Dataset Information

An expert botanical feature extraction technique based on phenetic features for identifying plant species.

ABSTRACT: In this paper, we present a new method to recognise the leaf type and identify plant species using phenetic parts of the leaf; lobes, apex and base detection. Most of the research in this area focuses on the popular features such as the shape, colour, vein, and texture, which consumes large amounts of computational processing and are not efficient, especially in the Acer database with a high complexity structure of the leaves. This paper is focused on phenetic parts of the leaf which increases accuracy. Detecting the local maxima and local minima are done based on Centroid Contour Distance for Every Boundary Point, using north and south region to recognise the apex and base. Digital morphology is used to measure the leaf shape and the leaf margin. Centroid Contour Gradient is presented to extract the curvature of leaf apex and base. We analyse 32 leaf images of tropical plants and evaluated with two different datasets, Flavia, and Acer. The best accuracy obtained is 94.76% and 82.6% respectively. Experimental results show the effectiveness of the proposed technique without considering the commonly used features with high computational cost.

SUBMITTER: Kolivand H

PROVIDER: S-EPMC5805256 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

An expert botanical feature extraction technique based on phenetic features for identifying plant species.

Kolivand Hoshang H Fern Bong Mei BM Rahim Mohd Shafry Mohd MSM Sulong Ghazali G Baker Thar T Tully David D

PloS one 20180208 2

In this paper, we present a new method to recognise the leaf type and identify plant species using phenetic parts of the leaf; lobes, apex and base detection. Most of the research in this area focuses on the popular features such as the shape, colour, vein, and texture, which consumes large amounts of computational processing and are not efficient, especially in the Acer database with a high complexity structure of the leaves. This paper is focused on phenetic parts of the leaf which increases a ...[more]

PMID: 29420568

Similar Datasets

Project description:ObjectiveTo investigate the effects of Image Biomarker Standardisation Initiative (IBSI) compliance, harmonisation of calculation settings and platform version on the statistical reliability of radiomic features and their corresponding ability to predict clinical outcome.MethodsThe statistical reliability of radiomic features was assessed retrospectively in three clinical datasets (patient numbers: 108 head and neck cancer, 37 small-cell lung cancer, 47 non-small-cell lung cancer). Features were calculated using four platforms (PyRadiomics, LIFEx, CERR and IBEX). PyRadiomics, LIFEx and CERR are IBSI-compliant, whereas IBEX is not. The effects of IBSI compliance, user-defined calculation settings and platform version were assessed by calculating intraclass correlation coefficients and confidence intervals. The influence of platform choice on the relationship between radiomic biomarkers and survival was evaluated using univariable cox regression in the largest dataset.ResultsThe reliability of radiomic features calculated by the different software platforms was only excellent (ICC > 0.9) for 4/17 radiomic features when comparing all four platforms. Reliability improved to ICC > 0.9 for 15/17 radiomic features when analysis was restricted to the three IBSI-compliant platforms. Failure to harmonise calculation settings resulted in poor reliability, even across the IBSI-compliant platforms. Software platform version also had a marked effect on feature reliability in CERR and LIFEx. Features identified as having significant relationship to survival varied between platforms, as did the direction of hazard ratios.ConclusionIBSI compliance, user-defined calculation settings and choice of platform version all influence the statistical reliability and corresponding performance of prognostic models in radiomics.Key points• Reliability of radiomic features varies between feature calculation platforms and with choice of software version. • Image Biomarker Standardisation Initiative (IBSI) compliance improves reliability of radiomic features across platforms, but only when calculation settings are harmonised. • IBSI compliance, user-defined calculation settings and choice of platform version collectively affect the prognostic value of features.

Project description:This study developed a technique system for the measurement, reconstruction, and trait extraction of rice canopy architectures, which have challenged functional-structural plant modeling for decades and have become the foundation of the design of ideo-plant architectures. The system uses the location-separation-measurement method (LSMM) for the collection of data on the canopy architecture and the analytic geometry method for the reconstruction and visualization of the three-dimensional (3D) digital architecture of the rice plant. It also uses the virtual clipping method for extracting the key traits of the canopy architecture such as the leaf area, inclination, and azimuth distribution in spatial coordinates. To establish the technique system, we developed (i) simple tools to measure the spatial position of the stem axis and azimuth of the leaf midrib and to capture images of tillers and leaves; (ii) computer software programs for extracting data on stem diameter, leaf nodes, and leaf midrib curves from the tiller images and data on leaf length, width, and shape from the leaf images; (iii) a database of digital architectures that stores the measured data and facilitates the reconstruction of the 3D visual architecture and the extraction of architectural traits; and (iv) computation algorithms for virtual clipping to stratify the rice canopy, to extend the stratified surface from the horizontal plane to a general curved surface (including a cylindrical surface), and to implement in silico. Each component of the technique system was quantitatively validated and visually compared to images, and the sensitivity of the virtual clipping algorithms was analyzed. This technique is inexpensive and accurate and provides high throughput for the measurement, reconstruction, and trait extraction of rice canopy architectures. The technique provides a more practical method of data collection to serve functional-structural plant models of rice and for the optimization of rice canopy types. Moreover, the technique can be easily adapted for other cereal crops such as wheat, which has numerous stems and leaves sheltering each other.

Dataset Information

An expert botanical feature extraction technique based on phenetic features for identifying plant species.

Publications

An expert botanical feature extraction technique based on phenetic features for identifying plant species.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets