Cloud-Native AI Architectures in Modern Agriculture
DOI:
https://doi.org/10.5281/zenodo.21509158Keywords:
Precision Agriculture, Cloud Computing In Agriculture, Smart Farming Systems, Agricultural Data Analytics, IoT Sensors In Farming, Real-Time Farm Data, Agricultural Data Sharing, Scalable Cloud Infrastructure, AI And ML In Agriculture, Farm-Level Decision Support, Data Ingestion Pipelines, Agricultural Data Lakes, Model Training And Inference, Edge–Cloud Agriculture Architectures, Food Security 2050, Stakeholder Data Accessibility, Agricultural Data Governance, Privacy And Security In Agri-Data, Latency And Reliability Challenges, Digital Agriculture Ecosystems.Abstract
Precision agriculture is considered one of the key solutions to satisfy world food demand by 2050. To achieve precision at the smallest farming unit, data collected, processed, and analysed to produce fit-for-use knowledge must be visible, sharable, and accessible among all stakeholders. Cloud computing technology on the foreshore has penetrated all segments of everyday life, not sparing the agriculture segment, making visible the nondirectional flow of information in real time. Cloud computing generally provides scalable infrastructure, powerful storage, data-sharing facilities, low operational costs, and powerful analytics, metering AI/ML model development and inference more affordable and enabling artificial intelligence and machine learning development and inference at regional scales. Its challenges include single-point failure, low-quality service, latency, and resource dependency. Conclusively, precision agriculture at the farm and field levels can also benefit from clustering agriculture stakeholders’ data on the cloud and making the data accessible to eminent researchers and agriculture domains using various algorithms to develop AI/ML models. The planned architecture helps shed light on the logical flow of information from the sensor to decision representation. It includes key functional modules, components required for data ingestion, integration towards data lakes, model training nested in the cloud, deployment in different environments for inference, and governing data with respect to modelling, privacy, and security of stakeholders’ data in the cloud.
To satisfy world food demand by 2050, precision agriculture is considered one of the key solutions. Precision requires collecting, processing, and analysing data to produce fit-for-use knowledge that is visible, sharable, and accessible among all stakeholders. Cloud-computing technology on the foreshore has penetrated all segments of everyday life, not sparing agriculture, and enabling a non-directional flow of real-time information. Cloud computing generally provides scalable infrastructure, powerful storage, data-sharing facilities, low operational costs, and powerful analytics, making artificial intelligence and machine learning development and inference at regional scales more affordable. The technology’s challenges include single-point failure, low-quality service, latency, and resource dependency. Precision agriculture at the farm and field levels can benefit from clustering agriculture stakeholders’ data on the cloud and making the data accessible to eminent researchers and agriculture domains.
References
[1] Agriculture and Horticulture Development Board. (2021). Digital farming: Technologies and benefits for agriculture. AHDB.
[2] Ayaz, M., Ammad-Uddin, M., Sharif, Z., Mansour, A., & Aggoune, E. H. M. (2019). Internet-of-Things (IoT)-based smart agriculture: Toward making the fields talk. IEEE Access, 7, 129551–129583.
[3] Bacco, M., Barsocchi, P., Ferro, E., Gotta, A., & Ruggeri, M. (2019). The digitisation of agriculture: A survey of precision agriculture and smart farming. Sensors, 19(12), 2673.
[4] Balafoutis, A., Beck, B., Fountas, S., Vangeyte, J., van der Wal, T., Soto, I., … Eory, V. (2017). Precision agriculture technologies positively contributing to GHG emissions mitigation, farm productivity and economics. Sustainability, 9(8), 1339.
[5] Basso, B., & Antle, J. (2020). Digital agriculture to design sustainable agricultural systems. Nature Sustainability, 3(4), 254–256.
[6] Benos, L., Tagarakis, A. C., Dolias, G., Berruto, R., Kateris, D., & Bochtis, D. (2021). Machine learning in agriculture: A comprehensive updated review. Sensors, 21(11), 3758.
[7] Bermejo, J. A., Insua, D. R., & Ruiz, M. (2022). Cloud-based decision support systems for precision agriculture: Architectures and trends. Computers and Electronics in Agriculture, 198, 107060.
[8] Camargo, A., & Smith, J. S. (2019). Image pattern classification for the identification of disease causing agents in plants. Computers and Electronics in Agriculture, 66(2), 121–125.
[9] Chlingaryan, A., Sukkarieh, S., & Whelan, B. (2018). Machine learning approaches for crop yield prediction and nitrogen status estimation in precision agriculture: A review. Computers and Electronics in Agriculture, 151, 61–69.
[10] Coleze, J., Huyghe, C., & Doré, T. (2021). Long-term management impacts on agricultural sustainability: A data-driven approach. Agricultural Systems, 190, 103090.
[11] Dutta, R., & Smith, D. (2022). Edge–cloud computing architectures for smart farming: A survey. IEEE Internet of Things Journal, 9(21), 21078–21098.
