Review Articles
Vol. 12 No. sp1 (2025): Recent Advances in Agriculture by Young Minds - II
The AI-viticulture nexus: Robotics and precision technologies for sustainable vineyards
Department of Fruit Science, Horticultural College and Research Institute, Tamil Nadu Agricultural University, Coimbatore 641 003, Tamil Nadu, India
Directorate of Planning and Monitoring, Tamil Nadu Agricultural University, Coimbatore 641 003, Tamil Nadu, India
Department of Fruit Science, Horticultural College and Research Institute (Women), Tamil Nadu Agricultural University, Trichy 620 001, Tamil Nadu, India
Department of Crop Physiology, Tamil Nadu Agricultural University, Coimbatore 641 003, Tamil Nadu, India
Department of Agricultural Microbiology, Tamil Nadu Agricultural University, Coimbatore 641 003, Tamil Nadu, India
Agricultural College and Research Institute, Tamil Nadu Agricultural University, Madurai 625 104, Tamil Nadu, India
Abstract
Automation technologies, such as Artificial Intelligence (AI), robotics, IoT and remote sensing, are transforming viticulture by addressing labour shortages, climate resilience challenges and resource optimization. AI-driven machine learning models process data from multispectral drones and IoT sensors to monitor soil health, water stress and canopy dynamics, enabling precision agriculture practices like targeted irrigation and nutrient delivery. Autonomous robotic systems perform tasks such as selective harvesting, pruning and pest management, enhancing operational efficiency while reducing manual labour. IoT networks provide real-time insights into microclimatic conditions, empowering growers to adopt climate-smart strategies that minimize chemical inputs and improve yield stability. Despite progress, key barriers persist: AI models require terroir-specific adaptation, fragmented datasets hinder interoperability and field validation of autonomous systems under diverse conditions remains limited. Future research must prioritize accessible solutions: low-cost sensor networks for smallholders, adaptive AI frameworks for climate volatility (e.g., drought or flood prediction) and edge computing for real-time analytics. Ethical concerns data privacy, algorithmic bias and technology access disparities demand inclusive governance. Additionally, user-friendly interfaces are essential for broad adoption. Addressing these gaps will unlock automation’s full potential in advancing sustainable viticulture: optimizing water/energy use, reducing agrochemical reliance, enhancing biodiversity and ensuring economic resilience for growers. Ultimately, integrated automation promises a balance between ecological stewardship, resource efficiency and sector-wide viability in a climate-constrained future.
References
- 1. Jones GV, White MA, Cooper OR, Storchmann K. Climate change and global wine quality. Climatic Change. 2005;73(3):319-43. https://doi.org/10.1007/s10584-005-4704-2
- 2. Botterill T, Paulin S, Green R, Williams S, Lin J, Saxton V, et al. A robot system for pruning grape vines. Journal of Field Robotics. 2017;34(6):1100-22. https://doi.org/10.1002/rob.21680
- 3. Naveed M. The adoption of 4.0 agriculture for wine production in order to improve efficiency, sustainability and competitiveness. PhD [thesis]. Università degli Studi di Foggia; 2024. https://doi.org/10.14274/naveed-mubshair_phd2024
- 4. Upadhyay A, Patel A, Patel A, Chandel NS, Chakraborty SK, Bhalekar DG. Leveraging AI and ML in precision farming for pest and disease management: benefits, challenges and future prospects. In: Jatav HS, Raiput VD, Minkina T, editors. Ecologically mediated development. Singapore: Springer; 2025. p. 511-28 https://doi.org/10.1007/978-981-96-2413-3_23
- 5. Sharma K, Shivandu SK. Integrating artificial intelligence and internet of things (IoT) for enhanced crop monitoring and management in precision agriculture. Sensors International. 2024;5:100292. https://doi.org/10.1016/j.sintl.2024.100292
- 6. Hundal GS, Laux CM, Buckmaster D, Sutton MJ, Langemeier M. Exploring barriers to the adoption of internet of things-based precision agriculture practices. Agriculture. 2023;13(1):163. https://doi.org/10.3390/agriculture13010163
- 7. Folorunso A, Olanipekun K, Adewumi T, Samuel B. A policy framework on AI usage in developing countries and its impact. Global Journal of Engineering and Technology Advances. 2024;21(01):154-66. https://doi.org/10.30574/gjeta.2024.21.1.0192
