Medicinal Plant Identification and Detection using CNN and Deep Learning (CNN-X) Xception Technique

Abstract

Classification and identification of medicinal plants has garnered a lot of attention in the last several years. Many are curious about these plants because of the potential advantages they may have on human health. This study introduces a new deep learning-based artificial intelligence approach to medicinal plant identification using the CNN-X architecture. We are quite proud of our Python-based model's 93% training accuracy and 97% validation accuracy. More than 18,000 images of medical plants organized into 200 distinct categories make up the VNPlant-200 dataset, which our model uses to reliably identify them using a number of visual features. After undergoing extensive training, the CNN-X model can correctly identify a variety of species and detect intricate patterns in the images. The model's performance was greatly enhanced with hyperparameter adjustments and CNN-X architectural modifications.

The high accuracies obtained indicate the model's capacity to accurately identify and classify therapeutic plants. This improves the accuracy and efficiency of identification procedures. By making it easier for researchers, botanists, and medical experts to identify therapeutic plants, our AI-based approach advances automated identification medicine. This study demonstrates how deep learning and CNN-X architecture, a kind of artificial intelligence (AI), may enhance the identification of medicinal plants. It expands the number of potential research avenues when applied to the VNPlant-200 dataset, advancing herbal medicine and botanical science. The experiment's overall findings indicate that deep learning techniques can be useful for identifying therapeutic plants, which should spur further developments in the field of herbal medicine.