@mastersthesis{Valiate dos Santos2025,
  type      = {Master Thesis},
  author    = {Valiate dos Santos, Bruna Karoline},
  title     = {Development of a computer vision model for the pecan crack identification, classification, and control},
  organization    = {University of Georgia},
  institution = {Fakult{\"a}t Maschinenbau und Verfahrenstechnik (M+V)},
  school    = {Hochschule Offenburg},
  pages     = {38},
  year      = {2025},
  abstract  = {The advent of computer vision and pattern detection capabilities has given rise to the use of imaging technology in feedback and control processes. One such example is the use of imaging in agricultural post-harvesting equipment to enable machine automation through the use of imaging at the discharge. To explore this, the thesis will consist of developing a simulation in Python of the cracking process of pecans using experimental data, computer vision, and artificial intelligence (AI). In the cracking process, there are some variables as data that are the input for the machine and the output as videos recorded on the exit of cracking. Some classifications for the type of crack are determined, and the goal is to know and be able to modify the inputs to obtain the desired class of crack. An AI model will be tested and trained in Python with experimental data.},
  language  = {en}
}