The guava plant has achieved viable significance in subtropics and tropics owing to its flexibility to climatic environments,soil conditions and higher human consumption.It is cultivated in vast areas of Asian and Non...The guava plant has achieved viable significance in subtropics and tropics owing to its flexibility to climatic environments,soil conditions and higher human consumption.It is cultivated in vast areas of Asian and Non-Asian countries,including Pakistan.The guava plant is vulnerable to diseases,specifically the leaves and fruit,which result in massive crop and profitability losses.The existing plant leaf disease detection techniques can detect only one disease from a leaf.However,a single leaf may contain symptoms of multiple diseases.This study has proposed a hybrid deep learning-based framework for the real-time detection of multiple diseases from a single guava leaf in several steps.Firstly,Guava Infected Patches Modified MobileNetV2 and U-Net(GIP-MU-NET)has been proposed to segment the infected guava patches.The proposed model consists of modified MobileNetv2 as an encoder,and the U-Net model’s up-sampling layers are used as a decoder part.Secondly,the Guava Leaf SegmentationModel(GLSM)is proposed to segment the healthy and infected leaves.In the final step,the Guava Multiple Leaf Diseases Detection(GMLDD)model based on the YOLOv5 model detects various diseases from a guava leaf.Two self-collected datasets(the Guava Patches Dataset and the Guava Leaf Diseases Dataset)are used for training and validation.The proposed method detected the various defects,including five distinct classes,i.e.,anthracnose,insect attack,nutrition deficiency,wilt,and healthy.On average,the GIP-MU-Net model achieved 92.41%accuracy,the GLSM gained 83.40%accuracy,whereas the proposed GMLDD technique achieved 73.3%precision,73.1%recall,71.0%mAP@0.5 and 50.3 mAP@0.5:0.95 scores for all the aforesaid classes.展开更多
The specificities of tissue culture of wheat greatly limit the use of chloroplast transformation technologies in this crop. One limitation in wheat tissue culture is that it is difficult to regenerate plantlets from l...The specificities of tissue culture of wheat greatly limit the use of chloroplast transformation technologies in this crop. One limitation in wheat tissue culture is that it is difficult to regenerate plantlets from leaf tissue explants of regenerated plantlets, resulting in difficulty in obtaining homoplastic plants via multiple rounds of antibiotic selection of chloroplast transformants. Thus, a repeated in vitro regeneration system from leaf tissues was studied in this research. Our results showed that 2 mm leaf basal segments of the 4 cm high leaves from regenerated plantlets can give the best callus induction at present study. The best callus induction medium was Murashige and Skoog (MS) basal medium supplemented with 2 mg/L 2,4-dichlorophenoxyacetic acid and 1 mg/L naphthalenacetic acid, which gave a callus induction rate of up to 87.2%. The optimal differentiation medium was MS basal medium supplemented with 10 mg/L silver nitrate and 1 mg/L 2,3,5-triiodobenzoic acid, which gave a regeneration rate up to 33.7% for the wheat lines tested. This is the first report showing that leaf basal segments of in vitro regenerated plantlets can be used for regeneration of wheat. The establishment of a repetitive regeneration system should pave the way for the development of chloroplast transformation and the plant regeneration systems starting from leaf material of in vitro regenerated wheat and other cereal crops.展开更多
To understand the responses of flag leaf shape in rice to elevated CO2 environment and their genetic characteristics, quantitative trait loci （QTLs） for flag leaf shape in rice were mapped onto the molecular marker ...To understand the responses of flag leaf shape in rice to elevated CO2 environment and their genetic characteristics, quantitative trait loci （QTLs） for flag leaf shape in rice were mapped onto the molecular marker linkage map of chromosome segment substitution lines （CSSLs） derived from a cross between a japonica variety Asominori and an indica variety IR24 under free air carbon dioxide enrichment （FACE, 200 μmol/mol above current levels） and current CO2 concentration （Ambient, about 370 μmol/mol）. Three flag-leaf traits, flag-leaf length （LL）, width （LW） and the ratio of LL to LW （RLW）, were estimated for each CSSL and their parental varieties. The differences in LL, LW and RLW between parents and in LL and LW within IR24 between FACE and Ambient were significant at 1% level. The continuous distributions and transgressive segregations of LL, LW and RLW were also observed in CSSL population, showing that the three traits were quantitatively inherited under both FACE and Ambient. A total of 16 QTLs for the three traits were detected on chromosomes 1, 2, 3, 4, 6, 8 and 11 with LOD （Log10-1ikelihood ratio） scores ranging from 3.0 to 6.7. Among them, four QTLs （qLL-6＊, qLL-8＊ qLW-4＊ and qRLW-6＊） were commonly detected under both FACE and Ambient. Therefore, based on the different responses to elevated CO2 in comparison with current CO2 level, it can be suggested that the expressions of several QTLs associated with flag-leaf shape in rice could be induced by the high CO2 level.展开更多
基金financially supported by the Deanship of Scientific Research,Qassim University,Saudi Arabia for funding the publication of this project.
