AI-Driven Predictive Analytics in Precision Agriculture
DOI:
https://doi.org/10.63345/sjaibt.v2.i3.102Keywords:
Predictive Analytics, Machine Learning, Crop Yield Prediction, Irrigation Optimization, Smart FarmingAbstract
The rapid advancement of artificial intelligence (AI) and big data analytics has revolutionized agricultural practices by enabling precise, data-driven decision-making. Precision agriculture, a paradigm that leverages technology to optimize farming processes, increasingly relies on AI-driven predictive analytics to address challenges such as food security, resource efficiency, and climate variability. This manuscript critically examines the role of AI-driven predictive analytics in enhancing precision agriculture, with a particular focus on yield forecasting, soil health monitoring, pest and disease prediction, irrigation optimization, and supply chain management. It explores a comprehensive body of literature that illustrates how machine learning (ML), deep learning (DL), and predictive models have been employed to reduce uncertainty in farming outcomes while maximizing productivity and sustainability.
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