In this paper, an approach is described to develop deep learning models that recognize plant diseases of commercial interest through the classification of leaf images. A Raspberry Pi 4 microcomputer was used for hardware implementation. Some models employed from a subset of artificial intelligence, called Deep Learning, were used for pest detection by finetuning Transfer Learning to obtain high accuracy rates based on the PlantVillage dataset containing thirty-eight different classes, including diseased and healthy leaves. The study’s main objective is to classify various images of plants and leaves with high precision using convolutional neural networks with transfer learning implemented in a hardware device. The models were evaluated through an analysis based on precision, recall, F-score, and accuracy. The results present significant values obtained by the VGG16 technique, with 90% sensitivity and 90% accuracy. It is possible to conclude that the VGG16 trained model can be a useful tool for farmers to help and protect plants from the diseases mentioned.