Smart Viniculture: Applying Artificial Intelligence for Improved Winemaking and Risk Management
Source abstract
Abstract
This review explores the transformative role of artificial intelligence (AI) in the entire winemaking process, from viticulture to bottling, with a particular focus on enhancing food safety and traceability. It discusses AI’s applications in optimizing grape cultivation, fermentation, bottling, and quality control, while emphasizing its critical role in managing microbiological risks such as mycotoxins. The review aims to show how AI technologies not only refine operational efficiencies but also raise safety standards and ensure traceability from vineyard to consumer. Challenges in AI implementation and future directions for integrating more advanced AI solutions into the winemaking industry will also be discussed, providing a comprehensive overview of AI’s potential to revolutionize traditional practices.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Horticultural and Viticultural Research · Smart Agriculture and AI · Forest Insect Ecology and Management
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Laboratorio de Microbiología, Departamento de Biomedicina, Biotecnología y Salud Pública, Facultad de Ciencias del Mar y Ambientales, Universidad de Cádiz, 11510 Puerto Real, Spain
Funding
MCIN/AEI/10.13039/501100011033
References (100+)
Show source-provided references
1. Russell, S., and Norvig, P. (2020). Artificial Intelligence: A Modern Approach, Pearson. [4th ed.].
2. A Guide to Deep Learning in Healthcare10.1038/s41591-018-0316-z ↗
3. Liakos, K.G., Busato, P., Moshou, D., Pearson, S., and Bochtis, D. (2018). Machine Learning in Agriculture: A Review. Sensors, 18.10.3390/s18082674 ↗
4. Deep Learning in Agriculture: A Survey10.1016/j.compag.2018.02.016 ↗
5. Chouhan, M., Banerjee, P.S., and Kumar, A. (2023). Identifying the Suitability of Artificial Intelligence Technology for Modern Farming, Association for Computing Machinery.10.1145/3607947.3607985 ↗
6. Machine Learning Approaches for Crop Yield Prediction and Nitrogen Status Estimation in Precision Agriculture: A Review10.1016/j.compag.2018.05.012 ↗
7. Morales, A., and Villalobos, F.J. (2023). Using Machine Learning for Crop Yield Prediction in the Past or the Future. Front. Plant Sci., 14.10.3389/fpls.2023.1128388 ↗
8. Industrial AI [Industrial and Governmental Activities]10.1109/MCI.2022.3223460 ↗
9. Artificial Intelligence in New Zealand: Applications and Innovation10.1080/03036758.2023.2170165 ↗
10. Nelson, J., Coleman, R., Chacón-Rodríguez, L., Runnebaum, R., Boulton, R., and Knoesen, A. (2022). Advanced Monitoring and Control of Redox Potential in Wine Fermentation across Scales. Fermentation, 9.10.3390/fermentation9010007 ↗
11. Introduction to the Special Section on AI in Manufacturing10.1145/3575637.3575650 ↗
12. How Can AI Help Improve Food Safety?10.1146/annurev-food-060721-013815 ↗
13. Liu, Z., Wang, S., Zhang, Y., Feng, Y., Liu, J., and Zhu, H. (2023). Artificial Intelligence in Food Safety: A Decade Review and Bibliometric Analysis. Foods, 12.10.3390/foods12061242 ↗
14. González-Rodríguez, V.E., Izquierdo-Bueno, I., Cantoral, J.M., Carbú, M., and Garrido, C. (2024). Artificial Intelligence: A Promising Tool for Application in Phytopathology. Horticulturae, 10.10.20944/preprints202401.1882.v1 ↗
15. The Transformative Potential of Artificial Intelligence10.1016/j.futures.2021.102884 ↗
16. Artificial Intelligence (AI): Multidisciplinary Perspectives on Emerging Challenges, Opportunities, and Agenda for Research, Practice and Policy10.1016/j.ijinfomgt.2019.08.002 ↗
17. Artificial Intelligence: Current Perspectives and Alternative Paths10.22430/22565337.2731 ↗
18. Xu, D., Liu, B., Wang, J., and Zhang, Z. (2022). Bibliometric Analysis of Artificial Intelligence for Biotechnology and Applied Microbiology: Exploring Research Hotspots and Frontiers. Front. Bioeng. Biotechnol., 10.10.3389/fbioe.2022.998298 ↗
19. Searching for the Right Metaphors to Understand and Interrogate the AI Age10.22682/bcrp.2023.6.2.65 ↗
20. The AI Chip Race10.1109/MIS.2022.3165668 ↗
21. ChatGPT, AI Advertising, and Advertising Research and Education10.1080/00913367.2023.2227013 ↗
22. Colmenarejo, A.B., Nannini, L., Rieger, A., Scott, K.M., Zhao, X., Patro, G.K., Kasneci, G., and Kinder-Kurlanda, K. (2022). State of AI Ethics Report (Volume 6, February 2022). arXiv.
