE Technological Innovations in ICT for Agriculture and Rural Development (TIAR). doi:10.1109/tiar.2017.8273682 | es_ES |
dc.description.references | Montecinos, J., Ouhimmou, M., Chauhan, S., & Paquet, M. (2018). Forecasting multiple waste collecting sites for the agro-food industry. Journal of Cleaner Production, 187, 932-939. doi:10.1016/j.jclepro.2018.03.127 | es_ES |
dc.description.references | Yandun Narváez, F., Gregorio, E., Escolà, A., Rosell-Polo, J. R., Torres-Torriti, M., & Auat Cheein, F. (2018). Terrain classification using ToF sensors for the enhancement of agricultural machinery traversability. Journal of Terramechanics, 76, 1-13. doi:10.1016/j.jterra.2017.10.005 | es_ES |
dc.description.references | Nguyen, T., ZHOU, L., Spiegler, V., Ieromonachou, P., & Lin, Y. (2018). Big data analytics in supply chain management: A state-of-the-art literature review. Computers & Operations Research, 98, 254-264. doi:10.1016/j.cor.2017.07.004 | es_ES |
dc.description.references | Nilsson, E., Hochrainer-Stigler, S., Mochizuki, J., & Uvo, C. B. (2016). Hydro-climatic variability and agricultural production on the shores of Lake Chad. Environmental Development, 20, 15-30. doi:10.1016/j.envdev.2016.09.001 | es_ES |
dc.description.references | Nolan, P., Paley, D. A., & Kroeger, K. (2017). Multi-UAS path planning for non-uniform data collection in precision agriculture. 2017 IEEE Aerospace Conference. doi:10.1109/aero.2017.7943794 | es_ES |
dc.description.references | Oberholster, C., Adendorff, C., & Jonker, K. (2015). Financing Agricultural Production from a Value Chain Perspective. Outlook on Agriculture, 44(1), 49-60. doi:10.5367/oa.2015.0197 | es_ES |
dc.description.references | Opara, L. U., & Mazaud, F. (2001). Food Traceability from Field to Plate. Outlook on Agriculture, 30(4), 239-247. doi:10.5367/000000001101293724 | es_ES |
dc.description.references | Ott, K.-H., Aranı́bar, N., Singh, B., & Stockton, G. W. (2003). Metabonomics classifies pathways affected by bioactive compounds. Artificial neural network classification of NMR spectra of plant extracts. Phytochemistry, 62(6), 971-985. doi:10.1016/s0031-9422(02)00717-3 | es_ES |
dc.description.references | Panetto, H. (2007). Towards a classification framework for interoperability of enterprise applications. International Journal of Computer Integrated Manufacturing, 20(8), 727-740. doi:10.1080/09511920600996419 | es_ES |
dc.description.references | Paulraj, G. J. L., Francis, S. A. J., Peter, J. D., & Jebadurai, I. J. (2018). Resource-aware virtual machine migration in IoT cloud. Future Generation Computer Systems, 85, 173-183. doi:10.1016/j.future.2018.03.024 | es_ES |
dc.description.references | Pilli, S. K., Nallathambi, B., George, S. J., & Diwanji, V. (2015). eAGROBOT — A robot for early crop disease detection using image processing. 2015 2nd International Conference on Electronics and Communication Systems (ICECS). doi:10.1109/ecs.2015.7124873 | es_ES |
dc.description.references | Pinho, P., Dias, T., Cruz, C., Sim Tang, Y., Sutton, M. A., Martins-Loução, M.-A., … Branquinho, C. (2011). Using lichen functional diversity to assess the effects of atmospheric ammonia in Mediterranean woodlands. Journal of Applied Ecology, 48(5), 1107-1116. doi:10.1111/j.1365-2664.2011.02033.x | es_ES |
dc.description.references | Prathibha, S. R., Hongal, A., & Jyothi, M. P. (2017). IOT Based Monitoring System in Smart Agriculture. 2017 International Conference on Recent Advances in Electronics and Communication Technology (ICRAECT). doi:10.1109/icraect.2017.52 | es_ES |
dc.description.references | Reardon, T., Echeverria, R., Berdegué, J., Minten, B., Liverpool-Tasie, S., Tschirley, D., & Zilberman, D. (2019). Rapid transformation of food systems in developing regions: Highlighting the role of agricultural research & innovations. Agricultural Systems, 172, 47-59. doi:10.1016/j.agsy.2018.01.022 | es_ES |
dc.description.references | Ribarics, P. (2016). Big Data and its impact on agriculture. Ecocycles, 2(1), 33-34. doi:10.19040/ecocycles.v2i1.54 | es_ES |
dc.description.references | Rosell, J. R., & Sanz, R. (2012). A review of methods and applications of the geometric characterization of tree crops in agricultural activities. Computers and Electronics in Agriculture, 81, 124-141. doi:10.1016/j.compag.2011.09.007 | es_ES |
