Agricultural cybersecurity is a rising concern because farming is becoming ever more reliant on computers and Internet access. During the last few years, the agrotechnology community, public sector and researchers have been alerted to the problem and a significant amount of research has focused on the issue.
Criminals think that larger businesses have the resources to pay the demands without a second thought and smaller businesses often lack the necessary updates that are needed to fend off cybercriminals. For instance, a farm services company in Iowa, NEW Cooperative Inc, recently took its systems offline to contain a security threat. A notorious criminal group known for ransomware attacks took credit for it.
Using AI and Machine learning-based surveillance systems to monitor every crop field's real-time video feed identifies animal or human breaches, sending an alert immediately. AI and Machine learning improve crop yield prediction through real-time sensor data and visual analytics data from drones.
Yield mapping is an agricultural technique that relies on supervised machine learning algorithms to find patterns in large-scale data sets and understand the orthogonality of them in real-time, all of which is invaluable for crop planning.