23. Gardashova, L., Ismayilova, A., and Tarverdiyeva, G. (2023, January 23). Expert System for Dental Diseases. Proceedings of the PAHTEI-Procedings of Azerbaijan High Technical Educational Institutions, Tallinn, Estonia.10.36962/PAHTEI31082023-23 ↗
24. Envisioning the Future of Learning and Teaching Engineering in the Artificial Intelligence Era: Opportunities and Challenges10.1002/jee.20539 ↗
25. Tracing the Advancements in Artificial Intelligence: Drawing Lessons for Tomorrow
26. A Comprehensive Research Workshop on Artificial Intelligence in Parliaments10.1163/26668912-bja10074 ↗
27. ‘ChatGPT et al.’: The Ethics of Using (Generative) Artificial Intelligence in Research and Science10.1177/02683962231200411 ↗
28. Opportunities and Challenges to Integrate Artificial Intelligence Into Manufacturing Systems: Thoughts from a Panel Discussion [Opinion]10.1109/MRA.2023.3262464 ↗
29. Prajapati, S., Qureshi, S., Rao, Y., Nadkarni, S., Retharekar, M., and Avhad, A. (2023, January 26–28). Plant Disease Identification Using Deep Learning. Proceedings of the 2023 4th International Conference for Emerging Technology, INCET 2023, Belgaum, India.10.1109/INCET57972.2023.10170463 ↗
30. Artificial Intelligence Technology in the Agricultural Sector: A Systematic Literature Review10.1109/ACCESS.2022.3232485 ↗
31. Plant Disease Detection Using Deep Learning
32. A Smart IoT-Based Irrigation System Design Using AI and Prediction Model10.1007/s00521-023-08987-y ↗
33. Seedling-Lump Integrated Non-Destructive Monitoring for Automatic Transplanting with Intel RealSense Depth Camera
34. Agricultural Robots for Field Operations: Concepts and Components10.1016/j.biosystemseng.2016.06.014 ↗
35. A Review on the Practice of Big Data Analysis in Agriculture10.1016/j.compag.2017.09.037 ↗
36. Virtualization of Food Supply Chains with the Internet of Things10.1016/j.jfoodeng.2015.11.009 ↗
37. Marcu, I., Drăgulinescu, A.M., Oprea, C., Suciu, G., and Bălăceanu, C. (2022). Predictive Analysis and Wine-Grapes Disease Risk Assessment Based on Atmospheric Parameters and Precision Agriculture Platform. Sustainability, 14.10.3390/su141811487 ↗
38. Wheat Yield Prediction Using Machine Learning and Advanced Sensing Techniques10.1016/j.compag.2015.11.018 ↗
39. Kleb, M., Merkt, N., and Zörb, C. (2022). New Aspects of In Situ Measurements for Downy Mildew Forecasting. Plants, 11.10.3390/plants11141807 ↗
40. Jovanovska, E.M., Chorbev, I., Davcev, D., and Mitreski, K. (2022, January 16–18). Integrated IoT System for Prediction of Diseases in the Vineyards. Proceedings of the International Conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2022, Maldives.10.1109/ICECCME55909.2022.9988415 ↗
41. Maddalena, G., Marone Fassolo, E., Bianco, P.A., and Toffolatti, S.L. (2023). Disease Forecasting for the Rational Management of Grapevine Mildews in the Chianti Bio-District (Tuscany). Plants, 12.10.3390/plants12020285 ↗
42. Rossi, V., Giosuè, S., and Caffi, T. (2010). Modelling Plant Diseases for Decision Making in Crop Protection. Precision Crop Protection—The Challenge and Use of Heterogeneity, Springer.10.1007/978-90-481-9277-9_15 ↗
43. AI-Powered Solution for Plant Disease Detection in Viticulture10.1016/j.procs.2024.06.049 ↗
44. Li, R., Liu, J., Shi, B., Zhao, H., Li, Y., Zheng, X., Peng, C., and Lv, C. (2024). High-Performance Grape Disease Detection Method Using Multimodal Data and Parallel Activation Functions. Plants, 13.10.3390/plants13192720 ↗
45. Advancements in Drone Technology for Fruit Crop Management: A Comprehensive Review10.9734/ijecc/2023/v13i113617 ↗
