Ethno medicine of Indigenous Communities: Tamil Traditional Medicinal Plants Leaf detection using Deep Learning Models

Abstract

In indigenous communities, ethno medicine has played a prominent role in healing for centuries and provides valuable insight into the use of traditional medicinal plants. Using local plant leaves as therapeutic agents, this research explores the importance of traditional Tamil medicine. Traditional healers and practitioners can benefit from technological advances and collaborate with modern healthcare systems through these advances. Based on deep learning models, a novel approach for recognizing leaves of traditional Tamil medicinal plants is presented. Pharmaceutical companies are increasingly paying attention to plants that contain medicinal factors that have fewer side effects during treatment. By identifying plants with medicinal factors and identifying their medicinal uses, this work addresses the identification of plants with medicinal factors. To classify medicinal plants and their uses, we will use deep learning models and image processing techniques to identify Tamil Traditional Medical Plant (TTMP). The input dataset for this work includes 18 different traditional Tamil plant leaves such as Azadirachta Indica (Neem), Ocimum Tenuiflorum (Tulsi) and Trigonella Foenum-graecum (Fenugreek). Pre-processing steps are performed on the input plant image, including RBZR Augmentation, noise removal and grayscale conversion, to detect the image area that is important for plant type identification. To identify the core area of the plant, this work using the HGAW Segmentation algorithm. Deep learning model is trained with segmented plants and their labels as inputs. The aim is to digitize and make accessible ancestral medical knowledge by incorporating deep learning-based leaf recognition with 96.71% accuracy for the proposed EEXR model.