dc.description.references | Roshanianfard, A., Kamata, T., & Noguchi, N. (2018). Performance evaluation of harvesting robot for heavy-weight crops. IFAC-PapersOnLine, 51(17), 332-338. doi:10.1016/j.ifacol.2018.08.200 | es_ES |
dc.description.references | Routroy, S., & Behera, A. (2017). Agriculture supply chain. Journal of Agribusiness in Developing and Emerging Economies, 7(3), 275-302. doi:10.1108/jadee-06-2016-0039 | es_ES |
dc.description.references | Ruiz-Garcia, L., Steinberger, G., & Rothmund, M. (2010). A model and prototype implementation for tracking and tracing agricultural batch products along the food chain. Food Control, 21(2), 112-121. doi:10.1016/j.foodcont.2008.12.003 | es_ES |
dc.description.references | Saggi, M. K., & Jain, S. (2018). A survey towards an integration of big data analytics to big insights for value-creation. Information Processing & Management, 54(5), 758-790. doi:10.1016/j.ipm.2018.01.010 | es_ES |
dc.description.references | Sánchez-Hermosilla, J., González, R., Rodríguez, F., & Donaire, J. (2013). Mechatronic Description of a Laser Autoguided Vehicle for Greenhouse Operations. Sensors, 13(1), 769-784. doi:10.3390/s130100769 | es_ES |
dc.description.references | Santiago, R. M. C., Jose, J. A., Bandala, A. A., & Dadios, E. P. (2017). Multiple objective optimization of LED lighting system design using genetic algorithm. 2017 5th International Conference on Information and Communication Technology (ICoIC7). doi:10.1109/icoict.2017.8074698 | es_ES |
dc.description.references | Senge, P. M. (1997). THE FIFTH DISCIPLINE. Measuring Business Excellence, 1(3), 46-51. doi:10.1108/eb025496 | es_ES |
dc.description.references | Shi, X., An, X., Zhao, Q., Liu, H., Xia, L., Sun, X., & Guo, Y. (2019). State-of-the-Art Internet of Things in Protected Agriculture. Sensors, 19(8), 1833. doi:10.3390/s19081833 | es_ES |
dc.description.references | Shin, D., & Ko, K. (2017). Comparative analysis of degradation rates for inland and seaside wind turbines in compliance with the International Electrotechnical Commission standard. Energy, 118, 1180-1186. doi:10.1016/j.energy.2016.10.140 | es_ES |
dc.description.references | Singh, R., Soni, S. K., Patel, R. P., & Kalra, A. (2013). Technology for improving essential oil yield of Ocimum basilicum L. (sweet basil) by application of bioinoculant colonized seeds under organic field conditions. Industrial Crops and Products, 45, 335-342. doi:10.1016/j.indcrop.2013.01.003 | es_ES |
dc.description.references | Soh, Y. W., Koo, C. H., Huang, Y. F., & Fung, K. F. (2018). Application of artificial intelligence models for the prediction of standardized precipitation evapotranspiration index (SPEI) at Langat River Basin, Malaysia. Computers and Electronics in Agriculture, 144, 164-173. doi:10.1016/j.compag.2017.12.002 | es_ES |
dc.description.references | Spekken, M., de Bruin, S., Molin, J. P., & Sparovek, G. (2016). Planning machine paths and row crop patterns on steep surfaces to minimize soil erosion. Computers and Electronics in Agriculture, 124, 194-210. doi:10.1016/j.compag.2016.03.013 | es_ES |
dc.description.references | Starr, G. C. (2005). Assessing temporal stability and spatial variability of soil water patterns with implications for precision water management. Agricultural Water Management, 72(3), 223-243. doi:10.1016/j.agwat.2004.09.020 | es_ES |
dc.description.references | Suprem, A., Mahalik, N., & Kim, K. (2013). A review on application of technology systems, standards and interfaces for agriculture and food sector. Computer Standards & Interfaces, 35(4), 355-364. doi:10.1016/j.csi.2012.09.002 | es_ES |
dc.description.references | Swisher, M. E., Ruiz-Menjivar, J., & Koenig, R. (2017). Value chains in renewable and sustainable food systems. Renewable Agriculture and Food Systems, 33(1), 1-5. doi:10.1017/s1742170517000667 | es_ES |
dc.description.references | Tabatabaie, S. M. H., Rafiee, S., Keyhani, A., & Ebrahimi, A. (2013). Energy and economic assessment of prune production in Tehran province of Iran. Journal of Cleaner Production, 39, 280-284. doi:10.1016/j.jclepro.2012.07.052 | es_ES |
