Feasibility assessment of multi-spectral satellite sensors in monitoring and discriminating wheat diseases and insects

Yuan, Lin; Zhang, Haibo; Zhang, Yuntao; Chen, Xing; Bao, Zhiyan

doi:10.1016/j.ijleo.2016.11.206

Cited by 24 publications

(16 citation statements)

References 29 publications

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“…, was proved to be highly correlated with rust disease severity by using airborne hyperspectral imaging. Satellite remote sensing was also adopted in [21,22] for the large-scale damage evaluation caused by yellow rust disease. Very recently, there are also several studies on yellow rust monitoring with an UAV at field scales.…”

Section: Introductionmentioning

confidence: 99%

Spatio-temporal monitoring of wheat yellow rust using UAV multispectral imagery

Liu

et al. 2019

Computers and Electronics in Agriculture

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This work is focused on the spatio-temporal monitoring of winter wheat inoculated with various levels of yellow rust inoculum during the entire growth season. A dedicated workflow is devised to obtain time-series five-bands (visible-infrared) aerial imageries with a multispectral camera and an Unmanned Aerial Vehicle. A number of spectral indices are drawn so that the sensitive ones can be identified by statistical dependency analysis; particularly, their discriminating capabilities are evaluated at different stages for both wheat pixel segmentation and yellow rust severity. Then the spatial-temporal changes of sensitive bands/indices are evaluated and analysed quantitatively. A validation field experiment was designed in 2017-2018 by inoculating wheat with one of the six levels of yellow rust inoculum. Five-bands RedEdge camera on-board DJI S1000 was used to capture aerial images at eight time points covering the entire growth season at an altitude of about 20 meters with a ground resolution of 1-1.5 cm/pixel. Experimental results via spatio-temporal analysis show that: (1) various bands/indices should be used for wheat segmentation at different stages; (2) no bands/indices differences are observed for yellow rust inoculated wheat plots in both incubation stage (9 days after inoculation) and early onset stage (25 days after inoculation); (3) NIR and Red are the sensitive bands for wheat yellow rust in disease stages (45 days after inoculation); and their normalized difference NDVI index provides an even higher statistical dependency; (4) bands/indices' sensitivity to yellow rust changes over time and decreases in later Heading stage until being very low in Ripening stage (61 days after inoculation). This experimental study provides a crucial guidance for future early spatio-temporal yellow rust monitoring at farmland scales.

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Section: Introductionmentioning

confidence: 99%

Spatio-temporal monitoring of wheat yellow rust using UAV multispectral imagery

Liu

et al. 2019

Computers and Electronics in Agriculture

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“…Hyperspectral analysis is an ideal tool to capture biophysical variations caused by infestations of crops on account of its abundant narrow bands and high spectral resolution. These advantageous characteristics have been proven to enable information about plant growth to be obtained efficiently and to discriminate among diseases [8,9,10]. Hyperspectral remote sensing can detect subtle changes in the biophysical and biochemical characteristics of plants caused by various types of stress [11].…”

Section: Introductionmentioning

confidence: 99%

Identification of Wheat Yellow Rust Using Optimal Three-Band Spectral Indices in Different Growth Stages

Zheng

Huang

Cui

et al. 2018

Sensors

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Yellow rust, a widely known destructive wheat disease, affects wheat quality and causes large economic losses in wheat production. Hyperspectral remote sensing has shown potential for the detection of plant disease. This study aimed to analyze the spectral reflectance of the wheat canopy in the range of 350–1000 nm and to develop optimal spectral indices to detect yellow rust disease in wheat at different growth stages. The sensitive wavebands of healthy and infected wheat were located in the range 460–720 nm in the early-mid growth stage (from booting to anthesis), and in the ranges 568–709 nm and 725–1000 nm in the mid-late growth stage (from filling to milky ripeness), respectively. All possible three-band combinations over these sensitive wavebands were calculated as the forms of PRI (Photochemical Reflectance Index) and ARI (Anthocyanin Reflectance Index) at different growth stages and assessed to determine whether they could be used for estimating the severity of yellow rust disease. The optimal spectral index for estimating wheat infected by yellow rust disease was PRI (570, 525, 705) during the early-mid growth stage with R2 of 0.669, and ARI (860, 790, 750) during the mid-late growth stage with R2 of 0.888. Comparison of the proposed spectral indices with previously reported vegetation indices were able to satisfactorily discriminate wheat yellow rust. The classification accuracy for PRI (570, 525, 705) was 80.6% and the kappa coefficient was 0.61 in early-mid growth stage, and the classification accuracy for ARI (860, 790, 750) was 91.9% and the kappa coefficient was 0.75 in mid-late growth stage. The classification accuracy of the two indices reached 84.1% and 93.2% in the early-mid and mid-late growth stages in the validated dataset, respectively. We conclude that the three-band spectral indices PRI (570, 525, 705) and ARI (860, 790, 750) are optimal for monitoring yellow rust infection in these two growth stages, respectively. Our method is expected to provide a technical basis for wheat disease detection and prevention in the early-mid growth stage, and the estimation of yield losses in the mid-late growth stage.

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“…Hyperspectral images can provide hundreds of thousands of continuous narrow band data points and are very sensitive to changes in the physical and chemical parameters of plants caused by disease infection. These changes have gradually developed into effective features for expressing plant growth information and have proven to be effective in identifying plant diseases and insect pests [4]. Zheng et al [5] used wavelengths of 570 nm, 525 nm, 705 nm, 860 nm, 790 nm, and 750 nm to identify yellow rust successfully in the early and middle stages of wheat growth.…”

Section: Introductionmentioning

confidence: 99%

Development and Evaluation of a New Spectral Disease Index to Detect Wheat Fusarium Head Blight Using Hyperspectral Imaging

Zhang

Wang

Lin

et al. 2020

Sensors

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Fusarium head blight (FHB) is a major disease threatening worldwide wheat production. FHB is a short cycle disease and is highly destructive under conducive environments. To provide technical support for the rapid detection of the FHB disease, we proposed to develop a new Fusarium disease index (FDI) based on the spectral data of 374–1050 nm. This study was conducted through the analysis of reflectance spectral data of healthy and diseased wheat ears at the flowering and filling stages by hyperspectral imaging technology and the random forest method. The characteristic wavelengths selected were 570 nm and 678 nm for the late flowering stage, 565 nm and 661 nm for the early filling stage, 560 nm and 663 nm for the combined stage (combining both flowering and filling stages) by random forest. FDI at each stage was derived from the wavebands of each corresponding stage. Compared with other 16 existing spectral indices, FDI demonstrated a stronger ability to determine the severity of the FHB disease. Its determination coefficients (R2) values exceeded 0.90 and the RMSEs were less than 0.08 in the models for each stage. Furthermore, the model for the combined stage performed better when used at single growth stage, but its effect was weaker than that of the models for the two individual growth stages. Therefore, using FDI can provide a new tool to detect the FHB disease at different growth stages in wheat.

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Feasibility assessment of multi-spectral satellite sensors in monitoring and discriminating wheat diseases and insects

Cited by 24 publications

References 29 publications

Spatio-temporal monitoring of wheat yellow rust using UAV multispectral imagery

Spatio-temporal monitoring of wheat yellow rust using UAV multispectral imagery

Identification of Wheat Yellow Rust Using Optimal Three-Band Spectral Indices in Different Growth Stages

Development and Evaluation of a New Spectral Disease Index to Detect Wheat Fusarium Head Blight Using Hyperspectral Imaging

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