A Decision Model for Reliability Analysis of Agricultural Sensor Data for Smart Irrigation 4.0

Abstract

 Agriculture is the backbone of an Agro-based Country's Economic System as it employs the majority of the population. Internet-of-Things (IoT)-based intelligent systems help reduce losses and make efficient use of available resources. This paper aims to detect anomaly conditions that might occur in sensor nodes related to day-t- -day smart irrigational activities in an agricultural field. IoT-based irrigation systems being prone to unauthorized intrusion can cause damage to smart farms in terms of crop damage and infertility of the soil. In this paper, we propose an intelligent decision-making system that can identify Anomalous Conditions and Suspicious Activities. The model discussed in this paper uses the idea of Gaussian distribution, which calculates the expected probability of a given state of an agricultural field and classifies anomalies based on what previous probabilities of an anomaly state looked like. The approach classifies the anomalies with an accuracy of 80.79%, a precision of 0.81, and a recall of 0.54 under test conditions.