References

Technological and Programming References

  1. React Native for Cross-Platform Mobile Development

    • Brown, A., & Wilson, J. (2020). Building Mobile Apps with React Native: A Cross-Platform Approach.
      Mobile Development Perspectives, 5(3), 92-105.
      Discusses the benefits of using React Native for cross-platform development, enabling code reuse across iOS and Android and offering a rich ecosystem for mobile app development.

  2. Containerization with Docker

    • Merkel, D. (2014). Docker: Lightweight Linux Containers for Consistent Development and Deployment.
      Linux Journal, 239, 2.
      Provides an introduction to Docker, its architecture, and how it facilitates isolated environments for deploying applications consistently across different platforms.

  3. Programming with Go

    • Donovan, A. A., & Kernighan, B. W. (2015). The Go Programming Language.
      Addison-Wesley Professional.
      A comprehensive guide to programming in Golang, covering core concepts, concurrency patterns, and efficient data handling, making it ideal for building high-performance web applications.

  4. Hyperledger Fabric for Blockchain Applications

    • Androulaki, E., Barger, A., & Bortnikov, V. (2018). Hyperledger Fabric: A Distributed Operating System for Permissioned Blockchains.
      Proceedings of the Thirteenth EuroSys Conference, 30, 1-15.
      Details the architecture of Hyperledger Fabric and its applications in creating private, permissioned blockchains, suitable for secure and reliable enterprise applications.

  5. AWS for Scalable Cloud Solutions

    • Amazon Web Services (2021). AWS Cloud Practitioner Essentials.
      Amazon Web Services, Inc.
      An official guide to understanding the services provided by AWS, covering cloud computing basics, scalability, and deployment strategies for modern applications.

  1. Blockchain in Agriculture

    • Moujahed, B., & Fakih, K. (2022). Blockchain Applications in the Agricultural Sector: A Comprehensive Review.
      Journal of Agricultural Informatics, 13(2), 123-135.
      This paper provides an overview of blockchain applications in agriculture, highlighting how decentralized technology can improve transparency, security, and efficiency across supply chains.

  2. Credit Scoring for Rural Finance

    • Babcock, B., & Lee, N. (2021). Credit Scoring in Rural Finance: Opportunities and Challenges.
      Rural Financial Perspectives, 9(1), 45-60.
      Discusses the impact of credit scoring on rural lending and financial access, with insights on the potential benefits and risks of adopting a data-driven scoring system for farmers.

  3. Green Finance and Sustainable Agriculture

    • UNEP. (2020). Green Finance for Sustainable Agriculture: Aligning Development Finance with Sustainable Farming Practices.
      United Nations Environment Programme.
      A report on the role of green finance in supporting sustainable agriculture, emphasizing the importance of financial products that align with environmental and social goals in agriculture.

  4. Data Security in Financial Technology

    • Mishra, S., & Jha, D. (2023). Blockchain and Data Privacy: Ensuring Security in Financial Transactions.
      Financial Technology Journal, 15(3), 67-89.
      Highlights blockchain’s role in enhancing data security for financial transactions, with particular attention to rural and underserved populations.

  5. Farmer Creditworthiness in Emerging Markets

    • World Bank Group. (2019). Agricultural Finance and Credit Access: A Global Perspective on Emerging Markets.
      World Bank Publications.
      Explores the barriers to credit access for farmers in emerging markets, offering insights into innovative solutions such as scoring systems and blockchain for secure lending.

  6. AI in Real-Time Data Analysis

    • Santos, F., & Kumar, R. (2021). Artificial Intelligence in Real-Time Data for Agriculture: Enhancing Decision-Making and Resource Allocation.
      Agricultural Data Insights, 8(2), 34-47.
      Provides insights into the use of AI for real-time data analysis in agriculture, discussing how it can enhance decision-making and productivity.