Microscopic image analysis for herbal plant classification. (16th June 2021)

• Record Type: Journal Article
• Authors: Fataniya, Bhupendra D.; Zaveri, Tanish
• Is Part Of: International journal of image mining. Volume 4:Number 1(2020)
• Page Start: 1
• Page End: 23
• Publication Date: 2021-06-16
• Subjects: shape feature -- texture feature -- object detection -- herbal plant -- microscopic image; Image processing -- Periodicals; Data mining -- Periodicals; 006.42
• Languages: English
• ISSNs: 2055-6039

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.