Yıl: 2021 Cilt: 27 Sayı: 1 Sayfa Aralığı: 373 - 399 Metin Dili: İngilizce DOI: 10.15832/ankutbd.986431 İndeks Tarihi: 29-07-2022

Digital Transformation for Sustainable Future - Agriculture 4.0: A review

Öz:
In the last few years, while the COVID-19 pandemic affects food supplychains around the world, the agriculture sector also has faced many globalproblems, such as global warming, environmental pollution, climatechange, and weather disasters. It has known that technologicalopportunities are available for human beings to get out of thesepredicaments, solving the interconnections between food-water-energyclimate nexus, and achieving agricultural transformation from traditionalto digital.The aim of this review is to gain holistic solutions in a systematicview, based on water-energy-food resources to agricultural digitaltransformation that will play role in sustainable development. Thetransition from primitive to digital is given with road maps coveringagricultural and industrial revolutions at four stages on timeline. Digitalagriculture combined under precision agriculture and Agriculture 4.0 arehandled based on domains covering monitoring, control, prediction, andlogistics. Digital technologies are explained with application examplessuch as the Internet of Things (IoT), cloud computing, big data, artificialintelligence, decision support systems, etc. Wearable sensor technologies,real-time monitoring systems tracking whole conditions of animals inlivestock, the IoT-based irrigation and fertilization systems that helpenhance the efficiency of irrigation processes and minimize water andfertilizer losses in agricultural fields and greenhouses, blockchain-basedelectronic agriculture, and solutions based on drones and robotics thatreduce herbicide and pesticide use are handled systematically. Moreover,renewable energy technologies to be provided synergy betweentechnologies such as agrivoltaics and aquavoltaics combining food andenergy production in rural are explained, besides solar-powered pivot anddrip irrigation systems and environmental monitoring systems. As aresult, for a sustainable future, technological innovations that increasecrop productivity and improve crop quality, protect the environment,provide efficient resource use and decrease input costs can help us facingin agriculture of today overwhelm many the economic, social, andenvironmental challenges.
Anahtar Kelime: Digital transformation Artificial intelligence in agriculture Sustainable development Information technologies Smart Farming Blockchain

Internal Transcribed Spacer (ITS) Fails Barcoding of the Genus Neotinea Rchb.f. (Orchidaceae)

Öz:
Internal Transcribed Spacer (ITS) is one of the most used barcoding regions for the molecular phylogenetics and barcoding of orchids. Our aim in this study is to test the reliability of ITS on barcoding of closely related Neotinea spp., including Neotinea tridentata, Neotinea ustulata subsp. ustulata and Neotinea ustulata subsp. aestivalis, by comparing it to the accD-psaI intergenic spacer of the plastid DNA. Both ITS and accD-psaI regions were amplified by specific primer sets and sequenced. Phylogenetic trees were regenerated by using Maximum Parsimony approach. The results showed that ITS separated some N. tridentata samples of Turkish, Greek, Hungarian and Croatian samples from the others on the phylogenetic trees due to the incomplete lineage sorting. In contrast to ITS, the accD-psaI marker could successfully separate N. tridentata and N. ustulata samples according to a priori species classification. Our findings refer to a hybridisation story between some N. tridentata and N. ustulata. We propose not to use ITS sequences directly as a barcode and to reconstruct the phylogeny of the Neotinea group. Instead, the inclusion of other nuclear regions such as LFY, ADH, etc., or utilisation of whole genome sequencing could give better barcoding results.
Anahtar Kelime: orchids phylogenetic incongruence DNA barcoding

Belge Türü: Makale Makale Türü: Derleme Erişim Türü: Erişime Açık
  • Álvarez, I., Wendel, J.F. (2003) Ribosomal ITS sequences and plant phylogenetic inference. Molecular Phylogenetics and Evolution 29: 417–434. doi: 10.1016/S1055-7903(03)00208-2
  • Abegaz B W, Datta T & Mahajan S M (2018). Sensor technologies for the energy-water nexus – A review. ACS Applied Energy Materials, 210: 451-466. https://doi.org/10.1016/j.apenergy.2017.01.033
  • Bailey, C. (2003) Characterization of angiosperm nrDNA polymorphism, paralogy, and pseudogenes. Molecular Phylogenetics and Evolution 29: 435–455. doi: 10.1016/j.ympev.2003.08.021
  • Aceto G, Persico V & Pescape A (2019). A Survey on Information and Communication Technologies for Industry 4.0: State-of-the-Art, Taxonomies, Perspectives, and Challenges, IEEE Commun. Surv. Tutorials, 21(4): 3467-3501. https://doi.org/10.1109/COMST.2019.2938259
  • Baldwin, B.G., Sanderson, M.J., Porter, J.M., Wojciechowski, M.F., Campbell, C.S. & Donoghue, M.J. (1995) The its Region of Nuclear Ribosomal DNA: A Valuable Source of Evidence on Angiosperm Phylogeny. Annals of the Missouri Botanical Garden 82: 247–277.
  • Adebanjo D, Laosirihongthong T, Samaranayake P & Teh P L (2020). Key enablers of industry 4.0 development at firm level: Findings from an emerging economy, IEEE Transactions on Engineering Management. https://doi.org/10.1109/TEM.2020.3046764
  • Bateman, R.M., Hollingsworth, P.M., Preston, J., YI-BO, L., Pridgeon, A.M. & Chase, M.W. (2003) Molecular phylogenetics and evolution of Orchidinae and selected Habenariinae (Orchidaceae). Botanical Journal of the Linnean Society 142: 1–40. doi: 10.1046/j.1095-8339.2003.00157.x
  • Agostini A, Colauzzi M & Amaducci S (2021). Innovative agrivoltaic systems to produce sustainable energy: An economic and environmental assessment. Applied Energy 281: 116102. https://doi.org/10.1016/j.apenergy.2020.116102
  • Chase, M.W., Fay, M.F. & Savolainen, V. (2000) Higher-level classification in the angiosperms: new insights from the perspective of DNA sequence data. Taxon 49: 685–704.
