Quantitative Assessment of Apple Mosaic Disease Severity Based on Hyperspectral Images and Chlorophyll Content

Liu, Yanfu; Zhang, Yu; Jiang, Danyao; Zhang, Zijuan; Chang, Qingrui

doi:10.3390/rs15082202

Cited by 21 publications

(8 citation statements)

References 56 publications

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“…Additionally, many researchers are studying the use of stacking models for the inversion of metrics, which can combine the advantages of multiple algorithms and be more generalizable [51]. For example, Liu et al, (2023) constructed a high-precision stacked inversion model for apple leaf chlorophyll content based on boosting and stacking strategies; the R 2 value of its validation set reached 0.9644, which was better than that of the traditional integrated learning model [52]. Lin et al, (2022) constructed a stacked AdaBoost model to invert the heavy metal content in soil; the stacked model was more stable and had better prediction accuracy than the traditional single machine learning model [53].…”

Section: Discussionmentioning

confidence: 99%

Hyperspectral Analysis and Regression Modeling of SPAD Measurements in Leaves of Three Mangrove Species

Li,

Cui,

Dou

et al. 2023

Forests

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Mangroves have important roles in regulating climate change, and in reducing the impact of wind and waves. Analysis of the chlorophyll content of mangroves is important for monitoring their health, and their conservation and management. Thus, this study aimed to apply four regression models, eXtreme Gradient Boosting (XGBoost), Random Forest (RF), Partial Least Squares (PLS) and Adaptive Boosting (AdaBoost), to study the inversion of Soil Plant Analysis Development (SPAD) values obtained from near-ground hyperspectral data of three dominant species, Bruguiera sexangula (Lour.) Poir. (B. sexangula), Ceriops tagal (Perr.) C. B. Rob. (C. tagal) and Rhizophora apiculata Blume (R. apiculata) in Qinglan Port Mangrove Nature Reserve. The accuracy of the model was evaluated using R2, RMSE, and MAE. The mean SPAD values of R. apiculata (SPADavg = 66.57), with a smaller dispersion (coefficient of variation of 6.59%), were higher than those of C. tagal (SPADavg = 61.56) and B. sexangula (SPADavg = 58.60). The first-order differential transformation of the spectral data improved the accuracy of the prediction model; R2 was mostly distributed in the interval of 0.4 to 0.8. The accuracy of the XGBoost model was less affected by species differences with the best stability, with RMSE at approximately 3.5 and MAE at approximately 2.85. This study provides a technical reference for large-scale detection and management of mangroves.

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Section: Discussionmentioning

confidence: 99%

Hyperspectral Analysis and Regression Modeling of SPAD Measurements in Leaves of Three Mangrove Species

Li,

Cui,

Dou

et al. 2023

Forests

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“…A white background was used for sample shooting shooting area was 14 by 20 cm. The design of the installation was based on the st [15][16][17], and is shown in Figure 1.…”

Section: Hyperspectral Camera Setupmentioning

confidence: 99%

Hyperspectral Remote Sensing for Early Detection of Wheat Leaf Rust Caused by Puccinia triticina

Terentev,

Badenko,

Shaydayuk

et al. 2023

Agriculture

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Early crop disease detection is one of the most important tasks in plant protection. The purpose of this work was to evaluate the early wheat leaf rust detection possibility using hyperspectral remote sensing. The first task of the study was to choose tools for processing and analyze hyperspectral remote sensing data. The second task was to analyze the wheat leaf biochemical profile by chromatographic and spectrophotometric methods. The third task was to discuss a possible relationship between hyperspectral remote sensing data and the results from the wheat leaves, biochemical profile analysis. The work used an interdisciplinary approach, including hyperspectral remote sensing and data processing methods, as well as spectrophotometric and chromatographic methods. As a result, (1) the VIS-NIR spectrometry data analysis showed a high correlation with the hyperspectral remote sensing data; (2) the most important wavebands for disease identification were revealed (502, 466, 598, 718, 534, 766, 694, 650, 866, 602, 858 nm). An early disease detection accuracy of 97–100% was achieved from fourth dai (day/s after inoculation) using SVM.

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“…All of them would lead to spectrum changes. Through quantitative analysis of spectral variations of the foliar, PRS will have a doorway through which to locate diseased plants [47]. Recently, the majority of studies mostly on the severity assessment of WSR were conducted during the symptomatic stage.…”

Section: The Severity Assessment Of Wsrmentioning

confidence: 99%

Detecting the Minimum Limit on Wheat Stripe Rust in the Latent Period Using Proximal Remote Sensing Coupled with Duplex Real-Time PCR and Machine Learning

Liu,

Sun,

Wen

et al. 2023

Plants

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Wheat stripe rust (WSR) is an airborne disease that causes severe damage to wheat. The rapid and early detection of WSR is essential for the prevention and control of this disease. The minimum detection limit (MDL) is one of the most important characteristics of quantitative methods that can be used to determine the scope and applicability of a measurement technique. Three wheat cultivars were inoculated with Puccinia striiformis f.sp. tritici (Pst), and a spectrometer was used to collect the canopy hyperspectral data, and the Pst content was obtained via a duplex real-time polymerase chain reaction (PCR) during the latent period, respectively. The disease index (DI) and molecular disease index (MDI) were calculated. The regression tree algorithm was used to determine the MDL of the Pst based on hyperspectral feature parameters. The logistic, IBK, and random committee algorithms were used to construct the classification model based on the MDL. The results showed that when the MDL was 0.7, IBK had the best recognition accuracy. The optimal model, which used the spectral feature R_2nd.dv ((the second derivative of the original hyperspectral value)) and the modeling ratio 2:1, had an accuracy of 91.67% on the testing set and 90.67% on the 10-fold cross-validation. Thus, during the latent period, the MDL of Pst was determined using hyperspectral technology as 0.7.

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Quantitative Assessment of Apple Mosaic Disease Severity Based on Hyperspectral Images and Chlorophyll Content

Cited by 21 publications

References 56 publications

Hyperspectral Analysis and Regression Modeling of SPAD Measurements in Leaves of Three Mangrove Species

Hyperspectral Analysis and Regression Modeling of SPAD Measurements in Leaves of Three Mangrove Species

Hyperspectral Remote Sensing for Early Detection of Wheat Leaf Rust Caused by Puccinia triticina

Detecting the Minimum Limit on Wheat Stripe Rust in the Latent Period Using Proximal Remote Sensing Coupled with Duplex Real-Time PCR and Machine Learning

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