Abstract : The agricultural sector is facing unprecedented challenges due to increasing global population, climate change, and resource limitations. Smart farming, which integrates modern technologies like artificial intelligence (AI) and the Internet of Things (IoT) into traditional agricultural practices, offers innovative solutions to address these challenges. This research paper examines the evolving role of AI in smart farming, its applications, benefits, challenges, and potential impact on agricultural efficiency and sustainability. Through a comprehensive review of literature as well as analysis of case-studies, this study emphasizes on how AI-driven systems can revolutionize various aspects of farming, including precision agriculture, crop management, livestock monitoring, disease detection, and resource optimization. Furthermore, it discusses ethical considerations and the necessity for data privacy and security in AI-enabled farming systems. This study underlines the transformative capacity of AI in reshaping the future of agriculture and ensuring food security in a rapidly changing world.
Keywords : Artificial intelligence, Internet of Things, crop management, livestock monitoring
Cite : Rawat, N., & Kumar, V. (2023). Shaping Tomorrow's Agriculture: The Impact of Artificial Intelligence in Smart Farming (1st ed., pp. 171-182). Noble Science Press. https://doi.org/10.52458/9789388996969.nsp2023.eb.ch-20
References :
Banerjee, G., Sarkar, U., Das, S., & Ghosh, I. (2018). Artificial Intelligence in Agriculture: A Literature Survey. International Journal of Scientific Research in Computer Science Applications and Management Studies, 7(3), 1-6.
Food and Agriculture Organization of the United Nations. (2020). 2050: A Third More Mouths to Feed. Retrieved from www.fao.org/news/story/en/item/35571/icode/
Food and Agriculture Organization of the United Nations. (2020). Hunger and Food Insecurity. Retrieved from www.fao.org/hunger/en/
Gertsis, A. C., Galanopoulou - Sendouca, S., Papathanasiou, G., & Symeonakis, A. (1997). Use of GOSSYM-A Cotton Growth Simulation Model to Manage a Low Input Cotton Production System in Greece. In First European Conference for Information Technology in Agriculture.
Kok, J. N., Boers, E. J. W., Kosters, W. A., et al. (2002). Artificial Intelligence: Definition, Trends, Techniques, and Cases. In International Symposium on Southeast Asia Water Environment.
M. Sowmiya, & S. Prabavathi. (2019). Smart Agriculture Using IoT and Cloud Computing. International Journal of Recent Technology and Engineering (IJRTE), 7(6S3), 2277-3878.
Mckinion, J. M., & Lemmon, H. E. (1985). Expert Systems for Agriculture. Computers & Electronics in Agriculture, 1(1), 31-40.
Mekala Srinivasa Rao, Erukala Suresh Babu, P. Siva Naga Raju, & Ilaiah Kavati. (2019). Smart Agriculture: Automated Controlled Monitoring System using Internet of Things. International Journal of Recent Technology and Engineering (IJRTE), 8(3), 2277-3878.
Michael, C., & Fu, E. (2016). Google Deep Mind's AlphaGo. OR/MS Today.
Popa, C. (2011). Adoption of Artificial Intelligence in Agriculture. Bulletin of the University of Agricultural Sciences & Veterinary.
Priyadharsnee, K., & Rathi, S. (2017). An IoT based smart irrigation system. International Journal of Scientific & Engineering Research, 8(5).
R. Mythili, Meenakshi Kumari, Apoorv Tripathi, & Neha Pal. (2019). IoT Based Smart Farm Monitoring System. International Journal of Recent Technology and Engineering (IJRTE), 8(4), 2277-3878.
Senbetu, T. H., Kishore Kumar, K., & Karpura Dheepan, G. M. (2019). IoT Based Irrigation Remote Real-Time Monitoring And Controlling Systems. International Journal of Innovative Technology and Exploring Engineering (IJITEE), 8(7), 2278-3075.
