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

S Roopashree,J Anitha

doi:10.1109/access.2021.3116207

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.

Highlights

India, a developing country is rich in biodiversity
The work proposed in the paper mainly concentrates on classifying the medicinal herbs to enhance the knowledge of medicinal plants available locally, to use and grow them for healthy living
The best use of advanced techniques such as transfer learning in computer vision and deep learning, motivate the building of an automatic recognition system for medicinal plants

Summary

INTRODUCTION

A developing country is rich in biodiversity. Conservation of biodiversity ensures the sustainment of natural resources by boosting ecosystem productivity. A model or system to accelerate the automatic recognition of Indian medicinal herbs is yet not concentrated in recent times by many researchers. This research work concentrates to overcome several issues on the recognition and classification of plant leaf images by maintaining high accuracy and reduced prediction time for the system. The paper contributes to two novel approaches, firstly, building a deep learning model for the classification of medicinal herbs. The paper projects two unique models out of six models incorporating the transfer learning approach on different pre-trained Deep Convolution Neural Network (DCNN) architectures (VGG-16, VGG-19, Inception and Xception) used exclusively to extract features. Using the transfer learning technique has proved as a better choice for training the CNN models with a dataset of a smaller size This result in reduced training time as well as overcoming the overfitting issue.

BACKGROUND

RESULT

Findings

CONCLUSION