Modelo predictivo basado en visión por computador para identificar la enfermedad del Tizón Tardío (Phytophthora infestans) en las hojas del tomate de riñón (Solanum Lycopersicum) del Cantón Saraguro. - Parra Tene, Sisa Ñusta (2024)
In Saraguro canton, many farmers are engaged in the production of kidney tomato; however, this activity has been affected by diseases such as late blight, which causes significant economic losses. This degree work aims to apply technologies based on artificial intelligence, specifically convolutional neural networks, to identify this disease in kidney tomato leaves, thus facilitating timely detection and more effective crop management. The methodology used was based on the Design Thinking approach, implementing the Define, Ideate, Prototype and Evaluate phases. In the first phase, photographs of healthy and late blight kidney tomato leaves were collected in different greenhouses in the Saraguro canton, achieving a total of 1,447 images, which were combined with 3,351 images from the PlantVillage dataset. In the second phase, the most effective models for the classification of plant leaf diseases were investigated, highlighting the ResNet50, VGG16 and InceptionV3 models. This allowed advancing to the third phase, where these models were trained, obtaining accuracies of 99.8%, 99.3% and 98.5%, respectively. In the final phase, the ResNet50 model was evaluated using a web application that integrated the trained model. One hundred images of healthy and diseased kidney tomato leaves were collected in a greenhouse and uploaded to the application for diagnosis. The results were compared with the evaluation of an agronomist from Saraguro canton, achieving an accuracy of 91% in the classification of the disease. Keywords: CNN, VGG16, ResNet50, InceptionV3, Deep Learning.