Detecting Malicious Data in Agricultural Systems: An Integrated Cyber Framework

Information is gathered in smart agriculture from a variety of sources, such as IoT devices with There are many different kinds of sensors, such as weather sensor stations, soil moisture sensors, and sensors used by insects to check crop health. The satellites and drones are used to gather this sensor data. Farm management software tracks plant schedules, pest control methods, and irrigation techniques using the collected valuable data. Any unauthorized person manipulating agricultural data in this way leads to serious issues in the agricultural food supply chain and increases market demand for food. To avoid data manipulation in agricultural information, many strategies are utilized. This work discusses man in the middle attacks, man in the cloud attacks, SQL injection attacks, and cross-site scripting (XSS) attacks. Our strategy was to efficiently test the modified data using cyber integrated agriculture management (CIAM). This strategy focuses on tailored and effective techniques for treating plant soil health, analyzing nutrition levels and pH to guide fertilization, managing water levels, and detecting early signs of pests using image recognition and IoT data. AI-driven tools can detect anomalies and prevent attacks in real time, allowing different farming practices or weather conditions and their impacts on productivity for water usage, fertilizer application, and planting strategies to be detected and avoided with an impressive 98% accuracy, affecting both local and global food security.