  • Agrovoltaics (2021). Agrovoltaics a Solar Innovation and Triple usage of Solar Energy. Retrieved in July, 29, 2021 from https://agrovoltaics.com/index.php
  • Nieto-Feliner, G.N. & Rosselló, J.A. (2007) Better the devil you know? Guidelines for insightful utilization of nrDNA ITS in species-level evolutionary studies in plants. Molecular Phylogenetics and Evolution 44: 911–919. doi: 10.1016/j.ympev.2007.01.013
  • Ahmad L & Nabi F (2021). Agriculture 5.0. CRC Press, London
  • Fuertes Aguilar, J. & Nieto-Feliner, G. (2003) Additive polymorphisms and reticulation in an ITS phylogeny of thrifts (Armeria, Plumbaginaceae). Molecular Phylogenetics and Evolution 28: 430–447. doi: 10.1016/S1055-7903(02)00301-9
  • Ahmed U, Mumtaz R, Anwar H, Mumtaz S & Qamar A M (2020). Water quality monitoring: From conventional to emerging technologies. Water Sci. Technol. Water Supply, 20: 28-45. https://doi.org/10.2166/ws.2019.144
  • Gulyás, G., Sramkó, G., Molnár, V.A. & et al. (2005) Nuclear ribosomal DNA ITS paralogs as evidence of recent interspecific hybridization in the genus Ophrys (Orchidaceae). Acta Biologica Cracoviensia Series Botanica 47: 61–67.
  • Alcon F, Tapsuwan S, Brouwer R, Yunes M, Mounzer O, de-Miguel M D (2019). Modelling farmer choices for water security measures in the Litani river basin in Lebanon. Sci. Total Environ. 647: 37-46. https://doi.org/10.1016/j.ins.2014.10.013
  • Hollingsworth, P.M. (2008) DNA barcoding plants in biodiversity hot spots: progress and outstanding questions. Heredity (Edinb) 101: 1–2. doi: 10.1038/hdy.2008.16
  • Allardyce C S, Fankhauser C, Zakeeruddin S M, Grätzel M & Dyson P J. (2017). The influence of greenhouse-integrated photovoltaics on crop production. Solar Energy 155: 517-522. http://dx.doi.org/10.1016/j.solener.2017.06.044
  • Hollingsworth, P.M., Graham, S.W. & Little, D.P. (2011) Choosing and Using a Plant DNA Barcode. PLoS One 6:e19254. doi: 10.1371/journal.pone.0019254
  • Araújo S O, Peres R S, Barata J, Lidon F & Ramalho J C (2021). Characterising the Agriculture 4.0 Landscape-Emerging Trends, Challenges and Opportunities. Agronomy 11(667): 1-37. https://doi.org/10.3390/agronomy11040667
  • Kearse, M., Moir, R., Wilson, A. & et al. (2012) Geneious Basic: An integrated and extendable desktop software platform for the organization and analysis of sequence data. Bioinformatics 28: 1647–1649. doi: 10.1093/bioinformatics/bts199
  • Asolkar P & Bhadade U (2015). An effective method of controlling the greenhouse and crop monitoring using GSM. In: 2015 International Conference on Computing, Communication Control and Automation, February 26 - 27, 2015, Washington pp. 214-219. https://doi.org/10.1109/iccubea.2015.47
  • Kim, S.T. & Donoghue, M.J. (2008) Incongruence between cpDNA and nrITS trees indicates extensive hybridization within Eupersicaria (Polygonaceae). American Journal of Botany 95: 1122–35. doi: 10.3732/ajb.0700008
  • ATLAS (2019). Project ATLAS (agricultural interoperability & analysis system) 2019. Retrieved in July, 1, 2021 from https://www.atlash2020.eu/
  • Larkin, M.A., Blackshields, G., Brown, N.P. & et al. (2007) Clustal W and Clustal X version 2.0. Bioinformatics 23: 2947–2948. doi: 10.1093/bioinformatics/btm404
  • Aubert B, Schroeder A & Grimaudo J (2012). IT as enabler of sustainable farming: an empirical analysis of farmers’ adoption decision of precision agriculture technology, Decis. Support Syst. 54: 510-520. https://doi.org/10.1016/j.dss.2012.07.002
  • Li, X., Yang, Y., Henry, R.J., Rossetto, M., Wang, Y. & Chen, S. (2015) Plant DNA barcoding: from gene to genome. Biological Reviews 90: 157–166. doi: 10.1111/brv.12104
  • Ayaz M, Ammad-Uddin M, Sharif Z, Mansour A & Aggoune E H (2019). Internet-of Things (IoT)-Based Smart Agriculture: Toward Making the Fields Talk. IEEE Access. 7: 129551-129583. https://doi.org/10.1109/Access.628763910.1109/ ACCESS.2019.2932609
  • Liao, D. (2008) Concerted Evolution. In: John Wiley & Sons Ltd. Encyclopedia of Life Sciences. Chichester, UK, pp. 1–5.
  • Babatunde O M, Denwigwe I H, Adedoja S O, Babatunde D E & Gbadamosi S L (2019). Harnessing renewable energy for sustainable agricultural applications. International Journal of Energy Economics and Policy 9(5): 308-315. https://doi.org/10.32479/ijeep.7775
  • Mayol, M. & Rosselló, J.A. (2001) Why Nuclear Ribosomal DNA Spacers (ITS) Tell Different Stories in Quercus. Molecular Phylogenetics and Evolution 19: 167–176. doi: 10.1006/mpev.2001.0934
  • Balafoutis A T, Evert F K V & Fountas S (2020). Smart Farming Technology Trends: Economic and Environmental Effects, Labor Impact, and Adoption Readiness. Agronomy, 10: 743. https://doi.org/10.3390/agronomy10050743
  • Pillon, Y., Fay, M.F., Hedrén, M., Bateman, R.M., Devey, D.S., Shipunov, A.B., van der Bank, M., Chase, M.W. & Hedren, M. (2007) Evolution and temporal diversification of western European polyploid species complexes in Dactylorhiza (Orchidaceae). Taxon 56: 1185–1208
  • Bazilian M, Rogner H, Howells M, Hermann S, Arent D & Gielen D (2011). Considering the energy, water and food nexus: towards an integrated modelling approach. Energy Policy 39: 7896-7906. https://doi.org/10.1016/j.enpol.2011.09.039
  • Pridgeon, A.M., Bateman, R.M., Cox, A.V., Hapeman, J.R. & Chase, M.W. (1997) Phylogenetics of subtribe Orchidinae (Orchidoideae, Orchidaceae) based on nuclear ITS sequences. 1. Intergeneric relationships and polyphyly of Orchis sensu lato. Lindleyana 12: 89–109.
