Automatic mapping of cracking patterns on concrete surfaces with biological stains using hyper-spectral images processing

2019 ◽  
Vol 26 (3) ◽  
pp. e2320 ◽  
Author(s):  
Bruno Oliveira Santos ◽  
Jonatas Valença ◽  
Eduardo Júlio
Keyword(s):  
Hyper Spectral ◽  
Automatic Mapping ◽  
Images Processing ◽  
Biological Stains

The solar radiation evaluation by satellite images processing

2010 ◽  
Vol 1 (08) ◽  
pp. 344-347 ◽  
Author(s):  
M. Boulifa ◽  
A. Adane ◽  
A. Mefti ◽  
S. Ameur ◽  
Z. Ameur
Keyword(s):  
Solar Radiation ◽  
Satellite Images ◽  
Images Processing

Eco-smart pest management in rice farming: prospects and challenges

2019 ◽  
Vol 56 (Special) ◽  
pp. 143-155
Author(s):  
SD Mohapatra ◽  
R Tripathi ◽  
Anjani Kumar ◽  
Suchismita Kar ◽  
Minati Mohapatra ◽  
...  
Keyword(s):  
Insect Pests ◽  
Mobile Apps ◽  
Insect Species ◽  
Economic Damage ◽  
Pests And Diseases ◽  
Pest Damage ◽  
Disruptive Technologies ◽  
Hyper Spectral ◽  
Rice Pest ◽  
Computer Based

The insect problem is accentuated in intensive rice cropping where the insects occur throughout the year in overlapping generations. Over 800 insect species damaging rice in one way or another, although the majority of them do very little damage. In India, about a dozen of insect species are of major importance but the economic damage caused by these species varies greatly from field to field and from year to year. Insect pests cause about 10-15 per cent yield losses. Farmers lose an estimated average of 37% of their rice crop to insect pests and diseases every year. This review focuses on precision farming tools being used in rice pest and diseases management viz., forecasting model for real-time pest-advisory services, hyper-spectral remote sensing in pest damage assessment, computer-based decision support system, disruptive technologies (mobile apps).

Updated Efficient Area -Carry Select Adder for Low Complexity D LATCH Configuration by disease identification in brain tumor hyper spectral image

2019 ◽  
Vol 14 ◽  
Author(s):  
Teresa V.V ◽  
Anand. B
Keyword(s):  
Research Work ◽  
Low Complexity ◽  
Propagation Delay ◽  
Maximum Speed ◽  
Hyper Spectral ◽  
Ripple Carry Adder ◽  
Minimum Power Consumption ◽  
Carry Select Adder ◽  
The Look ◽  
Propagation Result

Objective: In this research work presents an efficient way Carry Select Adder (CSLA) performance and estimation. The CSLA is utilized in several system to mitigate the issue of carry propagation delay that is happens by severally generating various carries and to get the sum, select a carry because of the uses of various pairs of RCA to provide the sum of the partial section also carry by consisting carry input but the CSLA isn't time economical, then by the multiplexers extreme total and carry is chosen in the selected section. Methodology: The fundamental plan of this work is to attain maximum speed and minimum power consumption by using Binary to Excess-1. Convertor rather than RCA within the regular CSLA. Here RCA denotes the Ripple Carry Adder section. At the span to more cut back the facility consumption, a method of CSLA with D LATCH is implemented during this research work. The look of Updated Efficient Area -Carry Select Adder (UEA-CSLA) is evaluated and intended in XILINX ISE design suite 14. 5 tools. This VLSI arrangement is utilized in picture preparing application by concluding the cerebrum tumor discovery. Conclusion: In this study, medicinal pictures estimation, investigation districts in the multi phantom picture isn't that much proficient to defeat this disadvantage here utilized hyper spectral picture method is presented a sifting procedure in VLSI innovation restriction of cerebrum tumor is performed Updated Efficient Area - Carry Select Adder propagation result dependent on Matrix Laboratory in the adaptation of R2018b.

scholarly journals Hyper-spectral estimation of soil organic matter in apple orchard based on CWT

2021 ◽  
Vol 734 (1) ◽  
pp. 012030
Author(s):  
Ruiyan Li ◽  
Mingxiu Gao ◽  
Zexin XU ◽  
Jiafan Wang ◽  
Xiaoxin Xie
Keyword(s):  
Organic Matter ◽  
Soil Organic Matter ◽  
Spectral Estimation ◽  
Apple Orchard ◽  
Hyper Spectral

Phenotypic evaluation of Brown Swiss dairy cattle using images processing

2020 ◽  
Vol 18 (11) ◽  
pp. 1996-2002
Author(s):  
Hugo Alatrista-Salas ◽  
Julianna Milagros Apumayta Lopez ◽  
Eduardo Leuman Fuentes Navarro ◽  
Miguel Nunez-del-Prado
Keyword(s):  
Dairy Cattle ◽  
Brown Swiss ◽  
Images Processing

FPGA support for satellite computations of hyper spectral images

2009 ◽  
Author(s):  
Carlos Gonzalez ◽  
Daniel Mozos ◽  
Javier Resano
Keyword(s):  
Hyper Spectral
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