
Web Base application for leaf identification
· Orchestrated an ML initiative, achieving a 93.58% accuracy rate in medicinal leaf classification using CNN, setting a new benchmark in botanical analytics.
· Assembled and annotated a distinct dataset of 750 images from 15 species, enhancing feature detection through advanced image segmentation techniques.
· Employed SVM and CNN for a dual-model approach, reaching a 91.68% precision rate by extracting 17 critical morphological and textural features per sample.
Student Marketplace
Certified Information System and Ethical Hacking