Computer-aided Interpretable Features for Leaf Image Classification

• URL: http://arxiv.org/abs/2106.08077v1
• Date: Tue, 15 Jun 2021 12:11:10 GMT
• Title: Computer-aided Interpretable Features for Leaf Image Classification
• Authors: Jayani P. G. Lakshika, Thiyanga S. Talagala
• Abstract summary: We introduce 52 computationally efficient features to classify plant species. Length, width, area, texture correlation, monotonicity and scagnostics are to name few of them. The results show that the features are sufficient to discriminate the classes of interest under both supervised and unsupervised learning settings.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Plant species identification is time consuming, costly, and requires lots of efforts, and expertise knowledge. In recent, many researchers use deep learning methods to classify plants directly using plant images. While deep learning models have achieved a great success, the lack of interpretability limit their widespread application. To overcome this, we explore the use of interpretable, measurable and computer-aided features extracted from plant leaf images. Image processing is one of the most challenging, and crucial steps in feature-extraction. The purpose of image processing is to improve the leaf image by removing undesired distortion. The main image processing steps of our algorithm involves: i) Convert original image to RGB (Red-Green-Blue) image, ii) Gray scaling, iii) Gaussian smoothing, iv) Binary thresholding, v) Remove stalk, vi) Closing holes, and vii) Resize image. The next step after image processing is to extract features from plant leaf images. We introduced 52 computationally efficient features to classify plant species. These features are mainly classified into four groups as: i) shape-based features, ii) color-based features, iii) texture-based features, and iv) scagnostic features. Length, width, area, texture correlation, monotonicity and scagnostics are to name few of them. We explore the ability of features to discriminate the classes of interest under supervised learning and unsupervised learning settings. For that, supervised dimensionality reduction technique, Linear Discriminant Analysis (LDA), and unsupervised dimensionality reduction technique, Principal Component Analysis (PCA) are used to convert and visualize the images from digital-image space to feature space. The results show that the features are sufficient to discriminate the classes of interest under both supervised and unsupervised learning settings.

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