Rapid quantitative characterization of tea seedlings under lead-containing aerosol particles stress using Vis-NIR spectra

Sanaeifar, Alireza; Zhu, Fengle; Sha, Junjing; Liu, Yuanyuan; He, Yong; Zhan, Zhihao

doi:10.1016/j.scitotenv.2021.149824

Cited by 23 publications

(8 citation statements)

References 49 publications

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“…In recent years, nondestructive methods have been developed to detect internal components of plants. Hyperspectral technology has been widely studied and has proven effective in determining Chl content in various plants [4,[6][7][8].…”

Section: Introductionmentioning

confidence: 99%

“…There are two main approaches for the research on the detection of leaf Chl based on hyperspectral technology: building models based on direct spectral data or vegetation index. For the former, the model is established based on the full spectra or a few bands with characteristic spectral responses [7,[9][10][11]. For the latter, the model is constructed based on multispectral vegetation indices established according to the characteristic bands [12,13].…”

Section: Introductionmentioning

confidence: 99%

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Spectral Preprocessing Combined with Deep Transfer Learning to Evaluate Chlorophyll Content in Cotton Leaves

et al. 2022

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Rapid determination of chlorophyll content is significant for evaluating cotton’s nutritional and physiological status. Hyperspectral technology equipped with multivariate analysis methods has been widely used for chlorophyll content detection. However, the model developed on one batch or variety cannot produce the same effect for another due to variations, such as samples and measurement conditions. Considering that it is costly to establish models for each batch or variety, the feasibility of using spectral preprocessing combined with deep transfer learning for model transfer was explored. Seven different spectral preprocessing methods were discussed, and a self-designed convolutional neural network (CNN) was developed to build models and conduct transfer tasks by fine-tuning. The approach combined first-derivative (FD) and standard normal variate transformation (SNV) was chosen as the best pretreatment. For the dataset of the target domain, fine-tuned CNN based on spectra processed by FD + SNV outperformed conventional partial least squares (PLS) and squares-support vector machine regression (SVR). Although the performance of fine-tuned CNN with a smaller dataset was slightly lower, it was still better than conventional models and achieved satisfactory results. Ensemble preprocessing combined with deep transfer learning could be an effective approach to estimate the chlorophyll content between different cotton varieties, offering a new possibility for evaluating the nutritional status of cotton in the field.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Spectral Preprocessing Combined with Deep Transfer Learning to Evaluate Chlorophyll Content in Cotton Leaves

et al. 2022

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“…The wavebands mainly selected were 450–550 nm, 650–700 nm, and 750–900 nm. Of these, 450–500 nm and 650–700 nm are related to the absorption of blue light and red light by chlorophyll, and 550 nm is the reflection band of green light, which is mainly related to the content of chlorophyll 19 . The 750–900 nm band is related to the overtones of XH (X = C, O, N, etc.)…”

Section: Resultsmentioning

confidence: 99%

“…In the process of hyperspectral data acquisition, noise and baseline drift of the instrument and light scattering are unavoidable, which will affect the modeling performance. 19 Therefore, spectral preprocessing is an essential step. The standard normal variate method can eliminate the influence of light scattering.…”

Section: Spectral Preprocessingmentioning

confidence: 99%

Hyperspectral technique combined with stacking and blending ensemble learning method for detection of cadmium content in oilseed rape leaves

et al. 2022

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Background: Oilseed rape, as one of the most important oil crops, is an important source of vegetable oil and protein for mankind. As a non-essential element for plant growth, heavy metal cadmium (Cd) is easily absorbed by plants. Cd will inhibit the photosynthesis of plants, destroy the cell structure, slow the growth of plants, and affect their development and yield. It is necessary to develop a method based on visible near-infrared (NIR) hyperspectral imaging (HSI) technology to quickly and nondestructively determine the Cd content in rape leaves.Results: Two-layer estimation models were established by combining visible-NIR HSI with ensemble learning methods (stacking and blending). One layer used support vector regression, extreme learning machine, decision tree, and random forest (RF) as basic learners, and the other layer used support vector regression or RF as a meta learner. Different models were used to analyze the spectra of rape treated with five Cd concentrations to obtain the best prediction method. The results showed that the best model to predict Cd content was the stacking ensemble model with RF as the meta learner, with coefficient of determination for prediction of 0.9815 and root-mean-square error for prediction of 5.8969 mg kg −1 . A pseudo-color image was developed using this stacking model to visualize the content and distribution of Cd. Conclusion:The combination of visible-NIR HSI technology and the stacking ensemble learning method is a feasible method to detect the Cd content in rape leaves, which has the potential of being rapid and nondestructive.

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“…In feature band selection, SPA and CARS methods are often employed for band screening, followed by model establishment using the extracted spectral data [ 14 , 36 , 37 ]. Wang et al.…”

Section: Spectral Features For Plant Heavy Metal Stress Inspectionmentioning

confidence: 99%

Application of Visible/Near-Infrared Spectroscopy and Hyperspectral Imaging with Machine Learning for High-Throughput Plant Heavy Metal Stress Phenotyping: A Review

Zhai,

Zhou,

et al. 2023

Plant Phenomics

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Heavy metal pollution is becoming a prominent stress on plants. Plants contaminated with heavy metals undergo changes in external morphology and internal structure, and heavy metals can accumulate through the food chain, threatening human health. Detecting heavy metal stress on plants quickly, accurately, and nondestructively helps to achieve precise management of plant growth status and accelerate the breeding of heavy metal-resistant plant varieties. Traditional chemical reagent-based detection methods are laborious, destructive, time-consuming, and costly. The internal and external structures of plants can be altered by heavy metal contamination, which can lead to changes in plants’ absorption and reflection of light. Visible/near-infrared (V/NIR) spectroscopy can obtain plant spectral information, and hyperspectral imaging (HSI) can obtain spectral and spatial information in simple, speedy, and nondestructive ways. These 2 technologies have been the most widely used high-throughput phenotyping technologies of plants. This review summarizes the application of V/NIR spectroscopy and HSI in plant heavy metal stress phenotype analysis as well as introduces the method of combining spectroscopy with machine learning approaches for high-throughput phenotyping of plant heavy metal stress, including unstressed and stressed identification, stress types identification, stress degrees identification, and heavy metal content estimation. The vegetation indexes, full-range spectra, and feature bands identified by different plant heavy metal stress phenotyping methods are reviewed. The advantages, limitations, challenges, and prospects of V/NIR spectroscopy and HSI for plant heavy metal stress phenotyping are discussed. Further studies are needed to promote the research and application of V/NIR spectroscopy and HSI for plant heavy metal stress phenotyping.

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Rapid quantitative characterization of tea seedlings under lead-containing aerosol particles stress using Vis-NIR spectra

Cited by 23 publications

References 49 publications

Spectral Preprocessing Combined with Deep Transfer Learning to Evaluate Chlorophyll Content in Cotton Leaves

Spectral Preprocessing Combined with Deep Transfer Learning to Evaluate Chlorophyll Content in Cotton Leaves

Hyperspectral technique combined with stacking and blending ensemble learning method for detection of cadmium content in oilseed rape leaves

Application of Visible/Near-Infrared Spectroscopy and Hyperspectral Imaging with Machine Learning for High-Throughput Plant Heavy Metal Stress Phenotyping: A Review

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