Work

Automatic food bio-hazard detection system

Year: 2023

Type: article

Abstract: This paper presents the design of a convolutional neural network architecture oriented to the detection of food waste, to generate a low, medium, or critical-level alarm. An architecture based on four convolution layers is used, for which a database of 100 samples is prepared. The database is used with the different hyperparameters that make up the final architecture, after the training process. By means of confusion matrix analysis, a 100% performance of the network is obtained, which delivers its output to a fuzzy system that, depending on the duration of the detection time, generates the different alarm levels associated with the risk.

Source: International Journal of Power Electronics and Drive Systems/International Journal of Electrical and Computer Engineering

DOI: 10.11591/ijece.v13i3.pp2652-2659

Authors: Robinson Jimenez Moreno, Javier Eduardo Martinez Baquero

Institutions: Universidad Militar Nueva Granada, Universidad de los Llanos

Language: English

Topic: Advanced Neural Network Applications

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

SDGs: Responsible consumption and production, Zero hunger

Open Access status: diamond

Funders: Universidad Militar Nueva Granada, Universidad de los Llanos