Large-scale farming of agriculture crops requires real-time detection of disease for field pest management. Hyperspectral remote sensing data generally have high spectral resolution, which could be very useful for det...Large-scale farming of agriculture crops requires real-time detection of disease for field pest management. Hyperspectral remote sensing data generally have high spectral resolution, which could be very useful for detecting disease stress in green vegetation at the leaf and canopy levels. In this study, hyperspectral reflectances of rice in the laboratory and field were measured to characterize the spectral regions and wavebands, which were the most sensitive to rice brown spot infected by Bipolaris oryzae （Helminthosporium oryzae Breda. de Hann）. Leaf reflectance increased at the ranges of 450 to 500 nm and 630 to 680 nm with the increasing percentage of infected leaf surface, and decreased at the ranges of 520 to 580 nm, 760 to 790 nm, 1550 to 1750 nm, and 2080 to 2350 nm with the increasing percentage of infected leaf surface respectively. The sensitivity analysis and derivative technique were used to select the sensitive wavebands for the detection of rice brown spot infected by B. oryzae. Ratios of rice leaf reflectance were evaluated as indicators of brown spot. R669/R746 （the reflectance at 669 nm divided by the reflectance at 746 nm, the following ratios may be deduced by analogy）, R702/R718, R692/R530, R692/R732, R535/R746, R521/R718, and R569/R718 increased significantly as the incidence of rice brown spot increased regardless of whether it＇s at the leaf or canopy level. R702/R718, R692/R530, R692/R732 were the best three ratios for estimating the disease severity of rice brown spot at the leaf and canopy levels. This result not only confirms the capability of hyperspectral remote sensing data in characterizing crop disease for precision pest management in the real world, but also testifies that the ratios of crop reflectance is a useful method to estimate crop disease severity.展开更多
Detecting plant health conditions plays a key role in farm pest management and crop protection. In this study, measurement of hyperspectral leaf reflectance in rice crop (Oryzasativa L.) was conducted on groups of hea...Detecting plant health conditions plays a key role in farm pest management and crop protection. In this study, measurement of hyperspectral leaf reflectance in rice crop (Oryzasativa L.) was conducted on groups of healthy and infected leaves by the fungus Bipolaris oryzae (Helminthosporium oryzae Breda. de Hann) through the wavelength range from 350 to 2 500 nm. The percentage of leaf surface lesions was estimated and defined as the disease severity. Statistical methods like multiple stepwise regression, principal component analysis and partial least-square regression were utilized to calculate and estimate the disease severity of rice brown spot at the leaf level. Our results revealed that multiple stepwise linear regressions could efficiently estimate disease severity with three wavebands in seven steps. The root mean square errors (RMSEs) for training (n=210) and testing (n=53) dataset were 6.5% and 5.8%, respectively. Principal component analysis showed that the first principal component could explain approximately 80% of the variance of the original hyperspectral reflectance. The regression model with the first two principal components predicted a disease severity with RMSEs of 16.3% and 13.9% for the training and testing dataset, respec-tively. Partial least-square regression with seven extracted factors could most effectively predict disease severity compared with other statistical methods with RMSEs of 4.1% and 2.0% for the training and testing dataset, respectively. Our research demon-strates that it is feasible to estimate the disease severity of rice brown spot using hyperspectral reflectance data at the leaf level.展开更多
Based on the field hyperspectral data from the analytical spectral devices （ASD） spectrometer, we characterized the spectral properties of rice canopies infested with brown spot disease and selected spectral regions...Based on the field hyperspectral data from the analytical spectral devices （ASD） spectrometer, we characterized the spectral properties of rice canopies infested with brown spot disease and selected spectral regions and bands sensitive to four severity degrees （severe, moderate, light, and healthy）. The results show that the curves＇ variation on the original and the first- and second-order de- rivative curves are greatly different, but the spectral difference in the near-infrared region is the most obvious for each level. Specifically, the peaks are located at 822, 738, and 793 nm, while the valleys are located at 402, 570, and 753 run, respectively. The sensitive regions are between 430-520, 530-550, and 650-710 nm, and the bands are 498, 539, and 673 nm in the sensitivity analysis, while they are in the ranges of 401-530, 550-730 as well as at 498 nm and 678 nm in the continuum removal.展开更多
[目的]从土壤中筛选对水稻胡麻斑病病原菌(Helminthospotium oryzae Breda de Hann)具有稳定拮抗作用的细菌。[方法]利用稀释平板法对从土样中分离出的细菌进行拮抗菌的初筛,再采用室内平板筛选法进行复筛。将拮抗效果最佳的菌株进行培...[目的]从土壤中筛选对水稻胡麻斑病病原菌(Helminthospotium oryzae Breda de Hann)具有稳定拮抗作用的细菌。[方法]利用稀释平板法对从土样中分离出的细菌进行拮抗菌的初筛,再采用室内平板筛选法进行复筛。将拮抗效果最佳的菌株进行培养,通过菌体形态特征观察和生理生化测试进行菌种鉴定,并采用平板对峙法进行拮抗菌的抑菌谱分析。[结果]从38份土样中分离了494株细菌,初筛获得拮抗菌株24株,复筛得到4株拮抗能力较强的菌株,经鉴定4种拮抗菌均为地衣芽孢杆菌属(Bacillus licheniformis),并得到其抑菌谱。[结论]为利用拮抗菌生物防治水稻胡麻斑病提供了理论依据。展开更多
基金supported by the National High Technology Research and Development Program of China (Grant No. 2006AA10Z203) the National Natural Science Foundation of China (Grant No. 40571115).
