AI and CV based 2D-CNN algorithm: botanical authentication of Indian honey

Abstract

The market and aesthetic value of honey relies on the source of nectar collected by a honeybee from a specific flower, and the authenticity of honey based on botanical origin is of prime concern in the market. A deep learning framework based on the 2D-CNN model was used for the botanical authentication of Indian unifloral honey varieties. An inexpensive and robust analysis methodology based on computer vision (CV) was fabricated to determine the botanical authentication of honey varieties. The required .mp4 videos were recorded using a camera fixed on a stand with an adjustable distance. The developed model was trained using images which were extracted from the captured .mp4. The extracted data set of images for classification was fed to a developed 2D-CNN which was further validated using various performance metrics, namely, accuracy, precision, specificity, F1-score, and AUC-ROC. The value of AUC-ROC was more than 0.98 for most classes of unifloral honey varieties used for classification. The obtained results demonstrated that this experimental approach, in amalgamation with the developed 2D-CNN model, outperforms the existing algorithms used for evaluating food quality attributes. Henceforth, this novel approach would positively benefit the honey industry and the honey consumer regarding honey authentication—moreover, it encourages researchers to exploit this application of hybridised technology in food quality assurance and control.