{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T08:09:05Z","timestamp":1775635745569,"version":"3.50.1"},"reference-count":199,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2021,11,26]],"date-time":"2021-11-26T00:00:00Z","timestamp":1637884800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The agriculture sector is one of the backbones of many countries\u2019 economies. Its processes have been changing to enable technology adoption to increase productivity, quality, and sustainable development. In this research, we present a scientific mapping of the adoption of precision techniques and breakthrough technologies in agriculture, so-called Digital Agriculture. To do this, we used 4694 documents from the Web of Science database to perform a Bibliometric Performance and Network Analysis of the literature using SciMAT software with the support of the PICOC protocol. Our findings presented 22 strategic themes related to Digital Agriculture, such as Internet of Things (IoT), Unmanned Aerial Vehicles (UAV) and Climate-smart Agriculture (CSA), among others. The thematic network structure of the nine most important clusters (motor themes) was presented and an in-depth discussion was performed. The thematic evolution map provides a broad perspective of how the field has evolved over time from 1994 to 2020. In addition, our results discuss the main challenges and opportunities for research and practice in the field of study. Our findings provide a comprehensive overview of the main themes related to Digital Agriculture. These results show the main subjects analyzed on this topic and provide a basis for insights for future research.<\/jats:p>","DOI":"10.3390\/s21237889","type":"journal-article","created":{"date-parts":[[2021,12,1]],"date-time":"2021-12-01T01:45:02Z","timestamp":1638323102000},"page":"7889","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":63,"title":["A Bibliometric Network Analysis of Recent Publications on Digital Agriculture to Depict Strategic Themes and Evolution Structure"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7428-3993","authenticated-orcid":false,"given":"Michele Kremer","family":"Sott","sequence":"first","affiliation":[{"name":"Business School, Unisinos University, Porto Alegre 91330-002, RS, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0981-1718","authenticated-orcid":false,"given":"Leandro da Silva","family":"Nascimento","sequence":"additional","affiliation":[{"name":"School of Management, Federal University of Rio Grande do Sul, Porto Alegre 90040-060, RS, Brazil"}]},{"given":"Cristian Rog\u00e9rio","family":"Foguesatto","sequence":"additional","affiliation":[{"name":"Business School, Unisinos University, Porto Alegre 91330-002, RS, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8984-7774","authenticated-orcid":false,"given":"Leonardo B.","family":"Furstenau","sequence":"additional","affiliation":[{"name":"Department of Industrial Engineering, Federal University of Rio Grande do Sul, Porto Alegre 90040-060, RS, Brazil"}]},{"given":"Kad\u00edgia","family":"Faccin","sequence":"additional","affiliation":[{"name":"Business School, Unisinos University, Porto Alegre 91330-002, RS, Brazil"}]},{"given":"Paulo Ant\u00f4nio","family":"Zawislak","sequence":"additional","affiliation":[{"name":"School of Management, Federal University of Rio Grande do Sul, Porto Alegre 90040-060, RS, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4838-1546","authenticated-orcid":false,"given":"Bruce","family":"Mellado","sequence":"additional","affiliation":[{"name":"School of Physics and Institute for Collider Particle Physics, University of the Witwatersrand, Johannesburg 2050, South Africa"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7557-5672","authenticated-orcid":false,"given":"Jude Dzevela","family":"Kong","sequence":"additional","affiliation":[{"name":"Department of Mathematics and Statistics, York University, Toronto, ON M3J 1P3, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8409-868X","authenticated-orcid":false,"given":"Nicola Luigi","family":"Bragazzi","sequence":"additional","affiliation":[{"name":"Department of Mathematics and Statistics, York University, Toronto, ON M3J 1P3, Canada"}]}],"member":"1968","published-online":{"date-parts":[[2021,11,26]]},"reference":[{"key":"ref_1","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_2","unstructured":"Harari, Y.N. (2014). Sapiens: A Brief History of Humankind, Harper Collins."},{"key":"ref_3","unstructured":"Tekinerdogan, B. (2018). Strategies for Technological Innovation in Agriculture 4.0, Wageningen University."},{"key":"ref_4","first-page":"237","article-title":"Population Growth and Agrarian Change: An Historical Perspective","volume":"149","author":"Grigg","year":"1980","journal-title":"Geogr. J."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"683","DOI":"10.2307\/1836733","article-title":"The Agricultural Revolution in New England","volume":"26","author":"Bidwell","year":"1921","journal-title":"Am. Hist. Rev."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2211","DOI":"10.1093\/jxb\/eraa110","article-title":"From green to gold: Agricultural revolution for food security","volume":"71","author":"Evans","year":"2020","journal-title":"J. Exp. Bot."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"12302","DOI":"10.1073\/pnas.0912953109","article-title":"Green revolution: Impacts, limits, andthe path ahead","volume":"109","author":"Pingali","year":"2012","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_8","first-page":"815","article-title":"Green revolution: The way forward","volume":"2","author":"Khush","year":"2001","journal-title":"Nature"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"713","DOI":"10.1007\/s11119-012-9273-6","article-title":"Factors influencing the adoption of precision agricultural technologies: A review for policy implications","volume":"13","author":"Tey","year":"2012","journal-title":"Precis. Agric."},{"key":"ref_10","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_11","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_12","first-page":"111","article-title":"Towards an alert system for coffee diseases and pests in a smart farming approach based on semi-supervised learning and graph similarity","volume":"Volume 687","author":"Lasso","year":"2018","journal-title":"Proceedings of the International Conference of ICT for Adapting Agriculture to Climate Change (AACC\u201917)"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1016\/j.cosust.2014.07.002","article-title":"Sustainable intensification: What is its role in climate smart agriculture?","volume":"8","author":"Campbell","year":"2014","journal-title":"Curr. Opin. Environ. Sustain."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Adamides, G., Kalatzis, N., Stylianou, A., Marianos, N., Chatzipapadopoulos, F., Giannakopoulou, M., Papadavid, G., Vassiliou, V., and Neocleous, D. (2020). Smart farming techniques for climate change adaptation in Cyprus. Atmosphere, 11.","DOI":"10.3390\/atmos11060557"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1068","DOI":"10.1038\/nclimate2437","article-title":"Climate-smart agriculture for food security","volume":"4","author":"Lipper","year":"2014","journal-title":"Nat. Clim. Chang."