  • Benyezza H, Bouhedda M & Rebouh, S (2021). Zoning irrigation smart system based on fuzzy control technology and IoT for water and energy saving. Journal of Cleaner Production 302(2021): 127001. https://doi.org/10.1016/j.jclepro.2021.127001
  • Rieseberg, L.H. (1997) Hybrid origins of plant species. Annual Review of Ecology, Evolution, and Systematics 28: 359–389.
  • Bera B, Vangala A, Das A K, Lorenz P, Khan M K (2022). Private blockchain-envisioned drones-assisted authentication scheme in IoTenabled agricultural environment, Computer Standards & Interfaces 80(2022): 103567. https://doi.org/10.1016/j.csi.2021.103567
  • Rieseberg, L.H., Whitton, J. & Linder, C.R. (1996) Molecular marker incongruence in plant hybrid zones and phylogenetic trees. Acta Botanica Neerlandica 45: 243–262.
  • Berger R (2019). Farming 4.0: How precision agriculture might save the world Precision farming improves farmer livelihoods and ensures sustainable food production. Global Focus Report. Publisher Roland Berger GMBH. Germany
  • Shipunov, A.B., Fay, M.F., Pillon, Y., Bateman, R.M. & Chase, M.W. (2004) Dactylorhiza (Orchidaceae) in European Russia: Combined molecular and morphological analysis. American Journal of Botany 91: 1419–1426.
  • Bieber N, Ker J H, Wang X, Triantafyllidis C, van Dam K H, Koppelaar R H E M & Shah N (2018). Sustainable planning of the energy-waterfood nexus using decision making tools, Energy Policy 113: 584-607. https://doi.org/10.1016/j.enpol.2017.11.037
  • Small, R.L., Ryburn, J.A., Cronn, R.C., Seelanan, T. & Wendel, J.F. (1998) The tortoise and the hare: Choosing between noncoding plastome and nuclear ADH sequences for phylogeny reconstruction in a recently diverged plant group. American Journal of Botany 85: 1301–1315.
  • Boursianis A D, Papadopoulou M S, Diamantoulakis P, Liopa-Tsakalidi A, Barouchas P, Salahas G, Karagiannidis G, Wan S & Goudos S K (2020). Internet of Things (IoT) and Agricultural Unmanned Aerial Vehicles (UAVs) in Smart Farming: A Comprehensive Review. Internet of Things. 100187. https://doi.org/10.1016/j.iot.2020.100187
  • Sramkó, G. (2008) Sequence variability of the nrITS in the Ophrys fuciflora species-complex of the Mediterranean bee-orchid (Ophrys L.) genus. Department of Botany. University of Debrecen, Debrecen.
  • Bundschuh J, Chen G, Tomaszewska B, Ghaffour N, Mushtaq S, Hamawand I, Reardon-Smith K, Maraseni T. Banhazi T H, Mahmoudi M. Goosen & Antille L D (2017). Solar, wind and geothermal energy applications in agriculture: back to the future? In: J. Bundschuh, G. Chen, D. Chandrasekharam, J. Piechocki (Eds.), Geothermal, Wind and Solar Energy Applications in Agriculture and Aquaculture, London: CRC Press, London pp. 1-32
  • Sramko, G., Attila, M.V., Hawkins, J.A. & Bateman, R.M .(2014) Molecular phylogeny and evolutionary history of the Eurasiatic orchid genus Himantoglossum s.l. (Orchidaceae). Annals of Botany 114: 1609–1626. doi: 10.1093/aob/mcu179
  • Cadero A, Aubry A, Dourmad J Y, Salaun Y & Garcia-Launay F (2018). Towards a decision support tool with an individual-based model of a pig fattening unit. Computers and Electronics in Agriculture 147: 44-50. https://doi.org/10.1016/j.compag.2018.02.012
  • Sramkó, G., Gulyás, G., Matus, G., Rudnóy, S., Illyés, Z., Bratek, Z. & Molnár, A. (2008) Leaf width, nrDNA and cpDNA its sequence variation within central European Bulbocodium vernum and B. versicolor (Colchicaceae) populations: Are there really two taxa? Acta Biologica Academiae Scientiarum Hungaricae 59: 103–114.
  • Carolan M (2018) “Smart” farming techniques as political ontology: access, sovereignty and the performance of neoliberal and not-soneoliberal worlds. Sociol. Rural. 58(4): 745-764. https://doi.org/10.1111/soru.12202
  • Sramkó, G., Molnár, V.A., Hawkins, J.A. & Bateman, R.M. (2011) Evolution of the Eurasiatic genus Himantoglossum(Orchideae, Orchidoideae): an integrativephylogenetic approach. In: In: Abstracts of the XVIII International Botanical Congress. Comittee of the XVIII IBC 2011, Melbourne, pp. 286–287.
  • Carolan M (2019). Automated agrifood futures: robotics, labor and the distributive politics of digital agriculture. The Journal of Peasant Studies 1-24. https://doi.org/10.1080/03066150.2019.1584189
  • Swofford, D.L. (2003) PAUP*. Phylogenetic Analysis Using Parsimony (*and Other Methods). Version 4. Sinauer Associates, Sunderland, Massachusetts.
  • CEMA (2017a). Digital Farming: what does it really mean? Retrieved in July, 1, 2021 from http://www.cema-agri.org/page/digital-farmingwhat-does it-really-mean
  • White, T.J., Bruns, T.D., Lee, S. & Taylor, J.W. (1990) Amplification and direct sequencing of fungal ribosomal RNA genes for phylogenetics. In: Innis, M.A., Gelfand, D.H., Sninsky, J.J. & White, T.J. (Eds.) PCR protocols: A guide to methods and applications. Academic Press, San-Diego, pp. 315–322.
