Abstract
Plant identification via leaf images is very meaningful to agricultural information. The existing methods were based on one or two kinds of the three distinct characteristics in leaf images including leaf contours, textures and veins. This limits their recognition performance and scope of application. This paper describes a novel counting-based leaf recognition method, which can directly and effectively combine all of the three kinds of significant characteristics in leaf images. In order to obtain the stable and independent local line responses from leaf contour, texture and vein, elliptical half Gabor is introduced and convoluted with the raw grayscale leaf images, and then maximum gap local line direction patterns are extracted from the local line responses and normalized in direction by cyclically right shifting these patterns until the most numerous bit plane with a value of 1 to the left bit. The histogram of the normalized patterns is calculated and regarded as the counting-based local structure descriptor, and support vector machine is utilized as the classifier. Experimental results on three frequently used leaf databases show that the proposed approach yields a better performance in terms of the classification accuracy, applicability and feasibility in comparison with the state of the art methods.
Highlights
Automatic plant identification systems are very meaningful to agricultural information and ecological protection
In order to fair combine the three most significant characteristics including shape, venation and main texture in leaf images, we design a new kind of Gabor wavelet namely elliptical half Gabor wavelet to highlight the local dominant orientation information of leaf contours, veins and main textures, and we present an improved local line direction coding approach named as Maximum Gap Local Line Direction Pattern (MGLLDP) to extract local dominant orientation structure patterns from the elliptical half Gabor wavelet domain
It has been proved empirically that Local Line Directional Pattern (LLDP) defined in Eq.1 outperforms over the Local Binary Patterns (LBP)-like codes based on edge gradients or pixel intensities [20], but there are still some weaknesses in the original definition of LLDP
Summary
Automatic plant identification systems are very meaningful to agricultural information and ecological protection. Benefitting from the idea of counting, these methods generally outperform over the conventional contour-based methods in terms of classification accuracy and applicability, but they extract counting-based shape features only from the boundaries of leaves, and neglect other useful characteristics such as veins and textures in the leaf images Their sensitivity to the quality of the pre-processing results is in line with the conventional contour-based methods. In order to take advantage of the three kinds of significant characteristics in leaf images and make leaf recognition free of the pre-processing course, motivated by aforementioned inspirational conclusions about leaf identification including importance of venation structure [18] and advantage of counting the number of local shape patterns over matching global shape features point by point [21]–[24], we propose a novel counting-based local structure descriptor for identifying plant species. From the experimental results in [20], the LLDP outperforms over the LBP-like codes based on edge gradients
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