Title: Microscopic image analysis for herbal plant classification

Authors: Bhupendra D. Fataniya; Tanish Zaveri

Addresses: Department of Electronics and Communication Engineering, School of Technology, Nirma University, Ahmedabad, 382481, India ' Department of Electronics and Communication Engineering, School of Technology, Nirma University, Ahmedabad, 382481, India

Abstract: An identification of herbal plants from its powder form is a challenging task. In this paper, a new method for identification and classification of herbal plants liquorice, rhubarb and dhatura using the microscopic image is proposed. This paper evaluates the effectiveness of the shape and texture-based features with a different classifier for herbal plants classification. Three-shape and five-texture features are computed for each object. The effectiveness of the individual shape and texture-based features set and their combinations are investigated using a support vector machine, K-nearest neighbour and ensemble classifier. The highest 94.9% classification accuracy was achieved by combining all shape features using the bagged tree ensemble classifier. While using a combination of texture-based features almost 99.8% classification accuracy is obtained using fine K-nearest neighbour and cubic-support vector machine classifier. Further, by combining shape and texture-based features classification efficiency achieved is 99.3% with quadratic-support vector machine. From the analysis of simulation results, it is found that texture-based features are more effective to classify a microscopic image of herbal plants.

Keywords: shape feature; texture feature; object detection; herbal plant; microscopic image.

DOI: 10.1504/IJIM.2021.115744

International Journal of Image Mining, 2021 Vol.4 No.1, pp.1 - 23

Received: 02 Nov 2017
Accepted: 06 Nov 2018
Published online: 21 Jun 2021 *

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