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Optimizing Agriculture with Intelligent Solutions
Abstract
This paper explores the optimization of agriculture through intelligent solutions enabled by AI-driven engineering. Through case studies and research insights, it investigates how artificial intelligence is transforming traditional agricultural practices, including crop monitoring, pest management, and yield prediction. The study highlights the application of AI techniques such as remote sensing, machine learning, and data analytics in improving efficiency, sustainability, and profitability in farming operations. Additionally, it discusses the integration of AI with precision agriculture technologies, drones, and sensor networks to enable precision irrigation, autonomous farming, and soil health monitoring. The paper also addresses challenges such as data privacy, technology adoption, and rural infrastructure in the deployment of AI-driven engineering solutions in agriculture. It emphasizes the importance of farmer education, policy support, and research collaboration in leveraging AI's potential to address food security and environmental sustainability challenges in the agricultural sector.
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- Gao, L., Turner, D., Huete, A., & Dechant, B. (2019). A novel approach for vegetation fraction estimation from NDVI for the Australian continent using MODIS imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 148, 1-14.
- Gong, L., Qiao, X., Shi, Z., & Wu, W. (2019). Smart agriculture: An intelligent perception, wireless communication and IoT solution. IEEE Internet of Things Journal, 7(5), 3662-3670.
- Kumar, M., & Kaur, G. (2019). Artificial intelligence and its applications in agriculture. In Artificial Intelligence and Emerging Technologies (pp. 117-128). Springer, Singapore.
- Nigon, T., Benini, L., & Preusser, T. (2018). Opportunities and challenges in embedded machine learning for the Internet of Things. IEEE Internet of Things Journal, 6(3), 5889-5901.
- Padhee, A. K., Chakraborty, D., & Mohanty, R. P. (2019). Deep learning for plant stress phenotyping: Trends and future perspectives. Trends in Plant Science, 24(10), 906-939.
- Singh, A., Ganapathysubramanian, B., Singh, A. K., & Sarkar, S. (2018). Machine learning for high-throughput stress phenotyping in plants. Trends in Plant Science, 23(11), 882-890.
- Zhang, J., Zhang, J., Zhang, Q., Zhu, J., Xu, G., & Cheng, S. (2019). Real-time crop yield prediction in paddy fields with UAV remote sensing. IEEE Transactions on Geoscience and Remote Sensing, 57(11), 9125-9138.