{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T05:44:05Z","timestamp":1774676645495,"version":"3.50.1"},"reference-count":169,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2021,4,1]],"date-time":"2021-04-01T00:00:00Z","timestamp":1617235200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"UNINOVA-CTS","award":["UIDB\/00066\/2020"],"award-info":[{"award-number":["UIDB\/00066\/2020"]}]},{"name":"GeoBioTec","award":["UIDP\/04035\/2020"],"award-info":[{"award-number":["UIDP\/04035\/2020"]}]},{"DOI":"10.13039\/100001599","name":"CEF","doi-asserted-by":"publisher","award":["UIDB\/00239\/2020"],"award-info":[{"award-number":["UIDB\/00239\/2020"]}],"id":[{"id":"10.13039\/100001599","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Agronomy"],"abstract":"<jats:p>Investment in technological research is imperative to stimulate the development of sustainable solutions for the agricultural sector. Advances in Internet of Things, sensors and sensor networks, robotics, artificial intelligence, big data, cloud computing, etc. foster the transition towards the Agriculture 4.0 era. This fourth revolution is currently seen as a possible solution for improving agricultural growth, ensuring the future needs of the global population in a fair, resilient and sustainable way. In this context, this article aims at characterising the current Agriculture 4.0 landscape. Emerging trends were compiled using a semi-automated process by analysing relevant scientific publications published in the past ten years. Subsequently, a literature review focusing these trends was conducted, with a particular emphasis on their applications in real environments. From the results of the study, some challenges are discussed, as well as opportunities for future research. Finally, a high-level cloud-based IoT architecture is presented, serving as foundation for designing future smart agricultural systems. It is expected that this work will positively impact the research around Agriculture 4.0 systems, providing a clear characterisation of the concept along with guidelines to assist the actors in a successful transition towards the digitalisation of the sector.<\/jats:p>","DOI":"10.3390\/agronomy11040667","type":"journal-article","created":{"date-parts":[[2021,4,1]],"date-time":"2021-04-01T07:23:17Z","timestamp":1617261797000},"page":"667","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":270,"title":["Characterising the Agriculture 4.0 Landscape\u2014Emerging Trends, Challenges and Opportunities"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6192-8409","authenticated-orcid":false,"given":"Sara Oleiro","family":"Ara\u00fajo","sequence":"first","affiliation":[{"name":"UNINOVA\u2014Centre of Technology and Systems (CTS), FCT Campus, 2829-516 Caparica, Portugal"},{"name":"Earth Sciences Department (DCT), School of Sciences and Technology, NOVA University of Lisbon, 2829-516 Caparica, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3777-1346","authenticated-orcid":false,"given":"Ricardo Silva","family":"Peres","sequence":"additional","affiliation":[{"name":"UNINOVA\u2014Centre of Technology and Systems (CTS), FCT Campus, 2829-516 Caparica, Portugal"},{"name":"Electrical and Computer Engineering Department (DEEC), School of Sciences and Technology, NOVA University of Lisbon, 2829-516 Caparica, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6348-1847","authenticated-orcid":false,"given":"Jos\u00e9","family":"Barata","sequence":"additional","affiliation":[{"name":"UNINOVA\u2014Centre of Technology and Systems (CTS), FCT Campus, 2829-516 Caparica, Portugal"},{"name":"Electrical and Computer Engineering Department (DEEC), School of Sciences and Technology, NOVA University of Lisbon, 2829-516 Caparica, Portugal"}]},{"given":"Fernando","family":"Lidon","sequence":"additional","affiliation":[{"name":"Earth Sciences Department (DCT), School of Sciences and Technology, NOVA University of Lisbon, 2829-516 Caparica, Portugal"},{"name":"Unit of GeoBioSciences, GeoTechnologies and GeoEngineering (GeoBioTec), School of Sciences and Technology, NOVA University of Lisbon, 2829-516 Caparica, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7639-7214","authenticated-orcid":false,"given":"Jos\u00e9 Cochicho","family":"Ramalho","sequence":"additional","affiliation":[{"name":"Unit of GeoBioSciences, GeoTechnologies and GeoEngineering (GeoBioTec), School of Sciences and Technology, NOVA University of Lisbon, 2829-516 Caparica, Portugal"},{"name":"PlantStress &amp; Biodiversity Lab, Forest Research Center (CEF), School of Agriculture (ISA), University of Lisbon, (ULisboa), Quinta do Marqu\u00eas, Av. Rep\u00fablica, 2784-505 Oeiras, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2021,4,1]]},"reference":[{"key":"ref_1","unstructured":"Food and Agriculture Organization (2017). The Future of Food and Agriculture\u2014Trends and Challenges, Food and Agriculture Organization of the United Nations."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"539","DOI":"10.1071\/FP12078","article-title":"Yield stability for cereals in a changing climate","volume":"39","author":"Powell","year":"2012","journal-title":"Funct. Plant Biol."},{"key":"ref_3","unstructured":"Food and Agriculture Organization (2016). The State of Food and Agriculture. Climate Change, Agriculture and Food Security, Food and Agriculture Organization of the United Nations."},{"key":"ref_4","unstructured":"Stocker, T.F., Qin, D., Plattner, G.K., Tignor, M.M., Allen, S.K., Boschung, J., Nauels, A., Xia, Y., Bex, V., and Midgley, P.M. (2014). Climate Change 2013: The Physical Science Basis. Working Group I Contribution to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC), Cambridge University Press."},{"key":"ref_5","unstructured":"European Commission (2020, November 23). The European Green Deal. Available online: https:\/\/eur-lex.europa.eu\/legal-content\/EN\/TXT\/?uri=COM%3A2019%3A640%3AFIN."},{"key":"ref_6","unstructured":"European Commission (2020, November 23). Farm to Fork Strategy: For a Fair, Healthy and Environmentally-Friendly Food System. Available online: https:\/\/ec.europa.eu\/food\/sites\/food\/files\/safety\/docs\/f2f_action-plan_2020_strategy-info_en.pdf."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Mukhopadhyay, S.C. (2012). Smart sensing technology for agriculture and environmental monitoring. Lecture Notes in Electrical Engineering, 146, Springer.","DOI":"10.1007\/978-3-642-27638-5"},{"key":"ref_8","unstructured":"Trendov, N.M., Varas, S., and Zeng, M. (2019). Digital Technologies in Agriculture and Rural Areas: Status Report, Licence: cc by-nc-sa 3.0 igo."