Advancing agriculture through IoT, Big Data, and AI: A review of smart technologies enabling sustainability

被引：1
作者：
Ahmed, Nurzaman [1 ]
Shakoor, Nadia [1 ]
机构：
[1] Donald Danforth Plant Sci Ctr, St Louis, MO 63132 USA
来源：
SMART AGRICULTURAL TECHNOLOGY | 2025年 / 10卷
关键词：
Carbon footprint; IoT; Big Data; AI; Machine learning; Climate change; Food security; Sustainable agriculture; GREENHOUSE-GAS EMISSIONS; CLIMATE-CHANGE; CROP RESIDUE; PRECISION AGRICULTURE; SUPPLY CHAIN; CARBON; MANAGEMENT; METHANE; TRENDS; ENERGY;
D O I：
10.1016/j.atech.2025.100848
中图分类号：
S2 [农业工程];
学科分类号：
0828 ;
摘要：
This review addresses a critical aspect of modern agriculture: integrating the Internet of Things (IoT), Big Data, and Artificial Intelligence (AI) technologies to monitor and mitigate agricultural carbon emissions. We focus on the role of these advanced technologies in enhancing Climate-Smart Agriculture (CSA) and promoting sustainable farming practices. The paper provides a comprehensive review of how IoT, Big Data, and AI can be combined to monitor carbon footprints and support broader sustainability objectives in agriculture. As a key contribution, we propose a feasible, end-to-end system architecture tailored to the assessment of carbon footprint, combining IoT-enabled sensing, real-time data analytics, and predictive modeling. This study highlights the tangible benefits of these technologies through real-world case studies and evaluates their effectiveness in improving emission monitoring, operational efficiency, and environmental compliance. Furthermore, challenges such as data interoperability, device energy efficiency, and implementation costs are critically analyzed, providing insights into existing research gaps. The paper also identifies future directions, including scalable IoT-based carbon markets, Machine Learning (ML) algorithms for precision agriculture, and blockchain solutions for transparent carbon credit trading. The goal is to offer actionable insights into the adoption of cutting-edge technologies to achieve carbon neutrality and environmental sustainability in the agriculture sector.
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页数：19
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共 180 条
  • [11] Akanbi A., Masinde M., A distributed stream processing middleware framework for real-time analysis of heterogeneous data on big data platform: case of environmental monitoring, Sensors, 20, (2020)
  • [12] Al Rakib M.A., Rahman M.M., Uddin S., Khan M.A.H., Rahman M.A., Hossain M.M., Samad M., Abbas F.I., Smart agriculture robot controlling using bluetooth, Eur. J. Eng. Technol. Res., 7, pp. 77-81, (2022)
  • [13] Alam M., Ahmed N., Matam R., Mukherjee M., Barbhuiya F.A., Sdn-based reconfigurable edge network architecture for industrial Internet of things, IEEE Internet Things J., 10, pp. 16494-16503, (2023)
  • [14] Alemu B., The role of forest and soil carbon sequestrations on climate change mitigation, Res. J. Agric. Environ. Manag., 3, pp. 492-505, (2014)
  • [15] Alharbi H.A., Aldossary M., Energy-efficient edge-fog-cloud architecture for iot-based smart agriculture environment, IEEE Access, 9, pp. 110480-110492, (2021)
  • [16] Ali N., Energy requirement for primary and secondary processing of agricultural produce, Agric. Eng. Today, 24, pp. 51-62, (2000)
  • [17] Ali S.M.F., Next-generation etl framework to address the challenges posed by big data, DOLAP, (2018)
  • [18] Alibabaei K., Gaspar P.D., Lima T.M., Crop yield estimation using deep learning based on climate big data and irrigation scheduling, Energies, 14, (2021)
  • [19] Altieri M.A., Nicholls C.I., Henao A., Lana M.A., Agroecology and the design of climate change-resilient farming systems, Agron. Sustain. Dev., 35, pp. 869-890, (2015)
  • [20] Anenberg S.C., Schwartz J., Shindell D., Amann M., Faluvegi G., Klimont Z., Janssens-Maenhout G., Pozzoli L., Van Dingenen R., Vignati E., Et al., Global air quality and health co-benefits of mitigating near-term climate change through methane and black carbon emission controls, Environ. Health Perspect., 120, pp. 831-839, (2012)
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