  • CEMA (2017b). Connected Agricultural Machines in Digital Farming. Retrieved in July, 1, 2021 from http://www.cemaagri.org/publication/connectedagricultural-machines-digital-farming
  • Won, H. & Renner, S.S. (2005) The internal transcribed spacer of nuclear ribosomal DNA in the gymnosperm Gnetum. Molecular Phylogenetics and Evolution 36: 581–597. doi: 10.1016/j.ympev.2005.03.011
  • CEMA (2018). Digital farming technology, CEMA association. Retrieved in July, 1, 2021 from https://www.cema-agri.org/digital-farming
  • Zimmer, E.A. & Wen, J. (2012) Using nuclear gene data for plant phylogenetics: Progress and prospects. Molecular Phylogenetics and Evolution 65: 774–785. doi: 10.1016/j.ympev.2012.07.015
  • Cevher C (2019). Determination of the Main Socio-Economic Factors of the Sustainable Production of Forage Crops: Research of Kayseri Province. Journal of Agricultural Sciences, 25(4):474-480. https://doi.org/10.15832/ankutbd.453983
  • Chen Y, Li Y & Li C (2020). Electronic agriculture, blockchain and digital agricultural democratization: Origin, theory and application, Journal of Cleaner Production 268(2020): 122071. https://doi.org/10.1016/j.jclepro.2020.122071
  • Crippa M, Solazzo, E Guizzardi, D Monforti-Ferrario, F Tubiello F N & Leip A (2021). Food systems are responsible for a third of global anthropogenic GHG emissions Nature Food 2: 198-209. https://doi.org/10.1038/s43016-021-00225-9)
  • Daher BT, Hannibal B, Portney KE & Mohtar RH (2019). Toward creating an environment of cooperation between water, energy, and food stakeholders in San Antonio. Sci. Total Environ. 651(2): 2913-2926. https://doi.org/10.1016/j.scitotenv.2018.09.395
  • De Clercq M, Vats A & Biel A (2018). Agriculture 4.0: The future of farming technology, in Proc. World Government Summit, Dubai, 2018, pp. 11-13
  • DEMETER (2019). Project DEMETER. Retrieved in July, 1, 2021 from https://h2020-demeter.eu/
  • Eastwood C, Klerkx L, Ayre M & Dela Rue B (2017). Managing socio-ethical challenges in the development of smart farming: from a fragmented to a comprehensive approach for responsible research and innovation. J Agric Environ Ethics 32: 741–768. https://doi.org/10.1007/s10806-017-9704-5
  • EC (2017). The Future of Food and Farming. European Commission, Brussels
  • EC (2019). The contribution of precision agriculture technologies to farm productivity and the mitigation of greenhouse gas emissions in the EU. European Commission Joint Research Center, Brussels
  • EC (2021a). European Commission Internal Market, Industry, Entrepreneurship and SMEs Advanced Technologies for Industry. Retrieved in July, 1, 2021 from https://ati.ec.europa.eu/technologies
  • EC (2021b). Shaping Europe’s digital future. Large-scale pilots in the digitisation of agriculture. Retrieved in July, 1, 2021 from https://digitalstrategy.ec.europa.eu/en/policies/large-scale-pilots-digitisation-agriculture
  • EIA (2016). World Energy Outlook. International Energy Agency. Paris, France
  • EIA (2019). International Energy Outlook 2019 with projections to 2050. Paris, France
  • ElMassaha S & Mohieldin M (2020). Digital transformation and localizing the Sustainable Development Goals (SDGs). Ecological Economics 169: 106490. https://doi.org/10.1016/j.ecolecon.2019.106490
  • FAO (2012). World agriculture towards 2030/2050. FAO of the UN, Rome
  • FAO (2014). The Water-Energy-Food Nexus - A new approach in support of food security and sustainable agriculture. Food and Agriculture Organization of the United Nations (FAO), Rome
  • FAO (2016). AQUASTAT Database. Retrieved in July, 29, 2021 from http://www.fao.org/nr/water/aquastat/data/query/index.html?lang=en
  • FAO (2017a). The future of food and agriculture-trends and challenges. Food and Agriculture Organization of the United Nations, Rome
  • FAO (2017b). Water for Sustainable Food and Agriculture A report produced for the G20 Presidency of Germany, Food and Agriculture Organization of the United Nations, Rome
  • FAO (2018). Transforming food and agriculture to achieve the SDGs, FAO, Rome
  • FAO (2019). FAO’s work on climate change, The Food and Agriculture Organization of the United Nations (FAO), Rome
  • FAO (2020). Emissions due to agriculture. Global, regional and country trends 2000-2018. FAOSTAT Analytical Brief Series, No 18. Rome
  • FAO (2021). The impact of disasters and crises on agriculture and food security. Rome. https://doi.org/10.4060/cb3673en
  • Fielke S J, Garrard R, Jakku, E, Fleming A, Wiseman L & Taylor B M (2019). Conceptualising the DAIS: implications of the ‘Digitalisation of Agricultural Innovation Systems’ on technology and policy at multiple levels. NJAS–Wageningen J. Life Sci. 90. https://doi.org/10.1016/j.njas.2019.04.002
  • Fielke S, Taylor B & Jakku E (2020). Digitalisation of agricultural knowledge and advice networks: a state-of-the-art review. Agricultural Systems 180. https://doi.org/10.1016/j.agsy.2019.102763
  • Fielke S J, Botha N, Reid J, Gray D, Blackett P, Park N & Williams T (2018). Lessons for co-innovation in agricultural innovation systems: a multiple case study analysis and a conceptual model. J. Agric. Educ. Ext. 24(1): 9-27. https://doi.org/10.1080/1389224X.2017.1394885
  • Fleming A, Jakku E, Fielke S, Taylor B M, Lacey J, Terhorst A & Stitzlein C (2021). Foresighting Australian digital agricultural futures: Applying responsible innovation thinking to anticipate research and development impact under different scenarios. Agricultural Systems 190: 103120. doi.org/10.1016/j.agsy.2021.103120
  • Flourish (2018). Project Flourish (aerial data collection and analysis, and automated ground intervention for precision farming). Retrieved in July, 1, 2021 from http://flourish-project.eu
  • Fraunhofer (2021). Aquaculture Photovoltaics (Aqua-PV). Fraunhofer Institute for Solar Energy Systems ISE Retrieved in July, 1, 2021 from https://www.ise.fraunhofer.de/en/business-areas/photovoltaics/photovoltaic-modules-and-power-plants/integratedphotovoltaics/agrivoltaics/aqua-pv.html
  • García L, Parra L, Jimenez J M, Lloret J & Lorenz P (2020). IoT Based Smart Irrigation Systems: An Overview on the Recent Trends on Sensors and IoT Systems for Irrigation in Precision Agriculture. Sensors. 20(4): 1042. https://doi.org/10.3390/s20041042
  • GHD and AgThentic (2018). Emerging agricultural technologies: Consumer perceptions around emerging agtech, Publication No. 18/048, Project No. PRJ-011141, 2018 AgriFutures Australia
  • Goh C S, Ahl A & Woo W T (2021). Sustainable Transformation of Land-Based Economic Development in the Era of Digital Revolution, Trends in Biotechnology, 39(1): 1-4. https://doi.org/10.1016/j.tibtech.2020.05.010
  • Hatfield J L, Boote K J, Kimball B A, Ziska L H, Izaurralde R C, Ort D, Thomson A M & Wolfe D W (2011). Climate impacts on agriculture: Implications for crop production. Agronomy Journal, 103: 351-370. https://doi.org/10.2134/agronj2010.0303
  • Hatfield J L & Prueger J H (2015). Temperature extremes: Effect on plant growth and development. Weather and Climate Extremes. 10: 4-10.