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"87","DOI":"10.3389\/fsufs.2018.00087","article-title":"Agriculture 4.0: Broadening responsible innovation in an era of smart farming","volume":"2","author":"Rose","year":"2018","journal-title":"Front. Sustain. Food Syst."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Kov\u00e1cs, I., and Husti, I. (2018). The role of digitalization in the agricultural 4.0\u2014How to connect the industry 4.0 to agriculture?. Hung. Agric. Eng.","DOI":"10.17676\/HAE.2018.32.38"},{"key":"ref_11","unstructured":"De Clercq, M., Vats, A., and Biel, A. (,  2018). Agriculture 4.0: The future of farming technology. Proceedings of the World Government Summit, Dubai, United Arab Emirates."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Zambon, I., Cecchini, M., Egidi, G., Saporito, M.G., and Colantoni, A. (2019). Revolution 4.0: Industry vs. agriculture in a future development for SMEs. Processes, 7.","DOI":"10.3390\/pr7010036"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Liu, Y., Ma, X., Shu, L., Hancke, G.P., and Abu-Mahfouz, A.M. (2020). From Industry 4.0 to Agriculture 4.0: Current Status, Enabling Technologies, and Research Challenges. IEEE Trans. Ind. Inform.","DOI":"10.1109\/TII.2020.3003910"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"105256","DOI":"10.1016\/j.compag.2020.105256","article-title":"Decision support systems for agriculture 4.0: Survey and challenges","volume":"170","author":"Zhai","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_15","unstructured":"European Agricultural Machinery (2020, August 11). Digital Farming: What Does It Really Mean?. Available online: https:\/\/www.cema-agri.org\/images\/publications\/position-papers\/CEMA_Digital_Farming_-_Agriculture_4.0__13_02_2017_0.pdf."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"103187","DOI":"10.1016\/j.compind.2020.103187","article-title":"Agri-food 4.0: A survey of the supply chains and technologies for the future agriculture","volume":"117","author":"Lezoche","year":"2020","journal-title":"Comput. Ind."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"149854","DOI":"10.1109\/ACCESS.2020.3016325","article-title":"Precision Techniques and Agriculture 4.0 Technologies to Promote Sustainability in the Coffee Sector: State of the Art, Challenges and Future Trends","volume":"8","author":"Sott","year":"2020","journal-title":"IEEE Access"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Zhou, K., Liu, T., and Zhou, L. (,  2015). Industry 4.0: Towards future industrial opportunities and challenges. Proceedings of the 12th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), Zhangjiajie, China.","DOI":"10.1109\/FSKD.2015.7382284"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"467","DOI":"10.1016\/j.compag.2018.12.011","article-title":"IoT and agriculture data analysis for smart farm","volume":"156","author":"Muangprathub","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1016\/j.compag.2017.09.015","article-title":"Review of IoT applications in agro-industrial and environmental fields","volume":"142","author":"Talavera","year":"2017","journal-title":"Comput. Electron. Agric."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Shi, X., An, X., Zhao, Q., Liu, H., Xia, L., Sun, X., and Guo, Y. (2019). State-of-the-art Internet of things in protected agriculture. Sensors, 19.","DOI":"10.3390\/s19081833"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"220121","DOI":"10.1109\/ACCESS.2020.3042874","article-title":"Industrial Artificial Intelligence in Industry 4.0-Systematic Review, Challenges and Outlook","volume":"8","author":"Peres","year":"2020","journal-title":"IEEE Access"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Loper, E., and Bird, S. (,  2002). NLTK: The Natural Language Toolkit. Proceedings of the ACL-02 Workshop on Effective Tools and Methodologies for Teaching Natural Language Processing and Computational Linguistics, Philadelphia, PA, USA.","DOI":"10.3115\/1118108.1118117"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"111402","DOI":"10.1016\/j.rse.2019.111402","article-title":"Remote sensing for agricultural applications: A meta-review","volume":"236","author":"Weiss","year":"2020","journal-title":"Remote. Sens. Environ."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.biosystemseng.2019.12.013","article-title":"Internet of Things in arable farming: Implementation, applications, challenges and potential","volume":"191","author":"Edwards","year":"2020","journal-title":"Biosyst. Eng."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1016\/j.csi.2011.03.004","article-title":"A review of wireless sensors and networks\u2019 applications in agriculture","volume":"36","author":"Abbasi","year":"2014","journal-title":"Comput. Stand. Interfaces"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Kassim, M.R.M., and Harun, A.N. (,  2016). Applications of WSN in agricultural environment monitoring systems. Proceedings of the 2016 International Conference on Information and Communication Technology Convergence (ICTC), Jeju, Korea.","DOI":"10.1109\/ICTC.2016.7763493"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1016\/j.biosystemseng.2017.09.007","article-title":"Internet of Things in agriculture, recent advances and future challenges","volume":"164","author":"Tzounis","year":"2017","journal-title":"Biosyst. Eng."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1016\/S0168-1699(02)00096-0","article-title":"Precision agriculture\u2014A worldwide overview","volume":"36","author":"Zhang","year":"2002","journal-title":"Comput. Electron. Agric."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"358","DOI":"10.1016\/j.biosystemseng.2012.08.009","article-title":"Twenty five years of remote sensing in precision agriculture: Key advances and remaining knowledge gaps","volume":"114","author":"Mulla","year":"2013","journal-title":"Biosyst. Eng."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Morris, D., Johannsen, C., Brouder, S., and Steinhardt, G. (2005). Remote Sensing\/Organic Matter, Elsevier Ltd.","DOI":"10.1016\/B0-12-348530-4\/00292-7"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Pinter-Wollman, N., and Mabry, K. (2010). Remote-Sensing of Behavior. Encyclopedia of Animal Behaviour, Elsevier Ltd.","DOI":"10.1016\/B978-0-08-045337-8.00232-1"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Shafi, U., Mumtaz, R., Garc\u00eda-Nieto, J., Hassan, S.A., Zaidi, S.A.R., and Iqbal, N. (2019). Precision agriculture techniques and practices: From considerations to applications. Sensors, 19.","DOI":"10.3390\/s19173796"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1016\/j.agrformet.2015.11.003","article-title":"Crop yield forecasting on the Canadian Prairies by remotely sensed vegetation indices and machine learning methods","volume":"218","author":"Johnson","year":"2016","journal-title":"Agric. For. Meteorol."