Search

Leaf chlorophyll content (LCC) is an important indicator of plant nutritional status and can be a guide for plant disease diagnosis. In this study, we took apple leaves infected with mosaic disease as a research object and extracted two types of information on spectral and textural features from hyperspectral images, with a view to realizing non-destructive detection of LCC. First, the collected hyperspectral images were preprocessed and spectral reflectance was extracted in the region of interest. Subsequently, we used the successive projections algorithm (SPA) to select the optimal wavelengths (OWs) and extracted eight basic textural features using the gray-level co-occurrence matrix (GLCM). In addition, composite spectral and textural metrics, including vegetation indices (VIs), normalized difference texture indices (NDTIs), difference texture indices (DTIs), and ratio texture indices (RTIs) were calculated. Third, we applied the maximal information coefficient (MIC) algorithm to select significant VIs and basic textures, as well as the tandem method was used to fuse the spectral and textural features. Finally, we employ support vector regression (SVR), backpropagation neural network (BPNN), and K-nearest neighbors regression (KNNR) methods to explore the efficacy of single and combined feature models for estimating LCC. The results showed that the VIs model (R2 = 0.8532, RMSE = 2.1444, RPD = 2.6179) and the NDTIs model (R2 = 0.7927, RMSE = 2.7453, RPD = 2.2032) achieved the best results among the single feature models for spectra and texture, respectively. However, textural features generally exhibit inferior regression performance compared to spectral features and are unsuitable for standalone applications. Combining textural and spectral information can potentially improve the single feature models. Specifically, when combining NDTIs with VIs as input parameters, three machine learning models outperform the best single feature model. Ultimately, SVR achieves the highest performance among the LCC regression models (R2 = 0.8665, RMSE = 1.8871, RPD = 2.7454). This study reveals that combining textural and spectral information improves the quantitative detection of LCC in apple leaves infected with mosaic disease, leading to higher estimation accuracy. [ABSTRACT FROM AUTHOR]

Leaf chlorophyll content (LCC) is an important indicator of plant nutritional status and can be a guide for plant disease diagnosis. In this study, we took apple leaves infected with mosaic disease as a research object and extracted two types of information on spectral and textural features from hyperspectral images, with a view to realizing non-destructive detection of LCC. First, the collected hyperspectral images were preprocessed and spectral reflectance was extracted in the region of interest. Subsequently, we used the successive projections algorithm (SPA) to select the optimal wavelengths (OWs) and extracted eight basic textural features using the gray-level co-occurrence matrix (GLCM). In addition, composite spectral and textural metrics, including vegetation indices (VIs), normalized difference texture indices (NDTIs), difference texture indices (DTIs), and ratio texture indices (RTIs) were calculated. Third, we applied the maximal information coefficient (MIC) algorithm to select significant VIs and basic textures, as well as the tandem method was used to fuse the spectral and textural features. Finally, we employ support vector regression (SVR), backpropagation neural network (BPNN), and K-nearest neighbors regression (KNNR) methods to explore the efficacy of single and combined feature models for estimating LCC. The results showed that the VIs model (R2 = 0.8532, RMSE = 2.1444, RPD = 2.6179) and the NDTIs model (R2 = 0.7927, RMSE = 2.7453, RPD = 2.2032) achieved the best results among the single feature models for spectra and texture, respectively. However, textural features generally exhibit inferior regression performance compared to spectral features and are unsuitable for standalone applications. Combining textural and spectral information can potentially improve the single feature models. Specifically, when combining NDTIs with VIs as input parameters, three machine learning models outperform the best single feature model. Ultimately, SVR achieves the highest performance among the LCC regression models (R2 = 0.8665, RMSE = 1.8871, RPD = 2.7454). This study reveals that combining textural and spectral information improves the quantitative detection of LCC in apple leaves infected with mosaic disease, leading to higher estimation accuracy.

Apple mosaic disease is one of the most widely distributed and destructive diseases in apple cultivation worldwide, especially in China, whose apple yields account for more than 50% of the global total. Apple necrotic mosaic virus (ApNMV) is a newly identified ilarvirus that is closely associated with apple mosaic disease in China; however, basic viral protein interactions that play key roles in virus replication and the viral life cycle have not been determined in ApNMV. Here, we first identify an ApNMV–Lw isolate that belongs to subgroup 3 in the genus Ilarvirus. ApNMV–Lw was used to investigate interactions among viral components. ApNMV 1a and 2apol, encoded by RNA1 and RNA2, respectively, were co-localized in plant cell cytoplasm. ApNMV 1a interacted with itself at both the inter- and intramolecular levels, and its N-terminal portion played a key role in these interactions. 1a also interacted with 2apol, and 1a’s C-terminal, together with 2apol’s N-terminal, was required for this interaction. Moreover, the first 115 amino acids of 2apol were sufficient for permitting the 1a–2apol interaction. This study provides insight into the protein interactions among viral replication components of ApNMV, facilitating future investigations on its pathogenicity, as well as the development of strategies to control the virus and disease.

