In the realm of computer vision, paddy (Oryza Sativa) plays a pivotal role as a globally consumed staple crop. Its cultivation, harvesting, processing, and storage involve intricate quality control. Numerous factors, including weather conditions and irrigation frequency, influence grain quality. To address this, we present an innovative approach that combines image processing and machine learning (ML). Existing methods for rice grain quality assessment, while valuable, are tailored to rice-specific characteristics, employing complex and costly setups and opaque ML models. Our research overcomes these limitations with a robust ML-based IoT system for paddy grain quality assessment, using affordable sensors, a comprehensive data collection process, and an ML-driven image processing model. Importantly, our approach utilizes interpretable features like Shape, Size, Moisture, and Maturity for paddy grain classification. Rigorous real-world testing confirms its precision, marking it as the first automated system capable of providing a reliable overall quality metric. Our system’s unique feature lies in its transparency, with clear features and fuzzy rules, inspiring confidence and trust. While our experiments primarily feature Indian Subcontinent grain varieties, the system’s adaptability to diverse paddy types is evident, contributing significantly to computer vision.