},{"key":"ref_35","unstructured":"Sessa, R., and Dolman, H. (2008). Terrestrial Essential Climate Variables for Climate Change Assessment, Mitigation and Adaptation (GTOS 52), Food and Agriculture Organization of the United Nations."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1016\/j.compag.2017.08.026","article-title":"Crowdsourcing for agricultural applications: A review of uses and opportunities for a farmsourcing approach","volume":"142","author":"Minet","year":"2017","journal-title":"Comput. Electron. Agric."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1016\/j.compag.2015.08.011","article-title":"Wireless sensor networks for agriculture: The state-of-the-art in practice and future challenges","volume":"118","author":"Ojha","year":"2015","journal-title":"Comput. Electron. Agric."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Jawad, H.M., Nordin, R., Gharghan, S.K., Jawad, A.M., and Ismail, M. (2017). Energy-efficient wireless sensor networks for precision agriculture: A review. Sensors, 17.","DOI":"10.3390\/s17081781"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Moschitta, A., and Neri, I. (2014). Power consumption assessment in wireless sensor networks. ICT-Energy-Concepts Towards Zero-Power Information and Communication Technology, IntechOpen.","DOI":"10.5772\/57201"},{"key":"ref_40","unstructured":"Kassim, M.R.M., Mat, I., and Harun, A.N. (,  2014). Wireless Sensor Network in precision agriculture application. Proceedings of the International Conference on Computer, Information and Telecommunication Systems (CITS), Jeju, Korea."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Ferr\u00e1ndez-Pastor, F.J., Garc\u00eda-Chamizo, J.M., Nieto-Hidalgo, M., Mora-Pascual, J., and Mora-Mart\u00ednez, J. (2016). Developing ubiquitous sensor network platform using Internet of things: Application in precision agriculture. Sensors, 16.","DOI":"10.3390\/s16071141"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Fountas, S., Mylonas, N., Malounas, I., Rodias, E., Hellmann Santos, C., and Pekkeriet, E. (2020). Agricultural Robotics for Field Operations. Sensors, 20.","DOI":"10.3390\/s20092672"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Shamshiri, R.R., Weltzien, C., Hameed, I.A., Yule, J.I., Grift, E.T., Balasundram, S.K., Pitonakova, L., Ahmad, D., and Chowdhary, G. (2018). Research and development in agricultural robotics: A perspective of digital farming. Int. J. Agric. Biol.","DOI":"10.25165\/j.ijabe.20181104.4278"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Rold\u00e1n, J.J., del Cerro, J., Garz\u00f3n-Ramos, D., Garcia-Aunon, P., Garz\u00f3n, M., de Le\u00f3n, J., and Barrientos, A. (2018). Robots in agriculture: State of art and practical experiences. Serv. Robot.","DOI":"10.5772\/intechopen.69874"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1386","DOI":"10.1002\/ps.5651","article-title":"Integration of remote-weed mapping and an autonomous spraying unmanned aerial vehicle for site-specific weed management","volume":"76","author":"Hunter","year":"2019","journal-title":"Pest Manag. Sci."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"126030","DOI":"10.1016\/j.eja.2020.126030","article-title":"Deep learning techniques for estimation of the yield and size of citrus fruits using a UAV","volume":"115","author":"Egea","year":"2020","journal-title":"Eur. J. Agron."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"404","DOI":"10.1016\/j.compag.2016.01.008","article-title":"Detecting Bakanae disease in rice seedlings by machine vision","volume":"121","author":"Chung","year":"2016","journal-title":"Comput. Electron. Agric."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"224","DOI":"10.1016\/j.compag.2017.05.026","article-title":"Evaluation of hierarchical self-organising maps for weed mapping using UAS multispectral imagery","volume":"139","author":"Pantazi","year":"2017","journal-title":"Comput. Electron. Agric."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"502","DOI":"10.1016\/j.procs.2018.07.063","article-title":"Review on application of drone systems in precision agriculture","volume":"133","author":"Mogili","year":"2018","journal-title":"Procedia Comput. Sci."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Bonadies, S., Lefcourt, A., and Gadsden, S.A. (,  2016). A survey of unmanned ground vehicles with applications to agricultural and environmental sensing. Proceedings of the Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping, International Society for Optics and Photonics, Baltimore, MD, USA.","DOI":"10.1117\/12.2224248"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Arad, B., Balendonck, J., Barth, R., Ben-Shahar, O., Edan, Y., Hellstr\u00f6m, T., Hemming, J., Kurtser, P., Ringdahl, O., and Tielen, T. (2020). Development of a sweet pepper harvesting robot. J. Field Robot.","DOI":"10.1002\/rob.21937"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Farooq, M.S., Riaz, S., Abid, A., Umer, T., and Zikria, Y.B. (2020). Role of IoT Technology in Agriculture: A Systematic Literature Review. Electronics, 9.","DOI":"10.3390\/electronics9020319"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Yang, F., Wang, K., Han, Y., and Qiao, Z. (2018). A cloud-based digital farm management system for vegetable production process management and quality traceability. Sustainability, 10.","DOI":"10.3390\/su10114007"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1016\/j.compag.2013.11.014","article-title":"A cloud-based Farm Management System: Architecture and implementation","volume":"100","author":"Kaloxylos","year":"2014","journal-title":"Comput. Electron. Agric."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"637","DOI":"10.1109\/JIOT.2016.2579198","article-title":"Edge computing: Vision and challenges","volume":"3","author":"Shi","year":"2016","journal-title":"IEEE Internet Things J."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Bonomi, F., Milito, R., Zhu, J., and Addepalli, S. (,  2012). Fog computing and its role in the internet of things. Proceedings of the First Edition of the MCC Workshop on Mobile cloud computing, Helsinki, Finland.","DOI":"10.1145\/2342509.2342513"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/j.agsy.2017.01.023","article-title":"Big data in smart farming-a review","volume":"153","author":"Wolfert","year":"2017","journal-title":"Agric. Syst."