Characterization of Deep Green Infection in Tobacco Leaves Using a Hand-Held Digital Light Projection Based Near-Infrared Spectrometer and an Extreme Learning Machine Algorithm

Zhang, Jianqiang; Liu, Yan; He, Yufeng; Hu, Grace; Nannan, Bai

doi:10.1080/00032719.2020.1738452

Cited by 10 publications

(1 citation statement)

References 16 publications

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“…High-quality production of tobacco leaves needs identification of deep green infection. Janqiang et al proposed an identification methodology for deep green tobacco infections using a portable NIR ( Jianqiang et al, 2020 ).…”

Section: Applications To Agriculturementioning

confidence: 99%

Portable NIR spectroscopy: the route to green analytical chemistry

Gullifa,

Barone,

Papa

et al. 2023

Front. Chem.

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There is a growing interest for cost-effective and nondestructive analytical techniques in both research and application fields. The growing approach by near-infrared spectroscopy (NIRs) pushes to develop handheld devices devoted to be easily applied for in situ determinations. Consequently, portable NIR spectrometers actually result definitively recognized as powerful instruments, able to perform nondestructive, online, or in situ analyses, and useful tools characterized by increasingly smaller size, lower cost, higher robustness, easy-to-use by operator, portable and with ergonomic profile. Chemometrics play a fundamental role to obtain useful and meaningful results from NIR spectra. In this review, portable NIRs applications, published in the period 2019–2022, have been selected to indicate starting references. These publications have been chosen among the many examples of the most recent applications to demonstrate the potential of this analytical approach which, not having the need for extraction processes or any other pre-treatment of the sample under examination, can be considered the “true green analytical chemistry” which allows the analysis where the sample to be characterized is located. In the case of industrial processes or plant or animal samples, it is even possible to follow the variation or evolution of fundamental parameters over time. Publications of specific applications in this field continuously appear in the literature, often in unfamiliar journal or in dedicated special issues. This review aims to give starting references, sometimes not easy to be found.

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Section: Applications To Agriculturementioning

confidence: 99%

Portable NIR spectroscopy: the route to green analytical chemistry

Gullifa,

Barone,

Papa

et al. 2023

Front. Chem.

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Near‐infrared spectroscopy and deep neural networks for early common root rot detection in wheat from multi‐season trials

Xiong,

McCarthy,

Humpal

et al. 2024

Agronomy Journal

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In Australia, the soil‐borne disease common root rot (Bipolaris sorokiniana) (CRR) in wheat (Triticum aestivum L.) leads to substantial yield losses, yet has limited visible aboveground symptoms, making detection and identification labor intensive. Near‐infrared (NIR) spectroscopy offers an early potential identification solution for CRR in wheat and has previously been reported with success for crop disease detection. This study investigated the ability of nondestructive NIR spectroscopy in combination with deep neural networks (DNN), logistic regression (LR), and principal component analysis combined with support vector machines (PCA‐SVM) for early‐stage CRR detection in wheat. NIR spectra of five different wheat varieties with varying resistance to CRR were collected in two seasons of glasshouse and three seasons of field trials using a portable spectrometer. Results demonstrated that DNN outperformed LR and PCA‐SVM, achieving 66%–91% average classification accuracy in glasshouse trials and an average accuracy of 73% with up to 87% in field trials, effectively distinguishing inoculated and non‐inoculated wheat plants from seedling to anthesis stages. Validation with a third season of field data achieved an average of 77% accuracy for the most susceptible variety during the stem elongation stage. NIR reflectance within 1600–1700 nm was identified as most important for estimating CRR presence, with initial detection occurring 35 days after sowing (DAS) in the glasshouse and 46 DAS in the field. In conclusion, a NIR spectrometer with a DNN model successfully performed disease classification, with the potential as a portable early disease detection tool to assist farm management decisions.

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Data stream classification using a deep transfer learning method based on extreme learning machine and recurrent neural network

Eskandari,

Khotanlou

2024

Multimed Tools Appl

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Characterization of Deep Green Infection in Tobacco Leaves Using a Hand-Held Digital Light Projection Based Near-Infrared Spectrometer and an Extreme Learning Machine Algorithm

Cited by 10 publications

References 16 publications

Portable NIR spectroscopy: the route to green analytical chemistry

Portable NIR spectroscopy: the route to green analytical chemistry

Near‐infrared spectroscopy and deep neural networks for early common root rot detection in wheat from multi‐season trials

Data stream classification using a deep transfer learning method based on extreme learning machine and recurrent neural network

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