Fruit bruise detection based on 3D meshes and machine learning technologies

Zilong Hu; Jinshan Tang; Ping Zhang

doi:10.1117/12.2223336

Translator Disclaimer

19 May 2016 Fruit bruise detection based on 3D meshes and machine learning technologies

Proceedings Volume 9869, Mobile Multimedia/Image Processing, Security, and Applications 2016; 98690A (2016) https://doi.org/10.1117/12.2223336
Event: SPIE Commercial + Scientific Sensing and Imaging, 2016, Baltimore, Maryland, United States

Abstract

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.

Citation Download Citation

Zilong Hu, Jinshan Tang, and Ping Zhang "Fruit bruise detection based on 3D meshes and machine learning technologies", Proc. SPIE 9869, Mobile Multimedia/Image Processing, Security, and Applications 2016, 98690A (19 May 2016); https://doi.org/10.1117/12.2223336

ACCESS THE FULL ARTICLE