International Journal of Research in Advanced Electronics Engineering

Sesame (Sesamum indicum L.) has gained substantial recognition as a functional food due to its rich composition of essential fatty acids, lignans, proteins, minerals, and antioxidants, prompting a growing demand for technologies that ensure its quality and traceability. Traditional methods for evaluating seed quality such as chemical assays, visual inspection, and manual grading are often laborious, subjective, and inadequate for large-scale industrial applications. Sensor-based technologies, including hyperspectral imaging, near-infrared spectroscopy, machine vision, electronic nose systems, and dielectric property sensors, have emerged as advanced, rapid, and non-destructive tools capable of characterizing the physicochemical attributes of sesame seeds with high precision. These technologies facilitate the assessment of moisture content, lipid oxidation, lignan concentration, seed purity, contamination, and mechanical damage, thereby enhancing real-time decision making across the value chain.

Recent advancements in feature extraction algorithms, pattern recognition models, and machine-learning-based classification have significantly enhanced the potential of sensor-based systems for quality grading, adulteration detection, and functional component estimation. When integrated with IoT platforms, these sensor technologies further allow remote monitoring and automation, supporting standardized quality protocols for the food and nutraceutical industries. Despite the promising potential, challenges persist in calibration robustness, cost feasibility, data complexity, and technology transfer to smallholder systems. Understanding the applicability, performance limits, and optimization strategies for sensor-based tools is therefore essential for strengthening sesame seed quality assurance frameworks.