Effect of Gryllotalpa gryllotalpa (Orthoptera: Gryllotalpidae) on plant and crop characteristics of sugar beet, Beta vulgaris L. and detection of associated damage using hyperspectral remote sensing

Moursey, Amira H. A.; Aboelghar, Mohamed; Khattab, M.; Awad, Mona; Hemeida, Ibtisam; Ahmed, Sayeda S.

doi:10.1007/s42690-021-00689-z

Cited by 1 publication

(1 citation statement)

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“…The correlation peaked in the bands of 446-725 nm and 1380-1600 nm [34]. Therefore, by detecting variations in the spectrum, PRS will be able to pinpoint the related stress factors [35][36][37][38][39][40]. And it is possible to quickly and non-destructively obtain the hyperspectral characteristics of objects in a large space.…”

Section: Introductionmentioning

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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