Enhancing Reliability in Smart Agriculture: Detecting Failures and Anomalies in Irrigation System

Smart Agriculture (SA), a pivotal component of Industry 4.0, has revolutionized the agricultural sector by harnessing advanced technologies to optimize crop production, resource utilization, and environmental sustainability. However, integrating intelligent systems, particularly in Smart Irrigation (SI), introduces a complex network of interconnected assets susceptible to various failures. The realization of Industry 4.0's potential in agriculture relies on the ability to detect and address these failures. This paper introduces the challenges and potential pitfalls encountered by SA, focusing on the critical domain of SI systems, notably concerning Water Pump failures and anomalies. We propose an innovative approach that combines both failure detection and anomaly detection in SA. By leveraging ML algorithms, we aim to develop tools capable of not only detecting failures but also detecting anomalies in irrigation systems and other vital components. Our interdisciplinary approach presents a clear roadmap to strengthen SA's resilience and promote sustainable growth in the ever-changing landscape of Industry 4.0.