Leaf Shape Features Research Articles

3D Morphological Feature Quantification and Analysis of Corn Leaves.

Marked variations in the 3-dimensional (3D) shape of corn leaves can be discerned as a function of various influences, including genetics, environmental factors, and the management of cultivation processes. However, the causes of these variations remain unclear, primarily due to the absence of quantitative methods to describe the 3D spatial morphology of leaves. To address this issue, this study acquired 3D digitized data of ear-position leaves from 478 corn inbred lines during the grain-filling stage. We propose quantitative calculation methods for 13 3D leaf shape features, such as the leaf length, 3D leaf area, leaf inclination angle, blade-included angle, blade self-twisting, blade planarity, and margin amplitude. Correlation analysis, cluster analysis, and heritability analysis were conducted among the 13 leaf traits. Leaf morphology differences among subpopulations of the inbred lines were also analyzed. The results revealed that the 3D leaf traits are capable of revealing the morphological differences among different leaf surfaces, and the genetic analysis revealed that 84.62% of the 3D phenotypic traits of ear-position leaves had a heritability greater than 0.3. However, the majority of 3D leaf shape traits were strongly affected by environmental conditions. Overall, this study quantitatively investigated 3D leaf shape in corn, providing a reliable basis for further research on the genetic regulation of corn leaf morphology and advancing the understanding of the complex interplay among crop genetics, phenotypes, and the environment.

Callicarpayongshunensis (Lamiaceae): A new species from Hunan, China.

This study provides detailed description of a newly-discovered Callicarpayongshunensis Wen B. Xu, Xiao D. Li & Yan Ling Liu (Lamiaceae) species from Hunan, China. The species shares similarities in the inflorescence, glandular colour and leaf shape features with C.luteopunctata H. T. Chang and C.giraldii Hesse ex Rehd., while its white fruits are similar to those of C.longifolia Lamk. However, its procumbent, evergreen shrub and white fruits are distinctly different from those of C.luteopunctata and C.giraldii, while its procumbent, scarless nodes and stellate pubescence free fruits distinguishes it from C.longifolia. Images, distribution, morphological features, molecular phylogenetic classification and conservation assessment of this new Callicarpa species are explored.

Invariant leaf image recognition with histogram of Gaussian convolution vectors

Employing leaf shape features for plant species recognition, especially for cultivar recognition is very challenging due to the high similarities of leaf shapes across different species and cultivars. In this paper, we attempted a new strategy of depicting leaf shapes by convolving the contour vector functions with Gaussian functions of different widths. The resulting Gaussian convolution vector (GCV) has the following traits that are desirable for shape characterization: (1) It provides an overall description to the shape in which both the curvature features and the proportional relationship of leaf contour are encoded. (2) It is intrinsically invariant to the group transformations including translation, scaling and rotation. (3) It depicts local shape geometry at multiple scales which enhance the discriminative power of the shape descriptors. The 2D histogram that reflects the distribution information of GCV is generated for efficiently yet accurately matching shapes. Two types of leaf image datasets, the middle European Woody plants (MEW2012) and the Flavia leaf dataset that are widely used for plant species recognition, and the soybean leaf dataset we built especially for cultivar recognition, are utilized to examine the effectiveness of the proposed method. The experiments demonstrate that no matter on species recognition or on cultivar recognition, the proposed method achieves higher retrieval accuracies over the state-of-the-art benchmark methods. In addition, a self-overlapped leaf image dataset is built to validate the robustness of the proposed method to self-intersection of leaves.

Leaf identification using radial basis function neural networks and SSA based support vector machine.

In this research, an efficient scheme to identify leaf types is proposed. In that scheme, the leaf boundary points are fitted in a continuous contour using Radial Basis Function Neural Networks (RBFNN) to calculate the centroid of the leaf shape. Afterwards, the distances between predetermined points and the centroid were computed and normalized. In addition, the time complexity of the features’ extraction algorithm was calculated. The merit of this scheme is objects’ independence to translation, rotation and scaling. Moreover, different classification techniques were evaluated against the leaf shape features. Those techniques included two of the most commonly used classification methods; RBFNN and SVM that were evaluated and compared with other researches that used complex features extraction algorithms with much higher dimensionality. Furthermore, a third classification method with an optimization technique for the SVM using Salp Swarm Algorithm (SSA) was utilized showing a significant improvement over RBFNN and SVM.

Plant Leaf Image Reconstruction Based on Point Cloud Characteristics

We present a method of leaf image reconstruction for plants whose midribs are close to straight. First, tangerine leaves are used as an example to illustrate the geometric and leaf-shape features of such leaves and suggest an extraction method using principal component analysis. Hence, this method was used to extract the geometric features, and additional methods such as coordinate conversion, mathematical morphology, and scan line analysis were applied to extract the leaf shape features. Then, based on these leaf shape features, a Bezier deformation function of the tangerine leaf edges and their curved Bezier deformation function in the normal vector direction were employed to obtain a parametric equation of the leaves according to their rectangular deformation. Finally, a simple illumination model for the leaves is presented. The reconstruction results show that the method is highly efficient and geometrically realistic. These results will be useful for the further study of plant leaf image reconstruction.

