Abstract: In the quest to enhance agricultural productivity, predicting crop yield plays important part in optimizing resource allocation and planning. This study explores the uses of machine learning model to forecast crop yield, leveraging various models to analyse and interpret historical data. By integrating parameter like crop type, temperature, rainfall, and pesticide use, Machine learning techniques yield precise outcomes. predictions that support decision-making processes in agriculture. The results demonstrate the potential of these advanced analytical methods to provide actionable insights, improve yield forecasting accuracy, and ultimately contribute to sustainable agricultural practices.
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DOI:
10.17148/IJARCCE.2024.13903
