Global Journal of Science Frontier Research, A: Physics and Space Science, Volume 23 Issue 1

2. Anil, Robin & Capan, (2020). Apache Mahout: Machine Learning on Distributed Dataflow Systems. Journal of Machine Learning Research. 21. 1-6. 3. Apache Hadoop Documentation. (2014). http:// hadoop.apache.org/ 4. Asakiewicz, Christopher, Cognitive Analytics for Making Better Evidence-Based Decisions (August 24, 2016). Available at SSRN: https://ssrn.com/ abstract=2965767 or http://dx.doi.org/10.2139/ssrn. 2965767 5. AWS. Mantle Labs Improves Global Food Supply Chain Financing on AWS, https://aws.amazon.com/ solutions/case-studies/mantle-labs/ 6. Brown, L.R. (2013, July 09). Peak Water: What Happens When the Wells Go Dry? Earth Policy Institute . http://www.earth-policy.org/plan_b_ updates/2013/update115 7. CropX Agronomic Farm Management System, https://cropx.com 8. Definitions.net. STANDS4 LLC.(2022). Knowledge agriculture. https://www.definitions.net/definition/ knowledge+agriculture 9. Digital Farming, Smart Solutions for a Sustainable Future, Bayer . https://www.bayer.com/en/agriculture /digital-farming. 10. European Commission. (03 June 2021 - 03 September 2021). Data Act & amended rules on the legal protection of databases, https://ec.europa.eu/ info/law/better-regulation/have-your-say/initiatives/ 13045-Data-Act-including-the-review-of-the-Direc- tive-96-9-EC-on-the-legal-protection-of-databases- /public-consultation_en 11. Food and Agriculture Organization of United Nations (FAO). (2018). Guidelines for the measure- ment of productivity and efficiency in agriculture. https://www.fao.org/3/ca6395en/ca6395en.pdf 12. Food and Agriculture Organization of United Nations (FAO). (2019). Global Symposium on Soil Erosion (GSER19), https://www.fao.org/about/ meetings/soil-erosion-symposium/key- messages/en/ 13. Food and Agriculture Organization of UN. (2022). The State of Food Security and Nutrition in the World 2022. https://www.fao.org/documents/card/ en/c/cc0639en 14. Fuglie, K., (2015). Accounting for growth in global agriculture. Bio-based and Applied Economics. 4(3), 201–234 15. GeoPard Agriculture, https://geopard.tech 16. GODAN. Global Partnership for Sustainable Development. Global Open Data for Agriculture and Nutrition. https://www.data4sdgs.org/partner/global- open-data-agriculture-and-nutrition 17. Gonzalez-Sanchez, Alberto., Frausto-Solis, Juan., Ojeda-Bustamante, W., (2014) Predictive ability of machine learning methods for massive crop yield prediction. Span J Agric Res. 12(2), 313–28. 18. Google Cloud: Helping farmers to feed the planet with cutting-edge drone imaging and AI, https://cloud.google.com/customers/taranis 19. Hiba. A., Abu-Alsaad, et. al. (2019). Retailing Analysis Using Hadoop and Apache Hive. https:// ijssst.info/Vol-20/No-1/paper8.pdf 20. IaaS vs. PaaS vs. SaaS, IBM Cloud Education, https://www.ibm.com/in-en/cloud/learn/iaas-paas- saas 21. Kannan P., (2015). Beyond hadoopma preduce apachetez and apache spark. San Jose State University. http://www.sjsu.edu/people/robert.chun/ courses/CS259Fall2013/s3/F.pdf 22. Li. Xue, G. Liu, J. Parfitt, X. Liu, E. Van Herpen, Å. Stenmarck, C. O’Connor, K. Östergren, S. Cheng,.(2017). Missing food, missing data? A critical review of global food losses and food waste data, Environ. Sci. Technol., 51 (2017), 6618-6633, https://doi.org/10.1021/acs.est.7b00401. 23. Majumdar, J., Naraseeyappa, S. & Ankalaki, S. (2017). Analysis of agriculture data using data mining techniques: application of big data. J Big Data. 4, 20. https://doi.org/10.1186/s40537-017- 0077-4. 24. Mantle Labs, https://www.mantle-labs.com 25. McCue, L. (2020, December 16). Supply chain analytics: What it is and why it matters. Oracle NetSuite. https://www.netsuite.com/portal/resource/articles/er p/supply-chain-analytics.shtml 26. Mukherjee Sanjeeb. (Tuesday, January 17, 2023). PM Narendra Modi withdraws three farm laws, asks farmers to go home. Business Standard . https://www.business-standard.com/article/current- affairs/pm-narendra-modi-withdraws-three-farm- laws-asks-farmers-to-go-home- 121111900296_1.html 27. Pantazi, X.E., et al. (2016). Wheat Yield Prediction Using Machine Learning and Advanced Sensing Techniques. Computers and electronics in agriculture, V.121, 57-65. https://doi.org/10.1016/ j.compag.2015.11.018 28. Project FarmVibes. Democratizing digital tools for sustainable agriculture, https://www.microsoft.com/ en-us/research/project/project-farmvibes/ 29. ScienceDaily, (10 January 2023). Project aims to expand language technologies: Research could bring automatic speech recognition to 2,000. Carnegie Mellon University. https://www.science - daily.com/releases/2023/01/230110151049.htm 30. Seema Maitrey, & C.K. Jha. (2015). MapReduce: Simplified Data Analysis of Big Data, Procedia Computer Science, Volume 57, 563-571, ISSN 1877-0509, https://doi.org/10.1016/j.procs.2015. 07.392. 31. Spark Core Programming. https://www.tutorials point.com/apache_spark/apache_spark_rdd.htm © 2023 Global Journals 1 Year 2023 26 Global Journal of Science Frontier Research Volume XXIII Issue ersion I VI ( A ) Data-Driven Knowledge Agriculture: A Paradigm Shift for Enhancing Farm Productivity & Global Food Security