Beijing LICA United Technology Limited

Novel Instruments Provide New Opportunities

Hotline: 010-51292601
Technical Technical
News Technical

ASD | Quantitative identification of yellow rust in winter wheat with a new spectral index

Date: 2021-08-02
浏览次数: 12

Quantitative identification of yellow rust in winter wheat with a new spectral index: Development and validation using simulated and experimental data

Yellow rust, caused by Puccinia striiformis f. sp. Tritici, is a serious disease attacking wheat (Triticum aestivum L.) across the globe. The occurrence of yellow rust can result in severe yield reduction and economic loss. Hyperspectral remote sensing has demonstrated potential in detecting yellow rust, with the majority of studies distinguishing qualitatively between diseased and healthy individuals or performing simple grading of disease severity. However, research on the quantification of the severity of yellow rust is limited.

Based on this, to fill the research gap, in this article, a group of scientists obtained spectral and disease severity data at the leaf and canopy scales at the Scientific Research and Experimental Station of the Chinese Academy of Agricultural Science (39°3040N, 116°3620E) in Langfang, Hebei province, China and collected data on VPCPs and disease severity at the leaf scale at the Xiaotangshan National Precision Agriculture Demonstration Research Base (40°1036N, 116°2618E), in Changping District, Beijing, China to 1) explore the variations in vegetation biophysical and biochemical parameters (VPCPs) under different disease severities; 2) investigate the spectral change patterns of wheat yellow rust under the impact of VPCPs and spore colonies (in combination with PROSPECT-D and sensitivity analysis) to determine the suitable feature bands for the index; 3) establish a new yellow rust index, YROI, for the quantitative estimation of yellow rust severity; and 4) assess the accuracy and robustness of the new index and commonly used indices in yellow rust monitoring using leaf- and canopy-scale field survey data.

The authors collected leaf spectra and canopy spectra using ASD Field Spec Spectrometer (Analytical Spectral Devices, Boulder, CO, USA), measured the total chlorophylls (Cab) and total carotenoids (Car) concentrations and leaf water content, and quantified the leaf disease severity using the disease ratio (DR).

ResultsIn the PROSPECT-D simulation, an increase in the Cab content decreased the reflectance at 450–750 nm. The key Cab absorption bands were located in the visible and red-edged bands, with the green and red-edge bands exhibiting the strongest response to a reduction in the Cab content. In particular, the reflectance of the green band increased and the red-edge band exhibited a blue-shift. The 450–550 nm wavelength range was observed to be affected by a decrease in Car content, with the most significant response near 520 nm. The principle leaf water absorption band is located in the infrared region between 900 and 2400 nm. The measurements revealed the reflectance to increase as the water content decreased within this region. Variations in N resulted in spectral reflectance responses throughout the spectral range. The principle dry matter absorption wavelength is located in the infrared region between 750 and 2400 nm. Spectral variations were not significant within this region. For spore spectrum,  the strongest response was observed in the spectral reflectance ranging from 530 to 800 nm. The response was most pronounced in the red band, where the reflectance gradually increased with the spore spectrum proportion increasing, and the morphology gradually flattened. In addition, a blue shift occurred in the red-edge band.

ASD | Quantitative identification of yellow rust in winter wheat with a new spectral index

Variations in the reflectance of simulated spectra with different contents of biophysical and biochemical parameters by PROSPECT-D model, and simulated spectra with different proportions of fungal spores: (a) leaf chlorophyll content, Cab (μg/cm2); (b) leaf carotenoid content, Car (μg/cm2); (c) leaf equivalent water thickness, Cw (cm); (d) leaf structure parameter, N; (e) leaf dry matter, Cm (g/cm2); (f) proportion of spore spectra to mixed spectra, P.

The newly proposed index, YROI, demonstrated strong linearity and the highest correlation (R2 = 0.822, RMSE = 0.070). The fitted line of the scatterplot was close to the 1:1 line (Slope = 0.834). The photochemical reflectance index, PRI, yielded the best estimation among the published indices (R2 = 0.704, RMSE =0.084), with a strong ability to reflect the physiological activity of the leaves. The R2 of two chlorophyll indices, SIPI and MTCI, were above 0.6, while the other chlorophyll index, MCARI, only achieved an accuracy of 0.509. Despite ranking relatively low in the selected indices, the R2 of PhRI, NDVI, and MSR were still close to 0.6, indicating a correlation with yellow rust severity. TVI, ARI and RVSI exhibited poor prediction ability, implying their possible insensitivity to the leaf DR of yellow rust.

ASD | Quantitative identification of yellow rust in winter wheat with a new spectral index

Scatterplots of the measured disease ratio (DR) versus predicted DR for vegetation indices with leaf-scale data (n = 84).

