Development of an artificial vision algorithm with neural networks to detect coffee berry borer in coffee beans

This research addresses the problem of coffee berry borer in coffee plantations, which causes significant economic losses to companies in the coffee industry. This pest affects the quality of the coffee beans, reducing their market value and decreasing revenues. To address this issue, the use of artificial vision with neural networks is proposed as a solution. The main objective of this study is to develop a computer vision algorithm using convolutional neural networks to detect coffee bean defects caused by the coffee berry borer through image processing. Ultimately, this research aims to implement a coffee bean classification solution that achieves an efficiency of 91.8% based on the given criteria.