- 8. Dellermann D, Calma A, Lipusch N, Weber T, Weigel S, Ebel P, et al. The future of human-AI collaboration: a taxonomy of design knowledge for hybrid intelligence systems. arXiv. 2021;2105.03354. https://doi.org/10.48550/arXiv.2105.03354
- 9. Jangirala S, Das AK, Vasilakos AV. Designing secure lightweight blockchain-enabled RFID-based authentication protocol for supply chains in 5G mobile edge computing environment. IEEE Transactions on Industrial Informatics. 2019;16(11):7081-93. https://doi.org/10.1109/TII.2019.2942389
- 10. Sampath V, Rangarajan N, Sharanappa C, Deori M, Veeraragavan M, Ghodake B, et al. Advancing crop improvement through CRISPR technology in precision agriculture trends-a review. International Journal of Environment and Climate Change. 2023;13(11):4683-94. https://doi.org/10.9734/ijecc/2023/v13i113647
- 11. Lidder P, Cattaneo A, Chaya M. Innovation and technology for achieving resilient and inclusive rural transformation. Global Food Security. 2025;44:100827. https://doi.org/10.1016/j.gfs.2025.100827
- 12. Velasquez-Camacho L, Otero M, Basile B, Pijuan J, Corrado G. Current trends and perspectives on predictive models for mildew diseases in vineyards. Microorganisms. 2022;11(1):73. https://doi.org/10.3390/microorganisms11010073
- 13. Portela F, Sousa JJ, Araújo-Paredes C, Peres E, Morais R, Pádua L. A systematic review on the advancements in remote sensing and proximity tools for grapevine disease detection. Sensors. 2024;24(24):8172. https://doi.org/10.3390/s24248172
- 14. Khan KH, Aljaedi A, Ishtiaq MS, Imam H, Bassfar Z, Jamal SS. Disease detection in grape cultivation using strategically placed cameras and machine learning algorithms with a focus on powdery mildew and blotches. IEEE Access; 2024.
- 15. Aldossary M, Almutairi J, Alzamil I. Federated LeViT-ResUNet for scalable and privacy-preserving agricultural monitoring using drone and internet of things data. Agronomy. 2025;15(4):928. https://doi.org/10.3390/agronomy15040928
- 16. Durrant A, Markovic M, Matthews D, May D, Enright J, Leontidis G. The role of cross-silo federated learning in facilitating data sharing in the agri-food sector. Computers and Electronics in Agriculture. 2022;193:106648. https://doi.org/10.1016/j.compag.2021.106648
- 17. MirhoseiniNejad SM, Abbasi-Moghadam D, Sharifi A. ConvLSTM-ViT: A deep neural network for crop yield prediction using earth observations and remotely sensed data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing; 2024. https://doi.org/10.1109/JSTARS.2024.3464411
- 18. Pesce L. Machine learning for predicting grape quality using spectral imaging techniques. Data Science and Engineering, MSc [thesis]. Turin (IT): Politecnico di Torino; 2024.
- 19. D Dottori E. Adaptation strategies to climate change in vineyard: innovation in vine training and pruning system, and cover crops. MSc [thesis]. Ancona (IT): Università Politecnica delle Marche; 2023.
- 20. B Bourzig DKD, Abed M, Merah M. Powdery mildew disease classification in laboratory and real-field images using convolutional neural networks for precision agriculture. In: Proceedings of the 2024 1st International Conference on Innovative and Intelligent Information Technologies (IC3IT); 2024 Dec 3–5; Batna, Algeria. Piscataway (NJ): IEEE; 2024. https://doi.org/10.1109/IC3IT63743.2024.10869354
- 21. Li Y, Wang J, Wu H, Yu Y, Sun H, Zhang H. Detection of powdery mildew on strawberry leaves based on DAC-YOLOv4 model. Computers and Electronics in Agriculture. 2022;202:107418. https://doi.org/10.1016/j.compag.2022.107418
- 22. Wang H, Qiu S, Ye H, Liao X. A plant disease classification algorithm based on attention MobileNet V2. Algorithms. 2023;16(9):442. https://doi.org/10.3390/a16090442
- 23. Singh AK, Rao A, Chattopadhyay P, Maurya R, Singh L. Effective plant disease diagnosis using Vision Transformer trained with leafy-generative adversarial network-generated images. Expert Systems with Applications. 2024;254:124387. https://doi.org/10.1016/j.eswa.2024.124387
- 24. Mohimont L, Alin F, Gaveau N, Steffenel LA, editors. Lite CNN models for real-time post-harvest grape disease detection. Workshops at 18th International Conference on Intelligent Environments (IE2022); 2022. https://doi.org/10.3233/AISE220029
- 25. Zhang H, Ren G. Intelligent leaf disease diagnosis: image algorithms using Swin Transformer and federated learning. The Visual Computer. 2024:1-24. https://doi.org/10.1007/s00371-024-03692-w
- 26. Nyakuri JP, Nkundineza C, Gatera O, Nkurikiyeyezu K. State-of-the-art deep learning algorithms for Internet of Things-based detection of crop pests and diseases: a comprehensive review. IEEE Access. 2024.