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1015","DOI":"10.1007\/s10098-012-0535-9","article-title":"Sustainable consumption and production: How to make it possible","volume":"14","year":"2012","journal-title":"Clean Technol. Environ. Policy"},{"key":"ref_17","first-page":"65","article-title":"Toward a More Resilient Agriculture","volume":"5","author":"Bennett","year":"2014","journal-title":"Solutions"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"105100","DOI":"10.1109\/ACCESS.2019.2932119","article-title":"Unmanned aerial vehicles in agriculture: A review of perspective of platform, control, and applications","volume":"7","author":"Kim","year":"2019","journal-title":"IEEE Access"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"9070","DOI":"10.1080\/01431161.2019.1569793","article-title":"A bibliometric analysis on the use of unmanned aerial vehicles in agricultural and forestry studies","volume":"40","author":"Raparelli","year":"2019","journal-title":"Int. J. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"e3958","DOI":"10.1002\/ett.3958","article-title":"Systematic review of Internet of Things in smart farming","volume":"31","author":"Terence","year":"2020","journal-title":"Trans. Emerg. Telecommun. Technol."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Navarro, E., Costa, N., and Pereira, A. (2020). A systematic review of iot solutions for smart farming. Sensors, 20.","DOI":"10.3390\/s20154231"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"037522","DOI":"10.1149\/2.0222003JES","article-title":"Review\u2014Machine Learning Techniques in Wireless Sensor Network Based Precision Agriculture","volume":"167","author":"Mekonnen","year":"2020","journal-title":"J. Electrochem. Soc."},{"key":"ref_23","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_24","doi-asserted-by":"crossref","first-page":"104926","DOI":"10.1016\/j.cor.2020.104926","article-title":"A systematic literature review on machine learning applications for sustainable agriculture supply chain performance","volume":"119","author":"Sharma","year":"2020","journal-title":"Comput. Oper. Res."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"e1968","DOI":"10.1002\/pa.1968","article-title":"A review: The role of geospatial technology in precision agriculture","volume":"20","author":"Praveen","year":"2020","journal-title":"J. Public Aff."},{"key":"ref_26","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_27","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/j.agsy.2017.01.023","article-title":"Big data in smart farming","volume":"153","author":"Wolfert","year":"2017","journal-title":"Agric. Syst."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"4878","DOI":"10.1002\/jsfa.9693","article-title":"State-of-the-art technologies in precision agriculture: A systematic review","volume":"99","author":"Bhakta","year":"2019","journal-title":"J. Sci. Food Agric."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1292","DOI":"10.1007\/s11119-019-09653-x","article-title":"A systematic literature review of the factors affecting the precision agriculture adoption process","volume":"20","author":"Pathak","year":"2019","journal-title":"Precis. Agric."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1609","DOI":"10.1002\/asi.22688","article-title":"SciMAT: A new science mapping analysis software tool","volume":"63","author":"Cobo","year":"2012","journal-title":"J. Am. Soc. Inf. Sci. Technol."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1019","DOI":"10.1007\/s11119-020-09760-0","article-title":"Drivers and challenges of precision agriculture: A social media perspective","volume":"22","author":"Ofori","year":"2020","journal-title":"Precis. Agric."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Hrustek, L. (2020). Sustainability driven by agriculture through digital transformation. Sustainability, 12.","DOI":"10.3390\/su12208596"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Khattab, A., Abdelgawad, A., and Yelmarthi, K. (2016, January 17\u201320). 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_34","first-page":"146","article-title":"Agricultura de precis\u00e3o: Inova\u00e7\u00e3o para a produ\u00e7\u00e3o mundial de alimentos e otimiza\u00e7\u00e3o de insumos agr\u00edcolas","volume":"13","author":"Artuzo","year":"2017","journal-title":"Rev. Tecnol. E Soc."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1007\/s11119-007-9040-2","article-title":"The economic feasibility of precision agriculture in Mato Grosso do Sul State, Brazil: A case study","volume":"8","author":"Silva","year":"2007","journal-title":"Precis. Agric."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Balafoutis, A., Beck, B., Fountas, S., Vangeyte, J., Van Der Wal, T., Soto, I., G\u00f3mez-Barbero, M., Barnes, A., and Eory, V. (2017). Precision agriculture technologies positively contributing to ghg emissions mitigation, farm productivity and economics. Sustainability, 9.","DOI":"10.3390\/su9081339"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"359","DOI":"10.1023\/B:PRAG.0000040806.39604.aa","article-title":"Precision agriculture and sustainability","volume":"5","author":"Bongiovanni","year":"2004","journal-title":"Precis. Agric."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1016\/j.biosystemseng.2019.02.019","article-title":"Identification of management zones in precision agriculture: An evaluation of alternative cluster analysis methods","volume":"181","author":"Gavioli","year":"2019","journal-title":"Biosyst. Eng."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"105556","DOI":"10.1016\/j.compag.2020.105556","article-title":"FastMapping: Software to create field maps and identify management zones in precision agriculture","volume":"175","author":"Paccioretti","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1016\/j.eja.2019.04.001","article-title":"Improving productivity and increasing the efficiency of soil nutrient management on grassland farms in the UK and Ireland using precision agriculture technology","volume":"106","author":"Higgins","year":"2019","journal-title":"Eur. J. Agron."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"136365","DOI":"10.1016\/j.scitotenv.2019.136365","article-title":"Controlled release micronutrient fertilizers for precision agriculture\u2014A review","volume":"712","author":"Mikula","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1002\/jsfa.6734","article-title":"The role of precision agriculture for improved nutrient management on farms","volume":"95","author":"Hedley","year":"2015","journal-title":"J. Sci. Food Agric."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1007\/s11119-013-9335-4","article-title":"Assessing the accuracy of mosaics from unmanned aerial vehicle (UAV) imagery for precision agriculture purposes in wheat","volume":"15","year":"2014","journal-title":"Precis. Agric."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"107148","DOI":"10.1016\/j.comnet.2020.107148","article-title":"A compilation of UAV applications for precision agriculture","volume":"172","author":"Sarigiannidis","year":"2020","journal-title":"Comput. Netw."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"647","DOI":"10.1108\/AFR-11-2019-0121","article-title":"Duration analyses of precision agriculture technology adoption: What\u2019s influencing farmers\u2019 time-to-adoption decisions?","volume":"80","author":"Ofori","year":"2020","journal-title":"Agric. Financ. Rev."