This is an outdated version published on 30-10-2025. Read the
most recent version.
Review Articles
Early Access
Digital technologies for precision agriculture: Current approaches, advances and future perspectives
Horticulture Department, Agriculture Faculty, Selcuk University, Konya 42075, Turkey
Radio, Television and Cinema Department, Fine Arts and Architecture Faculty, Necmettin Erbakan University, Konya 42075, Turkey
Abstract
The frequent occurrence of climatic extremes has been exerting significant pressure on the agricultural sector worldwide. This pressure forced the agriculturists to implement intensified agricultural practices such as digital technologies to cope with ever-increasing environmental stress factors. Given these challenges, this review aims to scrutinize current developments in digital technologies and the constraints hindering the adoption of digital agriculture. Digital technologies, such as Global Positioning System (GPS), Internet of Things (IoT), spectral sensors, unmanned aerial vehicles (UAVs), robotics, thermal infrared cameras (TIR), Artificial Intelligence (AI), virtual reality, Big Data, Metaverse, cloud computing, blockchain and digital mapping have been offering new pathways for companies to achieve economic, social and environmental purposes. Combined use of multiple technologies would be more beneficial for precision agriculture and economic efficiency. Advanced digital technologies are extensively adopted in many regions of the world, although there are significant differences among the countries in terms of the digital transformation. A bibliometric evaluation revealed that factors such as cost, ease of use of technical devices, technological infrastructure, farmers’ willingness, technology reliability and concerns about data security and privacy are key challenges in the adoption of digital agriculture. Therefore, policymakers should focus on particular challenges to promote the widespread adoption of digital technologies for sustainable agriculture. Sustainable agricultural production can be achieved through improved, precise use of affordable digital technologies and effective, site- and crop-specific data-driven decision support. Present developments demonstrate that AI and new autonomous approaches will become increasingly important for the detection and management of spatial variability. This review article focused on the bibliometric assessment of current approaches, feasibility, benefits, restrictions and future perspectives of digital technologies in increasing agricultural production. Overall investigations revealed that digitalization enables farmers to optimize resource use, enhance crop yields and address pressing challenges such as climate change and food security by integrating advanced technologies such as remote sensors, drones, robotics and data-driven decision-making tools. Data presented in this article is anticipated to guide agriculturists to speed up and employ the digital technology applications on the modern farming technologies.
References
- 1. Sadigov R. Rapid growth of the world population and its socioeconomic results. Scientific World Journal. 2022;1:8110229. https://doi.org/10.1155/2022/811022
- 2. Pawlak K, Kołodziejczak M. The role of agriculture in ensuring food security in developing countries: considerations in the context of the problem of sustainable food production. Sustainability. 2020;12(13):5488. https://doi.org/10.3390/su12135488
- 3. Afifa KA, Nazim H, Muhammad HA, Muhammad ZS. Air pollution and climate change as grand challenges to sustainability. Science of The Total Environment. 2024;928:172370. https://doi.org/10.1016/j.scitotenv.2024.172370
- 4. Erbasi A, Sabir A. Raising awareness on extending precision agriculture techniques for the effects of climatic changes on agricultural production. European Union Project. 2019;TR2013/0327.05.01-02/111:373p.
- 5. Babu GR, Gokuldhev M, Brahmanandam PS. Integrating IoT for soil monitoring and hybrid machine learning in predicting tomato crop disease in a typical south India station. Sensors. 2024;24(19):6177. https://doi.org/10.3390/s24196177
- 6. Liu J, Ma C, Wang S. Thermal-structure finite element simulation system architecture in a cloud-edge-end collaborative environment. Journal of Intelligent Manufacturing. 2023;36:1063-94. https://doi.org/10.1007/s10845-02302269-z
- 7. Stefko R, Frajtova Michalikova K, Strakova J, Novak A. Digital twin-based virtual factory and cyber-physical production systems, collaborative autonomous robotic and networked manufacturing technologies, and enterprise and business intelligence algorithms for industrial metaverse. Equilibrium. Quarterly Journal of Economics and Economic Policy. 2025;20(1):389-425. https://doi.org/10.24136/eq.3557
- 8. Dibbern T, Romani LAS. Main drivers and barriers to the adoption of digital agriculture technologies. Smart Agricultural Technology. 2024;8:100459. https://doi.org/10.1016/j.atech.2024.100459
- 9. Aslan B, Sabir A. Sensor technology for precision viticulture under the effects of climate change. In: 2nd International Conference on Agriculture, Food, Veterinary and Pharmacy Sciences. May 19-21, 2023 Belgrade. ICAFVP. p. 56-70.
