Big Data Analytics for Precision Irrigation in Smart Agriculture
DOI:
https://doi.org/10.15662/IJRAI.2020.0306004Keywords:
Precision Agriculture, Irrigation Management Optimization, Big Data Analytics in Agriculture, Water Resource Efficiency, Data-Driven Irrigation Strategies, Spatial–Temporal Irrigation Modeling, Machine Learning for Crop Productivity, Integrated Statistical and Physical Models, Sustainable Water Management, Smart Irrigation Systems, Agricultural Decision Support Systems, Multi-Source Agricultural Data Integration, Deficit Irrigation Strategies, Climate-Adaptive Irrigation Planning, Agri-Tech Innovation and Governance.Abstract
Precision agriculture is an approach capable of increasing the productivity of crops with the most efficient use of inputs for the production. A crucial part of precision agriculture is irrigation management which allows a more precise use of water and other inputs, producing a higher productivity and quality yield. The difficulty is to find the best irrigation strategies that guarantee the productivity of crops and minimizes water consumption. The use of big data analytics provides a powerful new approach to optimize irrigation management in precision agriculture capable of integrating large volumes of multi-source information into more accurate decision-making models in the irrigation management process. Such information can be acquired and analyzed from different sources and based on different techniques, including a combination of machine learning, statistical and physical models. Such techniques allow decision-making about irrigation directly or indirectly, identifying, among other aspects, the best spatial and temporal irrigation strategies. Nevertheless, the adoption of big data for irrigation is still incipient facing a large number of challenges related to people and organizations, technical aspects of data acquisition, quality and security, and also ethical issues.Irrigation is of paramount importance for irrigation-deficient countries. Therefore, given the scarcity of water resources and the adverse effects of irrigation on the environment caused by poor irrigation management, the development of innovative and efficient management approaches for irrigation is of utmost importance. New paradigm decisions, based on newer reservoirs and not only on historical series of rainfall and temperature, policy rules that go beyond the economic aspect of the irrigation decision, and new technology to run large-scale irrigation systems and to open new regions with deficit irrigation, are the requirements for the advancement of irrigation science in those countries with the greatest irrigation potential in the world.
References
[1] Wolfert, S., Ge, L., Verdouw, C., & Bogaardt, M. J. (2017). Big data in smart farming. Agricultural Systems, 153, 69–80.
[2] Zhang, Y., Wang, G., Du, J., & Li, X. (2019). Precision irrigation management using IoT and big data. Computers and Electronics in Agriculture, 162, 435–448.
[3] Liakos, K. G., Busato, P., Moshou, D., Pearson, S., & Bochtis, D. (2018). Machine learning in agriculture. Sensors, 18(8), 2674.
[4] Davuluri, P. N. (2020). Event-Driven Architectures for Real-Time Regulatory Monitoring in Global Banking.
[5] Bongiovanni, R., & Lowenberg-DeBoer, J. (2004). Precision agriculture and sustainability. Precision Agriculture, 5(4), 359–387.
[6] McBratney, A., Whelan, B., Ancev, T., & Bouma, J. (2005). Future directions of precision agriculture. Precision Agriculture, 6(1), 7–23.
[7] Gottimukkala, V. R. R. (2020). Energy-Efficient Design Patterns for Large-Scale Banking Applications Deployed on AWS Cloud. power, 9(12).
[8] Pierce, F. J., & Nowak, P. (1999). Aspects of precision agriculture. Advances in Agronomy, 67, 1–85.
[9] Gebbers, R., & Adamchuk, V. I. (2010). Precision agriculture and food security. Science, 327(5967), 828–831.
[10] Moran, M. S., Inoue, Y., & Barnes, E. M. (1997). Opportunities and limitations for image-based remote sensing in precision crop management. Remote Sensing of Environment, 61(3), 319–346.
[11] Singireddy, S., & Adusupalli, B. (2019). Cloud Security Challenges in Modernizing Insurance Operations with Multi-Tenant Architectures. International Journal of Engineering and Computer Science, 8, 12.
