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The inclusion of Internet of Things (IoT) devices is growing rapidly in allapplication domains. Smart Farming supports devices connected, and with thesupport of Internet, cloud or edge computing infrastructure provide remotecontrol of watering and fertilization, real time monitoring of farm conditions,and provide solutions to more sustainable practices. This could involve usingirrigation systems only when the detected soil moisture level is low or stopwhen the plant reaches a sufficient level of soil moisture content. Theseimprovements to efficiency and ease of use come with added risks to securityand privacy. Cyber attacks in large coordinated manner can disrupt economy ofagriculture-dependent nations. To the sensors in the system, an attack mayappear as anomalous behaviour. In this context, there are possibilities ofanomalies generated due to faulty hardware, issues in network connectivity (ifpresent), or simply abrupt changes to the environment due to weather, humanaccident, or other unforeseen circumstances. To make such systems more secure,it is imperative to detect such data discrepancies, and trigger appropriatemitigation mechanisms. In this paper, we propose an anomaly detection model forSmart Farming using an unsupervised Autoencoder machine learning model. Wechose to use an Autoencoder because it encodes and decodes data and attempts toignore outliers. When it encounters anomalous data the result will be a highreconstruction loss value, signaling that this data was not like the rest. Ourmodel was trained and tested on data collected from our designed greenhousetest-bed. Proposed Autoencoder model based anomaly detection achieved 98.98%and took 262 seconds to train and has a detection time of .0585 seconds.

Autoencoder-based Anomaly Detection in Smart Farming Ecosystem