  • Hoff H (2011). Understanding the Nexus: Background paper for the Bonn 2011 Nexus conference: The Water, Energy and Food Security Nexus. 19-18 Nov. 2011. Bonn
  • Howells M, Hermann S, Welsch M, Bazilian M, Segerstrom R & Alfstad T (2013). Integrated analysis of climate change, land-use, energy and water strategies. Nat. Clim. Chang. 3: 621-626. https://doi.org/10.1038/nclimate1789
  • IOF2020 (2017). Project INTERNET OF FOOD & FARM 2020. Retrieved in July, 1, 2021 from https://www.iof2020.eu/
  • IRENA (2015). Renewable Energy in the Water, Energy and Food Nexus. Abu Dhabi
  • ISPA (2021). Precision Ag Definition. International Society of Precision Agriculture Retrieved in July, 29, 2021 from https://www.ispag.org/about/definition
  • Jha K, Doshi A, Patel P & Shah M (2019). A comprehensive review on automation in agriculture using artificial intelligence. Artificial Intelligence in Agriculture. 2: 1-12. https://doi.org/10.1016/j.aiia.2019.05.004
  • Kelton K, Fleischmann K R & Wallace W A (2008). Trust in digital information. J. Assoc. Inf. Sci. Technol. 59(3): 363-374
  • Keogh M & Henry M (2016). The Implications of Digital Agriculture and Big Data for Australian Agriculture. Australian Farm Institute, Sydney, Australia
  • Keyhanpour M J, Jahromi S H M & Ebrahimi H (2021). System dynamics model of sustainable water resources management using the Nexus Water-Food-Energy approach, Ain Shams Engineering Journal 12: 1267-1281. https://doi.org/10.1016/j.asej.2020.07.029
  • Kim S, Lee M & Shin C (2018). IoT-Based Strawberry Disease Prediction System for Smart Farming. Sensors 18(11): 4051-4067. https://doi.org/10.3390/s18114051
  • Klerkx L & Begemann S (2020). Supporting food systems transformation: the what, why, who, where and how of mission-oriented agricultural innovation systems. Agric. Syst. 184: 102901. https://doi.org/10.1016/j.agsy.2020.102901
  • Klerkx L & Rose D (2020). Dealing with the game-changing technologies of Agriculture 4.0: how do we manage diversity and responsibility in food system transition pathways? Global Food Security 24: 100347. https://doi.org/10.1016/j. gfs.2019.100347
  • Klerkx L, Jakku E & Labarthe P (2019). A review of social science on digital agriculture, smart farming and agriculture 4.0: new contributions and a future research agenda. NJAS - Wageningen J. Life Sci. 90-91: 100315. https://doi.org/10.1016/j.njas.2019.100315
  • Klerkx L, Seuneke P, de Wolf P & Rossing W A (2017). Replication and translation of co-innovation: the influence of institutional context in large international participatory research projects. Land Use Policy 61: 276-292. https://doi.org/10.1016/j.landusepol.2016.11.027
  • Knight C H (2020). Sensor techniques in ruminants_ more than fitness trackers. Animal 14:187-195. doi:10.1017/S1751731119003276
  • Kochhar A & Kumar N (2019). Wireless sensor networks for greenhouses: An end-to-end review. Computers and Electronics in Agriculture, 163: 104877-104891. https://doi.org/10.1016/j.compag.2019.104877
  • Kour V P & Arora S (2020). Recent developments of the Internet of Things in Agriculture: A Survey. IEEE Access 8: 129924-129957. https://doi.org/10.1109/ Access.628763910.1109/ACCESS.2020.3009298
  • Lee J (2019). AgTech trends in 2019: Precision Agriculture, and Millennial Farmers. G2 Crowd learning Hub.3 Dec. 2019
  • Lehmann S (2018). Implementing the urban nexus approach for improved resource efficiency of developing cities in Southeast-Asia. City Cult. Soci., 13: 46-56. https://doi.org/10.1016/j.ccs.2017.10.003
  • Lezoche M, Hernandez J, Diaz M D M A, Panetto H & Kacprzyk J (2020). Agri-food 4.0: A survey of the supply chains and technologies for the future agriculture. Comput. Ind. 117: 103187-103201. https://doi.org/10.1016/j.compind.2020.103187
  • Li H M, Wang X C, Zhao X F & Qi Y (2021). Understanding systemic risk induced by climate change, Advances in Climate Change Research, 12: 384-394. https://doi.org/10.1016/j.accre.2021.05.006
  • Liakos KG, Busato P, Moshou D, Pearson S & Bochtis D (2018). Machine learning in agriculture: A review. Sensors, 18(2674): 1-29. https://doi.org/10.3390/s18082674
  • Liao M S, Chena S F, Chou C Y, Chen H Y, Yeh S H, Chang Y C & Jiang J A (2017). On precisely relating the growth of Phalaenopsisleaves to greenhouse environmental factors by using an IoT-based monitoring system Computers and Electronics in Agriculture 136: 125-139. https://doi.org/10.1016/j.compag.2017.03.003
  • Lin N, Wang X, Zhang Y, Hu X & RuanJ (2020). Fertigation management for sustainable precision agriculture based on Internet of Things. Journal of Cleaner Production 277: 124119. https://doi.org/10.1016/j.jclepro.2020.124119
  • Lioutas E D, Charatsari C & De Rosa M (2021). Digitalization of agriculture: A way to solve the food problem or a trolley dilemma? Technology in Society 67(2021): 101744. https://doi.org/10.1016/j.techsoc.2021.101744
  • Liu Y, Ma X, Shu L, Hancke G P, Abu-Mahfouz A M (2021). From Industry 4.0 to Agriculture 4.0: Current Status, Enabling Technologies, and Research Challenges. IEEE Transactions on Industrial Informatics, 17(6): 4322-4334. https://doi.org/10.1109/TII.2020.3003910
  • Lorentz (2012). Solar powered vineyard irrigation system. Chile Case Study 4, 09|2012 Retrieved in July, 29, 2021 from https://partnernet.lorentz.de/files/lorentz_casestudy_vineyardlurton_chile_en-en.pdf
  • Lorentz (2014). Solar Water Pumping for Center Pivot Irrigation. North Africa Case Study 7, 05|2014. Retrieved in July, 29, 2021 from https://partnernet.lorentz.de/files/lorentz_casestudy_pivot_north_africa_en-en.pdf
