Title page for ETD etd-11182005-095815


Type of Document Master's Thesis
Author Polsapalli, Sirisha
Author's Email Address spolsa1@lsu.edu
URN etd-11182005-095815
Title Development of Software to Process Aerial Images for Agricultural Purposes
Degree Master of Science in Biological & Agricultural Engineering (M.S.B.A.E.)
Department Biological & Agricultural Engineering
Advisory Committee
Advisor Name Title
Randy Price Committee Chair
Billy Rogers Leonard Committee Member
Keith Morris Committee Member
Keywords
  • software
  • aerial imagery
  • remote sensing
  • mosaic
  • GPS
Date of Defense 2005-11-04
Availability unrestricted
Abstract
Remote sensing has been used in precision agriculture for monitoring crop health, weed management, detecting nutrient stress, and yield forecasting. One method of implementing remote sensing is through aerial imagery. Aerial imagery is being used in precision agriculture for a variety causes such as to detect crop stress, fertilizer skips and overlaps, nitrogen excesses and deficiencies and detect irregular or reduced crop stand. These crop features are noted by extracting spectral information from the images. The spectral data is obtained from the images by using software programs. The software programs process the images one at a time or assemble them together and process them all at once. To obtain information about an extensive region of agricultural crop and save time, it is advisable to assemble the images and process them simultaneously.

This research provides a low cost software program to assemble images and process the images simultaneously to obtain data pertinent to make decision process regarding agricultural crops. This study utilized geographic location of the area being photographed as reference points for creating the mosaic of the images taken. The software has the ability to assemble images taken randomly over a specified area. Vegetative indices are used as the parameter to detect crop vigor and density. Normalized difference vegetative index and ratio vegetative index were measured from the spectral information in the images. The software achieved the capability of assembling 100 randomly taken images in less than two minutes and represents the variation in vegetative indices in varying shades of red, providing a map for detecting crop variability.

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