scholarly journals Relationships between Microsclerotia Content and Hyperspectral Reflectance Data in Soybean Tissue Infected by Macrophomina phaseolina

2014 ◽  
Vol 05 (25) ◽  
pp. 3737-3744 ◽  
Author(s):  
Reginald S. Fletcher ◽  
James R. Smith ◽  
Alemu Mengistu ◽  
Jeffery D. Ray
2011 ◽  
Vol 33 (2) ◽  
pp. 524-531 ◽  
Author(s):  
K.R. Thorp ◽  
D.A. Dierig ◽  
A.N. French ◽  
D.J. Hunsaker

Author(s):  
Austin Hayes ◽  
T. David Reed

Flue-cured tobacco (Nicotiana tabacum L.) is a high value-per-acre crop that is intensively managed to optimise the yield of high-quality cured leaf. A 15-day study assessed the potential of hyperspectral reflectance data for detecting Phytophthora nicotianae (black shank) incidence in flue-cured tobacco. Hyperspectral reflectance data were taken from a commercial flue-cured tobacco field with a progressing black shank infestation. The effort encompassed two key objectives. First, develop hyperspectral indices and/or machine learning classification models capable of detecting Phytophthora nicotianae (black shank) incidence in flue-cured tobacco. Second, evaluate the model’s ability to separate pre-symptomatic plants from healthy plants. Two hyperspectral indices were developed to detect black shank incidence based on differences in the spectral profiles of asymptomatic flue-cured tobacco plants compared to those with black shank symptoms. While one of the indices is a broad-band index and the other uses narrow wavelength values, the statistical difference between the two indices was not significant and both provided an accurate classification of symptomatic plants. Further analysis of the indices showed significant differences between the index values of healthy and symptomatic plants (α = 0.05). In addition, the indices were able to detect black shank symptoms pre-symptomatically (α = 0.09). Subspace linear discriminant analysis, a machine learning classification, was also used for prediction of black shank incidence with up to 85.7% classification accuracy. The implications of using either spectral indices or machine learning for classification for future black shank research are discussed.


Weed Science ◽  
2006 ◽  
Vol 54 (02) ◽  
pp. 335-339 ◽  
Author(s):  
Kendall C. Hutto ◽  
David R. Shaw ◽  
John D. Byrd ◽  
Roger L. King

Hand-held hyperspectral reflectance data were collected in the summers of 2002, 2003, and 2004 to differentiate unique spectral characteristics of common turfgrass and weed species. Turfgrass species evaluated were: bermudagrass, ‘Tifway 419’; zoysiagrass, ‘Meyer’; St. Augustinegrass, ‘Raleigh’; common centipedegrass; and creeping bentgrass, ‘Crenshaw’. Weed species evaluated were: dallisgrass, southern crabgrass, eclipta, and Virginia buttonweed. Reflectance data were collected from greenhouse and field locations. An overall classification accuracy of 85% was achieved for all species in the field. A total of 21 spectral bands between 378 and 1,000 nm that were consistent over the three data collection periods were used for analysis. Only centipedegrass, zoysiagrass, and dallisgrass were correctly classified less than 80% of the time. An overall classification accuracy of 69% was achieved for the greenhouse species. Spectral bands used in this analysis ranged from 353 to 799 nm. Creeping bentgrass and Virginia buttonweed were classified correctly at 96 and 92%, respectively.


2005 ◽  
Author(s):  
Guiling Sun ◽  
Yonghua Fang ◽  
Cuilan Zhang ◽  
Xianbing Wang ◽  
Benyong Yang

2006 ◽  
Vol 41 (2) ◽  
pp. 155-164 ◽  
Author(s):  
Osama A. El-Sebai ◽  
Robert Sanderson ◽  
Max P. Bleiweiss ◽  
Naomi Schmidt

Hyperspectral reflectance data were used to detect internal infestations of Angoumois grain moth, Sitotroga ceralella (Olivier), in wheat kernels. Kernel reflectance was measured with a spectroradiometer over a wavelength range of 350–2500 nm. Kernel samples were selected randomly and scanned every 7 d after infestation to determine the ability of the hyperspectral reflectance data to discriminate between infested and uninfested kernels. Immature stages of S. ceralella inside wheat kernels can be detected through changes in moisture, starch, and chitin content of the kernel. By using the spectrally-derived moisture variable (Log[1/R972nm]-Log[1/R1032nm]) and starch variable (Log[1/R982nm]-Log[1/R1014nm]), it was possible to discriminate between infested and uninfested wheat kernels with 100% classification accuracy based on 90% confidence intervals. Significant differences in the spectral reflectance between the infested and uninfested kernels were due to changes in moisture and starch content in wheat kernels. Three of the four chitin variables showed slight discrimination between the infested and uninfested wheat kernels based on 90% confidence intervals with 63.9%, 68.8%, 66.7%, and 41.6% classification accuracy of the three variables (Log[1/R1130nm]-Log[1/R1670nm]), (Log[1/R1139nm ]-Log[1/R1320nm]), (Log[1/R1202nm]-Log[1/R1300nm]), and (Log[1/R2046nm]-Log[1/R2302nm]), respectively. Spectral reflectance changes as a function of wheat kernel position relative to the spectroradiometer sensor did not differ significantly (P > 0.10).


2020 ◽  
Vol 20 (19) ◽  
pp. 11490-11498
Author(s):  
Vishnu Sudharshan ◽  
Peter Seidel ◽  
Pedram Ghamisi ◽  
Sandra Lorenz ◽  
Margret Fuchs ◽  
...  

2015 ◽  
Vol 48 (7) ◽  
pp. 492-498 ◽  
Author(s):  
Xiaoping Wang ◽  
Chuanyan Zhao ◽  
Ni Guo ◽  
Yaohui Li ◽  
Shenqi Jian ◽  
...  

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