Adaptive ML Analytics for AgriTech Business Intelligence on Azure
Keywords:
Adaptive analytics, agri-tech, business-intelligence, cloud-based machine-learning, data-lake, Microsoft-Azure, predictive-analytics, risk-assessment, scalable-architecture, supporting-systemsAbstract
Adaptive Predictive Analytics Pipelines on Microsoft Azure: A Machine Learning Approach for Dynamic Business Intelligence in AgriTech Systems
Business intelligence systems, including predictive analytics repositories, are essential for effective decision support in agriculture and agri-tech. Yet, many existing solutions are unable to adapt to changing conditions. Recent advances in machine learning (ML) provide opportunities to alleviate some of the associated problems. On the Microsoft Azure platform, a set of services built around data lakes and ML models enables the implementation of adaptive predictive analytics pipelines—data-processing assemblies that optimize performance by continuously learning from available data. These pipelines can react to changes, such as the emergence of new patterns, by automatically altering feature engineering processes, retraining models using updated sources, or providing predictions with new data representations, characteristics, or horizons.
The architecture and implementation of these adaptive predictive analytics pipelines are described in detail, with descriptions of the pipelines’ general structure and operation, along with a case study focused on the Agri-Tech Risk Mitigation Solutions (AT-RMS) predictive analytics service. AT-RMS assists executive-level actors in the agriculture and insurance sectors with strategic-level decision-making, and the implementation leverages Azure to support infrastructure-as-code and operational governance.
References
1. Alfred, R., Obit, J. H., Chin, C. P.-Y., Haviluddin, H., & Lim, Y. (2021). Towards paddy rice smart farming: A review on big data, machine learning, and rice production tasks. IEEE Access, 9, 50358–50380.
2. Hassan, S. I., Alam, M. M., Illahi, U., Al Ghamdi, M. A., Almotiri, S. H., & Su’ud, M. M. (2021). A systematic review on monitoring and advanced control strategies in smart agriculture. IEEE Access, 9, 32517–32548.
3. Kok, Z. H., Shariff, A. R. M., Alfatni, M. S. M., & Khairunniza-Bejo, S. (2021). Support vector machine in precision agriculture: A review. Computers and Electronics in Agriculture, 191, 106546.
4. Mattaparthi, R. (2022). Engineering Predictive Industrial Systems Through IoT-Driven Asset Monitoring and Machine Learning Prognostics. International Journal of Future Innovative Science and Technology (IJFIST), 5(1), 7790.
5. Li, Y., & Yang, J. (2021). Meta-learning baselines and database for few-shot classification in agriculture. Computers and Electronics in Agriculture, 182, 106055.
6. Rodríguez, J. P., Montoya-Munoz, A. I., Rodriguez-Pabon, C., Hoyos, J., & Corrales, J. C. (2021). IoT-Agro: A smart farming system to Colombian coffee farms. Computers and Electronics in Agriculture, 190, 106442.
7. Khanramaki, M., Askari Asli-Ardeh, E., & Kozegar, E. (2021). Citrus pests classification using an ensemble of deep learning models. Computers and Electronics in Agriculture, 186, 106192.
8. Wang, F., Wang, R., Xie, C., Zhang, J., Li, R., & Liu, L. (2021). Convolutional neural network based automatic pest monitoring system using hand-held mobile image analysis towards non-site-specific wild environment. Computers and Electronics in Agriculture, 187, 106268.
9. Kolla, S. H., & Loganathan, R. (2023). Cloud-Native Deep Learning Architectures For Secure Generative AI Deployment In Enterprise Workflow Platforms. Journal of International Crisis and Risk Communication Research, 603-618.
10. Gui, P., Dang, W., Zhu, F., & Zhao, Q. (2021). Towards automatic field plant disease recognition. Computers and Electronics in Agriculture, 191, 106523.
11. Liu, J., & Wang, X. (2021). Plant diseases and pests detection based on deep learning: A review. Plant Methods, 17, 22.
12. Bansal, P., Kumar, R., & Kumar, S. (2021). Disease detection in apple leaves using deep convolutional neural network. Agriculture, 11(7), 617.
13. Fuentes, A., Yoon, S., Lee, M. H., & Park, D. S. (2021). Improving accuracy of tomato plant disease diagnosis based on deep learning with explicit control of hidden classes. Frontiers in Plant Science, 12, 682230.
14. Syed, S. (2023). Shaping The Future Of Large-Scale Vehicle Manufacturing: Planet 2050 Initiatives And The Role Of Predictive Analytics. Nanotechnology Perceptions, 19(3), 103-116.
15. Niloofar, P., Francis, D. P., Lazarova-Molnar, S., Vulpe, A., Vochin, M.-C., Suciu, G., Balanescu, M., Anestis, V., & Bartzanas, T. (2021). Data-driven decision support in livestock farming for improved animal health, welfare and greenhouse gas emissions: Overview and challenges. Computers and Electronics in Agriculture, 190, 106406.