  • Marolia A, Narwanea V S & Gardas B B (2021). Applications of IoT for achieving sustainability in agricultural sector: A comprehensive review, Journal of Environmental Management 298: 113488. https://doi.org/10.1016/j.jenvman.2021.113488
  • McBratney A, Whelan B, Ancev T & Bouma J (2005). Future directions of precision agriculture, Precision Agriculture 6: 7-23. https://doi.org/10.1007/s11119-005-0681-8
  • Mohtar R H & Daher B (2016). Water-energy-food nexus framework for facilitating multi stakeholder dialogue. Water Int. 41(5): 655-661. https://doi.org/10.1080/02508060.2016.1149759
  • Moysiadis V, Sarigiannidis P Vitsas V & Khelifi A (2021). Smart Farming in Europe. Computer Science Review 39: 100345. https://doi.org/10.1016/j.cosrev.2020.100345
  • Namany S, Govindan R, Di Martino M, Pistikopoulos E N, Linke P, Avraamiou S & Al-Ansari T (2021). An energy-water-food nexus-based decision-making framework to guide national priorities in Qatar. Sustainable Cities and Society 75: 103342. https://doi.org/10.1016/j.scs.2021.103342
  • Newton J E, Nettle R & Pryce J E (2020). Farming smarter with big data: Insights from the case of Australia's national dairy herd milk recording scheme, Agricultural Systems 181: 102811. https://doi.org/10.1016/j.agsy.2020.102811
  • Norouzi N & Kalantari G (2020). The food-water-energy nexus governance model: A case study for Iran, Water-Energy Nexus 3: 72–80
  • OECD (2012). OECD Environmental Outlook to 2050: The Consequences of Inaction. https://doi.org/10.1787/9789264122246-en
  • Ojha T, Misra S, Raghuwanshi NS (2015). Wireless sensor networks for agriculture: The state-of-the-art in practice and future challenges. Computer and Electronic in Agriculture, 118: 66-84 https://doi.org/10.1016/j.compag.2015.08.011
  • Pedersen S M & Lind K M (2017). Precision Agriculture: Technology and Economic Perspectives, Springer, Switzerland
  • Platis D, Anagnostopoulos C, Tsaboula A, Menexes G, Kalburtji K & Mamolos A (2019). Energy analysis, and carbon and water footprint for environmentally friendly farming practices in agroecosystems and agroforestry. Sustainability 1664. https://doi.org/10.3390/su11061664
  • Pringle A M, Handler R M & Pearce J M (2017). Aquavoltaics: synergies for dual use of water area for solar photovoltaic electricity generation and aquaculture. Renewable and Sustainable Energy Reviews. 80: 572-84. https://doi.org/10.1016/j.rser.2017.05.191
  • Purwanto A, Susnik J, Suryadi FX & de Fraiture C (2019). Using group model building to develop a causal loop mapping of the water-energyfood security nexus in Karawangregency, Indonesia. J. Clean. Prod. 240: 118170. https://doi.org/10.1016/j.jclepro.2019.118170
  • pv magazine (2020). Premiers résultats de l’expérimentation agrivoltaïque de Sun’Agri à Piolenc. Retrieved in July, 29, 2021 from https://www.pvmagazine.fr/2020/03/31/premiers-resultats-de-lexperimentation-agrivoltaique-de-sunagri-a-piolenc/?utm_source=dlvr.it&utm_medium=twitter
  • pv magazine (2021). India’s largest floating solar plant commissioned. Retrieved in July, 29, 2021 from https://www.pv-magazineaustralia.com/2021/09/20/indias-largest-floating-solar-plant-commissioned/
  • PV TECH (2020). BayWa r.e. starts building 27.4MWp floating PV plant on Dutch lake. Retrieved in July, 29, 2021 from https://www.pvtech.org/news/baywa-r.e.-starts-construction-of-27.4mwp-plant-on-dutch-sandpit-lake
  • Pylianidis C, Osinga S & Athanasiadis IN (2021). Introducing digital twins to agriculture, Computers and Electronics in Agriculture 184, 105942. https://doi.org/10.1016/j.compag.2020.105942
  • Raj M, Gupta S, Chamola V, Elhence A, Garg T, Atiquzzaman M & Niyato D (2021). A survey on the role of Internet of Things for adopting and promoting Agriculture 4.0. Journal of Network and Computer Applications 187: 10310. https://doi.org/10.1016/j.jnca.2021.103107
  • Rasul G & Sharma B (2016). The nexus approach to water energy food security: An option for adaptation to climate change an option foradaptation to climate change. Climate Policy 16(6): 682-702. https://doi.org/10.1080/14693062.2015.1029865
  • Ravar Z, Zahraie B, Sharifinejad A, Gozini H & Jafari S (2020). System dynamics modeling for assessment of water–food–energy resources security and nexus in Gavkhuni basin in Iran. Ecological Indicators 108: 105682. https://doi.org/10.1016/j.ecolind.2019.105682105682
  • Regan Á (2019). ‘Smart farming’ in Ireland: a risk perception study with key governance actors. NJAS – Wageningen J. Life Sci 90-91: 100292. https://doi.org/10. 1016/j.njas.2019.02.003
  • Rijswijk K, Klerkx L, Bacco M, Bartolini F, Bulten E, Debruyne L, Dessein J, Scotti I & Brunori G (2021). Digital transformation of agriculture and rural areas: A socio-cyber-physical system framework to support responsibilisation, Journal of Rural Studies 85: 79-90. https://doi.org/10.1016/j.jrurstud.2021.05.003
  • Roidt M & Avellán T (2019). Learning from integrated management approaches to implement the nexus. J. Environ. Manag 237: 609-616. https://doi.org/10.1016/j.jenvman.2019.02.106
  • ROMI (2020). Project ROMI (RObotics for MIcrofarms). Retrieved in July, 1, 2021 from https://romi-project.eu
  • Rose D C & Chilvers J (2018). Agriculture 4.0: Broadening responsible innovation in an era of smart farming, Frontiers in Sustainable Food System 2(87): 1-6. https:// doi.org/10.3389/fsufs.2018.00087
  • Rose D C, Wheeler R, Winter M, Lobley M & Chivers CA (2021). Agriculture 4.0: Making it work for people, production, and the planet. Land Use Policy 100: 104933. https://doi.org/10.1016/j.landusepol.2020.104933
