Fernández Samacá, LilianaHiguera Martínez, Oscar IvánAlarcón Aranguren, Lorena MaríaMerchán Dehaquiz, Andrés FelipeValderrama Pineda, Félix Daniel2021-12-172022-02-222021-12-172022-02-222021-12978-958-52071-9-62414-6390http://laccei.org/LEIRD2021-VirtualEdition/meta/FP42.htmlhttp://dx.doi.org/10.18687/LEIRD2021.1.1.42https://axces.info/handle/10.18687/20210102_42Various methods are employed to prevent potato crops from being affected by diseases and pests, one of which is monitoring, which consists of people walking through the crops and using their cognitive abilities to recognize the presence of pests. However, limitations in human capacity such as inaccuracy due to the subjectivity introduced by the farmer can cause failures in the diagnosis. For this reason, a system capable of detecting the presence of the Andean weevil was implemented. For this purpose, artificial vision is used to perform the preprocessing of images extracted from photographs provided by farmers. In addition, a deep learning model based on the VGGNet architecture was developed. The architecture was taken to a mobile application using the model called MobileNet. The results showed an adequate recognition rate, obtaining a prediction accuracy of up to 84%.EnglishLACCEI Licensehttps://laccei.org/blog/copyright-laccei-papers/Deep learningneural networkfolioluspreprocessingmobile applicationArticle