文摘The guava plant has achieved viable significance in subtropics and tropics owing to its flexibility to climatic environments,soil conditions and higher human consumption.It is cultivated in vast areas of Asian and Non-Asian countries,including Pakistan.The guava plant is vulnerable to diseases,specifically the leaves and fruit,which result in massive crop and profitability losses.The existing plant leaf disease detection techniques can detect only one disease from a leaf.However,a single leaf may contain symptoms of multiple diseases.This study has proposed a hybrid deep learning-based framework for the real-time detection of multiple diseases from a single guava leaf in several steps.Firstly,Guava Infected Patches Modified MobileNetV2 and U-Net(GIP-MU-NET)has been proposed to segment the infected guava patches.The proposed model consists of modified MobileNetv2 as an encoder,and the U-Net model’s up-sampling layers are used as a decoder part.Secondly,the Guava Leaf SegmentationModel(GLSM)is proposed to segment the healthy and infected leaves.In the final step,the Guava Multiple Leaf Diseases Detection(GMLDD)model based on the YOLOv5 model detects various diseases from a guava leaf.Two self-collected datasets(the Guava Patches Dataset and the Guava Leaf Diseases Dataset)are used for training and validation.The proposed method detected the various defects,including five distinct classes,i.e.,anthracnose,insect attack,nutrition deficiency,wilt,and healthy.On average,the GIP-MU-Net model achieved 92.41%accuracy,the GLSM gained 83.40%accuracy,whereas the proposed GMLDD technique achieved 73.3%precision,73.1%recall,71.0%mAP@0.5 and 50.3 mAP@0.5:0.95 scores for all the aforesaid classes.
文摘The specificities of tissue culture of wheat greatly limit the use of chloroplast transformation technologies in this crop. One limitation in wheat tissue culture is that it is difficult to regenerate plantlets from leaf tissue explants of regenerated plantlets, resulting in difficulty in obtaining homoplastic plants via multiple rounds of antibiotic selection of chloroplast transformants. Thus, a repeated in vitro regeneration system from leaf tissues was studied in this research. Our results showed that 2 mm leaf basal segments of the 4 cm high leaves from regenerated plantlets can give the best callus induction at present study. The best callus induction medium was Murashige and Skoog (MS) basal medium supplemented with 2 mg/L 2,4-dichlorophenoxyacetic acid and 1 mg/L naphthalenacetic acid, which gave a callus induction rate of up to 87.2%. The optimal differentiation medium was MS basal medium supplemented with 10 mg/L silver nitrate and 1 mg/L 2,3,5-triiodobenzoic acid, which gave a regeneration rate up to 33.7% for the wheat lines tested. This is the first report showing that leaf basal segments of in vitro regenerated plantlets can be used for regeneration of wheat. The establishment of a repetitive regeneration system should pave the way for the development of chloroplast transformation and the plant regeneration systems starting from leaf material of in vitro regenerated wheat and other cereal crops.
基金The study was supported by the National Natural Science Foundation, China （Grant Nos. 30270800 and 40231003）
文摘To understand the responses of flag leaf shape in rice to elevated CO2 environment and their genetic characteristics, quantitative trait loci （QTLs） for flag leaf shape in rice were mapped onto the molecular marker linkage map of chromosome segment substitution lines （CSSLs） derived from a cross between a japonica variety Asominori and an indica variety IR24 under free air carbon dioxide enrichment （FACE, 200 μmol/mol above current levels） and current CO2 concentration （Ambient, about 370 μmol/mol）. Three flag-leaf traits, flag-leaf length （LL）, width （LW） and the ratio of LL to LW （RLW）, were estimated for each CSSL and their parental varieties. The differences in LL, LW and RLW between parents and in LL and LW within IR24 between FACE and Ambient were significant at 1% level. The continuous distributions and transgressive segregations of LL, LW and RLW were also observed in CSSL population, showing that the three traits were quantitatively inherited under both FACE and Ambient. A total of 16 QTLs for the three traits were detected on chromosomes 1, 2, 3, 4, 6, 8 and 11 with LOD （Log10-1ikelihood ratio） scores ranging from 3.0 to 6.7. Among them, four QTLs （qLL-6＊, qLL-8＊ qLW-4＊ and qRLW-6＊） were commonly detected under both FACE and Ambient. Therefore, based on the different responses to elevated CO2 in comparison with current CO2 level, it can be suggested that the expressions of several QTLs associated with flag-leaf shape in rice could be induced by the high CO2 level.
