This document presents an image processing for the detection and classification of biosecurity elements in real time by means of cascade classifiers. The operation was based on a Haar Cascade Classifier and data augmentation to complete the datasets. The images were acquired using an embedded Raspberry Pi 3B+ system connected to a Raspberry camera and then processed in Python. Both OpenCV and the Cascade Trainer GUI application, available for Windows versions 7 or higher, were used to create the classification models, so the images captured by Raspberry Pi had to be transferred to a personal computer. There were 4250 images that were converted by data augmentation techniques to 25401, with an average data increase accuracy of 88.492%. Also, 5 classification models were obtained corresponding to 5 categories of biosecurity elements referring to mask, gloves, glasses, anti-fluid clothing and anti-fluid footwears, with success rates in the classification of 90.2%, 92.7%, 92%, 89.7% and 94.1% respectively. In addition to the performance tests according to the hit rates, the system was evaluated by measuring the processing response time, obtaining fluctuating times between 0.475 seconds and 0.571 seconds.