Apple mosaic disease has a great influence on apple production. In this study, an investigation into the incidence of apple mosaic disease in southwest China was performed, and the pathogen associated with the disease was detected. The results show that 2869 apple trees with mosaic disease were found in the Sichuan, Yunnan, and Guizhou Provinces, with an average incidence of 9.6%. Although apple mosaic virus (ApMV) is widespread in apples worldwide, the diseased samples were negative when tested for ApMV. However, a novel ilarvirus (apple necrotic mosaic virus, ApNMV) was identified in mosaic apple leaves which tested negative for ApMV. RT-PCR analysis indicated that ApNMV was detected in 322 out of 357 samples with mosaic symptoms. Phylogenetic analysis of coat protein (CP) sequences of ApNMV isolates suggested that, compared with ApMV, ApNMV was closer to prunus necrotic ringspot virus (PNRSV). The CP sequences of the isolates showed the diversity of ApNMV, which may enable the virus to adapt to the changeable environments. In addition, the pathology of mosaic disease was observed by microscope, and the result showed that the arrangement of the tissue and the shape of the cell, including the organelle, were seriously destroyed or drastically changed.

The causal agent of apple mosaic disease has been previously thought to be solely caused by apple mosaic virus (ApMV). In this study, we report that a novel ilarvirus is also associated with apple mosaic disease. Next-generation sequencing analysis of an apple tree showing mosaic symptoms revealed that the tree was infected with three apple latent viruses (apple stem pitting virus, apple stem grooving virus, and apple chlorotic leaf spot virus) and a novel ilarvirus (given the name apple necrotic mosaic virus (ApNMV)) that is closely related to Prunus necrotic ringspot virus (PNRSV) and ApMV. The genome of ApNMV consists of RNA1 (3378 nt), RNA2 (2767 nt), and RNA3 (1956 nt). A phylogenetic analysis based on the coat protein amino acid sequences indicated that the novel virus belongs to the same subgroup 3 of the genus Ilarvirus as PNRSV and ApMV. The presence of mosaic leaves, which tend to be unevenly distributed in diseased apple trees, was correlated with the internal distribution of ApNMV. RT-PCR detection of mosaic-diseased apple trees in Japan indicated that ApNMV was detected in apple trees introduced from China, whereas ApMV was detected from cultivated apple trees in domestic orchards. Consistent with these findings, a survey of mosaic-diseased apple trees in major apple-producing provinces in China revealed that the majority of apple trees showing mosaic symptoms in China are infected with ApNMV. [ABSTRACT FROM AUTHOR]

Apple mosaic disease is one of the most widely distributed and destructive diseases in apple cultivation worldwide, especially in China, whose apple yields account for more than 50% of the global total. Apple necrotic mosaic virus (ApNMV) is a newly identified ilarvirus that is closely associated with apple mosaic disease in China; however, basic viral protein interactions that play key roles in virus replication and the viral life cycle have not been determined in ApNMV. Here, we first identify an ApNMV–Lw isolate that belongs to subgroup 3 in the genus Ilarvirus. ApNMV–Lw was used to investigate interactions among viral components. ApNMV 1a and 2apol, encoded by RNA1 and RNA2, respectively, were co-localized in plant cell cytoplasm. ApNMV 1a interacted with itself at both the inter- and intramolecular levels, and its N-terminal portion played a key role in these interactions. 1a also interacted with 2apol, and 1a's C-terminal, together with 2apol's N-terminal, was required for this interaction. Moreover, the first 115 amino acids of 2apol were sufficient for permitting the 1a–2apol interaction. This study provides insight into the protein interactions among viral replication components of ApNMV, facilitating future investigations on its pathogenicity, as well as the development of strategies to control the virus and disease. [ABSTRACT FROM AUTHOR]

Apple mosaic disease has a great influence on apple production. In this study, an investigation into the incidence of apple mosaic disease in southwest China was performed, and the pathogen associated with the disease was detected. The results show that 2869 apple trees with mosaic disease were found in the Sichuan, Yunnan, and Guizhou Provinces, with an average incidence of 9.6%. Although apple mosaic virus (ApMV) is widespread in apples worldwide, the diseased samples were negative when tested for ApMV. However, a novel ilarvirus (apple necrotic mosaic virus, ApNMV) was identified in mosaic apple leaves which tested negative for ApMV. RT-PCR analysis indicated that ApNMV was detected in 322 out of 357 samples with mosaic symptoms. Phylogenetic analysis of coat protein (CP) sequences of ApNMV isolates suggested that, compared with ApMV, ApNMV was closer to prunus necrotic ringspot virus (PNRSV). The CP sequences of the isolates showed the diversity of ApNMV, which may enable the virus to adapt to the changeable environments. In addition, the pathology of mosaic disease was observed by microscope, and the result showed that the arrangement of the tissue and the shape of the cell, including the organelle, were seriously destroyed or drastically changed. [ABSTRACT FROM AUTHOR]