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Red solar-induced chlorophyll fluorescence (SIFB) is closely related to the photosynthetically active radiation absorbed by chlorophyll. The scattering and reabsorption of SIFB by the vegetation canopy significantly change the spectral intensity and shape of SIF, which affects the relationship between SIF and crop stress. To address this, we propose a method of modifying SIFB using SIF spectral shape characteristic parameters to reduce this influence. A red pseudokurtosis (PKB) parameter that can characterize spectral shape features was calculated using full-spectrum SIF data. On this basis, we analyzed the photosynthetic physiological mechanism of PKB and found that it significantly correlates with both the fraction of photosynthetically active radiation absorbed by chlorophyll(fPARchl) and the red SIF escape rate (fesc680); thus, it is closely related to the scattering and reabsorption of SIFB by the vegetation canopy. Consequently, we constructed an expression of PKB to modify SIFB. To evaluate the modified SIFB (MSIFB) in monitoring the severity of wheat stripe rust, we analyzed the correlations between SIFB, MSIFB, SIFB-VIs (a fusion of the vegetation index and SIFB), and MSIFB-VIs (a fusion of the vegetation index and MSIFB) with the severity level (SL), respectively. The results show that the correlation between MSIFB and the severity of wheat stripe rust increased by an average of 25.6% and at least 16.95% compared with that for SIFB. In addition, we constructed remote sensing monitoring models for wheat stripe rust using linear regression methods, with SIFB, MSIFB, SIFB-VIs, and MSIFB-VIs as independent variables. PKB significantly improves the accuracy and robustness of models based on SIFB and its fusion index SIFB-VIs in the constructed testing set. The R-value between the predicted SL and the measured SL of the remote sensing monitoring model for wheat stripe rust was established using MSIFB-VIs as the independent variable, and it was improved by an average of 39.49% compared with the model using SIFB-VIs. The RMSE was reduced by an average of 18.22%. Therefore, the SIFB modified by PKB can weaken the effects of chlorophyll reabsorption and canopy architecture on SIFB and improve the ability of SIFB to detect stress information. [ABSTRACT FROM AUTHOR]

Red solar-induced chlorophyll fluorescence (SIFB) is closely related to the photosynthetically active radiation absorbed by chlorophyll. The scattering and reabsorption of SIFB by the vegetation canopy significantly change the spectral intensity and shape of SIF, which affects the relationship between SIF and crop stress. To address this, we propose a method of modifying SIFB using SIF spectral shape characteristic parameters to reduce this influence. A red pseudokurtosis (PKB) parameter that can characterize spectral shape features was calculated using full-spectrum SIF data. On this basis, we analyzed the photosynthetic physiological mechanism of PKB and found that it significantly correlates with both the fraction of photosynthetically active radiation absorbed by chlorophyll(fPARchl) and the red SIF escape rate (fesc680); thus, it is closely related to the scattering and reabsorption of SIFB by the vegetation canopy. Consequently, we constructed an expression of PKB to modify SIFB. To evaluate the modified SIFB (MSIFB) in monitoring the severity of wheat stripe rust, we analyzed the correlations between SIFB, MSIFB, SIFB-VIs (a fusion of the vegetation index and SIFB), and MSIFB-VIs (a fusion of the vegetation index and MSIFB) with the severity level (SL), respectively. The results show that the correlation between MSIFB and the severity of wheat stripe rust increased by an average of 25.6% and at least 16.95% compared with that for SIFB. In addition, we constructed remote sensing monitoring models for wheat stripe rust using linear regression methods, with SIFB, MSIFB, SIFB-VIs, and MSIFB-VIs as independent variables. PKB significantly improves the accuracy and robustness of models based on SIFB and its fusion index SIFB-VIs in the constructed testing set. The R-value between the predicted SL and the measured SL of the remote sensing monitoring model for wheat stripe rust was established using MSIFB-VIs as the independent variable, and it was improved by an average of 39.49% compared with the model using SIFB-VIs. The RMSE was reduced by an average of 18.22%. Therefore, the SIFB modified by PKB can weaken the effects of chlorophyll reabsorption and canopy architecture on SIFB and improve the ability of SIFB to detect stress information.