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/j.compag.2017.09.037","article-title":"A review on the practice of big data analysis in agriculture","volume":"143","author":"Kamilaris","year":"2017","journal-title":"Comput. Electron. Agric."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Demchenko, Y., Grosso, P., De Laat, C., and Membrey, P. (,  2013). Addressing big data issues in scientific data infrastructure. Proceedings of the 2013 International Conference on Collaboration Technologies and Systems (CTS), San Diego, CA, USA.","DOI":"10.1109\/CTS.2013.6567203"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s40537-017-0077-4","article-title":"Analysis of agriculture data using data mining techniques: Application of big data","volume":"4","author":"Majumdar","year":"2017","journal-title":"J. Big Data"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"458","DOI":"10.1073\/pnas.1518384112","article-title":"Drivers of household food availability in sub-Saharan Africa based on big data from small farms","volume":"113","author":"Frelat","year":"2016","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Liakos, K.G., Busato, P., Moshou, D., Pearson, S., and Bochtis, D. (2018). Machine learning in agriculture: A review. Sensors, 18.","DOI":"10.3390\/s18082674"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"911","DOI":"10.1109\/TIFS.2019.2929409","article-title":"Verifynet: Secure and verifiable federated learning","volume":"15","author":"Xu","year":"2019","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_64","unstructured":"Turban, E., Aronson, J.E., and Liang, T.P. (2007). Decision Support Systems and Intelligent Systems, Prentice Hall. [7th ed.]."},{"key":"ref_65","unstructured":"Food and Agriculture Organization (2020). FAO Regional Conference for the Near East: Digital Innovation for Promoting Agriculture 4.0 in the Near East and North Africa, Food and Agriculture Organization of the United Nations."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.ejor.2008.02.014","article-title":"Application of planning models in the agri-food supply chain: A review","volume":"196","author":"Ahumada","year":"2009","journal-title":"Eur. J. Oper. Res."},{"key":"ref_67","unstructured":"Smith, P., Clark, H., Dong, H., Elsiddig, E., Haberl, H., Harper, R., House, J., Jafari, M., Masera, O., and Mbow, C. (2014). Agriculture, Forestry and Other Land Use (AFOLU). Climate Change 2014: Mitigation of Climate Change. IPCC Working Group III Contribution to AR5, Cambridge University Press."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"156237","DOI":"10.1109\/ACCESS.2019.2949703","article-title":"A Survey on the Role of IoT in Agriculture for the Implementation of Smart Farming","volume":"7","author":"Farooq","year":"2019","journal-title":"IEEE Access"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"4072","DOI":"10.3390\/s150204072","article-title":"Development and integration of a solar powered unmanned aerial vehicle and a wireless sensor network to monitor greenhouse gases","volume":"15","author":"Malaver","year":"2015","journal-title":"Sensors"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1016\/bs.agron.2017.10.005","article-title":"Limitations of existing weed control practices necessitate development of alternative techniques based on biological approaches","volume":"147","author":"Abbas","year":"2018","journal-title":"Adv. Agron."},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Dyrmann, M., Skovsen, S., S\u00f8rensen, R.A., Nielsen, P.R., and J\u00f8rgensen, R.N. (,  2018). Using a fully convolutional neural network for detecting locations of weeds in images from cereal fields. Proceedings of the 14th International Conference on Precision Agriculture, Montreal, QC, Canada.","DOI":"10.3920\/978-90-8686-888-9_94"},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Pflanz, M., Nordmeyer, H., and Schirrmann, M. (2018). Weed mapping with UAS imagery and a Bag of Visual Words based image classifier. Remote. Sens., 10.","DOI":"10.3390\/rs10101530"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"105174","DOI":"10.1016\/j.compag.2019.105174","article-title":"Crop pest recognition in natural scenes using convolutional neural networks","volume":"169","author":"Li","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Sarkar, S.K., Das, J., Ehsani, R., and Kumar, V. (,  2016). Towards autonomous phytopathology: Outcomes and challenges of citrus greening disease detection through close-range remote sensing. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Stockholm, Sweden.","DOI":"10.1109\/ICRA.2016.7487719"},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"575","DOI":"10.1016\/j.procs.2018.04.106","article-title":"Using Cloud IOT for disease prevention in precision agriculture","volume":"130","author":"Foughali","year":"2018","journal-title":"Procedia Comput. Sci."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"549","DOI":"10.1016\/j.compag.2018.12.018","article-title":"A remote sensing technique for detecting laurel wilt disease in avocado in presence of other biotic and abiotic stresses","volume":"156","author":"Abdulridha","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Sun, G., Jia, X., and Geng, T. (2018). Plant diseases recognition based on image processing technology. J. Electr. Comput. Eng., 2018.","DOI":"10.1155\/2018\/6070129"},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Na, A., Isaac, W., Varshney, S., and Khan, E. (,  2016). An IoT based system for remote monitoring of soil characteristics. Proceedings of the 2016 International Conference on Information Technology (InCITe)-The Next Generation IT Summit on the Theme-Internet of Things: Connect Your Worlds, Noida, India.","DOI":"10.1109\/INCITE.2016.7857638"},{"key":"ref_79","doi-asserted-by":"crossref","unstructured":"Yanes, A.R., Martinez, P., and Ahmad, R. (2020). Towards automated aquaponics: A review on monitoring, IoT, and smart systems. J. Clean. Prod., 121571.","DOI":"10.1016\/j.jclepro.2020.121571"},{"key":"ref_80","unstructured":"Food and Agriculture Organization (2014). Building a Common Vision for Sustainable Food and Agriculture: Principles and Approaches, Food and Agriculture Organization of the United Nations."