CROP YIELD PREDICTION USING FEATURE SELECTION TECHNIQUES AND ENSEMBLE MACHINE LEARNING CLASSIFIERS

In order to analyze the characteristics of the agricultural environment, this study focuses on crop forecasting using a variety of feature selection techniques and classifiers. Accurate crop projections are crucial for increasing agricultural productivity, optimizing resource utilization, and developing sustainable farming practices. In order to determine how environmental factors affect crop adaptability, the study looks at soil nutrients, temperature, humidity, rainfall, and pH levels. The most important elements influencing crop growth are identified using a variety of feature selection techniques, which improves forecast accuracy and reduces data dimensionality. To create predictive models and evaluate their efficacy, a range of machine learning classifiers are used, including decision trees, support vector machines, k-nearest neighbors, and random forest techniques. The experimental findings show that combining effective feature selection techniques with robust classification algorithms significantly improves crop prediction accuracy. This helps farmers and agricultural planners make well-informed decisions on crop cultivation.