- 10. Mu R, Rao J, Zhu J. Empowering breakthrough innovations through digital technology: The effects of digital technology dept and breath. Sustainability. 2025;17(5):1924. https://doi.org/10.3390/su17051924
- 11. Zengin H, Sabir A. Physiological and growth responses of grapevine rootstocks (Vitis spp.) to organic and synthetic mulch application in arid ecology under the effect of climate change. Journal of Central European Agriculture. 2022;23(3):655-64. https://doi.org/10.5513/JCEA01/23.3.3557
- 12. Ayaz M, Li CH, Ali Q, Zhao W, Chi YK, Shafiq M, et al. Bacterial and fungal biocontrol agents for plant disease protection: journey from lab to field, current status, challenges, and global perspectives. Molecules. 2023;28(18):6735. https://doi.org/10.3390/molecules28186735
- 13. Candiago S, Remondino F, De Giglio M, Dubbini M, Gattelli M. Evaluating multispectral images and vegetation indices for precision farming applications from UAV images. Remote Sensing. 2015;7(4):4026-47. https://doi.org/10.3390/rs70404026
- 14. Kucukbasmaci A, Sabir A. Long-term impact of deficit irrigation on the physiology and growth of grapevine cv. ‘Prima’ grafted on various rootstocks. Acta Scientiarum Polonorum Hortorum Cultus. 2019;18(4):57-70. https://dx.doi.org/10.24326/asphc.2019.4.6
- 15. Sujitha E, Valliammai A, Nagarajan M. Estimation of crop coefficient for radish using digital lysimeter under polyhouse. Plant Science Today. 2025;12(SP3). https://doi.org/10.14719/pst.8236
- 16. Faraboschi P, Frachtenberg E, Laplante P, Milojicic D, Saracco R. Digital transformation: lights and shadows. Computer. 2023;56(4):123-30. https://doi.org/10.1109/MC.2023.3241726
- 17. Chatterjee S, Kliestik T, Rowland Z, Bugaj M. Immersive collaborative business process and extended reality-driven industrial metaverse technologies for economic value co-creation in 3D digital twin factories. Oeconomia Copernicana. 2025;16(1):125-61. https://doi.org/10.24136/oc.3596
- 18. Minofar B, Milčić N, Maroušek J, Gavurová B, Maroušková A. Understanding the molecular mechanisms of interactions between biochar and denitrifiers in N₂O emissions reduction: pathway to more economical and sustainable fertilizers. Soil and Tillage Research. 2025;248:106405. https://doi.org/10.1016/j.still.2024.106405
- 19. Gayretli Y, Abdulhadi SAA, Demirkeser OK, Turkoglu I, Aslan B, Sabir A. Physiological responses of different grapevine genotypes (Vitis spp.) to variable temperatures artificially established as climate change scenery. AgroLife Scientific Journal. 2024;13:98-105. https://doi.org/10.17930/AGL2024110
- 20. Ali Z, Muhammad A, Lee N, Waqar M, Lee SW. Artificial intelligence for sustainable agriculture: a comprehensive review of AI-driven technologies in crop production. Sustainability. 2025;17(5):2281. https://doi.org/10.3390/su17052281
- 21. Kliestik T, Kral P, Bugaj M, Ďurana P. Generative artificial intelligence of things systems, multisensory immersive extended reality technologies, and algorithmic big data simulation and modelling tools in digital twin industrial metaverse. Equilibrium. 2024;19(2):429-61. https://doi.org/10.24136/eq.3108
- 22. Zvaríková K, Lubica Gajanova L, Horák J. Exploring CSR performance as a proxy for competitive ad-vantage across sectors in the Central European countries. Oeconomia Copernicana. 2024;15(3):991-1020. https://doi.org/10.24136/oc.3247
- 23. Huy TP, Hồng TP, Cuong NT, Tran P. AI innovation and economics growth: A global evidence. WSB Journal of Business and Finance. 2024;58(1):198-216. https://doi.org/10.2478/wsbjbf-2024-0017
- 24. Abiri R, Rizan N, Balasundram SK, Shahbazi AB, Abdul-Hamid H. Application of digital technologies for ensuring agricultural productivity. Helyon. 2023;9:e22601. https://doi.org/10.1016/j.heliyon.2023.e22601
