A Neutrosophic Approach to Edge-Based Anomaly Detection in Smart Farming Systems

Abstract

Neutrosophic sets have emerged as a powerful tool for addressing uncertainty and imprecision
in diverse domains, and their potential in anomaly detection within smart farming systems is the central
focus of this paper. We present a cutting-edge Neutrosophic Approach to Edge-Based Anomaly Detection,
specifically designed to cater to the intricacies of smart farming data. By harnessing the unique attributes
of single-valued neutrosophic sets, in conjunction with single-valued neutrosophic decision matrices, our
methodology adeptly handles the challenges posed by uncertain, dynamic, and multi-dimensional farm
data. Through a comprehensive analysis of sample data, we illustrate the precision and adaptability of our
approach, allowing for the quantification of intricate attribute relationships and the precise identification
of anomalies. By employing neutrosophic statistics and a weighted correlation coefficient, our approach
provides profound insights into the complex interactions within smart farming systems. This research
stands as a pivotal contribution within the scope of neutrosophic-based anomaly detection, promising to
advance the state of the art in the realm of precision agriculture