Pathogenetic process monitoring and early detection of pear black spot disease caused by Alternaria alternata using hyperspectral imaging

Pan, Ting-Tiao; Erkinbaev, Chyngyz; Sun, Da‐Wen; Paliwal, Jitendra; Pu, Hongbin

doi:10.1016/j.postharvbio.2019.04.005

Cited by 73 publications

(27 citation statements)

References 66 publications

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“…The moisture contents of the pulp, flesh, and peel were quite similar, up to 95 g/100 g (fresh weight basis), and were similar to those in watermelon (Cecilio et al, 2018), but higher than those in Kurt pear (Pan et al, 2019). As the flesh had high moisture content and is considered palatable and crisp, it is suitable for use as fresh food; however, its high moisture content relatively shortens its storage time.…”

Section: Basic Nutrientsmentioning

confidence: 73%

Nutrient evaluation of the seed, pulp, flesh, and peel of spaghetti squash

Ramaswamy

et al. 2022

Food Sci. Technol

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Some researchers have discussed the effect of agronomic factors of SS have been shown to have a significant effect on its chemical composition of various species of squash, although there are few studies concerning spaghetti squash. So far, the main research is to compare the contents of sugar, carotenoid, retinol and vitamin C in the fruit of several varieties of spaghetti spaghetti (Beany et al. 2003;Wadas & Kalinowski, 2010;Wadas et al. 2012;Wadas & Borysiak-Marciniak, 2016), and there has been relatively little attention paid to mineral components, amino acid, functional nutrients (Kulczyński & Gramza-Michalowska, 2019). In China, SS is grown mainly in Shanghai, Jiangsu, Shandong, and Anhui provinces. Among them, the Sixian County of Anhui Province employs a scientific planting method, which has contributed to an increase in the production, quality, and storage time of SS. SS is distributed widely throughout the county and country, with

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Section: Basic Nutrientsmentioning

confidence: 73%

Nutrient evaluation of the seed, pulp, flesh, and peel of spaghetti squash

Ramaswamy

et al. 2022

Food Sci. Technol

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“…(2020) Degree of aflatoxin contamination in peanut kernels 400–720 Fisher method: obtaining narrow band spectrum De-noising, contrast enhancement; Image thresholding Radial basis function Grid search optimization method: Kernel parameter values Five-fold 70:30 MATLAB R2015b 96% Zhongzhi et al. (2020) Detection of black spot disease in pear 400–1000 1st order derivative, multiplicative signal correction (MSC), and mean centering Image segmentation: Spectral angle mapper Radial basis function – Five-fold 70:30 MATLAB R2017a 98% Pan et al. (2019) Identification of adulterated cooked millet flour 900–1700 CARS (Competitive Adaptive Weighted sampling) Image thresholding Radial basis function Grid search optimization method: Kernel parameter values Ten-fold 67:33 MATLAB R2011b 100% Shao et al.…”

Section: Machine Learning Techniquesmentioning

confidence: 99%

Machine learning techniques for analysis of hyperspectral images to determine quality of food products: A review

Saha

Manickavasagan

2021

Current Research in Food Science

265

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Non-destructive testing techniques have gained importance in monitoring food quality over the years. Hyperspectral imaging is one of the important non-destructive quality testing techniques which provides both spatial and spectral information. Advancement in machine learning techniques for rapid analysis with higher classification accuracy have improved the potential of using this technique for food applications. This paper provides an overview of the application of different machine learning techniques in analysis of hyperspectral images for determination of food quality. It covers the principle underlying hyperspectral imaging, the advantages, and the limitations of each machine learning technique. The machine learning techniques exhibited rapid analysis of hyperspectral images of food products with high accuracy thereby enabling robust classification or regression models. The selection of effective wavelengths from the hyperspectral data is of paramount importance since it greatly reduces the computational load and time which enhances the scope for real time applications. Due to the feature learning nature of deep learning, it is one of the most promising and powerful techniques for real time applications. However, the field of deep learning is relatively new and need further research for its full utilization. Similarly, lifelong machine learning paves the way for real time HSI applications but needs further research to incorporate the seasonal variations in food quality. Further, the research gaps in machine learning techniques for hyperspectral image analysis, and the prospects are discussed.

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“…Hypersepctral imaging technique captures the image data with a large number of consecutive narrow bands spanning the visible-to-infrared spectrums (Chen et al 2019;Zhang, Li, and Du 2019). Owing to differences in reflectivity for different materials under different electromagnetic spectrums, this technique is very effective to discriminate the composition of material and has been widely used in the fields of agriculture, forestry, geology, oceanography, meteorology, hydrology, military, environmental monitoring (Cao et al 2019;Liu et al 2017;Pan et al 2019;Yakovliev et al 2019;Chen, Xiao, and Li 2016). In these applications, a fundamental task is to obtain the class types in Hyper-Spectral Image (HSI) (Amini et al 2018).…”

Section: Introductionmentioning

confidence: 99%

“…In these applications, a fundamental task is to obtain the class types in Hyper-Spectral Image (HSI) (Amini et al 2018). Based on the imaging procedure, HSI contains rich spectral and spatial features (Zhang and Du 2012;Natsagdorj et al 2017). For a large number of narrow bands, they have a strong correlation that results in massive redundant information in HSI (Du et al 2018;Mohanty, Happy, and Routray 2019a).…”

Section: Introductionmentioning

confidence: 99%

Review on graph learning for dimensionality reduction of hyperspectral image

Zhang

Luo

2020

Geo-spatial Information Science

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Graph learning is an effective manner to analyze the intrinsic properties of data. It has been widely used in the fields of dimensionality reduction and classification for data. In this paper, we focus on the graph learning-based dimensionality reduction for a hyperspectral image. Firstly, we review the development of graph learning and its application in a hyperspectral image. Then, we mainly discuss several representative graph methods including two manifold learning methods, two sparse graph learning methods, and two hypergraph learning methods. For manifold learning, we analyze neighborhood preserving embedding and locality preserving projections which are two classic manifold learning methods and can be transformed into the form of a graph. For sparse graph, we introduce sparsity preserving graph embedding and sparse graph-based discriminant analysis which can adaptively reveal data structure to construct a graph. For hypergraph learning, we review binary hypergraph and discriminant hyper-Laplacian projection which can represent the high-order relationship of data.

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Pathogenetic process monitoring and early detection of pear black spot disease caused by Alternaria alternata using hyperspectral imaging

Cited by 73 publications

References 66 publications

Nutrient evaluation of the seed, pulp, flesh, and peel of spaghetti squash

Nutrient evaluation of the seed, pulp, flesh, and peel of spaghetti squash

Machine learning techniques for analysis of hyperspectral images to determine quality of food products: A review

Review on graph learning for dimensionality reduction of hyperspectral image

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