- 25. Santesteban LG, Di Gennaro SF, Herrero-Langreo A, Miranda C, Royo JB, Matese A. High-resolution UAV-based thermal imaging to estimate the instantaneous and seasonal variability of plant water status within a vineyard. Agricultural Water Management. 2017;183:49-59. https://doi.org/10.1016/j.agwat.2016.08.026
- 26. Hitimana E, Kuradusenge M, Sinayobye OJ, Ufitinema C, Mukamugema J, Murangira T, et al. Revolutionizing coffee farming: a mobile app with GPS-enabled reporting for rapid and accurate on-site detection of coffee leaf diseases using integrated deep learning. Software. 2024;3(2):146-68. https://doi.org/10.3390/software3020007
- 27. Bellon-Maurel V, Huyghe C. L’innovation technologique dans l’agriculture. Géoéconomie. 2016;80:159-80. https://doi.org/10.3917/geoec.080.0159
- 28. Coll-Ribes G, Torres-Rodríguez IJ, Grau A, Guerra E, Sanfeliu A. Accurate detection and depth estimation of table grapes and peduncles for robot harvesting, combining monocular depth estimation and CNN methods. Computers and Electronics in Agriculture. 2023;215:108362. https://doi.org/10.1016/j.compag.2023.108362
- 29. Talaviya T, Shah D, Patel N, Yagnik H, Shah M. Implementation of artificial intelligence in agriculture for optimization of irrigation and application of pesticides and herbicides. Artificial Intelligence in Agriculture. 2020;4:58-73. https://doi.org/10.1016/j.aiia.2020.04.002
- 30. Lamb D, Weedon MM, Bramley RG, Using remote sensing to predict grape phenolics and colour at harvest in a cabernet sauvignon vineyard: timing observations against vine phenology and optimising image resolution. Australian Journal of Grape and Wine Research. 2004;10:46-54. https://doi.org/10.1111/j.1755-0238.2004.tb00007.x
- 31. Guebsi R, Mami S, Chokmani K. Drones in precision agriculture: a comprehensive review of applications, technologies, and challenges. Drones. 2024;8(11):686. https://doi.org/10.3390/drones8110686
- 32. Osamy W, Khedr AM, Salim A, Al-Ali AI, El-Sawy, AA. Coverage, deployment and localization challenges in wireless sensor networks based on artificial intelligence techniques: a review. IEEE. 2022;10:30232-57. https://doi.org/10.1109/ACCESS.2022.3156729
- 33. Ebrahimi HP, Schillo RS, Bronson K. Systematic stakeholder inclusion in digital agriculture: a framework and application to Canada. Sustainability. 2021;13(12):6879. https://doi.org/10.3390/su13126879
- 34. Sabir A, Sahin Z. The response of soilless grown ‘Michele Palieri’ (Vitis vinifera L.) grapevine cultivar to deficit irrigation under the effects of different rootstocks. Erwerbs-Obstbau. 2018;60(Suppl 1):S21-7. https://doi.org/10.1007/s10341-018-0378-6
- 35. Zhang Y, Cui S, Yang B, Wang X, Liu T. Research on 3D printing concrete mechanical properties prediction model based on machine learning. Case Studies in Construction Materials. 2025;22:e04254. https://doi.org/10.1016/j.cscm.2025.e04254
- 36. Neethirajan S. Net Zero dairy farming—advancing climate goals with big data and artificial intelligence. Climate. 2024;12(2):15. https://doi.org/10.3390/cli12020015
- 37. Jover V, Sempere S, Ferrándiz S. The creation of virtual stands in the metaverse: applications for the textile sector. Electronics. 2025;14(2):359. https://doi.org/10.3390/electronics14020359
- 38. Mushi GE, Burgi P-Y, Serugendo GM. Designing a farmers digital information system for sustainable agriculture: the perspective of Tanzanian agricultural stakeholders. The Electronic Journal of Information Systems in Developing Countries. 2025;91:e12344. https://doi.org/10.1002/isd2.12344
- 39. Verdouw CN, Wolfert J, Beulens AJM, Rialland A. Virtualization of food supply chains with the Internet of Things. Journal of Food Engineering. 2016;176:128-36. https://doi.org/10.1016/j.jfoodeng.2015.11.009
- 40. Sabir A, Yazar K. Diurnal dynamics of stomatal conductance and leaf temperature of grapevines (Vitis vinifera L.) in response to daily climatic variables. Acta Scientiarum Polonorum Hortorum Cultus. 2015;14:3-15.