  • Roy S K, Misra S, Raghuwanshi N S & Das S K (2021). AgriSens: IoT-based dynamic irrigation scheduling system for water management of irrigated crops, IEEE Internet Things J., 8(6): 5023-5030. https://doi.org/10.1109/JIOT.2020.3036126
  • Sachs J D, Schmidt-Traub G, Mazzucato M, Messner D, Nakicenovic N & Rockström J (2019). Six Transformations to achieve the Sustainable Development Goals, Nature Sustainability 2: 805-814. https://doi.org/10.1038/s41893-019-0352-9
  • Sadowski S & Spachos P (2020). Wireless technologies for smart agricultural monitoring using internet of things devices with energy harvesting capabilities. Computers Electronics in Agriculture 172: 105338-105347. https://doi.org/10.1016/j.compag.2020.105338
  • Santos Valle S & Kienzle J (2020). Agriculture 4.0 - Agricultural robotics and automated equipment for sustainable crop production. Integrated Crop Management Vol. 24. Rome, FAO
  • Say M S, Keskin M, Sehri M & Sekerli Y E (2017). Adoption of precision agriculture technologies in developed and developing countries. International Science and Technology Conference (ISTEC), July 17-19, 2017 Berlin, Germany, August 16-18 Cambridge, USA
  • Schwab K (2015) The fourth Industrial revolution: What it means and how to respond, Foreign Affairs. Retrieved in July, 1, 2021 from https://www.foreignaffairs.com/articles/2015-12-12/fourth-industrial-revolution
  • Shepherd M, Turner J A, Small B & Wheeler D (2018). Priorities for science to overcome hurdles thwarting the full promise of the ‘digital agriculture’ revolution. J. Sci. Food Agric. https://doi.org/10.1002/jsfa.9346
  • Sims R E H & Flammini A (2014). Chapter 6: Energy-smart food – technologies, practices and policies pp: 123-169, In: Sustainable Energy Solutions in Agriculture, Editors: Jochen Bundschuh, Guangnan Chen, CRC Press, London
  • Sinha B B & Dhanalakshmi R (2022). Recent advancements and challenges of Internet of Things in smart agriculture: A survey. Future Generation Computer Systems 126(2022): 169-184. https://doi.org/10.1016/j.future.2021.08.006
  • Sligo F X & Massey C (2007). Risk, trust and knowledge networks in farmers’ learning. J. Rural. Stud 23(2): 170-182. https://doi.org/10.1016/j.jrurstud.2006.06.001
  • Solaripedia (2021). Green Architecture & Building. Retrieved in July, 29, 2021 from http://www.solaripedia.com/13/147/wineries_and_thieves_go_solar_(california,_usa).html
  • Solarvibes (2021). Solar-powered Plug & Play Farm Monitoring System. Retrieved in July, 29, 2021 from https://www.solar-vibes.com/
  • Soto I, Barnes A, Balafoutis A, Beck B, Sanchez B, Vangeyte J, Fountas S, Van der Wal T, Eory V & Gómez-Barbero M (2019) The contribution of Precision Agriculture Technologies to farm productivity and the mitigation of greenhouse gas emissions in the EU, EUR (where available), Publications Office of the European Union, Luxembourg. https://doi.org/10.2760/016263, JRC112505
  • Talepbour B, Türker U & Yegül U (2015). The Role of Precision Agriculture in the promotion of Food Security. International Journal of Agricultural and Food Research-Science target. 4(1): 1-23. https://doi.org/10.24102/ijafr.v4i1.472
  • Tao W, Zhao L, Wang G & Liang R (2021). Review of the internet of things communication technologies in smart agriculture and challenges. Computers and Electronics in Agriculture 189(2021): 106352 https://doi.org/10.1016/j.compag.2021.106352
  • Tian H, Lu C, Pan C, Yang J, Miao R, Ren W, Yu Q, Fu B, Jin F, Lu Y, Melillo J, Ouyang Z, Palm C & Reilly J (2018) Optimizing resource use efficiencies in the food-energy-water nexus for sustainable agriculture: from conceptual model to decision support system. Current Opinion in Environmental Sustainability, 33: 104-113. https://doi.org/10.1016/j.cosust.2018.04.003
  • Trendov N M, Varas S & Zeng M (2019). Digital technologies in agriculture and rural areas - Status Report. Food and Agriculture Organization of the United Nations (FAO), Rome
  • TWI2050 (2018). Transformations to Achieve the Sustainable Development Goals, International Institute for Applied Systems Analysis. Laxenburg, Austria
  • Tzounis A, Katsoulas N, Bartzanas T & Kittas C (2017). Internet of Things in agriculture, recent advances and future challenges. Biosystems Engineering. 164: 31-48. https://doi.org/10.1016/j.biosystemseng.2017.09.007
  • UNECE (2021). Water-food-energy-ecosystem nexus (The United Nations Economic Commission for Europe. Retrieved in July, 1, 2021 from https://unece.org/environment-policy/water/areas-work-convention/water-food-energy-ecosystem-nexus)
  • United Nations (2015). Transforming our world: The 2030 agenda for sustainable development. In: United Nations General Assembly; Seventieth Session. September 18, New York, NY
  • Vaintrub M O, Levit H, Chincarini M, Fusaro I, Giammarco M & Vignola G (2021). Precision livestock farming, automats and new technologies: possible applications in extensive dairy sheep farming, Animal 15(2021): 100143. https://doi.org/10.1016/j.animal.2020.100143
  • VINBOT (2017). Project VINBOT (Autonomous Cloud-Computing Vineyard Robot to Optimize Yield Management and Wine Quality). Retrieved in July, 1, 2021 from http://vinbot.eu
  • VINEROBOT (2017). Project VINEROBOT (VINEyardROBOT). Retrieved in July, 1, 2021 from http://www.vinerobot.eu
  • WB (2017). ICT in Agriculture: Connecting Smallholders to Knowledge, Networks, and Institutions, Updated Edition. World Bank Washington, DC
  • WB (2019). Future of Food Harnessing Digital Technologies to Improve Food System Outcomes. World Bank Washington, DC.