- 27. Ashoka P, Devi BR, Sharma N, Behera M, Gautam A, Jha A, et al. Artificial intelligence in water management for sustainable farming: a review. Journal of Scientific Research and Reports. 2024;30(6):511-25. https://doi.org/10.9734/jsrr/2024/v30i62068
- 28. Seyar MH, Ahamed T. Optimization of soil-based irrigation scheduling through the integration of machine learning, remote sensing and soil moisture sensor technology. In: Ahamed T, editor. IoT and AI in agriculture. Singapore: Springer; 2024.2024. https://doi.org/10.1007/978-981-97-1263-2_18
- 29. Pooja K, Anandan P. Precision agronomy: leveraging technology for enhanced crop management. Singapore: Textify Publishers; 2024.
- 30. Sathya D, Thangamani R, Balaji BS. The revolution of edge computing in smart farming. In: Intelligent robots and drones for precision agriculture. Singapore: Springer; 2024:351-89. https://doi.org/10.1007/978-3-031-51195-0_17
- 31. Wang C, Pan W, Zou T, Li C, Han Q, Wang H, et al. A review of perception technologies for berry fruit-picking robots: advantages, disadvantages, challenges and prospects. Agriculture. 2024;14(8):1346. https://doi.org/10.3390/agriculture14081346
- 32. Teng T. Research on grapevine recognition, manipulation and winter pruning automation. PhD [thesis]. Milan: Università Cattolica del Sacro Cuore; 2023.
- 33. Upadhyay A, Zhang Y, Koparan C, Rai N, Howatt K, Bajwa S, et al. Advances in ground robotic technologies for site-specific weed management in precision agriculture: a review. Computers and Electronics in Agriculture. 2024;225:109363. https://doi.org/10.1016/j.compag.2024.109363
- 34. Nell N. Advanced vineyard based long range sensor networks. Stellenbosch University; 2022.
- 35. Vladucu M-V, Wu H, Medina J, Salehin KM, Dong Z, Rojas-Cessa R. Blockchain in environmental sustainability measures: a survey. arXiv preprint arXiv:241215261; 2024.
- 36. Masere TP, Worth SH. Factors influencing adoption, innovation of new technology and decision-making by small-scale resource constrained farmers: the perspective of farmers in lower Gweru, Zimbabwe. African Journal of Food, Agriculture, Nutrition and Development. 2022;22(3):20013-35. https://doi.org/10.18697/ajfand.108.20960
- 37. Blessing E, Hubert K. Technological infrastructure and challenges: integration challenges in implementing AI solutions in legacy systems. London: Figshare; 2024.
- 38. Olaoye G. Neuromorphic computing and the cloud: the next frontier in AI processing. SSRN; 2025. https://doi.org/10.2139/ssrn.5129536
- 39. George IE, Iniobong UU, Okhionkpamwunyi EP. The use of artificial intelligence in tractor field operations: a review. Poljoprivredna tehnika; 2022;47(4):1-14. https://doi.org/10.5937/poljteh2204001g
- 40. Gao Y, Spiteri C, Pham M-T, Al-Milli S. A survey on recent object detection techniques useful for monocular vision-based planetary terrain classification. Robotics and Autonomous Systems. 2014;62(2):151-67. https://doi.org/10.1016/j.robot.2013.11.003
- 41. Jensen TA, Antille DL, Tullberg JN. Improving on-farm energy use efficiency by optimizing machinery operations and management: a review. Agricultural Research. 2025;14(1):15-33. https://doi.org/10.1007/s40003-024-00824-5
- 42. Xu S. Vision-based autonomy stacks for farm tractors and intelligent spraying systems in orchards. Clemson University; 2024.
- 43. Jiang Y, Liu J, Wang J, Li W, Peng Y, Shan H. Development of a dual-arm rapid grape-harvesting robot for horizontal trellis cultivation. Frontiers in Plant Science. 2022;13:881904. https://doi.org/10.3389/fpls.2022.881904
- 44. Mendelson R, Steinhauer R. Napa Valley viticulture: a farmer’s outlook. Wines & Vines. 2011;92(11):32-9.
