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

Abstract

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.

Winter Durum Wheat Disease Severity Detection with Field Spectroscopy in Phenotyping Experiment at Leaf and Canopy Level

“…At the canopy level within the early group (Figure 5c), increasing disease levels led to increased VIS reflectance, a leftward shift of the red-edge, and an increase in NIR reflectance. Our observations across the VIS, red-edge, and NIR regions mirror those reported by Liu et al (2023) [72]. However, within the late group at the canopy level (Figure 5d), an increase in disease level resulted in decreased NIR reflectance, consistent with findings by Huang et al (2022) [35] and Zheng et al (2019) [25].…”

“…Our findings (refer to Tables 7 and 8) confirm that infections by pathogens lead to necrotic or chlorotic lesions, which subsequently diminish the chlorophyll content response in the spectrum's visible (VIS) and red-edge regions [18,19,32,72,74]. Moreover, our results suggest that the near-infrared (NIR) spectral region also plays a role in this process [35].…”

Screening of Endophytic Antagonistic Bacteria in Wheat and Evaluation of Biocontrol Potential against Wheat Stripe Rust

“…However, the screening of endophytic bacteria of different winter wheat varieties in Xinjiang and the use of strains with significant biocontrol potential to control wheat stripe rust have not been reported. At present, the main research on wheat stripe rust in Xinjiang focuses on physiological species identification [23,24], over-summering regions [25], resistance gene detection [26], deep learning recognition of disease [27], hyperspectral remote sensing [28][29][30], population genetic structures [31], and the minimum detection limit [32].…”

The Detection of Yr Genes in Xinjiang Wheat Cultivars Using Different Molecular Markers

“…The Yili Kazak Autonomous Prefecture and Kashgar Prefecture have the largest wheat planting areas [6]. As a prevalent wheat disease in Xinjiang, wheat stripe rust has emerged as a pivotal factor constraining local wheat production [7]. There are many ways to control wheat stripe rust, but breeding and promoting varieties carrying resistance genes is currently the most cost-effective and environmentally friendly method [8].…”