The timely and accurate identification of stripe rust and leaf rust is essential in effective disease control and the safe production of wheat worldwide. To investigate methods for identifying the two diseases on different wheat varieties based on image processing technology, single-leaf images of the diseases on different wheat varieties, acquired under field and laboratory environmental conditions, were processed. After image scaling, median filtering, morphological reconstruction, and lesion segmentation on the images, 140 color, texture, and shape features were extracted from the lesion images; then, feature selections were conducted using methods including ReliefF, 1R, correlation-based feature selection, and principal components analysis combined with support vector machine (SVM), back propagation neural network (BPNN), and random forest (RF), respectively. For the individual-variety disease identification SVM, BPNN, and RF models built with the optimal feature combinations, the identification accuracies of the training sets and the testing sets on the same individual varieties acquired under the same image acquisition conditions as the training sets used for modeling were 87.18–100.00%, but most of the identification accuracies of the testing sets for other individual varieties were low. For the multi-variety disease identification SVM, BPNN, and RF models built with the merged optimal feature combinations based on the multi-variety disease images acquired under field and laboratory environmental conditions, identification accuracies in the range of 82.05–100.00% were achieved on the training set, the corresponding multi-variety disease image testing set, and all the individual-variety disease image testing sets. The results indicated that the identification of images of stripe rust and leaf rust could be greatly affected by wheat varieties, but satisfactory identification performances could be achieved by building multi-variety disease identification models based on disease images from multiple varieties under different environments. This study provides an effective method for the accurate identification of stripe rust and leaf rust and could be a useful reference for the automatic identification of other plant diseases. [ABSTRACT FROM AUTHOR]

Wheat stripe rust-damaged leaves present challenges to automatic disease index calculation, including high similarity between spores and spots, and difficulty in distinguishing edge contours. In actual field applications, investigators rely on the naked eye to judge the disease extent, which is subjective, of low accuracy, and essentially qualitative. To address the above issues, this study undertook a task of semantic segmentation of wheat stripe rust damage images using deep learning. To address the problem of small available datasets, the first large-scale open dataset of wheat stripe rust images from Qinghai province was constructed through field and greenhouse image acquisition, screening, filtering, and manual annotation. There were 33,238 images in our dataset with a size of 512 × 512 pixels. A new segmentation paradigm was defined. Dividing indistinguishable spores and spots into different classes, the task of accurate segmentation of the background, leaf (containing spots), and spores was investigated. To assign different weights to high- and low-frequency features, we used the Octave-UNet model that replaces the original convolutional operation with the octave convolution in the U-Net model. The Octave-UNet model obtained the best benchmark results among four models (PSPNet, DeepLabv3, U-Net, Octave-UNet), the mean intersection over a union of the Octave-UNet model was 83.44%, the mean pixel accuracy was 94.58%, and the accuracy was 96.06%, respectively. The results showed that the state-of-art Octave-UNet model can better represent and discern the semantic information over a small region and improve the segmentation accuracy of spores, leaves, and backgrounds in our constructed dataset. [ABSTRACT FROM AUTHOR]

The timely and accurate identification of stripe rust and leaf rust is essential in effective disease control and the safe production of wheat worldwide. To investigate methods for identifying the two diseases on different wheat varieties based on image processing technology, single-leaf images of the diseases on different wheat varieties, acquired under field and laboratory environmental conditions, were processed. After image scaling, median filtering, morphological reconstruction, and lesion segmentation on the images, 140 color, texture, and shape features were extracted from the lesion images; then, feature selections were conducted using methods including ReliefF, 1R, correlation-based feature selection, and principal components analysis combined with support vector machine (SVM), back propagation neural network (BPNN), and random forest (RF), respectively. For the individual-variety disease identification SVM, BPNN, and RF models built with the optimal feature combinations, the identification accuracies of the training sets and the testing sets on the same individual varieties acquired under the same image acquisition conditions as the training sets used for modeling were 87.18–100.00%, but most of the identification accuracies of the testing sets for other individual varieties were low. For the multi-variety disease identification SVM, BPNN, and RF models built with the merged optimal feature combinations based on the multi-variety disease images acquired under field and laboratory environmental conditions, identification accuracies in the range of 82.05–100.00% were achieved on the training set, the corresponding multi-variety disease image testing set, and all the individual-variety disease image testing sets. The results indicated that the identification of images of stripe rust and leaf rust could be greatly affected by wheat varieties, but satisfactory identification performances could be achieved by building multi-variety disease identification models based on disease images from multiple varieties under different environments. This study provides an effective method for the accurate identification of stripe rust and leaf rust and could be a useful reference for the automatic identification of other plant diseases.