dc.description.references | Tan, D. (2012). Developing Agricultural Products Logistics in China from the Perspective of Green Supply Chain. International Journal of Business and Management, 7(21). doi:10.5539/ijbm.v7n21p106 | es_ES |
dc.description.references | Van der Vorst, J. G. A. J. (2005). Product traceability in food-supply chains. Accreditation and Quality Assurance, 11(1-2), 33-37. doi:10.1007/s00769-005-0028-1 | es_ES |
dc.description.references | Vergara, P. M., de la Cal, E., Villar, J. R., González, V. M., & Sedano, J. (2017). An IoT Platform for Epilepsy Monitoring and Supervising. Journal of Sensors, 2017, 1-18. doi:10.1155/2017/6043069 | es_ES |
dc.description.references | Wang, L., Aydin, N., Astaras, A., Ahmadian, M., Hammond, P. A., Tang, T. B., … Cumming, D. R. S. (s. f.). A Sensor System On Chip for Wireless Microsystems. 2006 IEEE International Symposium on Circuits and Systems. doi:10.1109/iscas.2006.1692720 | es_ES |
dc.description.references | Wang, B., Xiong, Y.-F., Hu, Z.-J., Zhao, H.-Y., Zhang, W., & Mei, H. (2014). Interactive Inconsistency Fixing in Feature Modeling. Journal of Computer Science and Technology, 29(4), 724-736. doi:10.1007/s11390-014-1462-5 | es_ES |
dc.description.references | Wang, H., Hohimer, C. J., Bhusal, S., Karkee, M., Mo, C., & Miller, J. H. (2018). Simulation As A Tool In Designing And Evaluating A Robotic Apple Harvesting System. IFAC-PapersOnLine, 51(17), 135-140. doi:10.1016/j.ifacol.2018.08.076 | es_ES |
dc.description.references | Rediers, H., Claes, M., Peeters, L., & Willems, K. A. (2009). Evaluation of the cold chain of fresh-cut endive from farmer to plate. Postharvest Biology and Technology, 51(2), 257-262. doi:10.1016/j.postharvbio.2008.07.017 | es_ES |
dc.description.references | Xavier, A., Freitas, M. de B. C., Rosário, M. do S., & Fragoso, R. (2018). Disaggregating statistical data at the field level: An entropy approach. Spatial Statistics, 23, 91-108. doi:10.1016/j.spasta.2017.11.005 | es_ES |
dc.description.references | Xie, Y., Wang, P., Bai, X., Khan, J., Zhang, S., Li, L., & Wang, L. (2017). Assimilation of the leaf area index and vegetation temperature condition index for winter wheat yield estimation using Landsat imagery and the CERES-Wheat model. Agricultural and Forest Meteorology, 246, 194-206. doi:10.1016/j.agrformet.2017.06.015 | es_ES |
dc.description.references | Yablonsky, S. (2018). A Multidimensional Framework for Digital Platform Innovation and Management: From Business to Technological Platforms. Systems Research and Behavioral Science, 35(4), 485-501. doi:10.1002/sres.2544 | es_ES |
dc.description.references | Yakovleva, N. (2007). Measuring the Sustainability of the Food Supply Chain: A Case Study of the UK. Journal of Environmental Policy & Planning, 9(1), 75-100. doi:10.1080/15239080701255005 | es_ES |
dc.description.references | Zhang, X., & Aramyan, L. H. (2009). A conceptual framework for supply chain governance. China Agricultural Economic Review, 1(2), 136-154. doi:10.1108/17561370910927408 | es_ES |
dc.description.references | Zhao, C., & Yao, X. (2016). A digital hardware platform for RF PA digital predistortion algorithms. 2016 9th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI). doi:10.1109/cisp-bmei.2016.7852884 | es_ES |
dc.description.references | Zhao, G., Liu, S., Lopez, C., Lu, H., Elgueta, S., Chen, H., & Boshkoska, B. M. (2019). Blockchain technology in agri-food value chain management: A synthesis of applications, challenges and future research directions. Computers in Industry, 109, 83-99. doi:10.1016/j.compind.2019.04.002 | es_ES |
dc.description.references | Zhou, J., Long, X.-M., & Luo, H.-J. (2018). Spectrum optimization of light-emitting diode insecticide lamp based on partial discharge evaluation. Measurement, 124, 72-80. doi:10.1016/j.measurement.2018.03.073 | es_ES |
dc.subject.ods | 02.- Poner fin al hambre, conseguir la seguridad alimentaria y una mejor nutrición, y promover la agricultura sostenible | es_ES |
dc.subject.ods | 09.- Desarrollar infraestructuras resilientes, promover la industrialización inclusiva y sostenible, y fomentar la innovación | es_ES |