Deep Learning based Aquatic and Semi Aquatic Plants Morphological Features Extraction and Classification

doi:10.23940/ijpe.22.10.p3.702-709

Abstract

Abstract: In Ayurveda, the ancient medicinal plant identification system is based on the morphological comparison of leaf, fruit, flower, root, stem etc. Botanists use morphometrics for aquatic and semi-aquatic medicinal plants classification. However, deep learning networks provide the highest image classification result in digital image processing. Existing deep learning algorithms generate feature maps for pixel-wise image classification. In the feature map of deep learning output, most of the morphological features are missing. This issue leads to the Catastrophic forgetting issue of deep learning. To generate a traditional morphological feature-based medicinal plant identification system, we are introducing morphometrics and morphological feature-based deep learning networks for aquatic and semi-aquatic plant classification. This article contains: (a) A detailed morphological features database of aquatic and semi-aquatic medicinal plants, (b) a summary of the importance of the morphological features-based leaf classification, (c) a morphological features extraction algorithm and (d) the morphological features-based deep learning approach for aquatic and semi-aquatic plant classification. This human brain-like procedure achieved 97% classification accuracy and reduced the Catastrophic forgetting issue of continual learning.

Key words: image processing, medicinal plants, aquatic plants, deep learning, morphometric

Jibi G. Thanikkal, Ashwani Kumar Dubey, and Thomas M. T.. Deep Learning based Aquatic and Semi Aquatic Plants Morphological Features Extraction and Classification [J]. Int J Performability Eng, 2022, 18(10): 702-709.

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