},{"key":"ref_81","doi-asserted-by":"crossref","unstructured":"Khelifa, B., Amel, D., Amel, B., Mohamed, C., and Tarek, B. (,  2015). Smart irrigation using Internet of things. Proceedings of the 2015 Fourth International Conference on Future Generation Communication Technology (FGCT), Luton, UK.","DOI":"10.1109\/FGCT.2015.7300252"},{"key":"ref_82","doi-asserted-by":"crossref","unstructured":"Viani, F., Bertolli, M., Salucci, M., and Polo, A. (2017). Low-Cost Wireless Monitoring and Decision Support for Water Saving in Agriculture. IEEE Sens. J., 17.","DOI":"10.1109\/JSEN.2017.2705043"},{"key":"ref_83","doi-asserted-by":"crossref","unstructured":"Ramachandran, V., Ramalakshmi, R., and Srinivasan, S. (,  2018). An automated irrigation system for smart agriculture using the Internet of Things. Proceedings of the 2018 15th International Conference on Control, Automation, Robotics and Vision (ICARCV), Singapore.","DOI":"10.1109\/ICARCV.2018.8581221"},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1016\/j.compag.2018.09.040","article-title":"An IoT based smart irrigation management system using Machine learning and open source technologies","volume":"155","author":"Goap","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_85","doi-asserted-by":"crossref","unstructured":"Suciu, G., Marcu, I., Balaceanu, C., Dobrea, M., and Botezat, E. (,  2019). Efficient IoT system for Precision Agriculture. Proceedings of the 2019 15th International Conference on Engineering of Modern Electric Systems (EMES), Oradea, Romania.","DOI":"10.1109\/EMES.2019.8795102"},{"key":"ref_86","doi-asserted-by":"crossref","unstructured":"Villalobos, F.J., Delgado, A., Lopez-Bernal, A., and Quemada, M. (2020). FertiliCalc: A Decision Support System for Fertilizer Management. Int. J. Plant Prod.","DOI":"10.1007\/s42106-019-00085-1"},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"226","DOI":"10.1016\/j.compag.2019.02.005","article-title":"A review on weed detection using ground-based machine vision and image processing techniques","volume":"158","author":"Wang","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_88","unstructured":"Barberi, P. (2003). Preventive and cultural methods for weed management. FAO Plant Prod. Prot., 120."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"377","DOI":"10.1590\/S0100-83582016340200019","article-title":"Weed control in clean agriculture: A review 1","volume":"34","author":"Abouziena","year":"2016","journal-title":"Planta Daninha"},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"339","DOI":"10.1016\/j.compag.2018.12.048","article-title":"Development and evaluation of a low-cost and smart technology for precision weed management utilizing artificial intelligence","volume":"157","author":"Partel","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1016\/j.enconman.2009.09.007","article-title":"Development of an intelligent indoor environment and energy management system for greenhouses","volume":"51","author":"Kolokotsa","year":"2010","journal-title":"Energy Convers. Manag."},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1016\/j.rser.2015.04.117","article-title":"LEDs for energy efficient greenhouse lighting","volume":"49","author":"Singh","year":"2015","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1016\/j.biosystemseng.2018.04.018","article-title":"Automatic carbon dioxide enrichment strategies in the greenhouse: A review","volume":"171","author":"Li","year":"2018","journal-title":"Biosyst. Eng."},{"key":"ref_94","unstructured":"Baudoin, W., Nono-Womdim, R., Lutaladio, N., Hodder, A., Castilla, N., Leonardi, C., De Pascale, S., Qaryouti, M., and Duffy, R. (2013). Good agricultural practices for greenhouse vegetable crops: Principles for mediterranean climate areas. FAO Plant Prod. Prot."},{"key":"ref_95","first-page":"115","article-title":"Identification of fruit and branch in natural scenes for citrus harvesting robot using machine vision and support vector machine","volume":"7","author":"Cai","year":"2014","journal-title":"Int. J. Agric. Biol."},{"key":"ref_96","unstructured":"Russello, H. (2018). Convolutional Neural Networks for Crop Yield Prediction Using Satellite Images. [Master\u2019s Thesis, IBM Center for Advanced Studies]."},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"621","DOI":"10.3389\/fpls.2019.00621","article-title":"Crop yield prediction using deep neural networks","volume":"10","author":"Khaki","year":"2019","journal-title":"Front. Plant Sci."},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"105709","DOI":"10.1016\/j.compag.2020.105709","article-title":"Crop yield prediction using machine learning: A systematic literature review","volume":"177","author":"Kassahun","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"1420","DOI":"10.1016\/j.ijforecast.2020.02.005","article-title":"Daily retail demand forecasting using machine learning with emphasis on calendric special days","volume":"36","author":"Huber","year":"2020","journal-title":"Int. J. Forecast."},{"key":"ref_100","doi-asserted-by":"crossref","unstructured":"Nukala, R., Panduru, K., Shields, A., Riordan, D., Doody, P., and Walsh, J. (,  2016). Internet of Things: A review from \u2018Farm to Fork\u2019. Proceedings of the 2016 27th Irish Signals and Systems Conference (ISSC), Londonderry, UK.","DOI":"10.1109\/ISSC.2016.7528456"},{"key":"ref_101","doi-asserted-by":"crossref","unstructured":"Prashar, D., Jha, N., Jha, S., Lee, Y., and Joshi, G.P. (2020). Blockchain-Based Traceability and Visibility for Agricultural Products: A Decentralized Way of Ensuring Food Safety in India. Sustainability, 12.","DOI":"10.3390\/su12083497"},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"1437","DOI":"10.1007\/s11277-015-2482-3","article-title":"Deployment of wireless sensor networks in crop storages","volume":"81","author":"Juul","year":"2015","journal-title":"Wirel. Pers. Commun."},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1016\/j.ijinfomgt.2017.12.005","article-title":"1 Blockchain\u2019s roles in meeting key supply chain management objectives","volume":"39","author":"Kshetri","year":"2018","journal-title":"Int. J. Inf. Manag."},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"222","DOI":"10.1016\/j.trac.2018.08.011","article-title":"Future challenges on the use of blockchain for food traceability analysis","volume":"107","author":"Galvez","year":"2018","journal-title":"TrAC Trends Anal. Chem."},{"key":"ref_105","doi-asserted-by":"crossref","unstructured":"Nechifor, S., Petrescu, A., Damian, D., Puiu, D., and T\u00e2rnauc\u0103, B. (,  2014). Predictive analytics based on CEP for logistic of sensitive goods. Proceedings of the 2014 International Conference on Optimization of Electrical and Electronic Equipment (OPTIM), Bran, Romania.","DOI":"10.1109\/OPTIM.2014.6850965"},{"key":"ref_106","doi-asserted-by":"crossref","unstructured":"Tenzin, S., Siyang, S., Pobkrut, T., and Kerdcharoen, T. (,  2017). Low cost weather station for climate-smart agriculture. Proceedings of the 9th international conference on knowledge and smart technology (KST), Chonburi, Thailand.","DOI":"10.1109\/KST.2017.7886085"},{"key":"ref_107","doi-asserted-by":"crossref","first-page":"1325","DOI":"10.3233\/JIFS-169676","article-title":"Field microclimate monitoring system based on wireless sensor network","volume":"35","author":"Yan","year":"2018","journal-title":"J. Intell. Fuzzy Syst."