The recent advancements in technologies such as Artificial Intelligence, the Internet of Things (IoT), automated drones, and embedded systems have led to significant changes in industrial systems architectures. This shift from non-interoperable simple systems to complex systems with hundreds of devices has caused confusion for operators in terms of understanding their systems, securing them, and recognizing faulty behavior. Intrusion Detection Systems (IDS) is an area where AI can be used to detect malicious traffic on heterogeneous systems involving IoT, embedded systems, and more classic LAN. In this research, we will focus on IDS for networks dedicated to agriculture, highlighting the challenges, pitfalls, and caveats when designing such a tool.Machine learning (ML)and deep learning(DL) has been extensively explored as a means to automate IDS giving them an edge to detect new forms of attacks.