Volltext-Downloads (blau) und Frontdoor-Views (grau)

Development of a computer vision model for the pecan crack identification, classification, and control

  • 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 crackingThe 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.show moreshow less

Download full text files

  • Masterthesis_Valiate_dos_Santos.pdf
    eng

Export metadata

Additional Services

Search Google Scholar

Statistics

frontdoor_oas
Metadaten
Document Type:Master's Thesis
Zitierlink: https://opus.hs-offenburg.de/10399
Bibliografische Angaben
Title (English):Development of a computer vision model for the pecan crack identification, classification, and control
Author:Bruna Karoline Valiate dos Santos
Advisor:Volker Sänger, Beshoy Morkos
Year of Publication:2025
Publishing Institution:Hochschule Offenburg
Granting Institution:Hochschule Offenburg
Contributing Corporation:University of Georgia
Place of publication:Offenburg
Publisher:Hochschule Offenburg
Page Number:38
Language:English
Inhaltliche Informationen
Institutes:Fakultät Maschinenbau und Verfahrenstechnik (M+V)
Collections of the Offenburg University:Abschlussarbeiten / Master-Studiengänge / MPE
DDC classes:600 Technik, Medizin, angewandte Wissenschaften
Tag:Artificial Intelligence; Computer Vision; Cracking Machine; Pecan
Formale Angaben
Open Access: Closed Access 
Licence (German):License LogoCreative Commons - CC0 1.0 - Universell - Public Domain Dedication