Analyzing Anomaly Detection Techniques in Privacy-Aware IoT-Based Smart Farming

Implementation of Internet of Things devices in modern agriculture have caused a notable change in agricultural methods,compliantforthesystematicsurveillanceofagricultural produce and the collecting of real-time statistics. Yet, the substantial surge of data from these devices presents considerable difficulties in guaranteeing data privacy and identifying anomalies that may indicate problems like equipment breakdowns, environmental risks, or security breaches. This study provides a thorough analysis of different algorithms developed for the purpose of detecting anomalies while ensuring privacy in smart agriculture systems based on the loT. We assess the efficacy of these algorithms by considering factors such as precision, computational speed, scalability, and the capacity to maintain privacy. The scope of our investigation includes conventional statistical methodologies, ML models, and sophisticated deep learning techniques. According to our analysis we found that LSTM surpasses among DNN, CNN and LSTM. By conducting thorough experiments on actual agricultural datasets. This allows us to gain valuable insights into their appropriateness for various agricultural scenarios. The aim of this study is to offer guidance to academics and practitioners in choosing the most suitable anomaly detection algorithms for improving the dependability and security of smart agriculture systems, while still upholding strict privacy standards.