},{"key":"ref_46","first-page":"100315","article-title":"A review of social science on digital agriculture, smart farming and agriculture 4.0: New contributions and a future research agenda","volume":"90","author":"Klerkx","year":"2019","journal-title":"NJAS\u2014Wagening. J. Life Sci."},{"key":"ref_47","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_48","doi-asserted-by":"crossref","unstructured":"Symeonaki, E., Arvanitis, K., and Piromalis, D. (2020). A context-aware middleware cloud approach for integrating precision farming facilities into the IoT toward agriculture 4.0. Appl. Sci., 10.","DOI":"10.3390\/app10030813"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Huh, J.H., and Kim, K.Y. (2018). Time-based trend of carbon emissions in the composting process of swine manure in the context of agriculture 4.0. Processes, 6.","DOI":"10.3390\/pr6090168"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"100347","DOI":"10.1016\/j.gfs.2019.100347","article-title":"Dealing with the game-changing technologies of Agriculture 4.0: How do we manage diversity and responsibility in food system transition pathways?","volume":"24","author":"Klerkx","year":"2020","journal-title":"Glob. Food Sec."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Pisanu, T., Garau, S., Ortu, P., Schirru, L., and Macci\u00f2, C. (2020). Prototype of a low-cost electronic platform for real time greenhouse environment monitoring: An agriculture 4.0 perspective. Electronics, 9.","DOI":"10.3390\/electronics9050726"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"140079","DOI":"10.1109\/ACCESS.2020.3012812","article-title":"Link between sustainability and industry 4.0: Trends, challenges and new perspectives","volume":"8","author":"Furstenau","year":"2020","journal-title":"IEEE Access"},{"key":"ref_53","first-page":"1131930","article-title":"Intelligent personal assistants: A systematic literature review","volume":"147","author":"Gomes","year":"2020","journal-title":"Expert Syst. Appl."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1016\/j.rcim.2017.06.002","article-title":"A systematic review of augmented reality applications in maintenance","volume":"49","author":"Palmarini","year":"2018","journal-title":"Robot. Comput. Integr. Manuf."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Bruzza, M., Cabrera, A., and Tupia, M. (2017, January 18\u201320). Survey of the state of art based on PICOC about the use of artificial intelligence tools and expert systems to manage and generate tourist packages. Proceedings of the 2017 International Conference on Infocom Technologies and Unmanned Systems (Trends and Future Directions)(ICTUS), Dubai, United Arab Emirates.","DOI":"10.1109\/ICTUS.2017.8286021"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"106426","DOI":"10.1016\/j.cie.2020.106426","article-title":"Looking at energy through the lens of Industry 4.0: A systematic literature review of concerns and challenges","volume":"143","year":"2020","journal-title":"Comput. Ind. Eng."},{"key":"ref_57","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_58","doi-asserted-by":"crossref","unstructured":"Saiz-Rubio, V., and Rovira-M\u00e1s, F. (2020). From smart farming towards agriculture 5.0: A review on crop data management. Agronomy, 10.","DOI":"10.3390\/agronomy10020207"},{"key":"ref_59","first-page":"314","article-title":"20 years of scientific evolution of cyber security: A science mapping","volume":"10","author":"Furstenau","year":"2020","journal-title":"IEOM Soc. Int."},{"key":"ref_60","unstructured":"L\u00f3pez-Robles, J.R., Otegi-Olaso, J.R., Cobo, M.J., Bertolin-Furstenau, L., Kremer-Sott, M., L\u00f3pez-Robles, L.D., and Gamboa-Rosales, N.K. (2020, January 20\u201321). The relationship between project management and industry 4.0: Bibliometric analysis of main research areas through Scopus. Proceedings of the 3rd International Conference on Research and Education in Project Management\u2014REPM 2020, Bilbao, Spain."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Severo, P.P., Furstenau, L.B., Sott, M.K., Cossul, D., Bender, M.S., and Bragazzi, N.L. (2021). Thirty years of human rights study in the web of science database (1990\u20132020). Int. J. Environ. Res. Public Health, 18.","DOI":"10.3390\/ijerph18042131"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Kolling, M.L., Furstenau, L.B., Sott, M.K., Rabaioli, B., Ulmi, P.H., Bragazzi, N.L., and Tedesco, L.P.C. (2021). Data mining in healthcare: Applying strategic intelligence techniques to depict 25 years of research development. Int. J. Environ. Res. Public Health, 18.","DOI":"10.3390\/ijerph18063099"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"1391","DOI":"10.1108\/BPMJ-05-2020-0181","article-title":"Process modeling for smart factories: Using science mapping to understand the strategic themes, main challenges and future trends","volume":"27","author":"Sott","year":"2021","journal-title":"Bus. Process Manag. J."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"1605","DOI":"10.1080\/00207543.2019.1671625","article-title":"Scopus scientific mapping production in industry 4.0 (2011\u20132018): A bibliometric analysis","volume":"58","author":"Kipper","year":"2020","journal-title":"Int. J. Prod. Res."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"364","DOI":"10.1016\/j.procs.2018.10.278","article-title":"Industry 4.0: A perspective based on bibliometric analysis","volume":"139","author":"Cobo","year":"2018","journal-title":"Procedia Comput. Sci."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Furstenau, L.B., Rabaioli, B., Sott, M.K., Cossul, D., Bender, M.S., Farina, E.M.J.D.M., Filho, F.N.B., Severo, P.P., Dohan, M.S., and Bragazzi, N.L. (2021). A Bibliometric Network Analysis of Coronavirus during the First Eight Months of COVID-19 in 2020. Int. J. Environ. Res. Public Health, 18.","DOI":"10.3390\/ijerph18030952"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"598676","DOI":"10.3389\/fpsyg.2020.598676","article-title":"100 Years of Scientific Evolution of Work and Organizational Psychology: A Bibliometric Network Analysis from 1919 to 2019","volume":"11","author":"Sott","year":"2020","journal-title":"Front. Psychol."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"1068","DOI":"10.1080\/09537325.2020.1865530","article-title":"An overview of 42 years of lean production: Applying bibliometric analysis to investigate strategic themes and scientific evolution structure","volume":"33","author":"Furstenau","year":"2021","journal-title":"Technol. Anal. Strateg. Manag."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1007\/BF02019280","article-title":"Co-word analysis as a tool for describing the network of interactions between basic and technological research: The case of polymer chemsitry","volume":"22","author":"Callon","year":"1991","journal-title":"Scientometrics"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"1206","DOI":"10.1002\/(SICI)1097-4571(1998)49:13<1206::AID-ASI7>3.0.CO;2-F","article-title":"Software engineering as seen through its research literature: A study in co-word analysis","volume":"49","author":"Coulter","year":"1998","journal-title":"J. Am. Soc. Inf. Sci."