Development of Shape Based Leaf Categorization

ArXiv, 2018

This paper presents a novel feature set for shape-only leaf identification motivated by real-world, mobile deployment. The feature set includes basic shape features, as well as signal features extracted from local area integral invariants (LAIIs), similar to curvature maps, at multiple scales. The proposed methodology is evaluated on a number of publicly available leaf datasets with comparable results to existing methods which make use of colour and texture features in addition to shape. Over 90% classification accuracy is achieved on most datasets, with top-four accuracy for these datasets reaching over 98%. Rotation and scale invariance of the proposed features are demonstrated, along with an evaluation of the generalisability of the approach for generic shape matching.

1963

This paper proposes an automated system for recognizing plant species based on leaf images. Plant leaf images corresponding to three plant types, are analyzed using three different shape modelling techniques, the first two based on the Moments-Invariant (M-I) model and the Centroid-Radii (C-R) model and the third based on a proposed technique of Binary-Superposition (B-S). For the M-I model the first four central normalized moments have been considered. For the C-R model an edge detector has been used to identify the boundary of the leaf shape and 36 radii at 10 degree angular separation have been used to build the shape vector. The proposed approach consists of comparing binary versions of the leaf images through superposition and using the sum of non-zero pixel values of the resultant as the feature vector. The data set for experimentations consists of 180 images divided into training and testing sets and comparison between them is done using Manhattan, Euclidean and intersection norms. Accuracies obtained using the proposed technique is seen to be an improvement over the M-I and C-R based techniques, and comparable to the best figures reported in extant literature.

International Journal

This paper proposes an automated system for recognizing plant species based on leaf images. Plant leaf images corresponding to three plant types, are analyzed using two different shape modeling techniques, the first based on the Moments-Invariant (M-I) model and the second on the Centroid-Radii (C-R) model. For the M-I model the first four normalized central moments have been considered and studied in various combinations viz. individually, in joint 2-D and 3-D feature spaces for producing optimum results. For the C-R model an edge detector has been used to identify the boundary of the leaf shape and 36 radii at 10 degree angular separation have been used to build the feature vector. To further improve the accuracy, a hybrid set of features involving both the M-I and C-R models has been generated and explored to find whether the combination feature vector can lead to better performance. Neural networks are used as classifiers for discrimination. The data set consists of 180 images divided into three classes with 60 images each. Accuracies ranging from 90%-100% are obtained which are comparable to the best figures reported in extant literature.

2011

Shape is an important aspects in recognizing plants. Several approaches have been introduced to identify objects, including plants. Combination of geometric features such as aspect ratio, compactness, and dispersion, or moments such as moment invariants were usually used toidentify plants. In this research, a comparative experiment of 4 methods to identify plants using shape features was accomplished. Two approaches have never been used in plants identification yet, Zernike moments and Polar Fourier Transform (PFT), were incorporated. The experimental comparison was done on 52 kinds of plants with various shapes. The result, PFT gave best performance with 64% in accuracy and outperformed the other methods.

2013

A number of shape features for automatic plant recognition based on digital image processing have been proposed by Pauwels et al. in 2009. A database with 15 classes and 171 leaf samples was considered for the evaluation of these measures using linear discriminant analysis and hierarchical clustering. The results obtained match the human visual shape perception with an overall accuracy of 87%.

Lecture Notes in Computer Science, 2012

Leaf morphological characters are a useful visual guide for constructing relationships between different plants and between plants and their environment. However, extracting and analysing these characters are carried out manually by botanists, which is a painstaking and time-consuming task. One way to accelerate and broaden the use of these characters is to automatically extract them directly from images. An indispensable step toward this goal is to automatically detect leaf parts (petiole, blade, base, apex, rachis) since foliar characters are key descriptions about their shapes. In this paper we present a novel approach that addresses this problem. It is based on two types of symmetry: the first is local translational symmetry (for petiole, rachis detection). The second is local symmetry of depth indentations (for base and apex detection). The main advantage of this method is its accuracy and its robustness to shape variability. This is confirmed by the high rate of correct detections (more than 90%) obtained for a large number of leaf species.

2017

Plants are the most important living organisms on our planet because they are sources of energy and protect our planet against global warming. Botanists were the first scientist to design techniques for plant species recognition using leaves. Although many techniques for plant recognition using leaf images have been proposed in the literature, the precision and the quality of feature descriptors for shape, texture, and color remain the major challenges. This thesis investigates the precision of geometric shape features extraction and improved the determination of the Minimum Bounding Rectangle (MBR). The comparison of the proposed improved MBR determination method to Chaudhuri’s method is performed using Mean Absolute Error (MAE) generated by each method on each edge point of the MBR. On the top left point of the determined MBR, Chaudhuri’s method has the MAE value of 26.37 and the proposed method has the MAE value of 8.14. This thesis also investigates the use of the Convexity Meas...

Shape is the most popular feature used in plant leaf identification, be it manual or automatic plant identification. In this pa per, a study is conducted to investigate the most contributing features among three low-level features for plant leaf identification. Intra-and inter-class identification are conducted using 455 herbal medicinal plant leaves, with 70% allocated for training and 30% f or testing dataset. Shape feature is extracted using Scale Invariant Feature Transform (SIFT); colour is represented using colour moments; and Segmentation-Based Fractal Texture Analysis (SFTA) is utilized to describe texture feature. Intra-class analysis showed that fusion of texture and shape surpassed fusion of text ure, shape and colour. Single texture feature identification al so achieved highest identification rate compared to identification using colour or shape. Inter-class analysis further support tex ture to be the discriminative feature among the low-level features. Results demonstrate that single texture feature outperformed col our or shape feature achieving 92% identification rate. Furthermore, fusion of all three features accomplished 94% identification rate .