DeepHerb: A Vision Based System for Medicinal Plants Using Xception Features

Abstract

The conservation of biodiversity is crucial as many plant species are critically under extinction. The traditional medicinal system, an alternative to synthetic drugs, promote healthy living and mainly depends on the wide repository of plants. A vision-based automatic medicinal plant identification system is proposed using different neural network techniques in computer vision and deep learning. The challenge lies in the unavailability of the medicinal herb dataset. The paper showcases a novel medicinal leaf dataset entitled DeepHerb dataset comprising of 2515 leaf images from 40 varied species of Indian herbs. The efficacy of the dataset is revealed by comparing pre-trained deep convolution neural network architectures such as VGG16, VGG19, InceptionV3 and Xception. The work concentrates on adopting the transfer learning technique on the pre-trained models to extract features and classify using Artificial Neural Network (ANN) and Support Vector Machine (SVM). The SVM hyperparameters are tuned further by Bayesian optimization to achieve a better performance model. The proposed DeepHerb model learned from Xception and ANN outperformed by 97.5% accuracy. A cross-platform mobile application entitled HerbSnap developed integrating the DeepHerb model identifies the herb image with a prediction time of 1 second per image and reveals the pertinent details of herbs from the database. This research will further focus on expanding the dataset to benefit stakeholders and thus, enriches society with the knowledge of herbs and their medicinal properties.

Keywords

• Bayesian optimization
• computer vision
• deep learning
• medicinal plants
• support vector machine
• transfer learning