Potential of Visible and Near-Infrared Hyperspectral Imaging for Detection of Diaphania pyloalis Larvae and Damage on Mulberry Leaves

Huang, Lingxia; Yang, Liang; Meng, Liuwei; Wang, Jingyu; Li, Shaojia; Fu, Xiaping; Du, Xiaoqiang; Wu, Di

doi:10.3390/s18072077

Cited by 12 publications

(6 citation statements)

References 30 publications

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“…However, in approach I, there were more classes used for the discriminant analysis. With more classes, each variable had different stabilities for each corresponding class (i.e., the number of stability values equaled the number of columns in the B matrix); then, the variable stability was selected as the median among the stability values of all the classes in the corresponding model [16]. For the case of four classes, the stabilities of the second-lowest class were taken.…”

Section: Uninformative Variable Elimination Partial-least Squares Discriminant Analysis (Uve-pls-da)mentioning

confidence: 99%

Combined Machine Learning and Molecular Modelling Workflow for the Recognition of Potentially Novel Fungicides

Jović

Šmuc

2020

Molecules

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Novel machine learning and molecular modelling filtering procedures for drug repurposing have been carried out for the recognition of the novel fungicide targets of Cyp51 and Erg2. Classification and regression approaches on molecular descriptors have been performed using stepwise multilinear regression (FS-MLR), uninformative-variable elimination partial-least square regression, and a non-linear method called Forward Stepwise Limited Correlation Random Forest (FS-LM-RF). Altogether, 112 prediction models from two different approaches have been built for the descriptor recognition of fungicide hit compounds. Aiming at the fungal targets of sterol biosynthesis in membranes, antifungal hit compounds have been selected for docking experiments from the Drugbank database using the Autodock4 molecular docking program. The results were verified by Gold Protein-Ligand Docking Software. The best-docked conformation, for each high-scored ligand considered, was submitted to quantum mechanics/molecular mechanics (QM/MM) gradient optimization with final single point calculations taking into account both the basis set superposition error and thermal corrections (with frequency calculations). Finally, seven Drugbank lead compounds were selected based on their high QM/MM scores for the Cyp51 target, and three were selected for the Erg2 target. These lead compounds could be recommended for further in vitro studies.

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Section: Uninformative Variable Elimination Partial-least Squares Discriminant Analysis (Uve-pls-da)mentioning

confidence: 99%

Combined Machine Learning and Molecular Modelling Workflow for the Recognition of Potentially Novel Fungicides

Jović

Šmuc

2020

Molecules

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“…Espectrógrafos 400-1000 nm HSI visibles e infrarrojas cercanas podrían distinguir las larvas de Diaphania pyloalis (Huang et al, 2018).…”

Section: Desarrollo Un Sistema Hsi En La Región Visible E Infrarroja ...unclassified

“…Del mismo modo Huang et al (2018) realizaron un estudio donde tuvieron como objetivo desarrollar un sistema de imágenes hiperespectrales en la región visible e infrarroja cercana (NIR) (400-1700 nm), los resultados de este estudio determinaron que las imágenes hiperespectrales visibles e infrarrojas cercanas podrían distinguir las larvas de Diaphania pyloalis y su daño de la vena de la hoja y el mesófilo sano de una manera rápida y no destructiva. Las imágenes hiperespectrales son una tecnología que se puede utilizar para monitorear las respuestas de las plantas al estrés, estas tienen un espectro completo para cada píxel de la imagen, brindando información detallada sobre la reflectancia espectral de la planta.…”

Section: Frambuesaunclassified

Uso de las imágenes hiperespectrales e imágenes digitales en bayas: Anomalías, enfermedades, daños mecánicos, firmeza, madurez y morfometría

Sánchez¹,

Reyes²

2023

Manglar

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There are different types of berries, one of the best-known, nutritious and important is the blueberry. The modern processing of these fruits guarantees high quality, better marketing of the product and an estimate of its useful life. The aim of this review was to provide scientific information on the physicochemical characteristics of different berries using hyperspectral imaging technology and digital imaging. These technologies present trends with satisfactory results in various technological and research fields. The findings obtained show that hyperspectral imaging technology and digital imaging technology have been of great interest in recent years, because they are non-destructive technologies, which allow good predictions in the detection of anomalies in berries, considering them Robust, reliable tools with high potential for use in the large industry in evaluating the quality of berries, making it possible to offer more suitable products for the consumer. With the advancement of technology, new possibilities for future studies are presented to obtain models that are faster to process and with greater statistical precision.

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“…Plant diseases and pest detection is a significant research field of hyperspectral imaging. Previous studies have proved that hyperspectral imaging can identify the outbreak and dynamics of plant diseases and pests ( Moghadam et al, 2017 ; Huang et al, 2018 ). In the application of hyperspectral imaging in pest detection as a branch of plant science research direction, the current main research includes pest identification ( Liu et al, 2016 ) and pest infection degree classification ( Lu and Ariana, 2013 ).…”

Section: Introductionmentioning

confidence: 99%

Combining Multi-Dimensional Convolutional Neural Network (CNN) With Visualization Method for Detection of Aphis gossypii Glover Infection in Cotton Leaves Using Hyperspectral Imaging

Yan

Jiao

et al. 2021

Front. Plant Sci.

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Cotton is a significant economic crop. It is vulnerable to aphids (Aphis gossypii Glovers) during the growth period. Rapid and early detection has become an important means to deal with aphids in cotton. In this study, the visible/near-infrared (Vis/NIR) hyperspectral imaging system (376–1044 nm) and machine learning methods were used to identify aphid infection in cotton leaves. Both tall and short cotton plants (Lumianyan 24) were inoculated with aphids, and the corresponding plants without aphids were used as control. The hyperspectral images (HSIs) were acquired five times at an interval of 5 days. The healthy and infected leaves were used to establish the datasets, with each leaf as a sample. The spectra and RGB images of each cotton leaf were extracted from the hyperspectral images for one-dimensional (1D) and two-dimensional (2D) analysis. The hyperspectral images of each leaf were used for three-dimensional (3D) analysis. Convolutional Neural Networks (CNNs) were used for identification and compared with conventional machine learning methods. For the extracted spectra, 1D CNN had a fine classification performance, and the classification accuracy could reach 98%. For RGB images, 2D CNN had a better classification performance. For HSIs, 3D CNN performed moderately and performed better than 2D CNN. On the whole, CNN performed relatively better than conventional machine learning methods. In the process of 1D, 2D, and 3D CNN visualization, the important wavelength ranges were analyzed in 1D and 3D CNN visualization, and the importance of wavelength ranges and spatial regions were analyzed in 2D and 3D CNN visualization. The overall results in this study illustrated the feasibility of using hyperspectral imaging combined with multi-dimensional CNN to detect aphid infection in cotton leaves, providing a new alternative for pest infection detection in plants.

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Potential of Visible and Near-Infrared Hyperspectral Imaging for Detection of Diaphania pyloalis Larvae and Damage on Mulberry Leaves

Cited by 12 publications

References 30 publications

Combined Machine Learning and Molecular Modelling Workflow for the Recognition of Potentially Novel Fungicides

Combined Machine Learning and Molecular Modelling Workflow for the Recognition of Potentially Novel Fungicides

Uso de las imágenes hiperespectrales e imágenes digitales en bayas: Anomalías, enfermedades, daños mecánicos, firmeza, madurez y morfometría

Combining Multi-Dimensional Convolutional Neural Network (CNN) With Visualization Method for Detection of Aphis gossypii Glover Infection in Cotton Leaves Using Hyperspectral Imaging

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