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"717","DOI":"10.13031\/2013.28132","article-title":"Effects of spatial variability of nitrogen, moisture, and weeds on the advantages of site-specific applications for wheat","volume":"37","author":"Chancellor","year":"1994","journal-title":"Trans. Am. Soc. Agric. Eng."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/0168-1923(94)90080-9","article-title":"The future of serving agriculture with weather\/climate information and forecasting: Some indications and observations","volume":"69","author":"Seeley","year":"1994","journal-title":"Agric. For. Meteorol."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1177\/003072709402300407","article-title":"Precision farming: An introduction","volume":"23","author":"Blackmore","year":"1994","journal-title":"Outlook Agric."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"335","DOI":"10.4141\/cjss94-046","article-title":"Soil salinity mapping with electromagnetic induction and satellite-based navigation methods","volume":"74","author":"Cannon","year":"1994","journal-title":"Can. J. Soil Sci."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1080\/00103629409369002","article-title":"High-precision agriculture is an excellent tool for conservation of natural resources","volume":"25","author":"Wallace","year":"1994","journal-title":"Commun. Soil Sci. Plant Anal."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"1382","DOI":"10.1002\/asi.21525","article-title":"Science mapping software tools: Review, analysis, and cooperative study among tools","volume":"62","author":"Cobo","year":"2011","journal-title":"J. Am. Soc. Inf. Sci. Technol."},{"key":"ref_77","first-page":"1275","article-title":"Some bibliometric procedures for analyzing and evaluating research fields","volume":"48","author":"Cobo","year":"2018","journal-title":"Appl. Intell."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1007\/978-90-481-9707-1_92","article-title":"Definitions and terminology","volume":"1","author":"Dalamagkidis","year":"2015","journal-title":"Handb. Unmanned Aer. Veh."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"3557","DOI":"10.3390\/s8053557","article-title":"Assessment of Unmanned Aerial Vehicles Imagery for Quantitative Monitoring of Wheat Crop in Small Plots","volume":"8","author":"Lelong","year":"2008","journal-title":"Sensors"},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"5078","DOI":"10.1080\/01431161.2017.1420941","article-title":"A meta-analysis and review of unmanned aircraft system (UAS) imagery for terrestrial applications","volume":"39","author":"Singh","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"339","DOI":"10.1080\/15481603.2014.926650","article-title":"Recent applications of unmanned aerial imagery in natural resource management","volume":"51","author":"Shahbazi","year":"2014","journal-title":"GIScience Remote Sens."},{"key":"ref_82","doi-asserted-by":"crossref","unstructured":"Eskandari, R., Mahdianpari, M., Mohammadimanesh, F., Salehi, B., Brisco, B., and Homayouni, S. (2020). Meta-analysis of unmanned aerial vehicle (UAV) imagery for agro-environmental monitoring using machine learning and statistical models. Remote Sens., 12.","DOI":"10.3390\/rs12213511"},{"key":"ref_83","doi-asserted-by":"crossref","unstructured":"Zha, H., Miao, Y., Wang, T., Li, Y., Zhang, J., Sun, W., Feng, Z., and Kusnierek, K. (2020). Improving unmanned aerial vehicle remote sensing-based rice nitrogen nutrition index prediction with machine learning. Remote Sens., 12.","DOI":"10.3390\/rs12020215"},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"411","DOI":"10.1002\/net.21818","article-title":"Optimization approaches for civil applications of unmanned aerial vehicles (UAVs) or aerial drones: A survey","volume":"72","author":"Otto","year":"2018","journal-title":"Networks"},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"139204","DOI":"10.1016\/j.scitotenv.2020.139204","article-title":"Unmanned aerial vehicles for biodiversity-friendly agricultural landscapes\u2014A systematic review","volume":"732","author":"Klaus","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_86","doi-asserted-by":"crossref","unstructured":"Fu, Z., Jiang, J., Gao, Y., Krienke, B., Wang, M., Zhong, K., Cao, Q., Tian, Y., Zhu, Y., and Cao, W. (2020). Wheat growth monitoring and yield estimation based on multi-rotor unmanned aerial vehicle. Remote Sens., 12.","DOI":"10.3390\/rs12030508"},{"key":"ref_87","first-page":"507","article-title":"Agriculture drones: A modern breakthrough in precision agriculture","volume":"20","author":"Puri","year":"2017","journal-title":"J. Stat. Manag. Syst."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"454","DOI":"10.1080\/07038992.2020.1788384","article-title":"Intra-Field Canopy Nitrogen Retrieval from Unmanned Aerial Vehicle Imagery for Wheat and Corn Fields","volume":"46","author":"Lee","year":"2020","journal-title":"Can. J. Remote Sens."},{"key":"ref_89","doi-asserted-by":"crossref","unstructured":"Garc\u00eda-Mart\u00ednez, H., Flores-Magdaleno, H., Ascencio-Hern\u00e1ndez, R., Khalil-Gardezi, A., Tijerina-Ch\u00e1vez, L., Mancilla-Villa, O.R., and V\u00e1zquez-Pe\u00f1a, M.A. (2020). Corn grain yield estimation from vegetation indices, canopy cover, plant density, and a neural network using multispectral and rgb images acquired with unmanned aerial vehicles. Agriculture, 10.","DOI":"10.3390\/agriculture10070277"},{"key":"ref_90","doi-asserted-by":"crossref","unstructured":"Zhang, S., and Zhao, G. (2019). A harmonious satellite-unmanned aerial vehicle-ground measurement inversion method for monitoring salinity in coastal saline soil. Remote Sens., 11.","DOI":"10.3390\/rs11141700"},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"526","DOI":"10.1080\/14693062.2017.1316968","article-title":"Climate-smart agriculture: Perspectives and framings","volume":"18","author":"Chandra","year":"2018","journal-title":"Clim. Policy"},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1080\/03066150.2017.1312355","article-title":"Climate-smart agriculture: What is it good for?","volume":"45","author":"Taylor","year":"2018","journal-title":"J. Peasant. Stud."},{"key":"ref_93","doi-asserted-by":"crossref","unstructured":"Fusco, G., Melgiovanni, M., Porrini, D., and Ricciardo, T.M. (2020). How to improve the diffusion of climate-smart agriculture: What the literature tells us. Sustainability, 12.","DOI":"10.3390\/su12125168"},{"key":"ref_94","doi-asserted-by":"crossref","unstructured":"Totin, E., Segnon, A.C., Schut, M., Affognon, H., Zougmor\u00e9, R.B., Rosenstock, T., and Thornton, P.K. (2018). Institutional perspectives of climate-smart agriculture: A systematic literature review. Sustainability, 10.","DOI":"10.3390\/su10061990"},{"key":"ref_95","first-page":"2411","article-title":"Climate-Smart agriculture and potato production in Kenya: Review of the determinants of practice","volume":"107","author":"Waaswa","year":"2021","journal-title":"Clim. Dev."},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"973","DOI":"10.1111\/rode.12670","article-title":"Does women\u2019s participation in agricultural technology adoption decisions affect the adoption of climate-smart agriculture? Insights from Indo-Gangetic Plains of India","volume":"24","author":"Aryal","year":"2020","journal-title":"Rev. Dev. Econ."