Wheat stripe rust-damaged leaves present challenges to automatic disease index calculation, including high similarity between spores and spots, and difficulty in distinguishing edge contours. In actual field applications, investigators rely on the naked eye to judge the disease extent, which is subjective, of low accuracy, and essentially qualitative. To address the above issues, this study undertook a task of semantic segmentation of wheat stripe rust damage images using deep learning. To address the problem of small available datasets, the first large-scale open dataset of wheat stripe rust images from Qinghai province was constructed through field and greenhouse image acquisition, screening, filtering, and manual annotation. There were 33,238 images in our dataset with a size of 512 × 512 pixels. A new segmentation paradigm was defined. Dividing indistinguishable spores and spots into different classes, the task of accurate segmentation of the background, leaf (containing spots), and spores was investigated. To assign different weights to high- and low-frequency features, we used the Octave-UNet model that replaces the original convolutional operation with the octave convolution in the U-Net model. The Octave-UNet model obtained the best benchmark results among four models (PSPNet, DeepLabv3, U-Net, Octave-UNet), the mean intersection over a union of the Octave-UNet model was 83.44%, the mean pixel accuracy was 94.58%, and the accuracy was 96.06%, respectively. The results showed that the state-of-art Octave-UNet model can better represent and discern the semantic information over a small region and improve the segmentation accuracy of spores, leaves, and backgrounds in our constructed dataset.

In this study, we successfully introgressed and validated Yr59 into four elite wheat cultivars, Jimai 22, Chuanmai 42, Zhengmai 9023 and Xinmai 26 through marker-assisted backcross selection. Used as female parents, these four cultivars were crossed with wheat line PI 660061 (Yr59). After two backcrosses and marker-assisted selection, the progenies were selfed and advanced to the BC2F4 generation. A total of 123 BC2F4 lines were selected based on agronomic traits and stripe rust resistance, and their BC2F5 and BC2F6 progenies were further evaluated for stripe rust resistance and agronomic traits. Seven markers linked with relevant genes, including Xbarc32, Xwgp5175, Xwmc557 and Xcfa2040 linked with Yr59; Xwmc658 with YrJ22; WE173 and Xbarc181 with Yr26, were used to genotype the breeding lines. A total of 109 introgression lines with positive markers for Yr59 were identified for further stripe rust and agronomic trait evaluation. Finally, 16 lines had higher levels resistance to stripe rust, and similar or superior agronomic traits compared to their parents were obtained. These lines can be released as new cultivars for various regions after regional tests and also can be used as resistance stocks for regional breeding programs to develop new cultivars with adequate and durable resistance to stripe rust.