文摘Large-scale farming of agriculture crops requires real-time detection of disease for field pest management. Hyperspectral remote sensing data generally have high spectral resolution, which could be very useful for detecting disease stress in green vegetation at the leaf and canopy levels. In this study, hyperspectral reflectances of rice in the laboratory and field were measured to characterize the spectral regions and wavebands, which were the most sensitive to rice brown spot infected by Bipolaris oryzae （Helminthosporium oryzae Breda. de Hann）. Leaf reflectance increased at the ranges of 450 to 500 nm and 630 to 680 nm with the increasing percentage of infected leaf surface, and decreased at the ranges of 520 to 580 nm, 760 to 790 nm, 1550 to 1750 nm, and 2080 to 2350 nm with the increasing percentage of infected leaf surface respectively. The sensitivity analysis and derivative technique were used to select the sensitive wavebands for the detection of rice brown spot infected by B. oryzae. Ratios of rice leaf reflectance were evaluated as indicators of brown spot. R669/R746 （the reflectance at 669 nm divided by the reflectance at 746 nm, the following ratios may be deduced by analogy）, R702/R718, R692/R530, R692/R732, R535/R746, R521/R718, and R569/R718 increased significantly as the incidence of rice brown spot increased regardless of whether it＇s at the leaf or canopy level. R702/R718, R692/R530, R692/R732 were the best three ratios for estimating the disease severity of rice brown spot at the leaf and canopy levels. This result not only confirms the capability of hyperspectral remote sensing data in characterizing crop disease for precision pest management in the real world, but also testifies that the ratios of crop reflectance is a useful method to estimate crop disease severity.
基金the Hi-Tech Research and Development Program (863) of China (No. 2006AA10Z203)the National Scienceand Technology Task Force Project (No. 2006BAD10A01), China
文摘Detecting plant health conditions plays a key role in farm pest management and crop protection. In this study, measurement of hyperspectral leaf reflectance in rice crop (Oryzasativa L.) was conducted on groups of healthy and infected leaves by the fungus Bipolaris oryzae (Helminthosporium oryzae Breda. de Hann) through the wavelength range from 350 to 2 500 nm. The percentage of leaf surface lesions was estimated and defined as the disease severity. Statistical methods like multiple stepwise regression, principal component analysis and partial least-square regression were utilized to calculate and estimate the disease severity of rice brown spot at the leaf level. Our results revealed that multiple stepwise linear regressions could efficiently estimate disease severity with three wavebands in seven steps. The root mean square errors (RMSEs) for training (n=210) and testing (n=53) dataset were 6.5% and 5.8%, respectively. Principal component analysis showed that the first principal component could explain approximately 80% of the variance of the original hyperspectral reflectance. The regression model with the first two principal components predicted a disease severity with RMSEs of 16.3% and 13.9% for the training and testing dataset, respec-tively. Partial least-square regression with seven extracted factors could most effectively predict disease severity compared with other statistical methods with RMSEs of 4.1% and 2.0% for the training and testing dataset, respectively. Our research demon-strates that it is feasible to estimate the disease severity of rice brown spot using hyperspectral reflectance data at the leaf level.
基金Supported by the National Natural Science Foundation of China (41071276 and 41101395)China Postdoctoral Science Foundation (20110490317)Postdoctoral Science Foundation of Beijing Academy of Agriculture and Forestry Sciences (2011)
文摘Based on the field hyperspectral data from the analytical spectral devices （ASD） spectrometer, we characterized the spectral properties of rice canopies infested with brown spot disease and selected spectral regions and bands sensitive to four severity degrees （severe, moderate, light, and healthy）. The results show that the curves＇ variation on the original and the first- and second-order de- rivative curves are greatly different, but the spectral difference in the near-infrared region is the most obvious for each level. Specifically, the peaks are located at 822, 738, and 793 nm, while the valleys are located at 402, 570, and 753 run, respectively. The sensitive regions are between 430-520, 530-550, and 650-710 nm, and the bands are 498, 539, and 673 nm in the sensitivity analysis, while they are in the ranges of 401-530, 550-730 as well as at 498 nm and 678 nm in the continuum removal.
