Title page for ETD etd-03232005-095121

Type of Document Master's Thesis
Author Nadimpalli, Uma Devi
Author's Email Address unadim1@lsu.edu
URN etd-03232005-095121
Title Image Processing Techniques to Identify Predatory Birds in Aquacultural Settings
Degree Master of Science in Biological & Agricultural Engineering (M.S.B.A.E.)
Department Biological & Agricultural Engineering
Advisory Committee
Advisor Name Title
Randy R. Price Committee Co-Chair
Steven G. Hall Committee Co-Chair
Chandra S. Theegala Committee Member
Marybeth Lima Committee Member
  • artificial neural networks
  • template matching
  • aquaculture
  • image processing
Date of Defense 2005-02-22
Availability unrestricted
Bird predation is a major problem in aquaculture. A novel method for dispersing birds is the use of a vehicle that can sense and chase birds. Image recognition software can improve their efficiency in chasing birds. Three recognition techniques were tested to identify birds 1) image morphology 2) artificial neural networks, and 3) template matching have been tested. A study was conducted on three species of birds 1) pelicans, 2) egrets, and 3) cormorants. Images were divided into 3 types 1) Type 1, 2) Type 2, and 3) Type 3 depending upon difficulty to separate from the others in the images. These types were clear, medium clear and unclear respectively. Image morphology resulted in 57.1% to 97.7%, 73.0% to 100%, and 46.1% to 95.5% correct classification rates (CCR) respectively on images of pelicans, cormorants and egrets before size thresholding. The artificial neural network model achieved 100% CCR while testing type 1 images and its classification success ranged from 63.5% to 70.0%, and 57.1% to 67.7% while testing type 2 and type 3 images respectively. The template matching algorithm succeeded in classifying 90%, 80%, and 60% of Type 1, Type 2 and Type 3 images of pelicans and egrets. This technique recognized 80%, 91.7%, and 80% of Type 1, Type 2, and Type 3 images of cormorants.

We developed a real time recognition algorithm that could capture images from a camera, process them, and send output to the autonomous boat in regular intervals of time. Future research will focus on testing the recognition algorithms in natural or aquacultural settings on autonomous boats.

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