Monitoring Wheat Stripe Rust Using Remote Sensing Technologies in China

Wang, Haiguang; Guo, Jingheng; Ma, Zhanhong

doi:10.1007/978-3-642-27275-2_18

Cited by 6 publications

(3 citation statements)

References 13 publications

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“…Wheat streak mosaic was assessed using multispectral satellite data (Landsat TM) with the maximum likelihood-supervised classification technique (Mirik et al 2011), or with spectral mixture analysis (SMA) techniques for subpixel classification (Mirik et al 2013). Wang et al (2012) discussed the multiscale capabilities (from leaf-to-satellite observation) offered by RS to detect winter wheat stripe rust.…”

Section: Spectroscopy-based Methodsmentioning

confidence: 99%

Advanced methods of plant disease detection. A review

Martinelli¹,

Scalenghe²,

Davino³

et al. 2014

Agron. Sustain. Dev.

692

341

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International audiencePlant diseases are responsible for major economic losses in the agricultural industry worldwide. Monitoring plant health and detecting pathogen early are essential to reduce disease spread and facilitate effective management practices. DNA-based and serological methods now provide essential tools for accurate plant disease diagnosis, in addition to the traditional visual scouting for symptoms. Although DNA-based and serological methods have revolutionized plant disease detection, they are not very reliable at asymptomatic stage, especially in case of pathogen with systemic diffusion. They need at least 1–2 days for sample harvest, processing, and analysis. Here, we describe modern methods based on nucleic acid and protein analysis. Then, we review innovative approaches currently under development. Our main findings are the following: (1) novel sensors based on the analysis of host responses, e.g., differential mobility spectrometer and lateral flow devices, deliver instantaneous results and can effectively detect early infections directly in the field; (2) biosensors based on phage display and biophotonics can also detect instantaneously infections although they can be integrated with other systems; and (3) remote sensing techniques coupled with spectroscopy-based methods allow high spatialization of results, these techniques may be very useful as a rapid preliminary identification of primary infections. We explain how these tools will help plant disease management and complement serological and DNA-based methods. While serological and PCR-based methods are the most available and effective to confirm disease diagnosis, volatile and biophotonic sensors provide instantaneous results and may be used to identify infections at asymptomatic stages. Remote sensing technologies will be extremely helpful to greatly spatialize diagnostic results. These innovative techniques represent unprecedented tools to render agriculture more sustainable and safe, avoiding expensive use of pesticides in crop protection

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Section: Spectroscopy-based Methodsmentioning

confidence: 99%

Advanced methods of plant disease detection. A review

Martinelli¹,

Scalenghe²,

Davino³

et al. 2014

Agron. Sustain. Dev.

692

341

View full text Add to dashboard Cite

show abstract

“…With the rapid development of science and technology, hyperspectral remote sensing monitoring technology is increasingly being applied to plant diseases and hyperspectral remote sensing of a variety of plant pests is gradually being concerned. However, many factors, such as leaf angle, soil, background plants, and meteorological conditions, have great influence on the canopy spectral data, and it is very likely to change the relative spectral differences [7]. Therefore, it is very important to find out a method to weaken or eliminate this kind of effects in further studies.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

“…Remote sensing techniques have been applied in the studies on identifying and monitoring plant diseases [4][5][6]. Remote sensing technology, especially the hyperspectral remote sensing technology, has been applied to remote sensing monitoring of wheat stripe rust [7]. Detection and identification of wheat stripe rust via remote sensing at the individual wheat leaves [8][9][10], monitoring of the disease via ground remote sensing [11][12][13], aerial remote sensing [14][15][16], and space remote sensing [17][18][19] have been investigated.…”

Section: Introductionmentioning

confidence: 99%

Identification and Disease Index Inversion of Wheat Stripe Rust and Wheat Leaf Rust Based on Hyperspectral Data at Canopy Level

Wang

Qin

Liu

et al. 2015

Journal of Spectroscopy

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Stripe rust and leaf rust with similar symptoms are two important wheat diseases. In this study, to investigate a method to identify and assess the two diseases, the canopy hyperspectral data of healthy wheat, wheat in incubation period, and wheat in diseased period of the diseases were collected, respectively. After data preprocessing, three support vector machine (SVM) models for disease identification and six support vector regression (SVR) models for disease index (DI) inversion were built. The results showed that the SVM model based on wavelet packet decomposition coefficients with the overall identification accuracy of the training set equal to 99.67% and that of the testing set equal to 82.00% was better than the other two models. To improve the identification accuracy, it was suggested that a combination model could be constructed with one SVM model and two models built usingK-nearest neighbors (KNN) method. Using the DI inversion SVR models, the satisfactory results were obtained for the two diseases. The results demonstrated that identification and DI inversion of stripe rust and leaf rust can be implemented based on hyperspectral data at the canopy level.

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