},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"4215","DOI":"10.1007\/s10668-020-00767-1","article-title":"Farmers\u2019 adaptation to climate-smart agriculture (CSA) in NW Turkey","volume":"23","author":"Everest","year":"2020","journal-title":"Environ. Dev. Sustain."},{"key":"ref_98","first-page":"29","article-title":"Economic modeling of climate-smart agriculture in Iran","volume":"2019","author":"Ardakani","year":"2019","journal-title":"New Medit"},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"858","DOI":"10.1016\/j.jenvman.2018.10.069","article-title":"Increasing resilience of smallholder farmers to climate change through multiple adoption of proven climate-smart agriculture innovations. Lessons from Southern Africa","volume":"231","author":"Makate","year":"2019","journal-title":"J. Environ. Manag."},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"1073","DOI":"10.1007\/s12571-018-0824-1","article-title":"Impact of climate-smart agriculture adoption on the food security of coastal farmers in Bangladesh","volume":"10","author":"Hasan","year":"2018","journal-title":"Food Secur."},{"key":"ref_101","doi-asserted-by":"crossref","unstructured":"Dorsemaine, B., Gaulier, J.P., Wary, J.P., Kheir, N., and Urien, P. (2015, January 9\u201311). Internet of Things: A Definition and Taxonomy. Proceedings of the 2015 9th International Conference on Next Generation Mobile Applications, Services and Technologies, Cambridge, UK.","DOI":"10.1109\/NGMAST.2015.71"},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"4309","DOI":"10.1007\/s11227-019-02774-0","article-title":"A network clock model for time awareness in the Internet of things and artificial intelligence applications","volume":"75","author":"Hwang","year":"2019","journal-title":"J. Supercomput."},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"129924","DOI":"10.1109\/ACCESS.2020.3009298","article-title":"Recent Developments of the Internet of Things in Agriculture: A Survey","volume":"8","author":"Kour","year":"2020","journal-title":"IEEE Access"},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"105338","DOI":"10.1016\/j.compag.2020.105338","article-title":"Wireless technologies for smart agricultural monitoring using internet of things devices with energy harvesting capabilities","volume":"172","author":"Sadowski","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_105","doi-asserted-by":"crossref","unstructured":"Jayaraman, P.P., Yavari, A., Georgakopoulos, D., Morshed, A., and Zaslavsky, A. (2016). Internet of things platform for smart farming: Experiences and lessons learnt. Sensors, 16.","DOI":"10.3390\/s16111884"},{"key":"ref_106","doi-asserted-by":"crossref","unstructured":"Farooq, M.S., Riaz, S., Abid, A., Umer, T., and Zikria, Y. (2020). Bin Role of iot technology in agriculture: A systematic literature review. Electronics, 9.","DOI":"10.3390\/electronics9020319"},{"key":"ref_107","doi-asserted-by":"crossref","first-page":"105476","DOI":"10.1016\/j.compag.2020.105476","article-title":"Integrating blockchain and the internet of things in precision agriculture: Analysis, opportunities, and challenges","volume":"178","author":"Torky","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_108","doi-asserted-by":"crossref","first-page":"127712","DOI":"10.1016\/j.jclepro.2021.127712","article-title":"Wearable Internet of Things enabled precision livestock farming in smart farms: A review of technical solutions for precise perception, biocompatibility, and sustainability monitoring","volume":"312","author":"Zhang","year":"2021","journal-title":"J. Clean. Prod."},{"key":"ref_109","doi-asserted-by":"crossref","unstructured":"Jin, X.B., Yu, X.H., Wang, X.Y., Bai, Y.T., Su, T.L., and Kong, J.L. (2020). Deep learning predictor for sustainable precision agriculture based on internet of things system. Sustainability, 12.","DOI":"10.3390\/su12041433"},{"key":"ref_110","first-page":"170","article-title":"Role of Internet of Things (IoT) with Blockchain Technology for the Development of Smart Farming","volume":"14","year":"2019","journal-title":"J. Mech. Contin. Math. Sci."},{"key":"ref_111","doi-asserted-by":"crossref","first-page":"3901","DOI":"10.1007\/s13762-020-02737-6","article-title":"Portable, wireless, and effective internet of things-based sensors for precision agriculture","volume":"17","author":"Gsangaya","year":"2020","journal-title":"Int. J. Environ. Sci. Technol."},{"key":"ref_112","doi-asserted-by":"crossref","unstructured":"Lombardi, F., and Lualdi, M. (2019). Step-frequency ground penetrating radar for agricultural soil morphology characterisation. Remote Sens., 11.","DOI":"10.3390\/rs11091075"},{"key":"ref_113","doi-asserted-by":"crossref","first-page":"1203","DOI":"10.1590\/s0100-204x2018001100002","article-title":"Soil chemical attributes restricting grain yield in Oxisols under no-tillage system","volume":"53","author":"Corassa","year":"2018","journal-title":"Pesqui. Agropecu. Bras."},{"key":"ref_114","doi-asserted-by":"crossref","first-page":"472","DOI":"10.1590\/1983-21252019v32n220rc","article-title":"Delineation of homogeneous zones based on geostatistical models robust to outliers","volume":"32","author":"Barbosa","year":"2019","journal-title":"Rev. Caatinga"},{"key":"ref_115","doi-asserted-by":"crossref","first-page":"410","DOI":"10.1590\/1807-1929\/agriambi.v21n6p410-414","article-title":"Geostatistical analysis of arabic coffee yield in two crop seasons","volume":"21","author":"Carvalho","year":"2017","journal-title":"Rev. Bras. Eng. Agric. Ambient."},{"key":"ref_116","first-page":"447","article-title":"Integration of GPS with remote sensing and GIS: Reality and prospect","volume":"68","author":"Gao","year":"2002","journal-title":"Photogramm. Eng. Remote. Sens."},{"key":"ref_117","unstructured":"Hofmann-Wellenhof, B., Lichtenegger, H., and Collins, J. (2012). Global Positioning System: Theory and Practice, Springer Science & Business Media."},{"key":"ref_118","doi-asserted-by":"crossref","first-page":"11738","DOI":"10.1109\/TVT.2019.2949298","article-title":"High-Precision Estimation of Steering Angle of Agricultural Tractors Using GPS and Low-Accuracy MEMS","volume":"68","author":"Si","year":"2019","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_119","doi-asserted-by":"crossref","first-page":"360","DOI":"10.1007\/s12355-011-0098-9","article-title":"Application of GPS and GIS in Sugarcane Agriculture","volume":"13","author":"Palaniswami","year":"2011","journal-title":"Sugar Tech."},{"key":"ref_120","doi-asserted-by":"crossref","first-page":"400","DOI":"10.1016\/j.compag.2018.10.038","article-title":"Development and application of a strawberry yield-monitoring picking cart","volume":"155","author":"Vougioukas","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_121","doi-asserted-by":"crossref","first-page":"603","DOI":"10.1007\/s11119-019-09685-3","article-title":"Row-crop planter performance to support variable-rate seeding of maize","volume":"21","author":"Virk","year":"2020","journal-title":"Precis. Agric."},{"key":"ref_122","doi-asserted-by":"crossref","first-page":"914","DOI":"10.2307\/1244334","article-title":"Economic Feasibility of Variable-Rate Technology for Nitrogen on Corn","volume":"81","author":"Thrikawala","year":"1999","journal-title":"Am. J. Agric. Econ."