  Honey, a natural product generated from organic sources, is widely recognized
for its revered reputation. Nevertheless, honey is susceptible to adulteration,
a situation that has substantial consequences for both the well-being of the
general population and the financial well-being of a country. Conventional
approaches for detecting honey adulteration are often associated with extensive
time requirements and restricted sensitivity. This paper presents a novel
approach to address the aforementioned issue by employing Convolutional Neural
Networks (CNNs) for the classification of honey samples based on thermal
images. The use of thermal imaging technique offers a significant advantage in
detecting adulterants, as it can reveal differences in temperature in honey
samples caused by variations in sugar composition, moisture levels, and other
substances used for adulteration. To establish a meticulous approach to
categorizing honey, a thorough dataset comprising thermal images of authentic
and tainted honey samples was collected. Several state-of-the-art Convolutional
Neural Network (CNN) models were trained and optimized using the dataset that
was gathered. Within this set of models, there exist pre-trained models such as
InceptionV3, Xception, VGG19, and ResNet that have exhibited exceptional
performance, achieving classification accuracies ranging from 88% to 98%.
Furthermore, we have implemented a more streamlined and less complex
convolutional neural network (CNN) model, outperforming comparable models with
an outstanding accuracy rate of 99%. This simplification offers not only the
sole advantage of the model, but it also concurrently offers a more efficient
solution in terms of resources and time. This approach offers a viable way to
implement quality control measures in the honey business, so guaranteeing the
genuineness and safety of this valuable organic commodity.
