Enhancing the Evaluation of Fault Detection Models in Smart Agriculture Using LLM Agents for Rule-Based Anomaly Generation

Abstract

In the context of Agriculture 4.0, advanced technologies such as the Internet of Things (IoT), artificial intelligence (AI), and big data analytics play a critical role in enhancing the efficiency and sustainability of farming operations. These innovations enable real-time monitoring and decision-making, improving the efficiency, sustainability, and productivity of agricultural systems. Central to Agriculture 4.0 is the deployment of sensors embedded in agricultural machinery, such as tractors, which continuously collect data on key operational metrics, including engine performance, fuel consumption, soil conditions, and equipment health. The effective analysis of such data is essential for predictive maintenance, as early detection of potential anomalies can prevent costly breakdowns and reduce downtime. However, finding real-world datasets containing examples of anomalies in agricultural machinery is highly challenging, making it difficult to develop and assess the effectiveness of anomaly detection models. Additionally, classical methods for anomaly generation, such as stochastic and adversarial approaches, may be difficult to apply given the intricate patterns and time dependency of these data. To address this gap, our work leverages Large Language Models (LLMs) and agentic workflows to generate realistic anomaly scenarios from agricultural data. Using a rule-based approach that combines prompt engineering techniques with a multi-agent system, we create synthetic anomalies that can later be used to evaluate anomaly detection models. These models would then enable the timely identification of potential machinery failures, reducing maintenance costs, minimizing downtime, and significantly lowering the environmental impact by preventing inefficiencies such as increased fuel consumption from faulty equipment, reducing the need for replacement parts, and conserving energy and resources used in repairs.