- 41. di Gennaro SF, Battiston E, di Marco S, Facini O, Matese A, Nocentini M, et al. Unmanned Aerial Vehicle (UAV)-based remote sensing to monitor grapevine leaf stripe disease within a vineyard affected by Esca complex. Phytopathologia Mediterranea. 2016;55:262-75.
- 42. Yuan H, Zhang J, Zhang H, Xu W, Peng J, Wang X, et al. Monitoring autumn phenology in understory plants with a fine-resolution camera. Remote Sensing. 2025;17(6):1025. https://doi.org/10.3390/rs17061025
- 43. Orphanidou C. A review of big data applications of physiological signal data. Biophysical Reviews. 2019;11(1):83-7. https://doi.org/10.1007/s12551-018-0495-3
- 44. Gupta RK. Revolutionizing agriculture: The magic of virtual reality (VR) and augmented reality (AR). Farm Chronicle. 2024;03(08):20-5. https://doi.org/10.5281/zenodo.15570294
- 45. Dolgui A, Ivanov D. Metaverse supply chain and operations management. International Journal of Production Research. 2023;61(23):8179-91. https://doi.org/10.1080/00207543.2023.2240900
- 46. Tudor C, Florescu M, Polychronidou P, Stamatiou P, Vlachos V, Kasabali K. Cloud adoption in the digital era: an interpretable machine learning analysis of national readiness and structural disparities across the EU. Applied Sciences. 2025;15(14):8019. https://doi.org/10.3390/app15148019
- 47. Zarbà C, Chinnici G, Matarazzo A, Privitera D, Scuderi A. The innovative role of blockchain in agri-food systems: a literature analysis. Food Control. 2024;164:110603. https://doi.org/10.1016/j.foodcont.2024.110603
- 48. Ramalingam K, Chidambaram PP, Mylsamy J, Moorthi NR, Ramasamy J, Dhanaraju M, et al. Digital soil mapping of soil subgroup class information in Coimbatore district using decision tree approach. Plant Science Today. 2025;12(2). https://doi.org/10.14719/pst.5295
- 49. Vasumathi V, Manivannan V, Raja R, Ragunath KP, Sakthivel N, Balachandar D, et al. A review on advancing agricultural practices using photogrammetric images. Plant Science Today. 2025. https://doi.org/10.14719/pst.8813
- 50. Bindeeba DS, Tukamushaba EK, Bakashaba R. Digital transformation and its multidimensional impact on sustainable business performance: evidence from a meta-analytic review. Future Business Journal. 2025;11:90. https://doi.org/10.1186/s43093-025-00511-z
- 51. Aleca OE, Mihai F. The role of digital infrastructure and skills in enhancing labor productivity: Insights from Industry 4.0 in the European Union. Systems. 2025;13(2):113. https://doi.org/10.3390/systems13020113
- 52. Vărzaru AA, Bocean CG. Digital transformation and innovation: the influence of digital technologies on turnover from innovation activities and types of innovation. Systems. 2024;12(9):359. https://doi.org/10.3390/systems12090359
- 53. Najem, R., Bahnasse, A., Fakhouri Amr, M. Talea M. Advanced AI and big data techniques in E-finance: a comprehensive survey. Discover Artificial Intelligence 2025;5:102. https://doi.org/10.1007/s44163-025-00365-y
- 54. Iliopoulos C, Theodorakopoulou I, Giotis T, Brunori G. Perceptions of costs and benefits of farm digitalization in Europe. International Food and Agribusiness Management Review. 2025;28(3):543-64. https://doi.org/10.22434/ifamr.1274
- 55. Meng Y, Dong L. Digital pathways to sustainable agriculture: examining the role of agricultural digitalization in green development in China. Sustainability. 2025;17(8):3652. https://doi.org/10.3390/su17083652