- 45. Agelli M, Corona N, Maggio F, Moi PV. Unmanned ground vehicles for continuous crop monitoring in agriculture: assessing the readiness of current ICT technology. Machines. 2024;12(11):750. https://doi.org/10.3390/machines12110750
- 46. Camel A, Belhadi A, Kamble S, Tiwari S, Touriki FE. Integrating smart green product platforming for carbon footprint reduction: the role of blockchain technology and stakeholders influence within the agri-food supply chain. International Journal of Production Economics. 2024;272:109251. https://doi.org/10.1016/j.ijpe.2024.109251
- 47. Dhillon R, Moncur Q. Small-scale farming: a review of challenges and potential opportunities offered by technological advancements. Sustainability. 2023;15(21):15478. https://doi.org/10.3390/su152115478
- 48. Ahern D. Regulatory lag, regulatory friction and regulatory transition as FinTech disenablers: calibrating an EU response to the regulatory sandbox phenomenon. European Business Organization Law Review. 2021;22(3):395-432. https://doi.org/10.1007/s40804-021-00217-z
- 49. Kashyap B, Kumar R. Sensing methodologies in agriculture for soil moisture and nutrient monitoring. IEEE Access. 2021;9:14095-121. https://doi.org/10.1109/ACCESS.2021.3052478
- 50. Schlank R, Kidman CM, Gautam D, Jeffery DW, Pagay V. Data-driven irrigation scheduling increases the crop water use efficiency of Cabernet Sauvignon grapevines. Irrigation Science. 2024;42(1):29-44. https://doi.org/10.1007/s00271-023-00866-7
- 51. Trilles Oliver S, González-Pérez A, Huerta Guijarro J. Adapting models to warn fungal diseases in vineyards using in-field internet of things (IoT) nodes. Sustainability. 2019;11(2):416. https://doi.org/10.3390/su11020416
- 52. Jawad HM, Nordin R, Gharghan SK, Jawad AM, Ismail M. Energy-efficient wireless sensor networks for precision agriculture: a review. Sensors. 2017;17(8):1781. https://doi.org/10.3390/s17081781
- 53. Khaliq A. Advancements in multi-temporal remote sensing data analysis techniques for precision agriculture. PhD [thesis]. Turin: Politecnico di Torino; 2020.
- 54. Dalezios NR, Faraslis IN. Remote sensing in agricultural production assessment. In: Vlontzos G, Ampatzidis Y, Manos B, Pardalos PM, editors. Modeling for sustainable management in agriculture, food and the environment. Boca Raton (FL): CRC Press; 2022. p. 172–98 https://doi.org/10.1201/9780429197529-6
- 55. Devine S. Soil ecosystem services at statewide and catchment scales: a climate change perspective. PhD [thesis]. Davis (CA): University of California; 2019.
- 56. Gu M. Improved Kalman filtering and adaptive weighted fusion algorithms for enhanced multi-sensor data fusion in precision measurement. Informatica. 2025;49(10). https://doi.org/10.31449/inf.v49i10.7122
- 57. Sharma H, Haque A, Jaffery ZA. Maximization of wireless sensor network lifetime using solar energy harvesting for smart agriculture monitoring. Ad Hoc Networks. 2019;94:101966. https://doi.org/10.1016/j.adhoc.2019.101966
- 58. Backman J, Linkolehto R, Koistinen M, Nikander J, Ronkainen A, Kaivosoja J, et al. Cropinfra research data collection platform for ISO 11783 compatible and retrofit farm equipment. Computers and Electronics in Agriculture. 2019;166:105008. https://doi.org/10.1016/j.compag.2019.105008
- 59. Gammanpila H, Sashika MN, Priyadarshani S. Advancing horticultural crop loss reduction through robotic and AI technologies: innovations, applications and practical implications. Advances in Agriculture. 2024;2024(1):2472111. https://doi.org/10.1155/2024/2472111
- 60. Van Der Woude AM, Peters W, Joetzjer E, Lafont S, Koren G, Ciais P, et al. Temperature extremes of 2022 reduced carbon uptake by forests in Europe. Nature Communications. 2023;14(1):6218. https://doi.org/10.1038/s41467-023-41851-0
