OPTIMIZING CROP YIELD AND FERTILITY MANAGEMENT USING KNN AND ML
DOI:
https://doi.org/10.29121/shodhkosh.v5.i5.2024.1888Keywords:
K-Nearest Neighbors (KNN), Predictive Modeling, Agricultural Sustainability, Fertility Management, Machine Learning AlgorithmsAbstract [English]
Agricultural productivity and sustainability are critical concerns in modern farming practices. This study explores the application of machine learning algorithms, specifically K-Nearest Neighbors (KNN) and boosting, for optimizing crop yield and fertilizer management. By leveraging historical data on crop characteristics, soil properties, weather conditions, and fertilizer application, predictive models are developed to recommend crop varieties and optimal fertilizer strategies. The KNN algorithm facilitates the identification of similar historical cases to predict crop performance and fertilizer requirements for a given set of conditions. Additionally, boosting techniques enhance model performance by iteratively improving predictive accuracy. This research aims to provide farmers with data-driven insights to enhance decision-making, maximize crop yield, and minimize environmental impact through efficient fertilizer usage.
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