},{"key":"ref_108","doi-asserted-by":"crossref","unstructured":"Math, R.K.M., and Dharwadkar, N.V. (,  2018). IoT Based low-cost weather station and monitoring system for precision agriculture in India. Proceedings of the 2018 2nd International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), Palladam, India.","DOI":"10.1109\/I-SMAC.2018.8653749"},{"key":"ref_109","doi-asserted-by":"crossref","unstructured":"Kodali, R.K., Rajanarayanan, S.C., and Boppana, L. (,  2019). IoT based Weather Monitoring and Notification System for Greenhouses. Proceedings of the 11th International Conference on Advanced Computing (ICoAC), Chennai, India.","DOI":"10.1109\/ICoAC48765.2019.246864"},{"key":"ref_110","doi-asserted-by":"crossref","unstructured":"Mao, H., Paul, O.K., Yang, N., and Li, L. (2018). Smart Arduino Sensor Integrated Drone for Weather Indices: Prototype. Drones-Applications, IntechOpen.","DOI":"10.5772\/intechopen.76872"},{"key":"ref_111","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1016\/j.compag.2017.03.003","article-title":"On precisely relating the growth of Phalaenopsis leaves to greenhouse environmental factors by using an IoT-based monitoring system","volume":"136","author":"Liao","year":"2017","journal-title":"Comput. Electron. Agric."},{"key":"ref_112","doi-asserted-by":"crossref","unstructured":"Daroya, R., and Ramos, M. (,  2017). NDVI image extraction of an agricultural land using an autonomous quadcopter with a filter-modified camera. Proceedings of the 7th IEEE International Conference on Control System, Computing and Engineering (ICCSCE), Penang, Malaysia.","DOI":"10.1109\/ICCSCE.2017.8284389"},{"key":"ref_113","doi-asserted-by":"crossref","first-page":"725","DOI":"10.1007\/s11277-017-5092-4","article-title":"Web enabled plant disease detection system for agricultural applications using WMSN","volume":"102","author":"Nandhini","year":"2018","journal-title":"Wirel. Pers. Commun."},{"key":"ref_114","doi-asserted-by":"crossref","first-page":"1749","DOI":"10.1109\/TLA.2018.8444395","article-title":"Annotated plant pathology databases for image-based detection and recognition of diseases","volume":"16","author":"Barbedo","year":"2018","journal-title":"IEEE Lat. Am. Trans."},{"key":"ref_115","doi-asserted-by":"crossref","unstructured":"Abdulridha, J., Ehsani, R., and De Castro, A. (2016). Detection and differentiation between laurel wilt disease, phytophthora disease, and salinity damage using a hyperspectral sensing technique. Agriculture, 6.","DOI":"10.3390\/agriculture6040056"},{"key":"ref_116","doi-asserted-by":"crossref","first-page":"1741","DOI":"10.3389\/fpls.2017.01741","article-title":"X-FIDO: An effective application for detecting olive quick decline syndrome with deep learning and data fusion","volume":"8","author":"Cruz","year":"2017","journal-title":"Front. Plant Sci."},{"key":"ref_117","doi-asserted-by":"crossref","unstructured":"Pavel, M.I., Kamruzzaman, S.M., Hasan, S.S., and Sabuj, S.R. (,  2019). An IoT Based Plant Health Monitoring System Implementing Image Processing. Proceedings of the 2019 IEEE 4th International Conference on Computer and Communication Systems (ICCCS), Singapore.","DOI":"10.1109\/CCOMS.2019.8821782"},{"key":"ref_118","doi-asserted-by":"crossref","first-page":"4057","DOI":"10.1016\/j.jclepro.2017.02.197","article-title":"A decision support system based on multisensor data fusion for sustainable greenhouse management","volume":"172","author":"Aiello","year":"2018","journal-title":"J. Clean. Prod."},{"key":"ref_119","doi-asserted-by":"crossref","unstructured":"Song, Y., Duan, X., Ren, Y., Xu, J., Luo, L., and Li, D. (,  2019). Identification of the Agricultural Pests Based on Deep Learning Models. Proceedings of the 2019 International Conference on Machine Learning, Big Data and Business Intelligence (MLBDBI), Taiyuan, China.","DOI":"10.1109\/MLBDBI48998.2019.00044"},{"key":"ref_120","doi-asserted-by":"crossref","unstructured":"Chen, K.T., Zhang, H.H., Wu, T.T., Hu, J., Zhai, C.Y., and Wang, D. (,  2014). Design of monitoring system for multilayer soil temperature and moisture based on WSN. Proceedings of the 2014 International Conference on Wireless Communication and Sensor Network, Wuhan, China.","DOI":"10.1109\/WCSN.2014.92"},{"key":"ref_121","doi-asserted-by":"crossref","unstructured":"Madhumathi, R., Arumuganathan, T., and Shruthi, R. (,  2020). Soil NPK and Moisture analysis using Wireless Sensor Networks. Proceedings of the 2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT), Kharagpur, India.","DOI":"10.1109\/ICCCNT49239.2020.9225547"},{"key":"ref_122","doi-asserted-by":"crossref","unstructured":"Zhang, X., Zhang, J., Li, L., Zhang, Y., and Yang, G. (2017). Monitoring citrus soil moisture and nutrients using an iot based system. Sensors, 17.","DOI":"10.3390\/s17030447"},{"key":"ref_123","doi-asserted-by":"crossref","first-page":"7333","DOI":"10.1109\/TIE.2017.2696508","article-title":"A temperature compensated smart nitrate-sensor for agricultural industry","volume":"64","author":"Alahi","year":"2017","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_124","doi-asserted-by":"crossref","unstructured":"Rau, A.J., Sankar, J., Mohan, A.R., Krishna, D.D., and Mathew, J. (,  2017). IoT based smart irrigation system and nutrient detection with disease analysis. Proceedings of the 2017 IEEE Region 10 Symposium (TENSYMP), Cochin, India.","DOI":"10.1109\/TENCONSpring.2017.8070100"},{"key":"ref_125","doi-asserted-by":"crossref","unstructured":"Raut, R., Varma, H., Mulla, C., and Pawar, V.R. (2018). Soil monitoring, fertigation, and irrigation system using IoT for agricultural application. Intelligent Communication and Computational Technologies, Springer.","DOI":"10.1007\/978-981-10-5523-2_7"},{"key":"ref_126","doi-asserted-by":"crossref","first-page":"2870","DOI":"10.1109\/LRA.2018.2846289","article-title":"Fully convolutional networks with sequential information for robust crop and weed detection in precision farming","volume":"3","author":"Lottes","year":"2018","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_127","doi-asserted-by":"crossref","unstructured":"Saranya, K., Dharini, P.U., Darshni, P.U., and Monisha, S. (,  2019). IoT Based Pest Controlling System for Smart Agriculture. Proceedings of the 2019 International Conference on Communication and Electronics Systems (ICCES), Coimbatore, India.","DOI":"10.1109\/ICCES45898.2019.9002046"},{"key":"ref_128","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1109\/MPRV.2018.2873849","article-title":"Pervasive agriculture: IoT-enabled greenhouse for plant growth control","volume":"17","author":"Somov","year":"2018","journal-title":"IEEE Pervasive Comput."