Tea gray blight disease and its existing control measures have had a negative impact on the sustainable development of tea gardens. However, our knowledge of safe and effective biological control measures is limited. It is critical to explore beneficial microbial communities in the tea rhizosphere for the control of tea gray blight. In this study, we prepared conditioned soil by inoculating Pseudopestalotiopsis camelliae-sinensis on tea seedling leaves. Thereafter, we examined the growth performance and disease resistance of fresh tea seedlings grown in conditioned and control soils. Next, the rhizosphere microbial community and root exudates of tea seedlings infected by the pathogen were analyzed. In addition, we also evaluated the effects of the rhizosphere microbial community and root exudates induced by pathogens on the performance of tea seedlings. The results showed that tea seedlings grown in conditioned soil had lower disease index values and higher growth vigor. Soil microbiome analysis revealed that the fungal and bacterial communities of the rhizosphere were altered upon infection with Ps. camelliae-sinensis. Genus-level analysis showed that the abundance of the fungi Trichoderma, Penicillium, and Gliocladiopsis and the bacteria Pseudomonas, Streptomyces, Bacillus, and Burkholderia were significantly (p < 0.05) increased in the conditioned soil. Through isolation, culture, and inoculation tests, we found that most isolates from the induced microbial genera could inhibit the infection of tea gray blight pathogen and promote tea seedling growth. The results of root exudate analysis showed that infected tea seedlings exhibited significantly higher exudate levels of phenolic acids and flavonoids and lower exudate levels of amino acids and organic acids. Exogenously applied phenolic acids and flavonoids suppressed gray blight disease by regulating the rhizosphere microbial community. In summary, our findings suggest that tea plants with gray blight can recruit beneficial rhizosphere microorganisms by altering their root exudates, thereby improving the disease resistance of tea plants growing in the same soil.

Citrus tangerines are famous fruits worldwide, and monitoring the water content of citrus leaves is highly important for citrus production. However, there are still challenges in quantitatively estimating the water content of citrus leaves using hyperspectral technology, and the random noise generated during spectral acquisition and the overlapping peaks in the sensitive band of the citrus leaf water content will affect estimation accuracy. To solve these problems and further explore the roles of the continuous wavelet transform (CWT) and fractional-order derivative (FOD) in the estimation of citrus leaf water content, this study intends to use of CWT and FOD to decompose the original spectrum, and then compare the correlation between the original spectrum and leaf water content to explore whether the decomposition treatment has improved the correlation between spectrum and leaf moisture content. Then, the successive projections algorithm (SPA) was used to select feature bands and combine spectral vegetation indices. Partial least squares regression (PLSR) was used to construct water-content inversion models for citrus leaves, and the inversion accuracies of two commonly used spectral preprocessing methods were compared. The results indicate that (1) the CWT can improve the sensitivity of the spectrum to the citrus leaf water content to a certain extent, and the inversion accuracy of the CWT is approximately 5% greater than that of the FOD. (2) On the basis of the CWT and FOD methods, the inversion accuracy of the citrus leaf water content based on SPA screening increased by 9.61% and 9.29%, respectively, compared with the original spectrum. (3) Under CWT decomposition, Scale4 of the Gaus1 wavelet was screened by the SPA, and the inversion model of citrus leaf water content was constructed by combining the spectral vegetation index NDVI with the best results. The R-squared (${\mathrm{R}}^2$) and root mean square error (RMSE) values were 0.7491 and 0.0284, respectively, which were both 0.0138 greater than those of the best inversion model for the FOD ${\mathrm{R}}^2$. In conclusion, the CWT-SPA combined with the spectral vegetation index can improve the sensitivity of the spectrum to the citrus leaf water content, eliminate a large amount of redundant data, and enhance the prediction ability and stability of the citrus leaf water content.

The metamaterial with artificial synthetic gauge field has been proved as an excellent platform to manipulate the transport of the electromagnetic wave. Here we propose an inhomogeneous spoof surface plasmonic metasurface to construct an in‐plane pseudo‐magnetic field, which is generated by engineering the gradient variation of the opened Dirac cone corresponding to spatially varying mass term. The chiral zeroth‐order Landau level is induced by the strong pseudo‐magnetic field. Based on the bulk state propagation of the chiral Landau level, the photonic Dirac waveguide is designed and demonstrated in the experimental measurement, in which the unidirectionally guided electromagnetic mode supports the high‐capacity of energy transport. Without breaking the time‐reversal symmetry, our proposal structure paves a new way for realizing the artificial in‐plane magnetic field and photonic Dirac waveguide in metamaterial, and have potential for designing integrated photonic devices in practical applications.

A one-pot synthetic strategy was developed for the synthesis of heterocyclic 1,4,2-oxazaphosphinanes via a three component Kabachnik-Fields reaction of 2-aminophenol, diphenyl H-phosphonate and carbonyl compounds. Through this newly developed method, 12 organophosphorus heterocycles and 2 related chrysin derivatives were synthesized with high yields. The target compounds were characterized by ¹H, ³¹P and 13C NMR and MS.

Rapid and nondestructive prediction of chlorophyll content and response to the growth of various crops using remote sensing technology is a prominent topic in agricultural remote sensing research. Bordeaux mixture has been extensively employed for managing citrus diseases, such as black star and ulcer disease. However, the presence of pesticide residues in Bordeaux mixture can significantly modify the spectral response of the citrus canopy, thereby exerting a substantial influence on the accurate prediction of agronomic indices in fruit trees. In this study, we used unmanned aerial vehicle (UAV) multispectral imaging technology to obtain remote sensing imagery of Bordeaux-covered citrus canopies during the months of July, September, and November. We integrated spectral and texture information to construct a high-dimensional feature dataset and performed data downscaling and feature optimization. Furthermore, we established four machine learning models, namely, partial least squares regression (PLS), ridge regression (RR), ridge, random forest (RF), and support vector regression (SVR). Our objectives were to identify the most effective prediction model for estimating the SPAD (soil plant analysis development) value of Bordeaux-covered citrus canopies, assess the variation in prediction accuracy between fused features and individual features, and investigate the impact of Bordeaux solution on the spectral reflectance of the citrus canopy. The results showed that (1) the impact of Bordeaux mixture on citrus canopy reflectance bands ranked from the highest to the lowest as follows: near-infrared band at 840 nm, red-edge band at 730 nm, blue band at 450 nm, green band at 560 nm, and red band at 650 nm. (2) Fused feature models had better prediction ability than single-feature modeling, with an average R² value of 0.641 for the four model test sets, improving by 0.117 and 0.039, respectively, compared with single-TF (texture feature) and -VI (vegetation index) modeling, and the test-set root-mean-square error (RMSE) was 2.594 on average, which was 0.533 and 0.264 lower than single-TF and -VI modeling, respectively. (3) Multiperiod data fusion effectively enhanced the correlation between features and SPAD values and consequently improved model prediction accuracy. Compared with accuracy based on individual months, R improved by 0.013 and 0.011, while RMSE decreased by 0.112 and 0.305. (4) The SVR model demonstrated the best performance in predicting citrus canopy SPAD under Bordeaux solution coverage, with R² values of 0.629 and 0.658, and RMSE values of 2.722 and 2.752 for the training and test sets, respectively.