Wheat stripe rust, powdery mildew, and Fusarium head blight (FHB) are the three most important diseases in wheat worldwide. Growing resistant cultivars is the most economic and effective method to control these diseases. To assess the disease resistance of commercial wheat cultivars and regional trial wheat lines in the Huang-Huai-Hai region of China, 146 wheat entries were inoculated with the Chinese prevalent Puccinia striiformis f. sp. tritici (Pst) races CYR32, CYR33, CYR34, and Blumeria graminis f. sp. tritici (Bgt) isolate E09 under controlled greenhouse conditions, respectively; these entries were also tested with the mixed Pst races, Bgt and FHB isolates at adult-plant stage in the field, respectively. The results showed that 108 (73.97%), 83 (56.85%), 99 (67.81%), and 22 (15.07%) entries were resistant to CYR32, CYR33, CYR34, and E09 at the seedling stage, respectively; 102 (69.86%), 24 (16.44%), and 2 (1.37%) entries were resistant to stripe rust, powdery mildew, and Fusarium head blight at the adult-plant stage, respectively. Additionally, the possible resistance gene(s) in these entries were postulated by the closely linked markers of stripe rust resistance genes Yr5, Yr9, Yr10, Yr15, Yr17, Yr18, Yr26, powdery mildew resistance gene Pm21, and Fusarium head blight resistance gene Fhb1. Combined with disease resistance and molecular markers tests, 62, nine, and three wheat entries were postulated to carry the Yr9, Yr17, Yr26 gene, respectively, and no entries contained Yr5, Yr10, Yr15, Yr18, Pm21, and Fhb1 gene. This study laid a theoretical foundation for rational utilization of these entries and gene in wheat breeding programs and disease control.

In this study, we successfully introgressed and validated Yr59 into four elite wheat cultivars, Jimai 22, Chuanmai 42, Zhengmai 9023 and Xinmai 26 through marker-assisted backcross selection. Used as female parents, these four cultivars were crossed with wheat line PI 660061 (Yr59). After two backcrosses and marker-assisted selection, the progenies were selfed and advanced to the BC2F4 generation. A total of 123 BC2F4 lines were selected based on agronomic traits and stripe rust resistance, and their BC2F5 and BC2F6 progenies were further evaluated for stripe rust resistance and agronomic traits. Seven markers linked with relevant genes, including Xbarc32, Xwgp5175, Xwmc557 and Xcfa2040 linked with Yr59; Xwmc658 with YrJ22; WE173 and Xbarc181 with Yr26, were used to genotype the breeding lines. A total of 109 introgression lines with positive markers for Yr59 were identified for further stripe rust and agronomic trait evaluation. Finally, 16 lines had higher levels resistance to stripe rust, and similar or superior agronomic traits compared to their parents were obtained. These lines can be released as new cultivars for various regions after regional tests and also can be used as resistance stocks for regional breeding programs to develop new cultivars with adequate and durable resistance to stripe rust. [ABSTRACT FROM AUTHOR]

Wheat stripe rust, powdery mildew, and Fusarium head blight (FHB) are the three most important diseases in wheat worldwide. Growing resistant cultivars is the most economic and effective method to control these diseases. To assess the disease resistance of commercial wheat cultivars and regional trial wheat lines in the Huang-Huai-Hai region of China, 146 wheat entries were inoculated with the Chinese prevalent Puccinia striiformis f. sp. tritici (Pst) races CYR32, CYR33, CYR34, and Blumeria graminis f. sp. tritici (Bgt) isolate E09 under controlled greenhouse conditions, respectively; these entries were also tested with the mixed Pst races, Bgt and FHB isolates at adult-plant stage in the field, respectively. The results showed that 108 (73.97%), 83 (56.85%), 99 (67.81%), and 22 (15.07%) entries were resistant to CYR32, CYR33, CYR34, and E09 at the seedling stage, respectively; 102 (69.86%), 24 (16.44%), and 2 (1.37%) entries were resistant to stripe rust, powdery mildew, and Fusarium head blight at the adult-plant stage, respectively. Additionally, the possible resistance gene(s) in these entries were postulated by the closely linked markers of stripe rust resistance genes Yr5, Yr9, Yr10, Yr15, Yr17, Yr18, Yr26, powdery mildew resistance gene Pm21, and Fusarium head blight resistance gene Fhb1. Combined with disease resistance and molecular markers tests, 62, nine, and three wheat entries were postulated to carry the Yr9, Yr17, Yr26 gene, respectively, and no entries contained Yr5, Yr10, Yr15, Yr18, Pm21, and Fhb1 gene. This study laid a theoretical foundation for rational utilization of these entries and gene in wheat breeding programs and disease control. [ABSTRACT FROM AUTHOR]