- 56. Yuan F, Ospina R, Perumal AB, Noguchi N, He Y, Liu Y. Smart agriculture in Asia. Plant Communications. 2025;6(7):101377. https://doi.org/10.1016/j.xplc.2025.101377
- 57. Suranto B, Kovač N, Haryono K, Rahman SFA, Shukri AFM, Suder M, et al. State of digitalization in the Southeast Asia region – bibliometric analysis. Quality and Quantity. 2025. https://doi.org/10.1007/s11135-025-02296-3
- 58. Egbeleo E, Sodokin K. Digital transformation, institutional quality and productivity in Sub-Saharan Africa. Cogent Economics & Finance. 2025;13(1):2519924. https://doi.org/10.1080/23322039.2025.2519924
- 59. Hardy A, Ma Y, Ooi CS. Tik tokking Antarctica: re-presentations of place. The Polar Journal. 2025;15(1):55-80. https://doi.org/10.1080/2154896X.2025.2492487
- 60. Menze S, Macaulay GJ, Zhang G, Lowther AD, Krafft BA. KRILLSCAN: An automated open-source software for processing and analysis of echosounder data from the Antarctic krill fishery. Fisheries Management and Ecology. 2024;32(1):e12739. https://doi.org/10.1111/fme.12739
- 61. Sabri S, Kurnia S. Digital transformation of urban planning in Australia: Influencing factors and key challenges. arXiv. 2025;2506:13333. https://doi.org/10.48550/arXiv.2506.13333
- 62. Gupta M, Arif RN. Digital shift in construction in Australia: unlocking potentials with AI and blockchain. Journal of Resilient Economies. 2024;4(2):1-8. https://doi.org/10.25120/jre.4.2.2024.4155
- 63. Rissing A, Spangler K. How do we know what we grow? Interrogating the datafication of agricultural landscapes in the United States. Economic Anthropology. 2025;12(2):e700032025. https://doi.org/10.1002/sea2.70003
- 64. Velden D, Klerkx L, Dessein J, Debruyne L. Governance by satellite: remote sensing, bureaucrats and agency in the common agricultural policy of the European Union. Journal of Rural Studies. 2025;114:103558. https://doi.org/10.1016/j.jrurstud.2024.103558
- 65. Osei DB. Digital infrastructure and innovation in Africa: Does human capital mediates the effect? Telematics and Informatics. 2024;89:102111. https://doi.org/10.1016/j.tele.2024.102111
- 66. Ruder SL, Wittman H, Duncan E, Satterfield T. Sociotechnical imaginaries for Canadian agri-food futures: a farmer survey. Agriculture and Human Values. 2025;42:1439-56. https://doi.org/10.1007/s10460-024-10675-z
- 67. Tubaro P, Casilli AA, Fernández Massi M, Longo J, Torres Cierpe J, Viana Braz M. The digital labour of artificial intelligence in Latin America: a comparison of Argentina, Brazil, and Venezuela. Globalizations. 2025;1-16. https://doi.org/10.1080/14747731.2025.2465171
- 68. Feng X, Bi S, Li H, Qi Y, Chen S, Shao L. Soil moisture forecasting for precision irrigation management using real-time electricity consumption records. Agricultural Water Management. 2024;291:108656. https://doi.org/10.1016/j.agwat.2023.108656
- 69. Mendes MP, Matias M, Gomes RC, Falcão AP. Delimitation of low topsoil moisture content areas in a vineyard using remote sensing imagery (Sentinel-1 and Sentinel-2) in a Mediterranean-climate region. Soil and Water Research. 202;16:85-94. https://doi.org/10.17221/101/2019-SWR
- 70. Šupčík A, Milics G, Matečný I. Predicting grape yield with vine canopy morphology analysis from 3D point clouds generated by UAV imagery. Drones. 2024;8(6):216. https://doi.org/10.3390/drones8060216
- 71. Ballesteros R, Intrigliolo DS, Ortega JF, Ramírez-Cuesta JM, Buesa I, Moreno MA. Vineyard yield estimation by combining remote sensing, computer vision and artificial neural network techniques. Precision Agriculture. 2020;21:1242-62.