},{"key":"ref_129","doi-asserted-by":"crossref","unstructured":"Vimal, P.V., and Shivaprakasha, K.S. (,  2017). IOT based greenhouse environment monitoring and controlling system using Arduino platform. Proceedings of the 2017 International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT), Kerala, India.","DOI":"10.1109\/ICICICT1.2017.8342795"},{"key":"ref_130","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1016\/j.isatra.2015.12.006","article-title":"Smart greenhouse fuzzy logic based control system enhanced with wireless data monitoring","volume":"61","author":"Azaza","year":"2016","journal-title":"ISA Trans."},{"key":"ref_131","doi-asserted-by":"crossref","unstructured":"Feng, Q., Wang, X., Wang, G., and Li, Z. (,  2015). Design and test of tomatoes harvesting robot. Proceedings of the 2015 International Conference on Information and Automation, Lijiang, China.","DOI":"10.1109\/ICInfA.2015.7279423"},{"key":"ref_132","doi-asserted-by":"crossref","unstructured":"Taqi, F., Al-Langawi, F., Abdulraheem, H., and El-Abd, M. (,  2017). A cherry-tomato harvesting robot. Proceedings of the 2017 18th International Conference on Advanced Robotics (ICAR), Hong Kong, China.","DOI":"10.1109\/ICAR.2017.8023650"},{"key":"ref_133","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1016\/j.protcy.2012.05.047","article-title":"Weather forecasting model using artificial neural network","volume":"4","author":"Abhishek","year":"2012","journal-title":"Procedia Technol."},{"key":"ref_134","doi-asserted-by":"crossref","unstructured":"Fente, D.N., and Singh, D.K. (,  2018). Weather forecasting using artificial neural network. Proceedings of the 2018 Second International Conference on Inventive Communication and Computational Technologies (ICICCT), Coimbatore, India.","DOI":"10.1109\/ICICCT.2018.8473167"},{"key":"ref_135","doi-asserted-by":"crossref","unstructured":"Kurniawan, A.P., Jati, A.N., and Azmi, F. (,  2017). Weather prediction based on fuzzy logic algorithm for supporting general farming automation system. Proceedings of the 2017 5th International Conference on Instrumentation, Control, and Automation (ICA), Yogyakarta, Indonesia.","DOI":"10.1109\/ICA.2017.8068431"},{"key":"ref_136","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/j.envsoft.2014.09.020","article-title":"A Fuzzy Decision Support System for irrigation and water conservation in agriculture","volume":"63","author":"Giusti","year":"2015","journal-title":"Environ. Model. Softw."},{"key":"ref_137","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1016\/j.compag.2016.04.003","article-title":"A decision support system for managing irrigation in agriculture","volume":"124","year":"2016","journal-title":"Comput. Electron. Agric."},{"key":"ref_138","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1007\/s00521-018-3737-1","article-title":"Adapting weather conditions based IoT enabled smart irrigation technique in precision agriculture mechanisms","volume":"31","author":"Keswani","year":"2019","journal-title":"Neural Comput. Appl."},{"key":"ref_139","doi-asserted-by":"crossref","first-page":"8913","DOI":"10.1109\/JSEN.2018.2867432","article-title":"Smart soil parameters estimation system using an autonomous wireless sensor network with dynamic power management strategy","volume":"18","year":"2018","journal-title":"IEEE Sens. J."},{"key":"ref_140","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1109\/JSEN.2016.2622244","article-title":"Low-cost wireless system for agrochemical dosage reduction in precision farming","volume":"17","author":"Viani","year":"2016","journal-title":"IEEE Sens. J."},{"key":"ref_141","doi-asserted-by":"crossref","unstructured":"Truong, T., Dinh, A., and Wahid, K. (,  2017). An IoT environmental data collection system for fungal detection in crop fields. Proceedings of the 2017 30th Canadian Conference on Electrical and Computer Engineering (CCECE), Windsor, ON, Canada.","DOI":"10.1109\/CCECE.2017.7946787"},{"key":"ref_142","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1016\/j.compag.2013.05.006","article-title":"Using classification algorithms for predicting durum wheat yield in the province of Buenos Aires","volume":"96","author":"Romero","year":"2013","journal-title":"Comput. Electron. Agric."},{"key":"ref_143","doi-asserted-by":"crossref","unstructured":"Haider, S.A., Naqvi, S.R., Akram, T., Umar, G.A., Shahzad, A., Sial, M.R., Khaliq, S., and Kamran, M. (2019). LSTM neural network based forecasting model for wheat production in Pakistan. Agronomy, 9.","DOI":"10.3390\/agronomy9020072"},{"key":"ref_144","doi-asserted-by":"crossref","unstructured":"Jeong, J.H., Resop, J.P., Mueller, N.D., Fleisher, D.H., Yun, K., Butler, E.E., Timlin, D.J., Shim, K.M., Gerber, J.S., and Reddy, V.R. (2016). Random forests for global and regional crop yield predictions. PLoS ONE, 11.","DOI":"10.1371\/journal.pone.0156571"},{"key":"ref_145","doi-asserted-by":"crossref","first-page":"699","DOI":"10.1016\/j.procs.2020.04.076","article-title":"Predictive analytics in Agriculture: Forecasting prices of Arecanuts in Kerala","volume":"171","author":"Sabu","year":"2020","journal-title":"Procedia Comput. Sci."},{"key":"ref_146","doi-asserted-by":"crossref","first-page":"1458","DOI":"10.1080\/00207543.2017.1367106","article-title":"A reliable decision support system for fresh food supply chain management","volume":"56","author":"Dellino","year":"2018","journal-title":"Int. J. Prod. Res."