  • Wolfert S, Ge L, Verdouw C & Bogaardt M J (2017). Big data in smart farming-A review, Agricultural Systems 153: 69-80. http://dx.doi.org/10.1016/j.agsy.2017.01.023
  • Yue Q, Wu H, Wang Y & Guo P (2021). Achieving sustainable development goals in agricultural energy-water-food nexus system: An integrated inexact multi-objective optimization approach, Resources. Conservation & Recycling 174: 105833. https://doi.org/10.1016/j.resconrec.2021.105833
  • Yue Q & Guo P (2021). Managing agricultural water-energy-food-environment nexus considering water footprint and carbon footprint under uncertainty, Agricultural Water Management 252: 106899. https://doi.org/10.1016/j.agwat.2021.106899
  • Yurtseven, E., Colak, M.S., Ozturk, A. & Ozturk, H.S. (2018). Drainage Water Salt Load Variations Related to the Salinity and Leaching Ratios of Irrigation Water. Journal of Agricultural Sciences, 24(3):394-402. https://doi.org/10.15832/ankutbd.456667
  • Yülek M A (2018). The industrialization process: A streamlined version. In: How Nations Succeed: Manufacturing, Trade, Industrial Policy, and Economic Development. Springer Heidelberg, Germany, pp. 171-182
  • Zambon I, Cecchini M, Egidi G, Saporito M G & Colantoni A (2019). Revolution 4.0: Industry vs. agriculture in a future development for SMEs. Processes 7(36): 1-16. https://doi.org/10.3390/pr7010036
  • Zamora-Izquierdo M A, Santa J, Martinez J A, Martinez V & Skarmeta A F (2019). Smart farming IoT platform based on edge and cloud computing. Biosystems Engineering. 177: 4-17. https://doi.org/10.1016/j.biosystemseng.2018.10.014
  • Zhai Z, Martínez J F, Beltran V & Martínez N L (2020). Decision support systems for agriculture 4.0: Survey and challenges. Computer Electronics and Agriculture. 170: 105256: 1-16. https://doi.org/10.1016/j.compag.2020.105256
  • Zhang X & Vesselinov V V (2016). Energy-water nexus: Balancing the tradeoffs between two-level decision makers. Appl. Energy 183: 77- 87. https://doi.org/10.1016/j.apenergy.2016.08.156
  • Zhang C, Chen X X, Li Y, Ding W & Fu G T (2018). Water-energy-food nexus: concepts, questions and methodologies. J. Clean. Prod. 195: 625-639. https://doi.org/10.1016/j.jclepro.2018.05.194
  • Zhang M, Wang X, Feng H, Huang Q, Xiao X & Zhang X (2021). Wearable Internet of Things enabled precision livestock farming in smart farms: A review of technical solutions for precise perception, biocompatibility, and sustainability monitoring. Journal of Cleaner Production 312: 127712. https://doi.org/10.1016/j.jclepro.2021.127712
APA Dayıoğlu M, HÜRKAN K, Türker U, TAŞKIN K (2021). Digital Transformation for Sustainable Future - Agriculture 4.0: A review. , 373 - 399. 10.15832/ankutbd.986431
Chicago Dayıoğlu Mehmet Ali,HÜRKAN Kaan,Türker Ufuk,TAŞKIN Kemal Melih Digital Transformation for Sustainable Future - Agriculture 4.0: A review. (2021): 373 - 399. 10.15832/ankutbd.986431
MLA Dayıoğlu Mehmet Ali,HÜRKAN Kaan,Türker Ufuk,TAŞKIN Kemal Melih Digital Transformation for Sustainable Future - Agriculture 4.0: A review. , 2021, ss.373 - 399. 10.15832/ankutbd.986431
AMA Dayıoğlu M,HÜRKAN K,Türker U,TAŞKIN K Digital Transformation for Sustainable Future - Agriculture 4.0: A review. . 2021; 373 - 399. 10.15832/ankutbd.986431
Vancouver Dayıoğlu M,HÜRKAN K,Türker U,TAŞKIN K Digital Transformation for Sustainable Future - Agriculture 4.0: A review. . 2021; 373 - 399. 10.15832/ankutbd.986431
IEEE Dayıoğlu M,HÜRKAN K,Türker U,TAŞKIN K "Digital Transformation for Sustainable Future - Agriculture 4.0: A review." , ss.373 - 399, 2021. 10.15832/ankutbd.986431
ISNAD Dayıoğlu, Mehmet Ali vd. "Digital Transformation for Sustainable Future - Agriculture 4.0: A review". (2021), 373-399. https://doi.org/10.15832/ankutbd.986431
APA Dayıoğlu M, HÜRKAN K, Türker U, TAŞKIN K (2021). Digital Transformation for Sustainable Future - Agriculture 4.0: A review. Tarım Bilimleri Dergisi, 27(1), 373 - 399. 10.15832/ankutbd.986431
Chicago Dayıoğlu Mehmet Ali,HÜRKAN Kaan,Türker Ufuk,TAŞKIN Kemal Melih Digital Transformation for Sustainable Future - Agriculture 4.0: A review. Tarım Bilimleri Dergisi 27, no.1 (2021): 373 - 399. 10.15832/ankutbd.986431
MLA Dayıoğlu Mehmet Ali,HÜRKAN Kaan,Türker Ufuk,TAŞKIN Kemal Melih Digital Transformation for Sustainable Future - Agriculture 4.0: A review. Tarım Bilimleri Dergisi, vol.27, no.1, 2021, ss.373 - 399. 10.15832/ankutbd.986431
AMA Dayıoğlu M,HÜRKAN K,Türker U,TAŞKIN K Digital Transformation for Sustainable Future - Agriculture 4.0: A review. Tarım Bilimleri Dergisi. 2021; 27(1): 373 - 399. 10.15832/ankutbd.986431
Vancouver Dayıoğlu M,HÜRKAN K,Türker U,TAŞKIN K Digital Transformation for Sustainable Future - Agriculture 4.0: A review. Tarım Bilimleri Dergisi. 2021; 27(1): 373 - 399. 10.15832/ankutbd.986431
IEEE Dayıoğlu M,HÜRKAN K,Türker U,TAŞKIN K "Digital Transformation for Sustainable Future - Agriculture 4.0: A review." Tarım Bilimleri Dergisi, 27, ss.373 - 399, 2021. 10.15832/ankutbd.986431
ISNAD Dayıoğlu, Mehmet Ali vd. "Digital Transformation for Sustainable Future - Agriculture 4.0: A review". Tarım Bilimleri Dergisi 27/1 (2021), 373-399. https://doi.org/10.15832/ankutbd.986431