{"title":"Precision variety identification of shelled and in-shell pecans using hyperspectral imaging with machine learning","authors":"Ebenezer Olaniyi , Christopher Kucha , Priyanka Dahiya , Allison Niu","doi":"10.1016/j.infrared.2024.105570","DOIUrl":null,"url":null,"abstract":"<div><div>Pecans are essential nuts containing polyunsaturated fatty acids and dietary fiber which offer health benefits to humans. They are exported and sold in both in-shell and shelled forms. However, varietal identification poses a challenge to both producers and processors, which results in variety substitution for economic advantages. The aim of this study was to investigate the efficacy of visible near-infrared (VNIR) and near-infrared (NIR) hyperspectral imaging systems (HSI) to accurately identify pecan cultivars (Cape fear, Desirable, Stuart blend, and Sumner). The in-shell and shelled spectra were acquired using VNIR and NIR-HSI at wavelengths 400–1000 nm and 900–1700 nm, respectively. The spectra dimensionality was reduced using principal component analysis (PCA). Thereafter, the selected principal components (PCs) were used to build six machine learning classifiers (Decision Tree, Random Forest, Gradient Boosting, Partial Least Square Discriminant Analysis, Support Vector Machine, and Linear Discriminant Analysis (LDA)) for four-class classification. LDA with and without PCA achieved the highest accuracy for both pecan forms. For shelled pecans, the LDA without PCA achieved 90.59 % and increased to 91.67 % accuracy with PCA on the VNIR spectra, while the LDA without PCA achieved 93.36 % and increased to 93.52 % accuracy with PCA on the NIR spectra. For the in-shell pecans, LDA without PCA achieved 98.59 % and increased to 99.12 % accuracy with PCA on the VNIR spectra, while LDA with and without PCA achieved 98.26 % accuracy for the NIR spectra. Moreover, Successive Projection Algorithm was also implemented for wavelength selection and modeling with satisfactory results. Overall, higher accuracy was achieved in the in-shell pecan. This study revealed the usefulness of HSI systems in identifying pecan varieties.</div></div>","PeriodicalId":13549,"journal":{"name":"Infrared Physics & Technology","volume":null,"pages":null},"PeriodicalIF":3.1000,"publicationDate":"2024-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Infrared Physics & Technology","FirstCategoryId":"101","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1350449524004547","RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"INSTRUMENTS & INSTRUMENTATION","Score":null,"Total":0}
引用次数: 0
Abstract
Pecans are essential nuts containing polyunsaturated fatty acids and dietary fiber which offer health benefits to humans. They are exported and sold in both in-shell and shelled forms. However, varietal identification poses a challenge to both producers and processors, which results in variety substitution for economic advantages. The aim of this study was to investigate the efficacy of visible near-infrared (VNIR) and near-infrared (NIR) hyperspectral imaging systems (HSI) to accurately identify pecan cultivars (Cape fear, Desirable, Stuart blend, and Sumner). The in-shell and shelled spectra were acquired using VNIR and NIR-HSI at wavelengths 400–1000 nm and 900–1700 nm, respectively. The spectra dimensionality was reduced using principal component analysis (PCA). Thereafter, the selected principal components (PCs) were used to build six machine learning classifiers (Decision Tree, Random Forest, Gradient Boosting, Partial Least Square Discriminant Analysis, Support Vector Machine, and Linear Discriminant Analysis (LDA)) for four-class classification. LDA with and without PCA achieved the highest accuracy for both pecan forms. For shelled pecans, the LDA without PCA achieved 90.59 % and increased to 91.67 % accuracy with PCA on the VNIR spectra, while the LDA without PCA achieved 93.36 % and increased to 93.52 % accuracy with PCA on the NIR spectra. For the in-shell pecans, LDA without PCA achieved 98.59 % and increased to 99.12 % accuracy with PCA on the VNIR spectra, while LDA with and without PCA achieved 98.26 % accuracy for the NIR spectra. Moreover, Successive Projection Algorithm was also implemented for wavelength selection and modeling with satisfactory results. Overall, higher accuracy was achieved in the in-shell pecan. This study revealed the usefulness of HSI systems in identifying pecan varieties.
期刊介绍:
The Journal covers the entire field of infrared physics and technology: theory, experiment, application, devices and instrumentation. Infrared'' is defined as covering the near, mid and far infrared (terahertz) regions from 0.75um (750nm) to 1mm (300GHz.) Submissions in the 300GHz to 100GHz region may be accepted at the editors discretion if their content is relevant to shorter wavelengths. Submissions must be primarily concerned with and directly relevant to this spectral region.
Its core topics can be summarized as the generation, propagation and detection, of infrared radiation; the associated optics, materials and devices; and its use in all fields of science, industry, engineering and medicine.
Infrared techniques occur in many different fields, notably spectroscopy and interferometry; material characterization and processing; atmospheric physics, astronomy and space research. Scientific aspects include lasers, quantum optics, quantum electronics, image processing and semiconductor physics. Some important applications are medical diagnostics and treatment, industrial inspection and environmental monitoring.