},{"key":"ref_123","doi-asserted-by":"crossref","unstructured":"Rivero, M.J., Grau-Campanario, P., Mullan, S., Held, S.D.E., Stokes, J.E., Lee, M.R.F., and Cardenas, L.M. (2021). Factors affecting site use preference of grazing cattle studied from 2000 to 2020 through GPS tracking: A review. Sensors, 21.","DOI":"10.3390\/s21082696"},{"key":"ref_124","first-page":"341","article-title":"The suitability analysis of soil moisture retrieval using GNSS-R technology","volume":"21","author":"Peng","year":"2017","journal-title":"Yaogan Xuebao\/J. Remote Sens."},{"key":"ref_125","first-page":"27","article-title":"Recent advances in image processing techniques for automated leaf pest and disease recognition\u2014A review","volume":"8","author":"Ngugi","year":"2021","journal-title":"Inf. Process. Agric."},{"key":"ref_126","doi-asserted-by":"crossref","first-page":"579","DOI":"10.1108\/WJE-09-2020-0459","article-title":"Advance control strategies using image processing, UAV and AI in agriculture: A review","volume":"18","author":"Syeda","year":"2021","journal-title":"World J. Eng."},{"key":"ref_127","first-page":"187","article-title":"A review on machine vision and image processing techniques for weed detection in agricultural crops","volume":"58","author":"Sohail","year":"2021","journal-title":"Pak. J. Agric. Sci."},{"key":"ref_128","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1016\/j.compag.2016.04.009","article-title":"Immature green citrus detection based on colour feature and sum of absolute transformed difference (SATD) using colour images in the citrus grove","volume":"124","author":"Zhao","year":"2016","journal-title":"Comput. Electron. Agric."},{"key":"ref_129","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1016\/j.measurement.2018.05.037","article-title":"Using video processing to classify potato plant and three types of weed using hybrid of artificial neural network and partincle swarm algorithm","volume":"126","author":"Sabzi","year":"2018","journal-title":"Meas. J. Int. Meas. Confed."},{"key":"ref_130","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_131","doi-asserted-by":"crossref","first-page":"585","DOI":"10.1016\/j.compag.2018.12.006","article-title":"Current and future applications of statistical machine learning algorithms for agricultural machine vision systems","volume":"156","author":"Rehman","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_132","doi-asserted-by":"crossref","unstructured":"Niedba\u0142a, G., Kurasiak-Popowska, D., Stuper-Szablewska, K., and Nawraca\u0142a, J. (2020). Application of artificial neural networks to analyze the concentration of ferulic acid, deoxynivalenol, and nivalenol in winter wheat grain. Agriculture, 10.","DOI":"10.3390\/agriculture10040127"},{"key":"ref_133","doi-asserted-by":"crossref","unstructured":"Mendes, J., Pinho, T.M., Dos Santos, F.N., Sousa, J.J., Peres, E., Boaventura-Cunha, J., Cunha, M., and Morais, R. (2020). Smartphone applications targeting precision agriculture practices\u2014A systematic review. Agronomy, 10.","DOI":"10.3390\/agronomy10060855"},{"key":"ref_134","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/j.compag.2016.08.004","article-title":"Growth tracking of basil by proximal remote sensing of chlorophyll fluorescence in growth chamber and greenhouse environments","volume":"128","author":"Wik","year":"2016","journal-title":"Comput. Electron. Agric."},{"key":"ref_135","doi-asserted-by":"crossref","first-page":"444","DOI":"10.1080\/14735903.2020.1792679","article-title":"Soil nutrient management: Fueling agroecosystem sustainability","volume":"18","author":"Young","year":"2020","journal-title":"Int. J. Agric. Sustain."},{"key":"ref_136","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1007\/s11540-009-9145-2","article-title":"Nitrogen responses and nitrogen management in potato","volume":"52","author":"Vos","year":"2009","journal-title":"Potato Res."},{"key":"ref_137","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1080\/00103624.2014.967862","article-title":"Precision Manure Management on Site-Specific Management Zones: Topsoil Quality and Environmental Impact","volume":"46","author":"Moshia","year":"2015","journal-title":"Commun. Soil Sci. Plant Anal."},{"key":"ref_138","doi-asserted-by":"crossref","first-page":"183","DOI":"10.4081\/ija.2019.1367","article-title":"Nanofertilisers. An outlook of crop nutrition in the fourth agricultural revolution","volume":"14","author":"Marchiol","year":"2019","journal-title":"Ital. J. Agron."},{"key":"ref_139","doi-asserted-by":"crossref","unstructured":"Storer, C.S., Coldrick, Z., Tate, D.J., Donoghue, J.M., and Grieve, B. (2018). Towards phosphate detection in hydroponics using molecularly imprinted polymer sensors. Sensors, 18.","DOI":"10.20944\/preprints201801.0054.v1"},{"key":"ref_140","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/j.eja.2011.11.003","article-title":"Prospects for ecological intensification of Australian agriculture","volume":"44","author":"Hochman","year":"2013","journal-title":"Eur. J. Agron."},{"key":"ref_141","doi-asserted-by":"crossref","first-page":"535","DOI":"10.1146\/annurev-phyto-080417-050100","article-title":"Hyperspectral sensors and imaging technologies in phytopathology: State of the art","volume":"56","author":"Mahlein","year":"2018","journal-title":"Annu. Rev. Phytopathol."},{"key":"ref_142","doi-asserted-by":"crossref","unstructured":"Lu, B., Dao, P.D., Liu, J., He, Y., and Shang, J. (2020). Recent advances of hyperspectral imaging technology and applications in agriculture. Remote Sens., 12.","DOI":"10.3390\/rs12162659"},{"key":"ref_143","doi-asserted-by":"crossref","first-page":"13586","DOI":"10.3390\/rs71013586","article-title":"Using high-resolution hyperspectral and thermal airborne imagery to assess physiological condition in the context of wheat phenotyping","volume":"7","author":"Hernandez","year":"2015","journal-title":"Remote Sens."},{"key":"ref_144","doi-asserted-by":"crossref","unstructured":"Zhang, N., Yang, G., Pan, Y., Yang, X., Chen, L., and Zhao, C. (2020). A review of advanced technologies and development for hyperspectral-based plant disease detection in the past three decades. Remote Sens., 12.","DOI":"10.3390\/rs12193188"},{"key":"ref_145","doi-asserted-by":"crossref","first-page":"1685","DOI":"10.1007\/s12145-021-00621-6","article-title":"Hyperspectral and multispectral image fusion techniques for high resolution applications: A review","volume":"14","author":"Sara","year":"2021","journal-title":"Earth Sci. Inform."},{"key":"ref_146","doi-asserted-by":"crossref","first-page":"2792","DOI":"10.1109\/JSTARS.2019.2917088","article-title":"Real-Time Hyperspectral Image Compression onto Embedded GPUS","volume":"12","author":"Diaz","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_147","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1007\/s11119-019-09640-2","article-title":"Hyperspectral remote sensing of grapevine drought stress","volume":"20","author":"Zovko","year":"2019","journal-title":"Precis. Agric."