Application of K-nearest neighbor algorithm in agricultural meteorological data mining

With the rapid development of agricultural practices and meteorological technologies, the integration of data mining techniques has become increasingly essential for analyzing complex meteorological data to improve agricultural productivity. This study aims to explore the application of the K-Nearest Neighbor (KNN) algorithm in mining agricultural meteorological data to support precision farming. The KNN algorithm is applied to analyze variables such as temperature, humidity, precipitation, and soil moisture, with the goal of predicting weather patterns, optimizing irrigation schedules, and forecasting crop yields. Through the identification of hidden patterns and trends, the KNN model enables more accurate predictions of adverse weather events and crop performance, thereby supporting informed decision-making in agricultural management. The results demonstrate that KNN can effectively classify and predict key meteorological events, contributing to improved resource planning, reduced waste, and enhanced productivity. In conclusion, the application of KNN offers significant potential for advancing sustainable and data-driven agricultural practices under changing climatic conditions.