Deep - Morpho Algorithm (DMA) for medicinal leaves features extraction
Pages 27905 - 27925
Abstract
Presently, for the identification and classification of images, various deep learning techniques are being used. In these techniques, the whole image is considered to produce similar feature sets for many images. As a result, this mechanism loses many of its features at the final stage. Therefore, to analyze and identify medicinal leaves through an artificial eye of botanists, it was emphasized that the leaf image features should remain preserved till the final stage of classification for better accuracy. The existing plant identification approaches are trained using the leaf images. So leaf features are lost in the different stages of the convolution process and the same feature values are generated for similar type leaf images. This raises ambiguity in the results and affects the accuracy of leaf image identification. But here, in this proposed deep learning-based plant leaves morphological feature recognition system, leaf morphological features are used to train the system. Morphological features are identified to recognize a plant leaf. Here, morphological features of medicinal plant leaves, venation, shapes, apices, and bases are extracted and analyzed to predict the image class. So, the leaf features remain persevered until the final stage. The proposed feature recognition analysis improves the accuracy of the leaf identification method. In this, more than 300 leaves from 18 different plant families are collected and trained to build the deep learning classifier and achieve 96% accuracy. The performance evaluation was also conducted over “Flavia”, “Swedish” and “Leaf” data set and obtained 91%, 87% and 91% accuracy. The performance of image classification and feature preservation algorithms with less computational power are indicating the potential applicability of the proposed Deep - Morpho Algorithm (DMA) in medicinal plants and leaves identification.
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Publication History
Published: 24 February 2023
Accepted: 31 January 2023
Revision received: 26 May 2022
Received: 30 September 2021
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