Fruit bruise detection based on 3D meshes and machine learning technologies
Department of Applied Computing; Center for Cyber-Physical Systems
This paper studies bruise detection in apples using 3-D imaging. Bruise detection based on 3-D imaging overcomes many limitations of bruise detection based on 2-D imaging, such as low accuracy, sensitive to light condition, and so on. In this paper, apple bruise detection is divided into two parts: feature extraction and classification. For feature extraction, we use a framework that can directly extract local binary patterns from mesh data. For classification, we studies support vector machine. Bruise detection using 3-D imaging is compared with bruise detection using 2-D imaging. 10-fold cross validation is used to evaluate the performance of the two systems. Experimental results show that bruise detection using 3-D imaging can achieve better classification accuracy than bruise detection based on 2-D imaging.
Proceedings Mobile Multimedia/Image Processing, Security, and Applications 2016
Fruit bruise detection based on 3D meshes and machine learning technologies.
Proceedings Mobile Multimedia/Image Processing, Security, and Applications 2016,
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p/845
© 2016 SPIE. Publisher's version of record: https://doi.org/10.1117/12.2223336