An enhanced support vector machine bbased multiclass classification method for crop prediction

Abstract

Crop type classification is a fundamental task in precision agriculture, enabling informed decision-making for crop management and resource allocation. Support vector machines (SVMs) have emerged as robust and effective tools for multiclass classification tasks. This study explores the application of SVM-based multiclass classification techniques to accurately categorize various crop types based on remote sensing data. The SVM algorithm is employed to create decision boundaries that maximize the margin between different crop classes while minimizing classification errors. To enhance classification performance, various kernel functions such as linear, polynomial, and radial basis functions are evaluated to capture complex relationships within the data. The proposed SVM-based approach is compared with other commonly used classification methods to assess its superiority in terms of accuracy, precision, recall, and F1 score.