[12] Elavarasan, D., Vincent, P. M. D., Sharma, V., Zomaya, A. Y., & Srinivasan, K. (2018). Forecasting yield by integrating agrarian factors and machine learning models: A survey. Computers and Electronics in Agriculture, 155, 257–282.
[13] FAO. (2022). The state of food and agriculture 2022: Leveraging automation in agriculture for transforming agrifood systems. Food and Agriculture Organization of the United Nations.
[14] FAO. (2023). Digital agriculture and innovation in agrifood systems: Policy and governance considerations. Food and Agriculture Organization of the United Nations.
[15] Farooq, M. S., Riaz, S., Abid, A., Abid, K., & Naeem, M. A. (2019). A survey on the role of IoT in agriculture for the implementation of smart farming. IEEE Access, 7, 156237–156271.
[16] Ferreira, L. B., da Silva, A. R., & da Costa, C. A. (2021). Cloud-based platforms for precision agriculture: A systematic mapping study. Journal of Cleaner Production, 318, 128545.
[17] Food and Agriculture Organization of the United Nations. (2021). Digital technologies in agriculture and rural areas: Status report. FAO.
[18] Gill, S. S., Tuli, S., Xu, M., Singh, I., Singh, K. V., Lindsay, D., … Buyya, R. (2019). Transformative effects of IoT, blockchain and artificial intelligence on cloud computing: Evolution, vision and challenges. Internet of Things, 8, 100118.
[19] Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press.
[20] Hashim, N., Zaki, N., & Al-Rawas, A. (2021). Precision agriculture using IoT and cloud computing: A review. International Journal of Agricultural and Biological Engineering, 14(4), 1–13.
[21] Hersh, W., Rogers, E., & Safran, C. (2022). Data governance frameworks for responsible AI systems: Concepts and practice. Journal of the American Medical Informatics Association, 29(7), 1240–1248.
[22] Jain, A., Sharma, S., & Verma, K. (2024). AI-enabled cloud platforms for precision agriculture: A systematic review and future research agenda. Computers and Electronics in Agriculture, 216, 108437.
[23] Jiang, Y., Li, C., & Xu, L. (2023). Deep learning for crop disease detection using UAV imagery: A review. Expert Systems with Applications, 213, 118907.
[24] Kamilaris, A., & Prenafeta-Boldú, F. X. (2018). Deep learning in agriculture: A survey. Computers and Electronics in Agriculture, 147, 70–90.
[25] Khanna, A., & Kaur, S. (2019). Evolution of Internet of Things (IoT) and its significant impact in the field of precision agriculture. Computers and Electronics in Agriculture, 157, 218–231.
[26] Kouadio, L., Deo, R. C., Byrareddy, V., Adamowski, J. F., & Mushtaq, S. (2018). Artificial intelligence and machine learning in agriculture: A review. Agricultural Systems, 168, 1–13.
[27] Kshetri, N. (2021). Blockchain and supply chain management in agriculture: Applications, challenges, and opportunities. International Journal of Information Management, 60, 102376.
[28] Liakos, K. G., Busato, P., Moshou, D., Pearson, S., & Bochtis, D. (2018). Machine learning in agriculture: A review. Sensors, 18(8), 2674.
[29] Lobell, D. B., & Burke, M. B. (2010). On the use of statistical models to predict crop yield responses to climate change. Agricultural and Forest Meteorology, 150(11), 1443–1452.
[30] Mishra, P., & Singh, R. K. (2022). Cloud-enabled machine learning frameworks for agricultural big data analytics. Journal of Ambient Intelligence and Humanized Computing, 13(4), 1967–1984.
[31] NIST. (2020). Security and privacy controls for information systems and organizations. NIST Special Publication 800-53 (Rev. 5).
[32] NIST. (2023). Artificial intelligence risk management framework (AI RMF 1.0). National Institute of Standards and Technology.
[33] OECD. (2019). Digital opportunities for better agricultural policies. Organisation for Economic Co-operation and Development.
[34] Paustian, K., Lehmann, J., Ogle, S., Reay, D., Robertson, G. P., & Smith, P. (2016). Climate-smart soils. Nature, 532(7597), 49–57.
[35] Patrício, D. I., & Rieder, R. (2018). Computer vision and artificial intelligence in precision agriculture for grain crops: A systematic review. Computers and Electronics in Agriculture, 153, 69–81.
[36] Ray, D. K., Mueller, N. D., West, P. C., & Foley, J. A. (2013). Yield trends are insufficient to double global crop production by 2050. PLOS ONE, 8(6), e66428.
[37] Wolfert, S., Ge, L., Verdouw, C., & Bogaardt, M. J. (2017). Big data in smart farming: A review. Agricultural Systems, 153, 69–80.
[38] Zhang, C., & Kovacs, J. M. (2012). The application of small unmanned aerial systems for precision agriculture: A review. Precision Agriculture, 13(6), 693–712.
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