Spectral analysis of winter wheat leaves for detection and differentiation of diseases and insects

Yellow rust (Puccinia striiformis f. sp. Tritici), powdery mildew (Blumeria graminis) and wheat aphid (Sitobion avenae F.) infestation are three serious conditions that have a severe impact on yield and grain quality of winter wheat worldwide. Discrimination among these three stressors is of practical importance, given that specific procedures (i.e. adoption of fungicide and insecticide) are needed to treat different diseases and insects. This study examines the potential of hyperspectral sensor systems in discriminating these three stressors at leaf level. Reflectance spectra of leaves infected with yellow rust, powdery mildew and aphids were measured at the early grain filling stage. Normalization was performed prior to spectral analysis on all three groups of samples for removing differences in the spectral baseline among different cultivars. To obtain appropriate bands and spectral features (SFs) for stressor discrimination and damage intensity estimation, a correlation analysis and an independent t-test were used jointly. Based on the most efficient bands/SFs, models for discriminating stressors and estimating stressor intensity were established by Fisher’s linear discriminant analysis (FLDA) and partial least square regression (PLSR), respectively. The results showed that the performance of the discrimination model was satisfactory in general, with an overall accuracy of 0.75. However, the discrimination model produced varied classification accuracies among different types of diseases and insects. The regression model produced reasonable estimates of stress intensity, with an R2 of 0.73 and a RMSE of 0.148. This study illustrates the potential use of hyperspectral information in discriminating yellow rust, powdery mildew and wheat aphid infestation in winter wheat. In practice, it is important to extend the discriminative analysis from leaf level to canopy level

Spectral analysis of winter wheat leaves for detection and differentiation of diseases and insects

Yellow rust (Puccinia striiformis f. sp. Tritici), powdery mildew (Blumeria graminis) and wheat aphid (Sitobion avenae F.) infestation are three serious conditions that have a severe impact on yield and grain quality of winter wheat worldwide. Discrimination among these three stressors is of practical importance, given that specific procedures (i.e. adoption of fungicide and insecticide) are needed to treat different diseases and insects. This study examines the potential of hyperspectral sensor systems in discriminating these three stressors at leaf level. Reflectance spectra of leaves infected with yellow rust, powdery mildew and aphids were measured at the early grain filling stage. Normalization was performed prior to spectral analysis on all three groups of samples for removing differences in the spectral baseline among different cultivars. To obtain appropriate bands and spectral features (SFs) for stressor discrimination and damage intensity estimation, a correlation analysis and an independent t-test were used jointly. Based on the most efficient bands/SFs, models for discriminating stressors and estimating stressor intensity were established by Fisher’s linear discriminant analysis (FLDA) and partial least square regression (PLSR), respectively. The results showed that the performance of the discrimination model was satisfactory in general, with an overall accuracy of 0.75. However, the discrimination model produced varied classification accuracies among different types of diseases and insects. The regression model produced reasonable estimates of stress intensity, with an R2 of 0.73 and a RMSE of 0.148. This study illustrates the potential use of hyperspectral information in discriminating yellow rust, powdery mildew and wheat aphid infestation in winter wheat. In practice, it is important to extend the discriminative analysis from leaf level to canopy level

Dynamic evaluation method of water-sealed gas for ultra-deep buried fractured tight gas reservoir in Kuqa Depression, Tarim Basin, China

The ultra-deep-buried fractured tight gas reservoir in the Kuqa Depression of the Tarim Basin has developed edge and bottom water. Faults and fractures have become “highways” for water invasion, resulting in the “water sealed gas” effect and reducing gas reservoir recovery. At present, there is a lack of effective evaluation methods. Therefore, based on an analysis of water invasion characteristics of the gas reservoir, a dynamic evaluation method for water-sealed gas in a fractured gas reservoir is established. This method considers two factors: fracture development scale and peripheral water body strength. It is then applied to three developed blocks in the Kuqa ultra-deep layer. The effectiveness of the evaluation results is verified by static and dynamic combination, and countermeasures to improve gas reservoir recovery are proposed. The results indicate that: (1) The non-uniform water invasion of fractures is jointly controlled by structural position, fracture development degree, and fracture network combination, which can be divided into three modes: edge water channeling along the large fracture in the core, edge and bottom water invading along the fracture in the wing, and rapid, violent water flooding of the bottom water along the fracture/small fault in the low part. (2) The replacement coefficient of water invasion in the three typical blocks is 0.2–0.3, indicating that they are sub active water-gas reservoirs. However, the severity of water-sealed gas varies greatly. The more severe the water-sealed gas is, the lower the recovery factor of the gas reservoir. (3) For directionally penetrating large fracture gas reservoirs, water shutoff should be carried out. For fracture network gas reservoirs with high fracture density, mild exploitation can control water, and early drainage can reduce the impact of water invasion, improving gas reservoir recovery. It is concluded that the new method of water-sealed gas dynamic evaluation can provide a reliable basis for evaluating fracture non-uniform water invasion dynamics of the ultra-deep gas reservoir and enhancing oil recovery of the gas reservoir in the Kuqa Depression. This method also supports the formulation of water control policies and the economic and efficient development of ultra-deep gas reservoirs in the Kuqa Depression

Manipulating Zn Metal Texture with Guided Zincophilic Sites via Electrochemical Stripping for Dendrite-Free Zn Anodes

Constructing a three-dimensional (3D) structure along with Zn (002) texture selective exposure is a promising strategy to tackle the issues faced by Zn metal anodes. Herein, for the first time, we proposed an electrochemical stripping strategy to achieve controlled modification of the texture and microstructure of zinc foils in one step, building a hierarchical structure with (002) texture preferred exposed Zn (SZ). The SZ with favorable zincophilic properties not only can reduce the concentration polarization at the interface but also allow Zn to grow horizontally on the edge of the (002) texture by guiding the adsorption sites for Zn2+. Moreover, the honeycomb-like structure is beneficial to rearrange the distribution of the Zn2+ flux as well as alleviating stress changes during cycling. Thus, the SZ||Cu cell exhibits excellent stability with a Coulombic efficiency of 99.76% over 1800 cycles. The SZ||NaV3O8·xH2O cell with inconspicuous self-discharge effect maintains a high areal capacity of 3.67 mA h cm–2 even after 700 cycles with a low N/P ratio of 3.6. This work achieves texture architecture and structure designing on Zn foils simultaneously by metallurgical electrochemical methods and opens up a potential strategy to implement the practicality of zinc metal anodes