Page 58 - WINGS OF AGRI-INNOVATIONS

18. A Multi/Hyper-Spectral Data Analyzing Process For Complete Quantification, Characterization And 19. Process for the Preparation of Pesticidal Oxime Esters
Compression Of Natural Resource Specific Information
Patent Number 214697 Patent Number 217763
Date Of Certificate Issue 14/02/2008 Date Of Certificate Issue 10/04/2008
Post Grant Journal Date 29/02/2008 Post Grant Journal Date 19/09/2008
Publication Number 49/2005 Publication Number 2/2006
Publication Date 23/12/2005 Publication Date 13/01/2006
Publication Type INA Publication Type INA
Application Number 825/DEL/2001 Application Number 846/DEL/2003
Application Filing Date 02/08/2001 Application Filing Date 27/06/2003
Field Of Invention COMPUTER SCIENCE Field Of Invention PHARMACEUTICALS
Classification (IPC) G01J 3/28 Classification (IPC) A01N 43/00

Inventor DR. (MRS) RAVINDER KAUR Inventor DR.SURESH WALIA, DR. BALRAJ SINGH PARMAR
Abstract: Abstract:
A process for the preparation of novel pesticidal oxime esters of formula I, RI/ARA-CH=N-OCOR2/ArB,
In remote sensing earth features are primarily characterized through multi-spectral signatures, and formula II, Rl/ARA-CH=N-OCO-Arc-COO-N=CH-ARA/RI characterized by the reaction of compounds
recorded either as per cent reflectance or gray levels in different wavebands. However, in order to make containing an oxime moiety Rl,MA-CHN-O- with compounds comprising of an acyl moiety RI/ArBCO-
characterization quantitative and more specific some spectral indices derived from information in these wherein the reaction is carried out in an organic solvent in a need based presence of a base as catalyst
spectral channels/wavebands are often used, which compress the data partially in two or more selected at 15 to 100 0C, and wherein MA, MB and Arc represent substituted or unsubstituted aryl, alkyl, ARAlkyl,
wavebands. Data analysis of simple gray scale, color, and color-infrared images is fairly straightforward. alkylaryl group(s), and R1 and R2, substituted or unsubstituted parafinic, olefinic or acetylenic group(s), to
Current techniques for analysis of Landsat-7 band images are adequate, but there are currently no methods yield geometrically isomeric compounds of formulae I and II. The configuration around the oxime double
for analysis of hyper-spectral data that are both powerful and fast. Current methods tend to either: 1) bond CH=N, in the molecule being Z or E or both. The application also describes the pesticidal compositions
Revert hyper-spectral images to Landsat channels; 2) Rely on information from a few selected bands; or based on the above esters for use in combating mosquito (Culex fatigans), agricultural insect pests namely
3) Explore the entire spectrum through complex data analysis procedures such as Partial Least Squares Spodoptera litura, and Helicoverpa armigera besides some phytophagous fungi and nematodes infecting
(PLS), whose computational requirements increase with the square of the data’s dimension (i.e. number of agricultural crops.
spectral channels). In fact all these techniques are based on a simple assumption that some wavelengths
or portions of the spectrum are rich in information about a feature of interest while the others are poor.
Thus all these techniques totally ignore the fact that the spectrum as a whole has another dimension
of information that is lost in treating it as discrete channels. Besides this, all these techniques involve
complicated class-separability and clustering analysis in n-dimensional space; where «n» is the number
of spectral channels. 1 developed a novel, powerful and fast hyper-spectral data analyzing method for
quantifying information contained in the whole spectrum, with any number of data/spectral channels from
2 to infinity, of any earth feature based on the basic principles of communication theory. Application of
this new hyper-spectral data analyzing method to multi-/ hyper-spectral databases from various platforms,
such as field, aircraft & satellite imaging spectrometers has shown that the new method can lead to: 1)
Easy identification of previously unrecognized systematic noise in the RDACS/H3 push broom hyper-
spectral sensor; 2) Distinct characterization of edges of linear/ non-linear natural/man-made resources
such as metallic roads, railway lines, canals, rivers, drains and water- bodies; 3) Distinct characterization of
and discrimination between vegetated areas, non- vegetated areas, natural resource mining sites, railway
lines, water-bodies, rivers & its tributaries and drains/ canals & their distributaries; 4) Easy discrimination
between structural and natural vegetation types thereby leading to more accurate estimates of areas
under these vegetation types; 5) Distinct discrimination between soil systems with different physico-
chemical characteristics; 6) Distinct characterization and discrimination of different moisture levels in soils;
7) Great reduction in data storage space requirement; and 8) Simplified 1-Dimensional clustering analysis.

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