16. Kong, J., Wang, H., Wang, X., Jin, X., Fang, X., & Lin, S. (2021). Multi-stream hybrid architecture based on cross-level fusion strategy for fine-grained crop species recognition in precision agriculture. Computers and Electronics in Agriculture, 185, 106134.
17. Abdullah, N., Durani, N. A. B., Shari, M. F. B., Siong, K. S., Hau, V. K. W., Siong, W. N., & Ahmad, K. A. (2021). Towards smart agriculture monitoring using fuzzy systems. IEEE Access, 9.
18. Despoudi, S., Sivarajah, U., Spanaki, K., & Irani, Z. (2021). Disruptive technologies in agricultural operations: A systematic review of AI-driven AgriTech research. Annals of Operations Research, 308, 491–524.
19. Condran, S., Bewong, M., Islam, M. Z., Maphosa, L., & Zheng, L. (2022). Machine learning in precision agriculture: A survey on trends, applications and evaluations over two decades. IEEE Access, 10, 73786–73803.
20. Kolla, S. K., & Reddy, V. A. R. (2023). Deep Learning Architectures For Multimodal Medical Data Integration. South Eastern European Journal of Public Health, 248–260.
21. Shaikh, T. A., Rasool, T., & Lone, F. R. (2022). Towards leveraging the role of machine learning and artificial intelligence in precision agriculture and smart farming. Computers and Electronics in Agriculture, 198, 107119.
22. Ghaffarian, S., van der Voort, M., Valente, J., Tekinerdogan, B., & de Mey, Y. (2022). Machine learning-based farm risk management: A systematic mapping review. Computers and Electronics in Agriculture, 192, 106631.
23. Rahaman, M. M., & Azharuddin, M. (2022). Wireless sensor networks in agriculture through machine learning: A survey. Computers and Electronics in Agriculture, 197, 106928.
24. Ruan, G., Li, X., Yuan, F., Cammarano, D., Ata-UI-Karim, S. T., Liu, X., Tian, Y., Zhu, Y., Cao, W., & Cao, Q. (2022). Improving wheat yield prediction integrating proximal sensing and weather data with machine learning. Computers and Electronics in Agriculture, 195, 106852.
25. Maestrini, B., Mimić, G., van Oort, P. A. J., Jindo, K., Brdar, S., Athanasiadis, I. N., & van Evert, F. K. (2022). Mixing process-based and data-driven approaches in yield prediction. European Journal of Agronomy, 139, 126569.
26. Iniyan, S., Varma, V. A., & Naidu, C. T. (2023). Crop yield prediction using machine learning techniques. Advances in Engineering Software, 175, 103326.
27. Godara, S., Toshniwal, D., Parsad, R., Bana, R. S., Singh, D., Bedi, J., Jhajhria, A., Dabas, J. P. S., & Marwaha, S. (2022). AgriMine: A deep learning integrated spatio-temporal analytics framework for diagnosing nationwide agricultural issues using farmers’ helpline data. Computers and Electronics in Agriculture, 201, 107308.
28. Diallo, A., et al. (2022). Agricultural decision system based on advanced machine learning models for yield prediction: Case of East African countries. Smart Agricultural Technology, 2, 100048.
29. Diallo, A., et al. (2022). Crops yield prediction based on machine learning models: Case of West African countries. Smart Agricultural Technology, 2, 100049.
30. Mesías-Ruiz, G. A., Pérez-Ortiz, M., Dorado, J., de Castro, A. I., & Peña, J. M. (2023). Boosting precision crop protection towards agriculture 5.0 via machine learning and emerging technologies: A contextual review. Frontiers in Plant Science, 14, 1143326.
31. Li, J., Chen, D., Qi, X., Li, Z., Huang, Y., Morris, D., & Tan, X. (2023). Label-efficient learning in agriculture: A comprehensive review. Computers and Electronics in Agriculture, 211, 108412.
32. Hu, T., Zhang, X., Bohrer, G., Liu, Y., Zhou, Y., Martin, J., Li, Y., & Zhao, K. (2023). Crop yield prediction via explainable AI and interpretable machine learning: Dangers of black box models for evaluating climate change impacts on crop yield. Agricultural and Forest Meteorology, 336, 109458.
33. Priyatikanto, R., Lu, Y., Dash, J., & Sheffield, J. (2023). Improving generalisability and transferability of machine-learning-based maize yield prediction model through domain adaptation. Agricultural and Forest Meteorology, 341, 109652.
34. Patil, S. B., Kulkarni, R. B., Kharade, P. A., & Patil, S. S. (2023). Review of machine learning model applications in precision agriculture. In Proceedings of the International Conference on Applications of Machine Intelligence and Data Analytics (ICAMIDA 2022) (pp. 916–930). Atlantis Press.
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Copyright (c) 2024 Christopher Wilson (Author)
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