- 61. Gatou P, Tsiara X, Spitalas A, Sioutas S, Vonitsanos G. Artificial intelligence techniques in grapevine research: a comparative study with an extensive review of datasets, diseases and techniques evaluation. Sensors. 2024;24(19):6211. https://doi.org/10.3390/s24196211
- 62. Rogiers SY, Greer DH, Liu Y, Baby T, Xiao Z. Impact of climate change on grape berry ripening: an assessment of adaptation strategies for the Australian vineyard. Frontiers in Plant Science. 2022;13:1094633. https://doi.org/10.3389/fpls.2022.1094633
- 63. Ye W, Xu W, Yan T, Yan J, Gao P, Zhang C. Application of near-infrared spectroscopy and hyperspectral imaging combined with machine learning algorithms for quality inspection of grape: a review. Foods. 2022;12(1):132. https://doi.org/10.3390/foods12010132
- 64. Farhan SM, Yin J, Chen Z, Memon MS. A comprehensive review of LiDAR applications in crop management for precision agriculture. Sensors. 2024;24(16):5409. https://doi.org/10.3390/s24165409
- 65. Ferro MV, Catania P, Micciche D, Pisciotta A, Vallone M, Orlando S. Assessment of vineyard vigour and yield spatio-temporal variability based on UAV high resolution multispectral images. Biosystems Engineering. 2023;231:36-56. https://doi.org/10.1016/j.biosystemseng.2023.06.001
- 66. Taylor JA, Bates TR, Jakubowski R, Jones H. Machine-learning methods to identify key predictors of site-specific vineyard yield and vine size. American Journal of Enology and Viticulture. 2023;74(1). https://doi.org/10.5344/ajev.2022.22050
- 67. Santosh K, Gaur L. Artificial intelligence and machine learning in public healthcare: opportunities and societal impact. Springer Nature; 2022. https://doi.org/10.1007/978-981-16-6768-8
- 68. Gopalakrishna Pillai S, Ngcobo-Onunkwo P, Al Rooq Y. Transparency uncorked: leveraging blockchain to tackle international wine fraud. Journal of Hospitality & Tourism Cases; 2024. https://doi.org/10.1177/21649987241290984
- 69. Diago MP. Vineyard water management. Advanced automation for tree fruit orchards and vineyards. Springer. 2023:75-92. https://doi.org/10.1007/978-3-031-26941-7_4
- 70. Carella A, Bulacio Fischer PT, Massenti R, Lo Bianco R. Continuous plant-based and remote sensing for determination of fruit tree water status. Horticulturae. 2024;10(5):516. https://doi.org/10.3390/horticulturae10050516
- 71. Das GP, Gould I, Zarafshan P, Heselden J, Badiee A, Wright I, et al. Applications of robotic and solar energy in precision agriculture and smart farming. Solar energy advancements in agriculture and food production systems. Elsevier. 2022:351-90. https://doi.org/10.1016/B978-0-323-89866-9.00011-0
- 72. Zanzotti R, Bertoldi D, Baldantoni D, Morelli R. Soil fertility and agronomic performance of green manure in vineyard. In: Proceedings of the IV Convegno AISSA# under 40; 2023 Jul 12–13; Fisciano (SA), Italy. Fisciano: AISSA. 2023. p. 142.
- 73. Bazán D. Alternatives to traditional agricultural biomass burning in Napa Valley. Environmental Management, MSc [project]. San Francisco: University of San Francisco; 2018.
- 74. Dhakshayani J, Surendiran B. M2F-Net: a deep learning-based multimodal classification with high-throughput phenotyping for identification of overabundance of fertilizers. Agriculture. 2023;13(6):1238. https://doi.org/10.3390/agriculture13061238
- 75. Rathod S, Kushwaha H, Kumar A, Khura TK, Kumar R, Dass A, et al. Comparative analysis on cost-economics evaluation of robotic tiller-planter against conventional tillage and planting operations. Int J Env Clim Change. 2024;14(1):433-42. https://doi.org/10.9734/ijecc/2024/v14i13853
- 76. Ghate U, Nydu P, Verma H, Ashraf S. Climate change and NTFP livelihood implications for the tribal. New Delhi: Indo Global Social Service Society; 2015. CCD report.
