Al-driven Precision Agriculture: Advancing Crop Yield Prediction

Author(s)	Sunny Guntuka
Country	United States
Abstract	This article explores the integration of Artificial Intelligence (AI) in precision agriculture, focusing on its role in advancing crop yield prediction and improving overall agricultural productivity. It examines three key areas: satellite imagery analysis, soil health monitoring, and weather data integration. The paper discusses how AI techniques such as Convolutional Neural Networks, IoT sensor networks, and ensemble methods are revolutionizing farming practices. It highlights the technical implementations of these technologies, their applications, and their significant impact on yield optimization, resource efficiency, and decision-making in agriculture. The study emphasizes the crucial role of AI in addressing global food security challenges and improving agricultural resilience to climate change.
Keywords	Keywords: Artificial Intelligence, Precision Agriculture, Crop Yield Prediction, Satellite Imagery Analysis, IoT Sensors
Field	Computer
Published In	Volume 6, Issue 5, September-October 2024
Published On	2024-10-31
Cite This	Al-driven Precision Agriculture: Advancing Crop Yield Prediction - Sunny Guntuka - IJFMR Volume 6, Issue 5, September-October 2024. DOI 10.36948/ijfmr.2024.v06i05.29555
DOI	https://doi.org/10.36948/ijfmr.2024.v06i05.29555
Short DOI	https://doi.org/g8p2vd

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