46. Dubuis, P.H., and Jaquerod, A. (2022). Evaluation of the Performance of Drone Treatments to Control Downy and Powdery Mildew in Grapevines. BIO Web Conf., 50.10.1051/bioconf/20225001006 ↗
47. Drone-Assisted Plant Disease Identification Using Artificial Intelligence: A Critical Review10.12785/ijcds/1401112 ↗
48. Rosado, L., Faria, P., Gonçalves, J., Silva, E., Vasconcelos, A., Braga, C., Oliveira, J., Gomes, R., Barbosa, T., and Ribeiro, D. (2022). EyesOnTraps: AI-Powered Mobile-Based Solution for Pest Monitoring in Viticulture. Sustainability, 14.10.3390/su14159729 ↗
49. Intelligent Agricultural Pest Manager Drone in Pakistan10.34016/pjbt.2023.20.02.816 ↗
50. Žibrat, U., and Knapič, M. (2024). Detection of Grapevine Yellows Using Multispectral Imaging. Remote Sensing in Precision Agriculture: Transforming Scientific Advancement into Innovation, Academic Press.10.1016/B978-0-323-91068-2.00001-1 ↗
51. Albetis, J., Jacquin, A., Goulard, M., Poilvé, H., Rousseau, J., Clenet, H., Dedieu, G., and Duthoit, S. (2018). On the Potentiality of UAV Multispectral Imagery to Detect Flavescence Dorée and Grapevine Trunk Diseases. Remote Sens., 11.10.3390/rs11010023 ↗
52. Operational Study of Drone Spraying Application of Phytosanitary Products in Vineyards
53. Unmanned Aerial Vehicle (UAV)-Based Remote Sensing to Monitor Grapevine Leaf Stripe Disease within a Vineyard Affected by Esca Complex
54. Detection of Grapevine Leaf Stripe Disease Symptoms by Hyperspectral Sensor
55. Remote Hyperspectral Imaging of Grapevine Leafroll-Associated Virus 3 in Cabernet Sauvignon Vineyards10.1016/j.compag.2016.10.003 ↗
56. Mapping the Spatial Variability of Botrytis Bunch Rot Risk in Vineyards Using UAV Multispectral Imagery10.1016/j.eja.2022.126691 ↗
57. Drone-Based AI/IoT Framework for Monitoring, Tracking and Fighting Pandemics
58. Chen, J., Ma, A., Huang, L., Su, Y., Li, W., Zhang, H., and Wang, Z. (2023). GA-YOLO: A Lightweight YOLO Model for Dense and Occluded Grape Target Detection. Horticulturae, 9.10.3390/horticulturae9040443 ↗
59. Xu, Z., Liu, J., Wang, J., Cai, L., Jin, Y., Zhao, S., and Xie, B. (2023). Realtime Picking Point Decision Algorithm of Trellis Grape for High-Speed Robotic Cut-and-Catch Harvesting. Agronomy, 13.10.3390/agronomy13061618 ↗
60. Wu, N., Huang, H., Meng, X., Xiong, Y., and Li, S. (2023). Design and Kinematic Modeling of Grape Picking Robot Arm, Association for Computing Machinery.10.1145/3627915.3628088 ↗
61. Liu, J., Liang, J., Zhao, S., Jiang, Y., Wang, J., Jin, Y., Zhang, B., Qiao, Y., Liu, J., and Liang, J. (2023). Design of a Virtual Multi-Interaction Operation System for Hand–Eye Coordination of Grape Harvesting Robots. Agronomy, 13.10.3390/agronomy13030829 ↗
62. Milburn, L., Gamba, J., Fernandes, M., and Semini, C. (2023, January 26–27). Computer-Vision Based Real Time Waypoint Generation for Autonomous Vineyard Navigation with Quadruped Robots. Proceedings of the 2023 IEEE International Conference on Autonomous Robot Systems and Competitions, ICARSC 2023, Tomar, Portugal.10.1109/ICARSC58346.2023.10129563 ↗
63. Zhang, T., Wu, F., Wang, M., Chen, Z., Li, L., and Zou, X. (2023). Grape-Bunch Identification and Location of Picking Points on Occluded Fruit Axis Based on YOLOv5-GAP. Horticulturae, 9.10.3390/horticulturae9040498 ↗
64. Jiang, Y., Liu, J., Wang, J., Li, W., Peng, Y., and Shan, H. (2022). Development of a Dual-Arm Rapid Grape-Harvesting Robot for Horizontal Trellis Cultivation. Front. Plant Sci., 13.10.3389/fpls.2022.881904 ↗
