SMART AGRICULTURE: EMPOWERING FARMERS THROUGH TECHNOLOGY

Main Article Content

Nirupama M

Abstract

Smart agriculture is revolutionizing modern farming by integrating digital technologies to improve productivity, sustainability, and resilience. Innovations such as the Internet of Things (IoT), Artificial Intelligence (AI), robotics, remote sensing, mobile applications, and renewable energy systems are enabling real-time decision-making, precision farming, and efficient resource management. These tools especially benefit small and marginal farmers by enhancing access to timely information, reducing manual labour dependency, and increasing profitability through data-driven practices. Global research and case studies reveal that smart farming effectively addresses key agricultural challenges, including food security, climate change, and land degradation. IoT-enabled sensors monitor soil conditions, water levels, and weather patterns, while AI aids in early detection of pests and diseases, crop monitoring, and yield prediction. Energy-efficient irrigation, app-controlled machinery, and solar-powered tools provide sustainable solutions adaptable even in remote areas. However, the adoption of smart agriculture is hindered by several barriers. These include high initial investment costs, limited digital infrastructure in rural regions, low digital literacy among farmers, and fragmented access to technology. Additionally, concerns about data privacy, cyber security, and the lack of strong linkages between technology providers and farming communities further constrain implementation, particularly in developing nations. This paper synthesizes recent academic findings and real-world practices to explore how smart agriculture can bridge technological divides and empower farming communities. It emphasizes the need for collaborative efforts among governments, private sector actors, research institutions, and grassroots organizations. Key recommendations include promoting inclusive policy frameworks, strengthening rural digital infrastructure, offering targeted training and capacity-building programs, and fostering farmer-centric innovation. By addressing these gaps, smart agriculture can become a transformative tool that not only boosts agricultural output but also ensures equitable growth and sustainability for future generations.

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How to Cite

SMART AGRICULTURE: EMPOWERING FARMERS THROUGH TECHNOLOGY. (2026). International Journal of Fundamental and Applied Sciences (IJFAS), 15(1), 121-127. https://doi.org/10.59415/ijfas.374

References

1. Arulmanikandan, M., Kumar, S., & Divya, K. (2024). Smart mobile applications in precision farming: Bridging the gap in rural India. International Journal of Agricultural Innovation, 12(1), 45–58.

2. Assimakopoulos, C., Tsoukalas, A., & Georgiou, K. (2024). Digital agriculture and infrastructure gaps in developing countries: A systems perspective. Journal of Agricultural Informatics, 15(2), 89–104.

3. Food and Agriculture Organization (FAO). (2017). The future of food and agriculture – Trends and challenges. Food and Agriculture Organization of the United Nations.

4. Jerhamre, E., Lundberg, M., & Bergström, P. (2022). Artificial intelligence in livestock farming: Case studies from Sweden. Agricultural Systems and Robotics, 8(4), 220–234.

5. Mandal, B., Roy, P., & Sen, A. (2024). Climate-smart agriculture in India: Policies, practices, and challenges. South Asian Journal of Agricultural Policy, 10(3), 125–139.

6. Mohamed, A., Karim, S., & Al-Shaibani, N. (2021). Emerging technologies in precision agriculture: Data ownership and security perspectives. Journal of Precision Farming, 7(2), 95–109.

7. Rehman, S., Al Dhaheri, M., & Khan, M. (2024). Smart agriculture in arid zones: Lessons from the UAE’s IoT-based greenhouse systems. International Journal of Sustainable Agriculture, 11(1), 30–47.

8. Thongnim, N., Kittipanya-Ngam, P., & Buranatrakoon, S. (2023). Automation and robotics in Southeast Asian smart farms: Challenges and opportunities. Asian Agricultural Review, 9(1), 67–83.

9. Klerkx, L., Jakku, E., & Labarthe, P. (2019). A review of social science on digital agriculture, smart farming, and agriculture 4.0: New contributions and a future research agenda. NJAS - Wageningen Journal of Life Sciences, 90–91. DOI: https://doi.org/10.1016/j.njas.2019.100315

10. Wolfert, S., Ge, L., Verdouw, C., & Bogaardt, M. J. (2017). Big data in smart farming: A review. Agricultural Systems, 153, 69–80. DOI: https://doi.org/10.1016/j.agsy.2017.01.023

11. Rose, D. C., et al. (2021). Agricultural digitalization and its implications for the future of farming: A review of current literature. Sustainability, 13(11), 5913.

12. Bhaskar, P., & Upadhyay, A. (2023). Role of IoT and AI in sustainable agriculture: A case study of Indian smart villages. International Journal of Rural Development and Technology, 6(2), 110–125.

13. Yadav, R. S., & Singh, P. (2024). Integration of machine learning and remote sensing for crop yield prediction in India. Journal of AgriTech Research, 10(1), 52–66.

14. Sundaramoorthy, V., & Rani, S. (2023). Precision irrigation and smart water management using IoT in Indian agriculture. Journal of Water and Soil Conservation Research, 8(3), 144–159.

15. Chandrasekaran, K., & Mehta, R. (2022). Blockchain for agricultural supply chains: Enhancing traceability and farmer empowerment. Agricultural Informatics Review, 7(2), 35–50.

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