Feature selection from hyperspectral imaging for guava fruit defects detection

2017 ◽  
Author(s):  
Mohamad Zubir Mat Jafri ◽  
Sou Ching Tan
Keyword(s):  
Feature Selection ◽  
Hyperspectral Imaging ◽  
Guava Fruit

Quantitative Analysis of Cadmium Content in Tomato Leaves Based on Hyperspectral Image and Feature Selection

2018 ◽  
Vol 34 (5) ◽  
pp. 789-798 ◽  
Author(s):  
Yuechun Zhang ◽  
Jun Sun ◽  
Junyan Li ◽  
Xiaohong Wu ◽  
Chunmei Dai
Keyword(s):  
Feature Selection ◽  
Hyperspectral Imaging ◽  
Hyperspectral Image ◽  
Imaging System ◽  
Population Analysis ◽  
Cadmium Stress ◽  
Hyperspectral Images ◽  
Cadmium Content ◽  
Variable Combination ◽  
Non Destructive

Abstract.In order to ensure that safe and healthy tomatoes can be provided to people, a method for quantitative determination of cadmium content in tomato leaves based on hyperspectral imaging technology was put forward in this study. Tomato leaves with seven cadmium stress gradients were studied. Hyperspectral images of all samples were firstly acquired by the hyperspectral imaging system, then the spectral data were extracted from the hyperspectral images. To simplify the model, three algorithms of competitive adaptive reweighted sampling (CARS), variable combination population analysis (VCPA) and bootstrapping soft shrinkage (BOSS) were used to select the feature wavelengths ranging from 431 to 962 nm. Final results showed that BOSS can improve prediction performance and greatly reduce features when compared with the other two selection methods. The BOSS model got the best accuracy in calibration and prediction with R2c of 0.9907 and RMSEC of 0.4257mg/kg, R2p of 0.9821, and RMSEP of 0.6461 mg/kg. Hence, the method of hyperspectral technology combined with the BOSS feature selection is feasible for detecting the cadmium content of tomato leaves, which can potentially provide a new method and thought for cadmium content detection of other crops. Keywords: Feature selection, Hyperspectral image technology, Non-destructive analysis, Regression model, Tomato leaves.

scholarly journals Data Fusion of Two Hyperspectral Imaging Systems with Complementary Spectral Sensing Ranges for Blueberry Bruising Detection

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4463 ◽  
Author(s):  
Shuxiang Fan ◽  
Changying Li ◽  
Wenqian Huang ◽  
Liping Chen
Keyword(s):  
Feature Selection ◽  
Data Fusion ◽  
Hyperspectral Imaging ◽  
Multispectral Imaging ◽  
Feature Selection Method ◽  
Tunable Filter ◽  
Support Vector ◽  
Spectroscopic Techniques ◽  
Decision Level ◽  
Spectral Ranges

Currently, the detection of blueberry internal bruising focuses mostly on single hyperspectral imaging (HSI) systems. Attempts to fuse different HSI systems with complementary spectral ranges are still lacking. A push broom based HSI system and a liquid crystal tunable filter (LCTF) based HSI system with different sensing ranges and detectors were investigated to jointly detect blueberry internal bruising in the lab. The mean reflectance spectrum of each berry sample was extracted from the data obtained by two HSI systems respectively. The spectral data from the two spectroscopic techniques were analyzed separately using feature selection method, partial least squares-discriminant analysis (PLS-DA), and support vector machine (SVM), and then fused with three data fusion strategies at the data level, feature level, and decision level. The three data fusion strategies achieved better classification results than using each HSI system alone. The decision level fusion integrating classification results from the two instruments with selected relevant features achieved more promising results, suggesting that the two HSI systems with complementary spectral ranges, combined with feature selection and data fusion strategies, could be used synergistically to improve blueberry internal bruising detection. This study was the first step in demonstrating the feasibility of the fusion of two HSI systems with complementary spectral ranges for detecting blueberry bruising, which could lead to a multispectral imaging system with a few selected wavelengths and an appropriate detector for bruising detection on the packing line.