},{"key":"ref_147","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Chen, B., and Lu, X. (,  2011). Intelligent monitoring system on refrigerator trucks based on the Internet of things. Proceedings of the International Conference on Wireless Communications and Applications (ICWCA), Sanya, China.","DOI":"10.1007\/978-3-642-29157-9_19"},{"key":"ref_148","doi-asserted-by":"crossref","first-page":"49990","DOI":"10.1109\/ACCESS.2018.2867872","article-title":"Big data driven agricultural products supply chain management: A trustworthy scheduling optimization approach","volume":"6","author":"Tao","year":"2018","journal-title":"IEEE Access"},{"key":"ref_149","doi-asserted-by":"crossref","unstructured":"Femling, F., Olsson, A., and Alonso-Fernandez, F. (,  2018). Fruit and vegetable identification using machine learning for retail applications. Proceedings of the 2018 14th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS), Las Palmas de Gran Canaria, Spain.","DOI":"10.1109\/SITIS.2018.00013"},{"key":"ref_150","doi-asserted-by":"crossref","unstructured":"Zhao, G., Yu, H., Wang, G., Sui, Y., and Zhang, L. (,  2014). Applied research of IOT and RFID technology in agricultural product traceability system. Proceedings of the International Conference on Computer and Computing Technologies in Agriculture (CCTA) VIII, Beijing, China.","DOI":"10.1007\/978-3-319-19620-6_57"},{"key":"ref_151","doi-asserted-by":"crossref","first-page":"107016","DOI":"10.1016\/j.foodcont.2019.107016","article-title":"Improving efficiency of RFID-based traceability system for perishable food by utilizing iot sensors and machine learning model","volume":"110","author":"Alfian","year":"2020","journal-title":"Food Control"},{"key":"ref_152","doi-asserted-by":"crossref","first-page":"1906","DOI":"10.1108\/IMDS-11-2016-0489","article-title":"IoT-based tracking and tracing platform for prepackaged food supply chain","volume":"117","author":"Li","year":"2017","journal-title":"Ind. Manag. Data Syst."},{"key":"ref_153","doi-asserted-by":"crossref","unstructured":"Pigini, D., and Conti, M. (2017). NFC-based traceability in the food chain. Sustainability, 9.","DOI":"10.3390\/su9101910"},{"key":"ref_154","unstructured":"Tian, F. (,  2017). A supply chain traceability system for food safety based on HACCP, Blockchain & Internet of Things. Proceedings of the 2017 International Conference on Service Systems and Service Management, Dalian, China."},{"key":"ref_155","doi-asserted-by":"crossref","unstructured":"Malik, S., Kanhere, S.S., and Jurdak, R. (,  2018). Productchain: Scalable blockchain framework to support provenance in supply chains. Proceedings of the 2018 17th International Symposium on Network Computing and Applications (NCA), Cambridge, MA, USA.","DOI":"10.1109\/NCA.2018.8548322"},{"key":"ref_156","doi-asserted-by":"crossref","unstructured":"Khan, P.W., Byun, Y.C., and Park, N. (2020). IoT-Blockchain Enabled Optimized Provenance System for Food Industry 4.0 Using Advanced Deep Learning. Sensors, 20.","DOI":"10.3390\/s20102990"},{"key":"ref_157","unstructured":"HORIZON 2020 (2021, March 23). Technology Readiness Levels (TRL). Available online: https:\/\/ec.europa.eu\/research\/participants\/data\/ref\/h2020\/wp\/2014_2015\/annexes\/h2020-wp1415-annex-g-trl_en.pdf."},{"key":"ref_158","doi-asserted-by":"crossref","unstructured":"Balafoutis, A.T., Evert, F.K.V., and Fountas, S. (2020). Smart farming technology trends: Economic and environmental effects, labor impact, and adoption readiness. Agronomy, 10.","DOI":"10.3390\/agronomy10050743"},{"key":"ref_159","doi-asserted-by":"crossref","unstructured":"Bahn, R.A., Yehya, A.A.K., and Zurayk, R. (2021). Digitalization for Sustainable Agri-Food Systems: Potential, Status, and Risks for the MENA Region. Sustainability, 13.","DOI":"10.3390\/su13063223"},{"key":"ref_160","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1016\/S0378-7206(02)00043-5","article-title":"AIMQ: A methodology for information quality assessment","volume":"40","author":"Lee","year":"2002","journal-title":"Inf. Manag."},{"key":"ref_161","unstructured":"Collins, S., Genova, F., Harrower, N., Hodson, S., Jones, S., Laaksonen, L., Mietchen, D., Petrauskait\u0117, R., and Wittenburg, P. (2021, February 09). Turning FAIR into Reality: Final Report and Action Plan from the European Commission Expert Group on FAIR Data. Available online: https:\/\/ec.europa.eu\/info\/sites\/info\/files\/turning_fair_into_reality_1.pdf."},{"key":"ref_162","unstructured":"Deng, J., Han, Y.S., Chen, P.N., and Varshney, P.K. (,  2004). Optimum transmission range for wireless ad hoc networks. Proceedings of the 2004 IEEE wireless communications and networking conference (IEEE Cat. No. 04TH8733), Atlanta, GA, USA."},{"key":"ref_163","unstructured":"Bing, F. (,  2016). The research of IOT of agriculture based on three layers architecture. Proceedings of the 2016 2nd International Conference on Cloud Computing and Internet of Things (CCIOT), Dalian, China."},{"key":"ref_164","doi-asserted-by":"crossref","unstructured":"Khattab, A., Abdelgawad, A., and Yelmarthi, K. (,  2016). Design and implementation of a cloud-based IoT scheme for precision agriculture. Proceedings of the 2016 28th International Conference on Microelectronics (ICM), Giza, Egypt.","DOI":"10.1109\/ICM.2016.7847850"},{"key":"ref_165","doi-asserted-by":"crossref","unstructured":"Ferr\u00e1ndez-Pastor, F.J., Garc\u00eda-Chamizo, J.M., Nieto-Hidalgo, M., and Mora-Mart\u00ednez, J. (2018). Precision agriculture design method using a distributed computing architecture on Internet of things context. Sensors, 18.","DOI":"10.3390\/s18061731"},{"key":"ref_166","doi-asserted-by":"crossref","unstructured":"Triantafyllou, A., Tsouros, D.C., Sarigiannidis, P., and Bibi, S. (,  2019). An Architecture model for Smart Farming. Proceedings of the 2019 15th International Conference on Distributed Computing in Sensor Systems (DCOSS), Santorini, Greece.","DOI":"10.1109\/DCOSS.2019.00081"},{"key":"ref_167","doi-asserted-by":"crossref","first-page":"260","DOI":"10.1111\/nph.16544","article-title":"Enabling reusability of plant phenomic datasets with MIAPPE 1.1","volume":"227","author":"Papoutsoglou","year":"2020","journal-title":"New Phytol."},{"key":"ref_168","unstructured":"Bonneau, V., Copigneaux, B., Probst, L., and Pedersen, B. (2021, February 18). Digital Transformation Monitor. Industry 4.0 in agriculture: Focus on IoT Aspects. European Commission, Internal Market, Industry, Entrepreneurship and SMEs. Available online: https:\/\/ati.ec.europa.eu\/sites\/default\/files\/2020-07\/Industry%204.0%20in%20Agriculture%20-%20Focus%20on%20IoT%20aspects%20%28v1%29.pdf."},{"key":"ref_169","first-page":"4","article-title":"The Reference Architectural Model Industrie 4.0 (RAMI 4.0)","volume":"2","author":"Hankel","year":"2015","journal-title":"ZWEI"}],"container-title":["Agronomy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-4395\/11\/4\/667\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T13:55:58Z","timestamp":1760363758000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-4395\/11\/4\/667"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,4,1]]},"references-count":169,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2021,4]]}},"alternative-id":["agronomy11040667"],"URL":"https:\/\/doi.org\/10.3390\/agronomy11040667","relation":{},"ISSN":["2073-4395"],"issn-type":[{"value":"2073-4395","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,4,1]]}}}