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.

KEYWORDS:

• AlexNet
• AousethNet
• Deep convolutional neural networks
• Majority vote classifier
• Medicinal leaves identification
• SoftMax

Additional information

Notes on contributors

C. P. Blesslin Elizabeth

C P Blesslin Elizabeth received her BE degree in electronics and instrumentation engineering from Anna University, Chennai, and ME degree in applied electronics from Anna University, Tirunelveli in 2008 and 2010, respectively. Presently, she is working as an assistant professor in the Department of Electronics and Communication Engineering in Arunachala College of Engineering for Women, Tamil Nadu, India, and pursuing her PhD at Anna University Chennai, India. Her research interests include digital image processing, digital signal processing, and soft computing. She has published many papers in reputed journals.

S. Baulkani

S Baulkani received her BE degree in electronics and communication engineering from Madurai Kamaraj University, Madurai, and ME degree in computer science and engineering from Bharathiyar University, Coimbatore in 1986 and 1998 respectively. She received her PhD degree in information and communication engineering from Anna University, Chennai in 2009. Presently, she is working as a professor in the Department of Electronics and Communication Engineering, Government College of Engineering, Tirunelveli, Tamil Nadu, India. Her areas of interest are digital image processing, network security, web mining, and soft computing and she has published many papers in reputed journals. Under her guidance, 6 scholars were awarded PhD and many are pursuing PhD. Email: [email protected]