The control effects of agricultural antibiotic 702 （ ag-antibiotic 702） on rice leaf blast and panicle blast were evaluated in field trials. With ag-antibiotic 702 and the two-line early hybrid rice 287 as test mate...The control effects of agricultural antibiotic 702 （ ag-antibiotic 702） on rice leaf blast and panicle blast were evaluated in field trials. With ag-antibiotic 702 and the two-line early hybrid rice 287 as test materials, different concentrations of ag-antibiotic 702 liquids were sprayed at tiUering stage and full heading stage of rice. In leaf blast test, the disease indexes of leaf blast at tillering stage were surveyed at 1 d prior to the first spraying and at 15 d post spraying, respectively. The disease indexes （DI） and the rice yield in panicle blast test was investigated at milky stage. Meanwhile, enzyme activities, including peroxidase （ POD）, catalase （CAT）, superoxide dismutase （SOD）, phenylalanine ammonia-lyase （PAL）, and polyphanol oxidase （PPO）, were measured. Kasugamycin （45 μg/mL） and deionized water were designed as positive and negative controls. When the concentrations of ag-antibiotie 702 were 15, 30 and 45 μg/mL, the control effects against leaf blast were 72.14%, 80.32% and 85.22%, respectively; the control effects on panicle blast were 74.08%, 84.87% and 86.58%, respectively; and the yield increase rates were 17.28%, 21.40% and 22.57%, respectively. So, ag-antibiotic 702, as a new agricultural antibiotic fungicide, could effectively cantrel rice blast by inducing defensive enzymes, and increase rice yield. The results will contribute to further development of ag-antibiotic 702 and its application in rice blast control.展开更多
Leaf blast is a significant global problem,severely affecting rice quality and yield,making swift,non-invasive detection crucial for effective field management.This study used hyperspectral remote sensing technology v...Leaf blast is a significant global problem,severely affecting rice quality and yield,making swift,non-invasive detection crucial for effective field management.This study used hyperspectral remote sensing technology via an unmanned aerial vehicle to gather spectral data from rice crops.ANOVA and the Relief-F algorithm were used to identify spectral bands sensitive to the disease and developed a new vegetation index,the rice blast index(RBI).This RBI was compared with 30 established vegetation indexes,using correlation analysis and visual comparison to further shortlist six superior indexes,including RBI.These were evaluated using the K-nearest neighbor(KNN)and random forests(RF)classification models.RBI demonstrated superior detection accuracy for leaf blast in both the KNN model(95.0% overall accuracy and 93.8% kappa coefficient)and the RF model(95.1%overall accuracy and 92.5% kappa coefficient).This study highlights the significant potential of RBI as an effective tool for precise leaf blast detection,offering a powerful new mechanism and theoretical basis for enhanced disease management in rice cultivation.展开更多
针对病斑在叶片上易形成封闭边缘的特性,采用色度学模型、边缘提取和形态学等方法对田间稻叶瘟病斑进行检测。利用2R-G色差分量提取图像上的图斑;采用Canny算法对2R-G色差分量图斑进行边缘检测,通过自定义边缘封闭修复模版对边缘进行修...针对病斑在叶片上易形成封闭边缘的特性,采用色度学模型、边缘提取和形态学等方法对田间稻叶瘟病斑进行检测。利用2R-G色差分量提取图像上的图斑;采用Canny算法对2R-G色差分量图斑进行边缘检测,通过自定义边缘封闭修复模版对边缘进行修复;利用HIS模型的H分量提取的叶片正常部位信息与修复后图像做掩膜运算,获得叶片范围内的病斑边界,然后,运用形态学运算剔除图斑中未闭合的边缘线;最后,采用归一化绿蓝差值指数(Normalized Difference Green and Blue Index,DNGBI)对封闭的非病斑区域进行阈值过滤,提取出稻瘟病病斑。试验结果表明:对叶瘟病斑的正确识别率可达到90.26%。展开更多
基金Supported by National Natural Science Foundation of China(31360450)Science and Technology Project of Jiangxi Province(20122BAB214027)
文摘The control effects of agricultural antibiotic 702 （ ag-antibiotic 702） on rice leaf blast and panicle blast were evaluated in field trials. With ag-antibiotic 702 and the two-line early hybrid rice 287 as test materials, different concentrations of ag-antibiotic 702 liquids were sprayed at tiUering stage and full heading stage of rice. In leaf blast test, the disease indexes of leaf blast at tillering stage were surveyed at 1 d prior to the first spraying and at 15 d post spraying, respectively. The disease indexes （DI） and the rice yield in panicle blast test was investigated at milky stage. Meanwhile, enzyme activities, including peroxidase （ POD）, catalase （CAT）, superoxide dismutase （SOD）, phenylalanine ammonia-lyase （PAL）, and polyphanol oxidase （PPO）, were measured. Kasugamycin （45 μg/mL） and deionized water were designed as positive and negative controls. When the concentrations of ag-antibiotie 702 were 15, 30 and 45 μg/mL, the control effects against leaf blast were 72.14%, 80.32% and 85.22%, respectively; the control effects on panicle blast were 74.08%, 84.87% and 86.58%, respectively; and the yield increase rates were 17.28%, 21.40% and 22.57%, respectively. So, ag-antibiotic 702, as a new agricultural antibiotic fungicide, could effectively cantrel rice blast by inducing defensive enzymes, and increase rice yield. The results will contribute to further development of ag-antibiotic 702 and its application in rice blast control.
文摘Leaf blast is a significant global problem,severely affecting rice quality and yield,making swift,non-invasive detection crucial for effective field management.This study used hyperspectral remote sensing technology via an unmanned aerial vehicle to gather spectral data from rice crops.ANOVA and the Relief-F algorithm were used to identify spectral bands sensitive to the disease and developed a new vegetation index,the rice blast index(RBI).This RBI was compared with 30 established vegetation indexes,using correlation analysis and visual comparison to further shortlist six superior indexes,including RBI.These were evaluated using the K-nearest neighbor(KNN)and random forests(RF)classification models.RBI demonstrated superior detection accuracy for leaf blast in both the KNN model(95.0% overall accuracy and 93.8% kappa coefficient)and the RF model(95.1%overall accuracy and 92.5% kappa coefficient).This study highlights the significant potential of RBI as an effective tool for precise leaf blast detection,offering a powerful new mechanism and theoretical basis for enhanced disease management in rice cultivation.
文摘针对病斑在叶片上易形成封闭边缘的特性,采用色度学模型、边缘提取和形态学等方法对田间稻叶瘟病斑进行检测。利用2R-G色差分量提取图像上的图斑;采用Canny算法对2R-G色差分量图斑进行边缘检测,通过自定义边缘封闭修复模版对边缘进行修复;利用HIS模型的H分量提取的叶片正常部位信息与修复后图像做掩膜运算,获得叶片范围内的病斑边界,然后,运用形态学运算剔除图斑中未闭合的边缘线;最后,采用归一化绿蓝差值指数(Normalized Difference Green and Blue Index,DNGBI)对封闭的非病斑区域进行阈值过滤,提取出稻瘟病病斑。试验结果表明:对叶瘟病斑的正确识别率可达到90.26%。