The wheat canopy spectral data and disease index (DI) were used to evaluate the feasibility of the new indices at the canopy scale. YROI exhibited the highest accuracy (R2 = 0.542; RMSE = 0.085) out of all indices. Furthermore, the stability and robustness of the newly proposed index surpassed those of the other indices. PRI exhibited the optimal estimation performance in the published indices, with a R2 and RMSE of 0.514 and 0.086, respectively. Although lower than that of PRI, the accuracy of PSRI was relatively high in the leaf data, and satisfactory results were also obtained in the canopy data. ARI exhibited a better performance in the canopy measurements compared to the leaf scale, indicating its suitability for canopy-scale disease severity estimations of yellow rust. PhRI was centrally ranked (fifth place) in the canopy-scale cross-validation, with a R2 of 0.358 and a RMSE of 0.084. The three chlorophyll indices performed relatively poor in the cross-validation of the canopy data compared to the leaf scale. SIPI (R2 = 0.354; RMSE = 0.083), MTCI (R2 = 0.251; RMSE =0.077) and MCARI (R2 = 0.033; RMSE = 0.046) ranked sixth, ninth and eleventh place, respectively. However, the performance of MCARI was poor at both the leaf- and canopy-scale. MSR and NDVI ranked seventh (R2 = 0.327; RMSE = 0.082) and eighth (R2 = 0.287; RMSE = 0.079) respectively. TVI and RVSI ranked tenth (R2 = 0.191; RMSE = 0.072) and twelfth. (R2= 0.020; RMSE = 0.034), with a similar performance at the leaf scale.

ASD | Quantitative identification of yellow rust in winter wheat with a new spectral index

Scatterplots of measured disease index (DI) versus predicted DI for vegetation indices with canopy-scale data (n = 143).

[Conclusions] In the current study, the authors analyzed the pathogenesis of wheat yellow rust and investigated the variations in VPCPs by increasing disease severity. PROSPECT-D was used to simulate and analyze the effects of VPCPs on the spectra, and the effect of spore colonies on the spectra was also considered using linear mixing. Sensitivity and correlation analysis were performed to obtain the sensitive bands for the construction of YROI. The authors tested the newly established index’s accuracy and robustness by comparing it with current commonly used indices using leaf- and canopy-scale spectral data. YROI surpassed the other indices in the leaf and canopy-scale cross-validation. The accurate and robust YROI for quantitatively determining the disease severity of yellow rust provides a basis for the timely and accurate judgment and treatment of yellow rust in precision agricultural applications. Although the index yielded optimal results in ground-based hyperspectral data, the authors did not validate it using imagery. YROI should be tested and improved using multispectral and hyperspectral imagery based on the numerous sources of uncertainty of such imagery (e.g., scale effect and atmosphere influence). This will provide a reference for the accurate and timely quantitative identification of crop diseases using remote sensing imagery over large areas.

ASD | Quantitative identification of yellow rust in winter wheat with a new spectral index6376351569630123304924221.pdf

News / Related News More
2022 - 11 - 14
Identification and characteristic analysis of urban vegetation spectra under different dust depositionIn recent years, the ecological environment of cities has suffered a lot from damages due to the acceleration of urbanization. The transportation network, traffic flow, industrial activities, and the use of fossil fuels are sources of serious particulate pollutants in the urban greening environmen...
2022 - 11 - 04
Application of Resonon Pika L Hyperspectral Imaging on Individual tree segmentation and tree species classification in subtropical broadleaf forestsAccurate information on tree species is essential in sustainable forest management, ecosystem service assessment, biodiversity monitoring, and ecological environment protection. Therefore, efficient, and cost-effective ways for classifying individual t...
2022 - 10 - 24
An advanced soil organic carbon content prediction model via fused temporal-spatial-spectral (TSS) information based on machine learning and deep learning algorithmsIn the global carbon cycle, soil organic carbon (SOC) is the largest terrestrial carbon reservoir, which accounts for approximately 50-80% of the total terrestrial carbon, contains more than three times as much as the atmosphere or veg...
2022 - 09 - 07
Application of Resonon Pika L Hyperspectral Imaging on the estimation of amino acid contents in maize leavesMaize is one of the most important crops in the world. In maize growth, nitrogen (N) is one of the most important nutrient elements. The nitrogen translocation in maize leaves was mainly in the form of glutamine. The maize yield is correlated well with the amino acids in leaves, such as glut...
Close window】【Print
Copyright ©2018-2023 LICA United Technology Limited

LICA United Technology Limited

Address:The 5th.Building,No.18,Anningzhuang East Road,Haidian District, 100085, Beijing, China.





  • Name:
  • *
  • 公司名称:
  • *
  • 地址:
  • *
  • 电话:
  • *
  • 传真:
  • *
  • E-mail:
  • *
  • 邮政编码:
  • *
  • 留言主题:
  • *
  • Details:
  • *
Follow us
  • Wechat
  • Mobile Website