Automatic Recognition of Medicinal Plants using Machine Learning Techniques

Adams Begue,Fawzi Mahomoodally,Upasana Singh,Sameerchand Pudaruth,Venitha Kowlessur

doi:10.14569/ijacsa.2017.080424

Adams Begue, Fawzi Mahomoodally + Show 3 more

Open Access

https://doi.org/10.14569/ijacsa.2017.080424

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Abstract

The proper identification of plant species has major benefits for a wide range of stakeholders ranging from forestry services, botanists, taxonomists, physicians, pharmaceutical laboratories, organisations fighting for endangered species, government and the public at large. Consequently, this has fueled an interest in developing automated systems for the recognition of different plant species. A fully automated method for the recognition of medicinal plants using computer vision and machine learning techniques has been presented. Leaves from 24 different medicinal plant species were collected and photographed using a smartphone in a laboratory setting. A large number of features were extracted from each leaf such as its length, width, perimeter, area, number of vertices, colour, perimeter and area of hull. Several derived features were then computed from these attributes. The best results were obtained from a random forest classifier using a 10-fold cross-validation technique. With an accuracy of 90.1%, the random forest classifier performed better than other machine learning approaches such as the k-nearest neighbour, naive Bayes, support vector machines and neural networks. These results are very encouraging and future work will be geared towards using a larger dataset and high-performance computing facilities to investigate the performance of deep learning neural networks to identify medicinal plants used in primary health care. To the best of our knowledge, this work is the first of its kind to have created a unique image dataset for medicinal plants that are available on the island of Mauritius. It is anticipated that a web-based or mobile computer system for the automatic recognition of medicinal plants will help the local population to improve their knowledge on medicinal plants, help taxonomists to develop more efficient species identification techniques and will also contribute significantly in the protection of endangered species.

Highlights

The world bears thousands of plant species, many of which have medicinal values, others are close to extinction, and still others that are harmful to man
The proper identification of plant species has major benefits for a wide range of stakeholders ranging from consumers, forestry services, botanists, taxonomists, physicians, pharmaceutical laboratories, organization fighting for endangered species, government, and the public at large
The Random Forest classifier achieves the best performance with an accuracy of 90.1%, i.e., out of 720 leaves, 649 leaves were classified correctly while 71 were not

Summary

Introduction

The world bears thousands of plant species, many of which have medicinal values, others are close to extinction, and still others that are harmful to man. Many of the processes involved in classifying these plant species is ‘dependent on knowledge accumulation and skills of human beings’ [1]. This process of manual recognition is often laborious and timeconsuming. Systems developed so far use varying number of steps to automate the process of automatic classification, though the processes are quite similar. These steps involve preparing the leaves collected, undertaking some pre-processing to identify their specific attributes, classification of the leaves, populating the database, training for recognition and evaluating the results. An automated plant identification system can be used by nonbotanical experts to quickly identify plant species quite effortlessly

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