Novel Network for Medicinal Leaves Identification

Abstract

From the ancient days, plant leaves are used to cure various infectious diseases. Even today, herbal leaves are preferred by medicinal experts for treating cancer, asthma, heart problems, etc. The recognition of these herbal plants is based on the visual perception of villagers. There are many kinds of species that seem to be very similar in color and shape. There is a high probability of human error in the identification of such plants. It is inevitable to correctly identify the species of plants to treat the patients. Therefore, a smart plant classification system is essentially required to eliminate human error. This research work develops a hybrid system that is based on deep convolutional neural networks. The system is named as AousethNet which is a modification of AlexNet by replacing its classifier namely, SoftMax with the Majority vote classifier. It is trained to predict the plant species from a huge number of leaf samples from four datasets namely Mendeley, D-Leaf, Flavia, and Folio. Typically, the performance of AousethNet with Mendeley dataset attained an accuracy of 99.89%, precision 98.61%, and very less recognition time of 0.087 s/image. This system is found to have good feature extraction and strong discrimination ability compared with the original version of AlexNet.