AI in Agriculture : Revolutionizing Precision Farming and Sustainable Crop Management

Rajesh Basa

doi:10.32628/CSEIT241051040

Authors

Rajesh Basa Indian Institute of Technology, Guwahati, India Author

DOI:

https://doi.org/10.32628/CSEIT241051040

Keywords:

Artificial Intelligence in Agriculture, Precision Farming, Sustainable Crop Management, Yield Prediction, Agricultural Robotics

Abstract

This article explores the transformative impact of Artificial Intelligence (AI) on agriculture, focusing on precision farming and sustainable crop management. It highlights how AI-driven technologies are revolutionizing agricultural practices by optimizing resource utilization, enhancing productivity, and promoting sustainability. The article discusses key applications of AI in agriculture, including crop health monitoring, predictive analytics, resource optimization, smart irrigation systems, variable rate application of chemicals, and pest and disease detection. It presents case studies and statistical data demonstrating the significant improvements in crop yields, resource efficiency, and environmental sustainability achieved through AI adoption. The article also addresses the challenges facing widespread AI implementation in agriculture, such as cost barriers, lack of technical expertise, data quality issues, and infrastructure limitations, while exploring future directions including the integration of AI with robotics and blockchain technology.

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References

J. Lowenberg-DeBoer and B. Erickson, "Setting the Record Straight on Precision Agriculture Adoption," Agronomy Journal, vol. 111, no. 4, pp. 1552-1569, 2019. [Online]. Available: https://doi.org/10.2134/agronj2018.12.0779

Food and Agriculture Organization of the United Nations, "The future of food and agriculture – Trends and challenges," FAO, Rome, 2017. [Online]. Available: http://www.fao.org/3/i6583e/i6583e.pdf

A. Kamilaris and F. X. Prenafeta-Boldú, "Deep learning in agriculture: A survey," Computers and Electronics in Agriculture, vol. 147, pp. 70-90, 2018. [Online]. Available: https://doi.org/10.1016/j.compag.2018.02.016

D. Rolnick et al., "Tackling Climate Change with Machine Learning," ACM Computing Surveys, vol. 55, no. 2, pp. 1-96, 2022. [Online]. Available: https://doi.org/10.1145/3485128

K. Liakos, P. Busato, D. Moshou, S. Pearson, and D. Bochtis, "Machine Learning in Agriculture: A Review," Sensors, vol. 18, no. 8, p. 2674, 2018. [Online]. Available: https://doi.org/10.3390/s18082674

European Commission, Joint Research Centre, "Precision agriculture and the future of farming in Europe," 2021. [Online]. Available: https://www.europarl.europa.eu/RegData/etudes/STUD/2016/581892/EPRS_STU(2016)581892_EN.pdf

J. Chlingaryan, S. Sukkarieh, and B. Whelan, "Machine learning approaches for crop yield prediction and nitrogen status estimation in precision agriculture: A review," Computers and Electronics in Agriculture, vol. 151, pp. 61-69, 2018. [Online]. Available: https://doi.org/10.1016/j.compag.2018.05.012

World Economic Forum, "Innovation with a Purpose: The role of technology innovation in accelerating food systems transformation," 2022. [Online]. Available: https://www3.weforum.org/docs/WEF_Innovation_with_a_Purpose_VF-reduced.pdf

A. Sharma, A. Jain, P. Gupta, and V. Chowdary, "Machine learning applications for precision agriculture: A comprehensive review," Computers and Electronics in Agriculture, vol. 178, p. 105823, 2021. [Online]. Available: https://ieeexplore.ieee.org/document/9311735

J. Lowenberg-DeBoer et al., "Economics of robots and automation in field crop production," Precision Agriculture, vol. 22, pp. 278-299, 2021. [Online]. Available: https://link.springer.com/article/10.1007/s11119-019-09667-5