},{"key":"ref_148","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1186\/s13007-018-0349-9","article-title":"Hyperspectral band selection using genetic algorithm and support vector machines for early identification of charcoal rot disease in soybean stems","volume":"14","author":"Nagasubramanian","year":"2018","journal-title":"Plant Methods"},{"key":"ref_149","doi-asserted-by":"crossref","first-page":"474","DOI":"10.3389\/fpls.2017.00474","article-title":"Hyperspectral technologies for assessing seed germination and Trifloxysulfuron-Methyl response in Amaranthus palmeri (Palmer amaranth)","volume":"8","author":"Matzrafi","year":"2017","journal-title":"Front. Plant Sci."},{"key":"ref_150","doi-asserted-by":"crossref","first-page":"105121","DOI":"10.1016\/j.compag.2019.105121","article-title":"Fruit detection, yield prediction and canopy geometric characterization using LiDAR with forced air flow","volume":"168","author":"Gregorio","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_151","doi-asserted-by":"crossref","first-page":"111599","DOI":"10.1016\/j.rse.2019.111599","article-title":"Soybean yield prediction from UAV using multimodal data fusion and deep learning","volume":"237","author":"Maimaitijiang","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_152","doi-asserted-by":"crossref","unstructured":"Ballester, C., Hornbuckle, J., Brinkhoff, J., Smith, J., and Quayle, W. (2017). Assessment of in-season cotton nitrogen status and lint yield prediction from unmanned aerial system imagery. Remote Sens., 9.","DOI":"10.3390\/rs9111149"},{"key":"ref_153","doi-asserted-by":"crossref","unstructured":"Zecha, C.W., Peteinatos, G.G., Link, J., and Claupein, W. (2018). Utilisation of ground and airborne optical sensors for nitrogen level identification and yield prediction in wheat. Agriculture, 8.","DOI":"10.3390\/agriculture8060079"},{"key":"ref_154","doi-asserted-by":"crossref","unstructured":"Niedba\u0142a, G. (2019). Application of artificial neural networks for multi-criteria yield prediction ofwinter rapeseed. Sustainability, 11.","DOI":"10.3390\/su11020533"},{"key":"ref_155","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.compag.2015.11.018","article-title":"Wheat yield prediction using machine learning and advanced sensing techniques","volume":"121","author":"Pantazi","year":"2016","journal-title":"Comput. Electron. Agric."},{"key":"ref_156","doi-asserted-by":"crossref","unstructured":"Abbas, F., Afzaal, H., Farooque, A.A., and Tang, S. (2020). Crop yield prediction through proximal sensing and machine learning algorithms. Agronomy, 10.","DOI":"10.3390\/agronomy10071046"},{"key":"ref_157","doi-asserted-by":"crossref","unstructured":"G\u00f3mez, D., Salvador, P., Sanz, J., and Casanova, J.L. (2019). Potato yield prediction using machine learning techniques and Sentinel 2 data. Remote Sens., 11.","DOI":"10.3390\/rs11151745"},{"key":"ref_158","doi-asserted-by":"crossref","unstructured":"Feng, L., Zhang, Z., Ma, Y., Du, Q., Williams, P., Drewry, J., and Luck, B. (2020). Alfalfa yield prediction using UAV-based hyperspectral imagery and ensemble learning. Remote Sens., 12.","DOI":"10.3390\/rs12122028"},{"key":"ref_159","doi-asserted-by":"crossref","first-page":"416","DOI":"10.1016\/S0034-4257(02)00018-4","article-title":"Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture","volume":"81","author":"Haboudane","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_160","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1016\/j.rse.2003.12.013","article-title":"Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture","volume":"90","author":"Haboudane","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_161","doi-asserted-by":"crossref","first-page":"S59","DOI":"10.1007\/BF02980332","article-title":"Imaging techniques for chemical application on crops","volume":"25","author":"Hetzroni","year":"1997","journal-title":"Phytoparasitica"},{"key":"ref_162","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1080\/00380768.2001.10408393","article-title":"Geostatistical analysis of soil chemical properties and rice yield in a paddy field and application to the analysis of yield-determining factors","volume":"47","author":"Yanai","year":"2001","journal-title":"Soil Sci. Plant Nutr."},{"key":"ref_163","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1023\/A:1021471531188","article-title":"Nitrate leaching in temperate agroecosystems: Sources, factors and mitigating strategies","volume":"64","author":"Di","year":"2002","journal-title":"Nutr. Cycl. Agroecosyst."},{"key":"ref_164","doi-asserted-by":"crossref","first-page":"1537","DOI":"10.1080\/00103629809370047","article-title":"Agriculture and change: The promises and pitfalls of precision","volume":"29","author":"Nowak","year":"1998","journal-title":"Commun. Soil Sci. Plant Anal."},{"key":"ref_165","doi-asserted-by":"crossref","first-page":"1275","DOI":"10.2307\/1243506","article-title":"Precision Farming and Spatial Economic Analysis: Research Challenges and Opportunities","volume":"78","author":"Weiss","year":"1996","journal-title":"Am. J. Agric. Econ."},{"key":"ref_166","first-page":"38","article-title":"The promise of precision agriculture","volume":"51","year":"1996","journal-title":"J. Soil Water Conserv."},{"key":"ref_167","doi-asserted-by":"crossref","first-page":"1281","DOI":"10.2307\/1243507","article-title":"Precision Farming and the New Information Technology: Implications for Farm Management, Policy, and Research: Discussion","volume":"78","year":"1996","journal-title":"Am. J. Agric. Econ."},{"key":"ref_168","doi-asserted-by":"crossref","first-page":"753","DOI":"10.1071\/EA97156","article-title":"Precision agriculture\u2014Opportunities, benefits and pitfalls of site-specific crop management in Australia","volume":"38","author":"Cook","year":"1998","journal-title":"Aust. J. Exp. Agric."},{"key":"ref_169","doi-asserted-by":"crossref","first-page":"1463","DOI":"10.1080\/00103629809370043","article-title":"Precision agriculture\u2014What\u2019s in our future","volume":"29","author":"Schepers","year":"1998","journal-title":"Commun. Soil Sci. Plant Anal."},{"key":"ref_170","doi-asserted-by":"crossref","first-page":"693","DOI":"10.1007\/s11119-012-9274-5","article-title":"The application of small unmanned aerial systems for precision agriculture: A review","volume":"13","author":"Zhang","year":"2012","journal-title":"Precis. Agric."},{"key":"ref_171","doi-asserted-by":"crossref","first-page":"342","DOI":"10.1016\/j.landusepol.2015.04.021","article-title":"\u201cBest available techniques\u201d as a mandatory basic standard for more sustainable agricultural land use in Europe?","volume":"47","year":"2015","journal-title":"Land Use Policy"},{"key":"ref_172","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.agwat.2015.01.020","article-title":"UAVs challenge to assess water stress for sustainable agriculture","volume":"153","author":"Gago","year":"2015","journal-title":"Agric. Water Manag."},{"key":"ref_173","doi-asserted-by":"crossref","first-page":"422","DOI":"10.1080\/13504509.2014.956677","article-title":"Agricultural production and sustainable development in a Brazilian region (Southwest, S\u00e3o Paulo State): Motivations and barriers to adopting sustainable and ecologically friendly practices","volume":"21","author":"Leite","year":"2014","journal-title":"Int. J. Sustain. Dev. World Ecol."},{"key":"ref_174","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1590\/0001-3765202020181112","article-title":"Seeding rate in soybean according to the soil apparent electrical conductivity","volume":"92","author":"Moura","year":"2020","journal-title":"An. Acad. Bras. Cienc."