65. Accurate Detection and Depth Estimation of Table Grapes and Peduncles for Robot Harvesting, Combining Monocular Depth Estimation and CNN Methods10.1016/j.compag.2023.108362 ↗
66. Lowe, M., Qin, R., and Mao, X. (2022). A Review on Machine Learning, Artificial Intelligence, and Smart Technology in Water Treatment and Monitoring. Water, 14.10.3390/w14091384 ↗
67. Deployment and Use of Artificial Intelligence (AI) in Water Resources and Water Management10.1088/1755-1315/1195/1/012056 ↗
68. Darouich, H., Ramos, T.B., Pereira, L.S., Rabino, D., Bagagiolo, G., Capello, G., Simionesei, L., Cavallo, E., and Biddoccu, M. (2022). Water Use and Soil Water Balance of Mediterranean Vineyards under Rainfed and Drip Irrigation Management: Evapotranspiration Partition and Soil Management Modelling for Resource Conservation. Water, 14.10.3390/w14040554 ↗
69. Alibabaei, K., Gaspar, P.D., Campos, R.M., Rodrigues, G.C., and Lopes, C.M. (2023). Evaluation of a Deep Learning Approach for Predicting the Fraction of Transpirable Soil Water in Vineyards. Appl. Sci., 13.10.3390/app13052815 ↗
70. D’amore, G., Di Vaio, A., Balsalobre-Lorente, D., and Boccia, F. (2022). Artificial Intelligence in the Water–Energy–Food Model: A Holistic Approach towards Sustainable Development Goals. Sustainability, 14.10.3390/su14020867 ↗
71. Peng, X., Chen, D., Zhou, Z., Zhang, Z., Xu, C., Zha, Q., Wang, F., and Hu, X. (2022). Prediction of the Nitrogen, Phosphorus and Potassium Contents in Grape Leaves at Different Growth Stages Based on UAV Multispectral Remote Sensing. Remote Sens., 14.10.3390/rs14112659 ↗
72. Lyu, H., Grafton, M., Ramilan, T., Irwin, M., and Sandoval, E. (2023). Assessing the Leaf Blade Nutrient Status of Pinot Noir Using Hyperspectral Reflectance and Machine Learning Models. Remote Sens., 15.10.3390/rs15061497 ↗
73. A Super Learner Ensemble to Map Potassium Fixation in California Vineyard Soils10.1016/j.geoderma.2024.116824 ↗
74. veer Optimizing Irrigation and Nutrient Management in Agriculture through Artificial Intelligence Implementation10.9734/ijecc/2023/v13i103077 ↗
75. Rachmadi, M.A., Nugroho, T.H., Taufiqurrohman, H., Cahya, Z., Cahya, D.E., and Prihantoro, G. (2023, January 15–16). Precise Temperature Control Strategy for Laboratory-Scale Fermentation Process. Proceedings of the 2023 International Conference on Radar, Antenna, Microwave, Electronics, and Telecommunications: Empowering Global Progress: Innovative Electronic and Telecommunication Solutions for a Sustainable Future, ICRAMET 2023, Bandung, Indonesia.10.1109/ICRAMET60171.2023.10366767 ↗
76. Development of an Innovative Temperature Control System in a Fermenter: Application to the Case of the Wine Industry10.1088/1742-6596/2177/1/012036 ↗
77. A Mobile Control System of Fermentation Temperature on Winemaking
78. Monitoring of Glucose and Ethanol during Wine Fermentation by Bienzymatic Biosensor10.1016/j.jelechem.2018.03.052 ↗
79. Yang, S., Bo, C., Li, J., Tan, Q., Wang, Y., Wu, H., Chu, Z., and Jin, W. (2022). Online Glucose Analysis System with High Precision and Wide Detection Range for Fermentation Monitoring. IEEE Transactions on Instrumentation and Measurement, IEEE.10.1109/TIM.2022.3218115 ↗
80. Fuller, H., Beaver, C., Harbertson, J., Jordão, M., and Michelle Wine Estates, S. (2021). Alcoholic Fermentation Monitoring and PH Prediction in Red and White Wine by Combining Spontaneous Raman Spectroscopy and Machine Learning Algorithms. Beverages, 7.10.3390/beverages7040078 ↗
81. Torello Pianale, L., Rugbjerg, P., and Olsson, L. (2022). Real-Time Monitoring of the Yeast Intracellular State During Bioprocesses with a Toolbox of Biosensors. Front. Microbiol., 12.10.3389/fmicb.2021.802169 ↗
