The project describes the design and implementation of an automatic system for detecting defects in plastic crates for glass bottles. In all companies there is damage and defects in their cases, crates, or containers due to constant use, as they are reusable, and therefore this problem causes various economic losses and a decrease in production, especially in beverage companies. This system was designed to solve and prevent the crates from having defects in their base and containing waste inside, to obtain less product losses in the bottle packaging area. In this research, it is proposed to design the automatic system, which consists of training a convolutional neural network with a database of 136 photographs of waste and defects in the boxes that will be taken by the HQ Raspberry Camera; then programmed into the Raspberry the process of activating the engine so that the box is moved to...