A Comprehensive Survey of Leaf Classification for Ayurvedic Herb Identification and Beyond

Within the principality of herbal medicine, Ayurvedic leaves have held the commitment of healing prospects since a long period of time, providing a treasure trove of simplest medication for several affliction. In the dynamic panorama of technology, the fusion of botanical proficiency and artificial intelligence has given birth to a remarkable attempt, enabling the classification of Ayurvedic leaves with unmatched accuracy. This review paper embarks on a extensive expedition through the records of scientific exploration, delving deep into the diverse assemblage of research efforts devoted to the image classification of Ayurvedic leaves. Meticulously surveying a plethora of studies, we reveal a variety of methodologies, each a brushstroke on the canvas of herbal identification. Amongst this rich variety, two reliable algorithms, Convolutional Neural Networks and KNearest Neighbour, emerge as recurrent stars, their ubiquity and efficacy shining brightly across the literature. As the paper unfolds, their prominence becomes conspicuous, as these algorithms consistently prove their tenacity, offering robust and reliable performance in the crucial mission of Ayurvedic leaf classification. The study explores the landscape of Ayurvedic herb identification with an eye toward recognizing the leaves, providing an essential foundation for further exploration of their curative potential, fostering a bridge between tradition and innovation that holds the promise of a healthier future.