[12] Allen, R. G., Pereira, L. S., Raes, D., & Smith, M. (1998). Crop evapotranspiration. FAO Irrigation and Drainage Paper 56.
[13] Pereira, L. S., Cordery, I., & Iacovides, I. (2012). Coping with water scarcity. Springer.
[14] Fereres, E., & Soriano, M. A. (2007). Deficit irrigation. Journal of Experimental Botany, 58(2), 147–159.
[15] Rongali, S. K. (2020). Predictive Modeling and Machine Learning Frameworks for Early Disease Detection in Healthcare Data Systems. Current Research in Public Health, 1(1), 1-15.
[16] Bastiaanssen, W. G. M., et al. (1998). SEBAL model. Journal of Hydrology, 212–213, 198–212.
[17] Anderson, M. C., et al. (2007). ALEXI model. Remote Sensing of Environment, 112(1), 59–74.
[18] Inala, R. Designing Scalable Technology Architectures for Customer Data in Group Insurance and Investment Platforms.
[19] Wagner, W., et al. (2007). Soil moisture from remote sensing. Remote Sensing of Environment, 101(3), 366–378.
[20] Dorigo, W., et al. (2017). ESA CCI soil moisture. Remote Sensing of Environment, 203, 185–215.
[21] Verhoef, A., & Egea, G. (2014). Modeling plant transpiration. Agricultural and Forest Meteorology, 182–183, 126–140.
[22] Jones, J. W., et al. (2003). The DSSAT cropping system model. European Journal of Agronomy, 18(3–4), 235–265.
[23] Pandiri, L., Singireddy, S., & Adusupalli, B. (2020). Digital Transformation of Underwriting Processes through Automation and Data Integration. Global Research Development (GRD) ISSN, 2455-5703.
[24] Lobell, D. B., et al. (2015). Remote sensing of yield. Annual Review of Environment and Resources, 40, 419–441.
[25] Schaap, M. G., et al. (2001). Rosetta pedotransfer functions. Soil Science Society of America Journal, 65(3), 659–668.
[26] Hillel, D. (1998). Environmental soil physics. Academic Press.
[27] Varri, D. B. S. (2020). Automated Vulnerability Detection and Remediation Framework for Enterprise Databases. Available at SSRN 5774865.
[28] Ojha, T., Misra, S., & Raghuwanshi, N. S. (2015). Wireless sensor networks for agriculture. Computers and Electronics in Agriculture, 118, 66–84.
[29] Kim, Y., Evans, R. G., & Iversen, W. M. (2008). Remote sensing and control of irrigation. Computers and Electronics in Agriculture, 61(2), 133–149.
[30] Vellidis, G., et al. (2016). Cloud-based irrigation scheduling. Computers and Electronics in Agriculture, 124, 1–12.
[31] Chávez, J. L., et al. (2010). Automated irrigation scheduling. Agricultural Water Management, 97(10), 1512–1520.
[32] Evett, S. R., et al. (2012). Soil water sensors. Vadose Zone Journal, 11(4).
[33] Amistapuram, K. Energy-Efficient System Design for High-Volume Insurance Applications in Cloud-Native Environments. International Journal of Innovative Research in Electrical, Electronics, Instrumentation and Control Engineering (IJIREEICE), DOI, 10.
[34] Khosla, R., et al. (2008). Precision nutrient management. Agronomy Journal, 100(6), 1528–1538.
[35] Zhang, N., Wang, M., & Wang, N. (2002). Precision agriculture—a worldwide overview. Computers and Electronics in Agriculture, 36(2–3), 113–132.
[36] McCready, M. S., Dukes, M. D., & Miller, G. L. (2009). Urban irrigation scheduling. Agricultural Water Management, 96(1), 67–75.
[37] Inala, R. (2020). Building Foundational Data Products for Financial Services: A MDM-Based Approach to Customer, and Product Data Integration. Universal Journal of Finance and Economics, 1(1), 1-18.
