Hyperspectral Measurements for Estimating Vertical Infection of Yellow Rust on Winter Wheat Plant

Abstract

Yellow rust (Puccinia striiformis f. sp. tritici) on winter wheat (Triticum aestivum L.) has resulted in significant reductions in the yield losses and wheat grain quality. It is extremely important to quantitatively detect and assess such a serious disease rather than visual qualitative description. In comparison with traditional diagnosis method, remote sensing has proven to be a cost-effective tool to achieve such a goal. In this study, we used yellow rust in winter wheat to illustrate the capability of estimating the infection index in different leaf layers of the plant using hyperspectral measurements on individual wheat diseased leaves. The analysis results indicated that the severities showed a gradual increasing trend from F-1 (F=Flag leaf) to F-3, while the relative chlorophyll and nitrogen showed an inverse change. The spectral reflectance gradually increased from F-1 to F-3 in the visible and short-wave infrared (SWIR) regions, while it was the very reverse in the near-infrared (NIR) region. In addition, an integral spectral index -yellow rust spectral index (YRSI) was constructed to quantitatively estimate the disease using the most sensitive bands in the visible (704 nm), NIR (1423 nm) and SWIR (1926 nm) regions. The coefficient of determination (R 2 ) reaches 0.88 between the disease severity (DS%) and YRSI, which shows that the index can be suitable and effective to estimate the infection severity for a wheat plant.

Remote sensing monitoring of wheat leaf rust based on UAV multispectral imagery and the BPNN method

“…It was also found that the reflectance in the green and red bands increased, while the reflectance in the blue, red‐edge, and near‐infrared bands decreased as the DI increased (Figure 10). This might be because in the visible band, wheat leaf rust destroys the chlorophyll cells of the leaves and the chlorophyll concentration decreases, and the decrease in pigment absorption leads to an increase in reflectance in this band range; the infested disease in the NI band severely damages the physiological structure, proteins, and biomass of the leaves (Sims & Gamon, 2002), thus causing a decrease in reflectance in the NIR band range, which is consistent with the results of other studies (Huang et al, 2012; Su et al, 2019). Figure 10 showed that the slopes of the red and infrared bands were larger in absolute value than those of the other three bands; so, the VIs with larger weights and those retained after dimensionality reduction were mostly calculated from the reflectance of these two bands (Figure 5).…”

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

“…The traditional estimation of disease severity Is conducted by field scouting where diseased wheat is already showing obvious symptoms, and it is difficult to control WSR. Field scouting is time-consuming, laborious, and has low accuracy [9]. The naked eye cannot identify the latent period of WSR because it does not show obvious symptoms.…”

Early Visual Detection of Wheat Stripe Rust Using Visible/Near-Infrared Hyperspectral Imaging

“…To monitor and detect wheat stripe rust, scouting methods by visual inspection of the foliage in the field are traditionally employed. However, this method is not only time consuming and labor intensive, but also somewhat subjective [ 10 ]. Other types of methods for plant diseases diagnosis and detection include the microscopic evaluation of morphology features to identify pathogens [ 11 ], as well as molecular diagnostic techniques [ 12 ], but these methods demand experienced individuals with well-developed skills in disease detection and are thus subject to human bias.…”