- 72. del Cerro J, Ulloa CC, Barrientos A, de Léon Rivas J. Unmanned aerial vehicles in agriculture: a survey. Agronomy. 2021;11(2):203. https://doi.org/10.3390/AGRONOMY11020203
- 73. Barnaba FE, Bellincontro A, Mencarelli F. Portable NIR‐AOTF spectroscopy combined with winery FTIR spectroscopy for an easy, rapid, in‐field monitoring of Sangiovese grape quality. Journal of the Science of Food and Agriculture. 2014;94(6):1071-7. https://doi.org/10.1002/jsfa.6391
- 74. Maroušek J, Žáková K. Techno-economic perspective on the use of pyrolysis oil from digestate in spark-ignition engines. Aircraft Engineering and Aerospace Technology. https://doi.org/10.1108/AEAT-01-2025-0020
- 75. Izquierdo-Bueno I, Moraga J, Cantoral JM, Carbú M, Garrido C, González-Rodríguez VE. Smart viniculture: applying artificial intelligence for improved winemaking and risk management. Applied Sciences. 2024;14(22):10277. https://doi.org/10.3390/app142210277
- 76. Samphao A, Butmee P, Saejueng P, Pukahuta C, Švorc Ľ, Kalcher K. Monitoring of glucose and ethanol during wine fermentation by bienzymatic biosensor. Journal of Electroanalytical Chemistry. 2018;816:179-88. https://doi.org/10.1016/j.jelechem.2018.03.052
- 77. Comba L, Biglia A, Ricauda Aimonino D, Tortia C, Mania E, Guidoni S, et al. Leaf area index evaluation in vineyards using 3D point clouds from UAV imagery. Precision Agriculture. 2020;21:881-96. https://doi.org/10.1007/s11119-019-09699-x
- 78. Pivoto DGS, de Almeida LFF, da Rosa RR, Rodrigues JJPC, Lugli AB, Alberti AM. Cyber-physical systems architectures for industrial internet of things applications in Industry 4.0: a literature review. Journal of Manufacturing Systems. 2021;58:176-92. https://doi.org/10.1016/j.jmsy.2020.11.017
- 79. Sun ZF, Du KM, Zhang FX. Perspectives of research and application of big data on smart agriculture. Journal of Agricultural Science and Technology. 2013;15(6):63-71. https://doi.org/10.3969/j.issn.1008-0864.2013.06.10
- 80. Wolfert S, Ge L, Verdouw C, Bogaardt MJ. Big data in smart farming. A review. Agricultural Systems. 2017;153:69-80. https://doi.org/10.1016/j.agsy.2017.01.023
- 81. al-Chalabi M. Vertical farming: Skyscraper sustainability? Sustainable Cities and Society. 2015;18:74-7. https://doi.org/10.1016/j.scs.2015.06.003
- 82. Benyam A, Soma T, Fraser E. Digital agricultural technologies for food loss and waste prevention and reduction: global trends, adoption opportunities and barriers. Journal of Cleaner Production. 2021;323:129099. https://doi.org/10.1016/J.JCLEPRO.2021.129099
- 83. Yatribi T. Factors affecting precision agriculture adoption: A systematic literature review. Economics. 2020;8(2):103-21. https://doi.org/10.2478/eoik-2020-0013
- 84. Chawade A, van Ham J, Blomquist H, Bagge O, Alexandersson E, Ortiz R. High-throughput field-phenotyping tools for plant breeding and precision agriculture Agronomy. 2019;9(5):258. https://doi.org/10.3390/agronomy9050258
- 85. Gebresenbet G, Bosona T, Patterson D, Persson H, Fischer B, Mandaluniz N, et al. A concept for application of integrated digital technologies to enhance future smart agricultural systems. Smart Agricultural Technology. 2023;5:100255. https://doi.org/10.1016/j.atech.2023.100255
- 86. Jouanjean MA, Casalini F, Wiseman L, Gray E. Issues around data governance in the digital transformation of agriculture: the farmers’ perspective. OECD Food, Agriculture and Fisheries Papers; 2020. https://doi.org/10.1787/53ecf2ab-en
- 87. Janssen M, Brous P, Estevez E, Barbosa LS, Janowski T. Data governance: organizing data for trustworthy Artificial Intelligence. Government Information Quarterly. 2020;37(3):101493. https://doi.org/10.1016/j.giq.2020.101493
- 88. Cao K, Liu Y, Meng G, Sun Q. An overview on edge computing research. IEEE. 2020;8:85714-28. https://doi.org/10.1109/ACCESS.2020.2991734
- 89. Poorna TK, Senthilkumar M, Manimekalai R, Saravanan PA, Vanitha G. Exploring the factors influencing the adoption of smart farming technologies in agriculture - A bibliometric analysis literature review. Plant Science Today. 2025;12(3). https://doi.org/10.14719/pst.8325
- 90. Sai S, Kumar S, Gaur A, Goyal S, Chamola V, Hussain A. Unleashing the power of generative AI in Agriculture 4.0 for smart and sustainable farming. Cognitive Computation. 2025;17:63. https://doi.org/10.1007/s12559-025-10420-6
Downloads
Download data is not yet available.