Abstract
Pumping systems play an important role in agriculture because they provide the necessary level of irrigation needed to increase crop yields. Pump malfunctions result in equipment downtime, reduced efficiency of agricultural production and significant financial losses. Thus, the development of an early fault detection and diagnosis system leveraging sensor analytic, filtering techniques, and machine learning (ML) technologies constitutes a critical applied research challenge. The aim of this research is to develop and validate early fault detection and classification methods for pumping systems using advanced machine learning algorithms and sensor data analysis.