Pixel-level aflatoxin detecting in maize based on feature selection and hyperspectral imaging

2020 ◽  
Vol 234 ◽  
pp. 118269
Author(s):  
Jiyue Gao ◽  
Jiangong Ni ◽  
Dawei Wang ◽  
Limiao Deng ◽  
Juan Li ◽  
...  
Keyword(s):  
Feature Selection ◽  
Hyperspectral Imaging

Methods for performing dimensionality reduction in hyperspectral image classification

2018 ◽  
Vol 26 (1) ◽  
pp. 61-75 ◽  
Author(s):  
Jun-Li Xu ◽  
Carlos Esquerre ◽  
Da-Wen Sun
Keyword(s):  
Monte Carlo ◽  
Feature Selection ◽  
Dimensionality Reduction ◽  
Hyperspectral Imaging ◽  
Hyperspectral Image ◽  
Selection Function ◽  
Selectivity Ratio ◽  
White Stripe ◽  
Vast Number ◽  
Linear Discriminant

This paper provides several useful strategies for performing the dimensionality reduction in hyperspectral imaging data, with detailed command line scripts in the Matlab computing language as the supplementary data. Due to the vast number of data dimensionality reduction methods available, this paper will mainly focus on some commonly used approaches adopted in hyperspectral imaging. In this work, transformation-based methods include principal component analysis and linear discriminant analysis, while band selection methods are comprised of partial least squares regression combined with the variable importance in the projection scores, selectivity ratio, and significance multivariate correlation; Monte Carlo sampling-based methods including enhanced Monte Carlo variable selection and competitive adaptive reweighted sampling; model population analysis-based methods from libPLS including uninformative variable elimination, random frog, and PHADIA; Matlab built-in functions for feature selection including Relieff, stepwise regression, and sequential feature selection; and the selection method guided by genetic algorithm. The example data included in supplementary material, also available for download, will be used to simplify decision tree models for differentiation of white stripe and red muscle pixels on salmon fillets, since classification is one of the main application domains of hyperspectral imaging. In this work, there are many original codes and functions developed, such as fast multiple scattering correction preprocessing, outlier detection, optimal cutoff value determination, spikes, and dead spectra identification and correction for hyperspectral image. More importantly, a further selection function based on variance inflation factor is proposed to diagnose and alleviate collinearity problem because collinearity and multicollinearity are always expected to be severe in the spectral data. In this work, step-by-step procedure is provided for easy adaptation of these strategies to individual case.

scholarly journals Nondestructive Detection of Codling Moth Infestation in Apples Using Pixel-Based NIR Hyperspectral Imaging with Machine Learning and Feature Selection

Foods ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 8
Author(s):  
Nader Ekramirad ◽  
Alfadhl Y. Khaled ◽  
Lauren E. Doyle ◽  
Julia R. Loeb ◽  
Kevin D. Donohue ◽  
...  
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Multispectral Imaging ◽  
Cydia Pomonella ◽  
Region Of Interest ◽  
Codling Moth ◽  
Ensemble Classifier ◽  
Validation Dataset

Codling moth (CM) (Cydia pomonella L.), a devastating pest, creates a serious issue for apple production and marketing in apple-producing countries. Therefore, effective nondestructive early detection of external and internal defects in CM-infested apples could remarkably prevent postharvest losses and improve the quality of the final product. In this study, near-infrared (NIR) hyperspectral reflectance imaging in the wavelength range of 900–1700 nm was applied to detect CM infestation at the pixel level for three organic apple cultivars, namely Gala, Fuji and Granny Smith. An effective region of interest (ROI) acquisition procedure along with different machine learning and data processing methods were used to build robust and high accuracy classification models. Optimal wavelength selection was implemented using sequential stepwise selection methods to build multispectral imaging models for fast and effective classification purposes. The results showed that the infested and healthy samples were classified at pixel level with up to 97.4% total accuracy for validation dataset using a gradient tree boosting (GTB) ensemble classifier, among others. The feature selection algorithm obtained a maximum accuracy of 91.6% with only 22 selected wavelengths. These findings indicate the high potential of NIR hyperspectral imaging (HSI) in detecting and classifying latent CM infestation in apples of different cultivars.

Hyperspectral Imaging Remote Sensing

2016 ◽  
Author(s):  
Dimitris Manolakis ◽  
Ronald Lockwood ◽  
Thomas Cooley
Keyword(s):  
Remote Sensing ◽  
Hyperspectral Imaging
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