- 77. Prasath VT, Reddy G, Kaanth K, Madanmohan Reddy K. Smart-Agro: enhancing crop management with Agribot. J IoT Soc Mobile Anal Cloud. 2024;6(3):212-26. https://doi.org/10.36548/jismac.2024.3.002
- 78. Nuwarapaksha TD, Udumann SS, Dissanayaka NS, Dilshan R, Atapattu AJ. AI driven solutions for sustainable irrigation: exploring smart technologies to enhance conservation and efficiency. In: Integrating agriculture, green marketing strategies and artificial intelligence. Hershey: IGI Global Scientific Publishing. 2025:1-32. https://doi.org/10.4018/979-8-3693-6468-0.ch001
- 79. Le Bellec F, Vélu A, Fournier P, Le Squin S, Michels T, Tendero A, et al. Helping farmers to reduce herbicide environmental impacts. Ecological Indicators. 2015;54:207-16. https://doi.org/10.1016/j.ecolind.2015.02.020
- 80. Jujjavarapu G, Hickok E, Sinha A, Mohandas S, Ray S, Bidare PM, et al. AI and the manufacturing and services industry in India. Bengaluru: Centre for Internet and Society; 2018.
- 81. Khobragade PJ, Bhatnagar P, Nigam S. Adoption of agritech innovations by the sugarcane industry in Maharashtra and Uttar Pradesh: a comparative analysis. IUP Journal of Operations Management. 2024;23(4):44-61.
- 82. Ardon O, Asa SL, Lloyd MC, Lujan G, Parwani A, Santa-Rosario JC, et al. Understanding the financial aspects of digital pathology: a dynamic customizable return on investment calculator for informed decision-making. Journal of Pathology Informatics. 2024;15:100376. https://doi.org/10.1016/j.jpi.2024.100376
- 83. Raza S. Innovative technologies in agriculture: leveraging AI, ML and IoT for sustainable food production and resource management. Int J Agric Sustain Develop. 2024;6(3):127-46. https://doi.org/10.5209/reve.95352
- 84. Bisht B. Yield prediction using spatial and temporal deep learning algorithms and data fusion. Computer Science, PhD [thesis]. Ottawa (ON): University of Ottawa; 2023. https://doi.org/10.1109/ICMLA58977.2023.00272
- 85. Saleh E. Trade-marking tradition: an ethnographic study of the Lebanese wine industry. PhD [thesis]. London: Goldsmiths University of London; 2014. https://doi.org/10.25602/GOLD.00011042
- 86. Babashahi L, Barbosa CE, Lima Y, Lyra A, Salazar H, Argôlo M, et al. AI in the workplace: a systematic review of skill transformation in the industry. Administrative Sciences. 2024;14(6):127. https://doi.org/10.3390/admsci14060127
- 87. Simeunović M, Ratković K, Kovač N, Racković T, Fernandes A. A knowledge-driven framework for a decision support platform in sustainable viticulture: integrating climate data and supporting stakeholder collaboration. Sustainability. 2025;17(4):1387. https://doi.org/10.3390/su17041387
- 88. 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
- 89. Fuentes S, Tongson E, Gonzalez Viejo C. New developments and opportunities for AI in viticulture, pomology and soft-fruit research: a mini-review and invitation to contribute articles. Frontiers in Horticulture. 2023;2:1282615. https://doi.org/10.3389/fhort.2023.1282615
- 90. Newlands NK. Artificial intelligence and big data analytics in vineyards: a review. IntechOpen; 2021. https://doi.org/10.5772/intechopen.99862
- 91. Madeira M, Porfírio RP, Santos PA, Madeira RN. AI-powered solution for plant disease detection in viticulture. Procedia Computer Science. 2024;238:468-75. https://doi.org/10.1016/j.procs.2024.06.049
- 92. Sharma S, Popli R, Singh S, Chhabra G, Saini GS, Singh M, et al. The role of 6G technologies in advancing smart city applications: opportunities and challenges. Sustainability. 2024;16(16):7039. https://doi.org/10.3390/su16167039
- 93. Lozano-Tello A, Luceño J, Caballero-Mancera A, Clemente PJ. Estimating olive tree density in delimited areas using Sentinel-2 images. Remote Sensing. 2025;17(3):508. https://doi.org/10.3390/rs17030508
- 94. Toscano F, Fiorentino C, Capece N, Erra U, Travascia D, Scopa A, et al. Unmanned aerial vehicle for precision agriculture: a review. IEEE Access; 2024. https://doi.org/10.1109/ACCESS.2024.3401018
- 95. Ibáñez-Jiménez J, Palomo R. Wine tokenisation: the opportunity of DLT technology for the economic and social challenges of the wine sector. REVESCO Rev Estud Coop. 2024;146:14. https://doi.org/10.5209/reve.95352
- 96. Vitiello M. Globalism and sustainable vineyard practices. U Pac L Rev. 2020;52:623.
Downloads
Download data is not yet available.