Agricultural produce grading by computer vision using Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4549

  author =       "Panitnat Yimyam and Adrian F. Clark",
  booktitle =    "IEEE International Conference on Robotics and
                 Biomimetics (ROBIO 2012)",
  title =        "Agricultural produce grading by computer vision using
                 Genetic Programming",
  year =         "2012",
  month =        "11-14 " # dec,
  address =      "Guangzhou",
  pages =        "458--463",
  keywords =     "genetic algorithms, genetic programming, agriculture,
                 computer vision, crops, image classification, image
                 colour analysis, image segmentation, image texture,
                 inspection, learning (artificial intelligence), shape
                 recognition, agricultural produce grading, apple
                 variety discrimination, barley classification, colour
                 feature, feature classification, feature segmentation,
                 generic component, machine learning, mango surface
                 inspection, maturity evaluation, purple sticky rice
                 grading, shape feature, task-specific computer vision
                 system, texture feature, wheat classification",
  isbn13 =       "978-1-4673-2125-9",
  DOI =          "doi:10.1109/ROBIO.2012.6491009",
  size =         "6 pages",
  abstract =     "An approach to generating task-specific computer
                 vision systems from generic components using machine
                 learning is presented. With this system, it is possible
                 to learn both feature segmentation and classification
                 from training data. This approach is applied to a
                 disparate range of problems in the domain of
                 agricultural produce grading: mango surface inspection
                 and maturity evaluation, apple variety discrimination,
                 wheat and barley classification and purple sticky rice
                 grading. It is shown that shape, colour and texture
                 features together produce more accurate classification
                 results than fewer categories of feature, and that
                 these evolved classifiers are competitive with neural
                 networks and support vector machines.",
  notes =        "Also known as \cite{6491009}",

Genetic Programming entries for Panitnat Yimyam Adrian F Clark