82. Temperature Control of an Alcoholic Fermentation Process through the Takagi–Sugeno Modeling10.1016/j.cherd.2018.10.021 ↗
83. Identification of Chemical Markers to Detect Abnormal Wine Fermentation Using Support Vector Machines10.1016/j.compchemeng.2020.107158 ↗
84. Impact of Fermentation Temperature on Required Heat Dissipation, Growth and Viability of Yeast, on Sensory Characteristics and on the Formation of Volatiles in Riesling10.1111/ajgw.12386 ↗
85. An Integrated Approach Based on Virtual Data Augmentation and Deep Neural Networks Modeling for VFA Production Prediction in Anaerobic Fermentation Process10.1016/j.watres.2020.116103 ↗
86. Florea, A., Sipos, A., and Stoisor, M.C. (2022). Applying AI Tools for Modeling, Predicting and Managing the White Wine Fermentation Process. Fermentation, 8.10.3390/fermentation8040137 ↗
87. Juuso, E.K. (2020, January 12–16). Intelligent Dynamic Simulation of Fed-Batch Fermentation Processes. Proceedings of the 60th SIMS Conference on Simulation and Modelling SIMS 2019, Västerås, Sweden.10.3384/ecp20170132 ↗
88. ATR-MIR Spectroscopy and Multivariate Analysis in Alcoholic Fermentation Monitoring and Lactic Acid Bacteria Spoilage Detection10.1016/j.foodcont.2019.106947 ↗
89. Berbegal, C., Khomenko, I., Russo, P., Spano, G., Fragasso, M., Biasioli, F., and Capozzi, V. (2020). PTR-ToF-MS for the Online Monitoring of Alcoholic Fermentation in Wine: Assessment of VOCs Variability Associated with Different Combinations of Saccharomyces/Non-Saccharomyces as a Case-Study. Fermentation, 6.10.3390/fermentation6020055 ↗
90. Existence, Uniqueness, and Numerical Modeling of Wine Fermentation Based on Integro-Differential Equations10.1137/20M1362309 ↗
91. Advanced Control of a Biochemical Reactor for Yeast Fermentation
92. Soares, M.C.P., Fujiwara, E., Gomes, M.K., Mendes, B.F., La Torre, L.G., and Suzuki, C.K. (2018, January 24–28). Application of Optical Fiber Sensor on Fermentation Monitoring. Proceedings of the 26th International Conference on Optical Fiber Sensors, Lausanne, Switzerland. Part F124-OFS 2018.10.1364/OFS.2018.ThE39 ↗
93. AI-Based Forecasting of Ethanol Fermentation Using Yeast Morphological Data10.1093/bbb/zbab188 ↗
94. Prediction of Intensified Ethanol Fermentation of Sugarcane Using a Deep Learning Soft Sensor and Process Analytical Technology10.1002/jctb.7525 ↗
95. Machine Vision Systems for Industrial Quality Control Inspections
96. Mahor, A., and Yadav, R.S. (November, January 30). In-Process Intelligent Inspection of the Specimen Using Machine Vision. Proceedings of the ASME International Mechanical Engineering Congress and Exposition, Proceedings (IMECE), Columbus, OH, USA.
97. State of the Art in Defect Detection Based on Machine Vision10.1007/s40684-021-00343-6 ↗
98. Gong, W., Zhang, K., Yang, C., Yi, M., and Wu, J. (2020, January 10–12). Adaptive Visual Inspection Method for Transparent Label Defect Detection of Curved Glass Bottle. Proceedings of the 2020 International Conference on Computer Vision, Image and Deep Learning, CVIDL 2020, Chongqing, China.10.1109/CVIDL51233.2020.00024 ↗
99. Wu, M.Y. (2019, January 16–18). Wine Label Image Recognition Using Convolutional Neural Network with Augmented Data. Proceedings of the International Conferences Interfaces and Human Computer Interaction 2019; Game and Entertainment Technologies 2019; and Computer Graphics, Visualization, Computer Vision and Image Processing 2019, Porto, Portugal.10.33965/cgv2019_201906P074 ↗
100. Distributed Search and Fusion for Wine Label Image Retrieval10.7717/peerj-cs.1116 ↗