Identification Of Medicinal Plants Using Deep Transfer Learning

Abstract

The thesis explores the identification of medicinal plants in Bangladesh from images of plant leaves with the approach of deep transfer learning. The objectives of the study were to determine the best and less time consuming method to which individual could differentiate among the leafy medicinal plants and for what purposes. For this, I proposed and evaluated several deep learning architectures (MobileNet V2, Inception V3, ResNet50, VGG16 & VGG19). Performance from the test results suggested that the performance of the model outperforms all of the other models in MobileNet V2. On the dataset, it achieved an accuracy of 93.78%. In terms of performance, the precision 94%, recall and F1-score are 93%. Combining these results illustrates the efficiency of the model to predict the medicinal plants via leaf images.