This work proposes a non-destructive quality control for a pitting process of cherries. A system composed of a video camera and a light source records pictures of backlit cherries. The images processing in MATLAB environment provides the dynamic histograms of the pictures, which are analysed to state the presence of the pit. A feedforward artificial neural network was implemented and trained with the histograms obtained. The network developed allows a fast detection of stone fractions not visible by human inspection and the reduction of the accidental reject of properly manufactured products.
Baiocco, G., Almonti, D., Guarino, S., Tagliaferri, F., Tagliaferri, V., Ucciardello, N. (2020). Image-based system and artificial neural network to automate a quality control system for cherries pitting process. In Procedia CIRP (pp.527-532). Elsevier B.V. [10.1016/j.procir.2020.05.091].
Image-based system and artificial neural network to automate a quality control system for cherries pitting process
Almonti D.;
2020-01-01
Abstract
This work proposes a non-destructive quality control for a pitting process of cherries. A system composed of a video camera and a light source records pictures of backlit cherries. The images processing in MATLAB environment provides the dynamic histograms of the pictures, which are analysed to state the presence of the pit. A feedforward artificial neural network was implemented and trained with the histograms obtained. The network developed allows a fast detection of stone fractions not visible by human inspection and the reduction of the accidental reject of properly manufactured products.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
