Classification of Indian Medicinal Leaves using Transfer Learning based Convolutional Neural Networks

There’s a rapid increment in the utilization of herbal-based items over the world. Medicinal herbs play a major part in creating ayurvedic drugs and homegrown items. Herbal medications are regarded as a substitute for synthetic drugs in both developing and developed countries, owing to their lack of adverse effects. Many medicinal species are extant in India. Identifying those medicinal plants/leaves over a bunch of different kinds of plant species is necessary as they are many health benefits with medicinal plants. Classification of medicinal leaves can be automated based on their respective textures, shape, and edge or margin of a leaf. In this paper, Indian medicinal leaves classifying utilizing transfer learning based convolutional neural networks has been proposed. In order to make the classification more accurate, the data augmentation technique had been utilized to enlarge the data which avoids insufficiency of data. The regularization technique was utilized to avoid the menace of overfitting. The dataset consists of 1835 images and thirty different types of beneficial Indian medicinal herbs such as Tulsi, Jasmine, etc. Data has been trained using different architectures such as VGG16, Xception, and efficieNetv2 whereas these architectures achieve 99.55%, 99.99%, and 99.46% test accuracy respectively. The preliminary results of the Indian medicinal herbs dataset showed that the Xception architecture performed better than EfficientNetv2 and VGG16.