BIG DATA ANALYTICS FOR SUSTAINABLE AGRICULTURE, ENERGY, AND ENVIRONMENTAL MONITORING
Main Article Content
Abstract
The accelerating convergence of big data technologies with sustainability science represents one of the most consequential technological developments of the twenty-first century, offering transformative potential to address three of humanity's most pressing existential challenges: global food insecurity, the clean energy transition, and ecological degradation. The global agricultural sector must increase food production by an estimated 70% by 2050 to feed a projected population of 9.7 billion, while simultaneously reducing its environmental footprint — a paradoxical imperative that demands unprecedented precision and efficiency in resource utilisation. Concurrently, the energy sector confronts the challenge of decarbonising global electricity systems by integrating variable renewable energy sources — solar and wind — whose output is intermittent, geographically distributed, and fundamentally dependent on atmospheric and environmental conditions that require sophisticated data analytics to forecast and manage. The environmental monitoring domain faces the challenge of characterising and responding to ecosystem changes occurring across spatial scales from microbial communities to continental biomes, and temporal scales from hourly pollution events to decadal climate trends, using sensor networks and satellite observation systems that generate petabytes of data annually.
Downloads
Article Details
Section
References
1. Atzori, L., Iera, A., & Morabito, G. (2010). The internet of things: A survey. Computer Networks, 54(15), 2787–2805.
2. Bauer, P., Thorpe, A., & Brunet, G. (2015). The quiet revolution of numerical weather prediction. Nature, 525(7567), 47–55.
3. Ben-Dor, E., Chabrillat, S., Demattê, J. A. M., Taylor, G. R., Hill, J., Whiting, M. L., & Sommer, S. (2009). Using imaging spectroscopy to study soil properties. Remote Sensing of Environment, 113(S1), S38–S55.
4. Blumenfeld, J., Bhatt, D. L., & Bhatt, D. (2020). Data science applications in sustainable agriculture: A systematic review. Nature Sustainability, 3(4), 274–283.
5. Chen, M., Mao, S., & Liu, Y. (2014). Big data: A survey. Mobile Networks and Applications, 19(2), 171–209.
6. Dutta, R., Morshed, A., Aryal, J., D'Este, C., & Das, A. (2015). Development of an intelligent environmental monitoring system for remote areas. IEEE Internet of Things Journal, 2(6), 569–578.
7. FAO. (2022). The state of food and agriculture 2022: Leveraging automation in agriculture. Food and Agriculture Organization of the United Nations.
8. Gao, J., Ding, M., Krupke, C., & Oikonomou, P. (2020). Precision agriculture using big data and machine learning: A comprehensive review. Computers and Electronics in Agriculture, 168, 105104.
9. Ghamisi, P., Yokoya, N., Li, J., Liao, W., Liu, S., Plaza, J., & Plaza, A. (2017). Advances in hyperspectral image and signal processing. IEEE Geoscience and Remote Sensing Magazine, 5(4), 37–78.
10. IEA. (2023). World energy outlook 2023. International Energy Agency.
11. Jain, A., Singh, P., & Raj, B. (2021). Big data analytics for smart grid energy management: Survey and future directions. Renewable and Sustainable Energy Reviews, 138, 110449.
12. Karpatne, A., Atluri, G., Faghmous, J. H., Steinbach, M., Banerjee, A., Ganguly, A., & Kumar, V. (2017). Theory-guided data science: A new paradigm for scientific discovery from data. IEEE Transactions on Knowledge and Data Engineering, 29(10), 2318–2331.
13. Kussul, N., Lavreniuk, M., Skakun, S., & Shelestov, A. (2017). Deep learning classification of land cover and crop types using remote sensing data. IEEE Geoscience and Remote Sensing Letters, 14(5), 778–782.
14. Li, Z., Liu, F., Yang, W., Peng, S., & Zhou, J. (2022). A survey of convolutional neural networks: Analysis, applications, and prospects. IEEE Transactions on Neural Networks and Learning Systems, 33(12), 6999–7019.
15. Liakos, K. G., Busato, P., Moshou, D., Pearson, S., & Bochtis, D. (2018). Machine learning in agriculture: A review. Sensors, 18(8), 2674.
16. Reichstein, M., Camps-Valls, G., Stevens, B., Jung, M., Denzler, J., Carvalhais, N., & Prabhat. (2019). Deep learning and process understanding for data-driven Earth system science. Nature, 566(7743), 195–204.
17. Shi, X., Chen, Z., Wang, H., Yeung, D. Y., Wong, W. K., & Woo, W. (2015). Convolutional LSTM network: A machine learning approach for precipitation nowcasting. Advances in Neural Information Processing Systems, 28, 802–810.
18. Tansley, S., & Tolle, K. M. (2009). The fourth paradigm: Data-intensive scientific discovery. Microsoft Research.
19. UNEP. (2023). Emissions gap report 2023: Broken record – temperatures hit new highs, yet world fails to cut emissions. United Nations Environment Programme.
20. Wolfert, S., Ge, L., Verdouw, C., & Bogaardt, M. J. (2017). Big data in smart farming: A review. Agricultural Systems, 153, 69–80.