CROP PREDICTION BASED ON CHARACTERISTICS OF THE AGRICULTURAL ENVIORNMENT USING VARIOUS FEATURE SELECTION TECHNIQUES AND CLASSSIFIERS

Abstract

Agriculture plays a vital role in ensuring food security and economic sustainability, making accurate crop prediction essential for maximizing agricultural productivity and resource utilization. The selection of suitable crops depends on various environmental factors such as soil characteristics, temperature, humidity, rainfall, nutrient content, and climatic conditions. Traditional crop selection methods often rely on farmer experience and manual assessment, which may not always result in optimal agricultural outcomes. This paper presents a crop prediction framework based on the characteristics of the agricultural environment using various feature selection techniques and classifiers. The proposed approach utilizes agricultural datasets containing environmental and soil parameters to identify the most influential features affecting crop growth and yield. Feature selection techniques are employed to reduce data dimensionality and improve model efficiency, while multiple machine learning classifiers are used to predict the most suitable crop for a given agricultural environment. Experimental analysis demonstrates that the integration of feature selection methods with advanced classification algorithms enhances prediction accuracy, improves computational efficiency, and supports informed decision-making in precision agriculture. The proposed framework provides an intelligent and scalable solution for sustainable crop planning and agricultural resource management.