Automated analysis of visual leaf shape features for plant classification

A large number of studies have been performed during the past few years to automatically identify the plant type in a given image. Besides common object recognition difficulties arising mainly due to light, pose and orientation variations, the plant type identification problem is further complicated by the differences in leaf shape overage and changing leaf color under different weather conditions. The limited accuracy of existing approaches can be improved using an appropriate selection of representative leaf based features. This study evaluates different handcrafted visual leaf features, their extraction techniques, and classification methods. Towards this end, a new five-step algorithm is presented (comprising image pre-processing, segmentation, feature extraction, dimensionality reduction, and classification steps) for recognition of plant type through leaf images. The proposed algorithm is evaluated on a publicly available standard dataset ‘Flavia’ of 1600 leaf images and on a self-collected dataset of 625 leaf images. With the proposed algorithm, different classifiers such as k-nearest neighbor (KNN), decision tree, naïve Bayes, and multi-support vector machines (SVM) are tested. The best performing KNN, claimed for the final results, reveals that the proposed algorithm gives precision and recall values of 97.6% and 98.8% respectively when tested on ‘Flavia’ dataset. The proposed technique is also tested on our self-collected dataset, giving respectively 96.1% and 97.3% precision and recall measure results. Results confirm that our approach, when augmented with efficient segmentation techniques on raw leaf images, can be a significantly accurate plant type recognition method in practical situations. AlexNet, a Convolutional Neural Network (CNN) based approach is also compared for classification on the datasets as oppose to handcrafted feature-based approach and it is found that the later outperforms the former in robustness when the training dataset is small.

Wavelet Energy and Shape Features for Plants Recognition

This work presents plants recognition system with rotation invariant based on plant leaf. Wavelet energy features are extracted for sub-images (blocks) beside three of leaf shape features: [area, perimeter, circularity ratio]. (8) species of leaves are used in different size and color, (15) samples for each leaf are used. Leaves images are rotated at angles: 90?, 180?, 270?(counterclockwise,clockwise). Euclidean distance is used, the recognition rate was 98.2% with/without rotation.

Comprehensive Methods for Leaf Geometric Morphometric Analyses

Leaf morphometrics are used frequently by several disciplines, including taxonomists, systematists, developmental biologists, morphologists, agronomists, and plant breeders to name just a few. Leaf shape is highly variable and can be used for identifying species or genotypes, developmental patterning within and among individuals, assessing plant health, and measuring environmental impacts on plant phenotype. Traditional leaf morphometrics requires hand tools and access to specimens, but modern efforts to digitize botanical collections make digital morphometrics a readily accessible and scientifically rigorous option. Here we provide detailed instructions for performing some of the most informative digital geometric morphometric analyses available: generalized Procrustes analysis, elliptical Fourier analysis, and shape features. This comprehensive procedure for leaf shape analysis is comprised of six main sections: A) scanning of material, B) acquiring landmarks, C) analysis of landmark data, D) isolating leaf outlines, E) analysis of leaf outlines, and F) shape features. This protocol provides a detailed reference for applying landmark and outline analysis to leaf shape as well as describing leaf shape features, thus empowering researchers to perform high throughput phenotyping for diverse applications.

Plant Recognition from Leaf Image through Artificial Neural Network

to know the details of plants growing around us is of great importance medicinally and economically. Conventionally, plants are categorized mainly by taxonomists through investigation of various parts of the plant. However, most of the plants can be classified based on the leaf shape and associated features. This article describes how Artificial Neural Network is used to identify plant by inputting leaf image. Compared to earlier approaches, new input features and image processing approach that matter in efficient classification in Artificial Neural Network have been introduced. Image processing techniques are used to extract leaf shape features such as aspect ratio, width ratio, apex angle, apex ratio, base angle, centroid deviation ratio, moment ratio and circularity. These extracted features are used as inputs to neural network for classifying the plants. Under the current research, 534 leaves of 20 kinds of plants were collected. Out of these, 400 leaves were trained. The 134 testing samples were recognised with 92% accuracy; even without considering types of leaf margins, vein and removal of the petiole. Software has also been developed to identify leaf automatically except two mouse clicks by the user.

Utilizing venation features for efficient leaf image retrieval

Most Content-Based Image Retrieval systems use image features such as textures, colors, and shapes. However, in the case of a leaf image, it is not appropriate to rely on color or texture features only as such features are very similar in most leaves. In this paper, we propose a new and effective leaf image retrieval scheme. In this scheme, we first analyze leaf venation which we use for leaf categorization. We then extract and utilize leaf shape features to find similar leaves from the already categorized group in a leaf database. The venation of a leaf corresponds to the blood vessels in organisms. Leaf venations are represented using points selected by a curvature scale scope corner detection method on the venation image. The selected points are then categorized by calculating the density of feature points using a non-parametric estimation density. We show this technique’s effectiveness by performing several experiments on a prototype system.

Application of Neural Network to Identification of Soybean Varieties Using Leaflet Shape Images

We suggested a model that substitutes of experts' visual evaluation on leafshape. In previous studies, several researchers successfully created leafshape evaluation models based on shape features, but it is difficult to find the common leaf shape features to cover various species simultaneously We, therefore, contrived a new generalized model requiring no features using neural network with direct image input. In this study, we applied this model to the varietal discrimination of soybean leaflet shape. The identification rate of 38 varieties among 364 individual leaflets given by the model was 80%.