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.
Keywords: Smart Farming, Internet of Things, Anomaly Detection, Sensor Nodes, Smart Irrigation.
