Leveraging RegNet and CBAM for precise detection of honey adulteration using thermal image analysis

Honey adulteration poses a huge challenge with considerable health and economic consequences, underscoring the necessity for effective and precise quality evaluation techniques. This research introduces a novel approach for classifying levels of honey adulteration through thermal imaging and Artificial Intelligence (AI). Traditional detection methods are frequently marked by protracted processing durations, elevated expenses, and restricted sensitivity. To mitigate these constraints, a dataset of thermal images was compiled from 15 pure honey samples and 69 adulterated samples including glucose syrup at amounts between 1% and 30%. An adaptable AI model was created to categorize various honey types, attaining elevated accuracy, sensitivity, and specificity across different levels of adulteration. The model achieved a precision and specificity of 100% for pure honey and 1% adulteration, demonstrating strong performance at higher adulteration levels (0.98 and 0.97 for 3% and 5% adulteration, respectively). This methodology offers significant benefits, such as swift identification and versatility across various honey varieties. The results indicate that the integration of thermal imaging and AI can improve quality control in the honey sector, providing a dependable method for verifying the authenticity and safety of natural bee products. This approach facilitates enhanced quality assurance methods and bolsters consumer confidence in honey products.