[38] Tilling, A. K., et al. (2007). Remote sensing of nitrogen. Precision Agriculture, 8(3), 177–190.
[39] Daccache, A., et al. (2014). Climate change and irrigation demand. Agricultural Water Management, 142, 21–29.
[40] Fischer, R. A., et al. (2014). Crop yield potential. Field Crops Research, 164, 3–11.
[41] Vadisetty, R., Polamarasetti, A., Guntupalli, R., Rongali, S. K., Raghunath, V., Jyothi, V. K., & Kudithipudi, K. (2020). Generative AI for Cloud Infrastructure Automation. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 1(3), 15-20.
[42] Molden, D., et al. (2010). Improving agricultural water productivity. Agricultural Water Management, 97(4), 528–535.
[43] FAO. (2017). Water for sustainable food and agriculture. FAO.
[44] Zhang, C., & Kovacs, J. M. (2012). UAV remote sensing. Precision Agriculture, 13(6), 693–712.
[45] Hunt, E. R., et al. (2010). UAVs for crop monitoring. Precision Agriculture, 11(1), 1–13.
[46] Dorigo, W., et al. (2015). Global soil moisture products. Hydrology and Earth System Sciences, 19, 1783–1803.
[47] Sishodia, R. P., Ray, R. L., & Singh, S. K. (2020). Applications of remote sensing. Environmental Monitoring and Assessment, 192, 1–17.
[48] Kamilaris, A., et al. (2018). Agri-food AI review. Computers and Electronics in Agriculture, 147, 70–90.
[49] Wolfert, S., et al. (2014). Big data in agriculture. NJAS Wageningen Journal of Life Sciences, 70–71, 1–5.
[50] Raghupathi, W., & Raghupathi, V. (2014). Big data analytics in healthcare. Health Information Science and Systems, 2, 3.
[51] Davuluri, P. N. (2020). Improving Data Quality and Lineage in Regulated Financial Data Platforms. Finance and Economics, 1(1), 1-14.
[52] McAfee, A., & Brynjolfsson, E. (2012). Big data. Harvard Business Review, 90(10), 60–68.
[53] Chen, M., Mao, S., & Liu, Y. (2014). Big data survey. Mobile Networks and Applications, 19(2), 171–209.
[54] Segireddy, A. R. (2020). Cloud Migration Strategies for High-Volume Financial Messaging Systems.
[55] Han, J., Pei, J., & Kamber, M. (2011). Data mining (3rd ed.). Morgan Kaufmann.
[56] Bishop, C. M. (2006). Pattern recognition and machine learning. Springer.
[57] Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning. Springer.
[58] Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32.
[59] Vapnik, V. N. (1998). Statistical learning theory. Wiley.
[60] Quinlan, J. R. (1993). C4.5. Morgan Kaufmann.
[61] Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273–297.
[62] Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press.
[63] LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521, 436–444.
[64] Krizhevsky, A., et al. (2012). ImageNet classification. NeurIPS, 1097–1105.
[65] Schmidhuber, J. (2015). Deep learning overview. Neural Networks, 61, 85–117.
[66] Adusupalli, B., Pandiri, L., & Singireddy, S. (2019). DevOps Enablement in Legacy Insurance Infrastructure for Agile Policy and Claims Deployment. risk, 7(12).
[67] Dietterich, T. G. (2000). Ensemble methods. Multiple Classifier Systems, 1–15.
[68] Witten, I. H., Frank, E., & Hall, M. A. (2011). Data mining. Morgan Kaufmann.
[69] Domingos, P. (2012). A few useful things to know about machine learning. Communications of the ACM, 55(10), 78–87.
[70] Mitchell, T. M. (1997). Machine learning. McGraw-Hill.
[71] Russell, S., & Norvig, P. (2010). Artificial intelligence (3rd ed.). Pearson.
[72] Provost, F., & Fawcett, T. (2013). Data science for business. O’Reilly.
[73] Shmueli, G., et al. (2017). Data mining for business analytics. Wiley.