},{"key":"ref_175","doi-asserted-by":"crossref","first-page":"105880","DOI":"10.1016\/j.agwat.2019.105880","article-title":"Differential irrigation scheduling by an automated algorithm of water balance tuned by capacitance-type soil moisture sensors","volume":"228","author":"Girona","year":"2020","journal-title":"Agric. Water Manag."},{"key":"ref_176","doi-asserted-by":"crossref","first-page":"724","DOI":"10.1139\/cjss-2018-0063","article-title":"Delineating soil management zones using a proximal soil sensing system in two commercial potato fields in new brunswick, canada","volume":"98","author":"Perron","year":"2018","journal-title":"Can. J. Soil Sci."},{"key":"ref_177","doi-asserted-by":"crossref","unstructured":"Zhao, Q., and Huang, J. (2011). Roadmap of Resource Saving Agricultural Science and Technology Development. Agricultural Science & Technology in China: A Roadmap to 2050, Springer.","DOI":"10.1007\/978-3-642-19128-2"},{"key":"ref_178","doi-asserted-by":"crossref","unstructured":"Bolfe, \u00c9.L., de Jorge, L.A.C., Sanches, I.D., J\u00fanior, A.L., da Costa, C.C., de Victoria, D.C., Inamasu, R.Y., Grego, C.R., Ferreira, V.R., and Ramirez, A.R. (2020). Precision and digital agriculture: Adoption of technologies and perception of Brazilian farmers. Agriculture, 10.","DOI":"10.3390\/agriculture10120653"},{"key":"ref_179","first-page":"5860905","article-title":"Technology roadmapping architecture based on knowledge management: Case study for improved indigenous coffee production from Guerrero, Mexico","volume":"2019","year":"2019","journal-title":"J. Sens."},{"key":"ref_180","first-page":"4","article-title":"Innovative ideas: Thailand 4.0 and the fourth industrial revolution","volume":"17","author":"Jones","year":"2017","journal-title":"Asian Int. J. Soc. Sci."},{"key":"ref_181","first-page":"91","article-title":"The policy drive of Thailand 4.0","volume":"3","author":"Puncreobutr","year":"2017","journal-title":"St. J. Humanit. Soc. Sci."},{"key":"ref_182","doi-asserted-by":"crossref","first-page":"32031","DOI":"10.1109\/ACCESS.2020.2973178","article-title":"Security and Privacy for Green IoT-Based Agriculture: Review, Blockchain Solutions, and Challenges","volume":"8","author":"Ferrag","year":"2020","journal-title":"IEEE Access"},{"key":"ref_183","first-page":"77","article-title":"Internet of Things (IOT): Research challenges and future applications","volume":"10","author":"Hussein","year":"2019","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"key":"ref_184","doi-asserted-by":"crossref","first-page":"141748","DOI":"10.1109\/ACCESS.2020.3013005","article-title":"Overview of Edge Computing in the Agricultural Internet of Things: Key Technologies, Applications, Challenges","volume":"8","author":"Zhang","year":"2020","journal-title":"IEEE Access"},{"key":"ref_185","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1109\/MITP.2019.2963412","article-title":"The Future of Digital Agriculture: Technologies and Opportunities","volume":"22","author":"Fountas","year":"2020","journal-title":"IT Prof."},{"key":"ref_186","first-page":"607","article-title":"Sustainable development: A critical review","volume":"19","year":"1991","journal-title":"Elsevier"},{"key":"ref_187","doi-asserted-by":"crossref","first-page":"e225","DOI":"10.1016\/S2214-109X(16)00038-3","article-title":"Paradoxes of sustainability with consequences for health","volume":"4","author":"Engebretsen","year":"2016","journal-title":"Lancet Glob. Health"},{"key":"ref_188","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.strueco.2011.08.002","article-title":"Time-to-build, Obsolescence and the Technological Paradox","volume":"23","author":"Patriarca","year":"2012","journal-title":"Struct. Chang. Econ. Dyn."},{"key":"ref_189","first-page":"44","article-title":"Comparative analysis of circular agriculture development in selected Western Balkan countries based on sustainable performance indicators","volume":"168","author":"Vasa","year":"2017","journal-title":"Econ. Ann."},{"key":"ref_190","doi-asserted-by":"crossref","unstructured":"Odara, S., Khan, Z., and Ustun, T.S. (2015, January 10\u201312). Integration of Precision Agriculture and SmartGrid technologies for sustainable development. Proceedings of the 2015 IEEE Technological Innovation in ICT for Agriculture and Rural Development (TIAR), Chennai, India.","DOI":"10.1109\/TIAR.2015.7358536"},{"key":"ref_191","first-page":"275","article-title":"Dynamic interactions among knowledge management, strategic foresight and emerging technologies","volume":"25","author":"Reichert","year":"2020","journal-title":"J. Knowl. Manag."},{"key":"ref_192","doi-asserted-by":"crossref","first-page":"14","DOI":"10.4067\/S0718-27242012000200002","article-title":"Innovation capability: From technology development to transaction capability","volume":"7","author":"Zawislak","year":"2012","journal-title":"J. Technol. Manag. Innov."},{"key":"ref_193","doi-asserted-by":"crossref","first-page":"2901","DOI":"10.1108\/BFJ-10-2018-0647","article-title":"Innovation capabilities in the food processing industry in Brazil","volume":"121","author":"Oliveira","year":"2019","journal-title":"Br. Food J."},{"key":"ref_194","first-page":"1","article-title":"4.0 For Agriculture","volume":"5","year":"2020","journal-title":"Eur. J. Bus. Manag. Res."},{"key":"ref_195","doi-asserted-by":"crossref","first-page":"43","DOI":"10.22215\/timreview\/1260","article-title":"How to Develop a Digital Ecosystem\u2014A Practical Framework","volume":"9","year":"2019","journal-title":"Technol. Innov. Manag. Rev."},{"key":"ref_196","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1016\/j.jbusres.2020.12.018","article-title":"How the relational structure of universities influences research and development results","volume":"125","author":"Santini","year":"2021","journal-title":"J. Bus. Res."},{"key":"ref_197","doi-asserted-by":"crossref","first-page":"770","DOI":"10.1111\/grow.12442","article-title":"Universities as orchestrators of the development of regional innovation ecosystems in emerging economies","volume":"52","author":"Thomas","year":"2020","journal-title":"Growth Chang."},{"key":"ref_198","first-page":"8","article-title":"Advances in control of agriculture and the environment","volume":"21","author":"Sigrimis","year":"2002","journal-title":"IEEE. Contr. Syst. Mag."},{"key":"ref_199","doi-asserted-by":"crossref","first-page":"350","DOI":"10.1016\/j.biosystemseng.2020.09.001","article-title":"An invited editorial interview with Professor Nick Sigrimis, Agricultural University of Athens, on Smart Agriculture and the digital revolution","volume":"198","author":"Day","year":"2020","journal-title":"Biosyst. Eng."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/23\/7889\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:36:32Z","timestamp":1760168192000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/23\/7889"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,11,26]]},"references-count":199,"journal-issue":{"issue":"23","published-online":{"date-parts":[[2021,12]]}},"alternative-id":["s21237889"],"URL":"https:\/\/doi.org\/10.3390\/s21237889","relation":{"has-preprint":[{"id-type":"doi","id":"10.20944\/preprints202105.0758.v1","asserted-by":"object"}]},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,11,26]]}}}