Automatic food bio-hazard detection system

Robinson Jimenez Moreno (Universidad Militar Nueva Granada)
Javier Eduardo Martinez Baquero (Universidad de los Llanos)

International Journal of Electrical and Computer Engineering
Vol 13, No 3: June 2023

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.