Sistema para el diagnóstico de plagas de Solanum tuberosum L. mediante técnicas de inteligencia artificial.

In order to detect potato crop pests from symptom images, a web platform was developed to facilitate the diagnosis of early and late blight from images of affected leaves. The Python programming language, the Flask framework, Bootstrap for platform styling, the necessary libraries, and Pycharm as development environment were used. Image recognition and artificial intelligence software tools, specifically artificial neural networks, were used to implement the diagnostic module. The system also includes the field monitoring record with the incidences of pests, allows incorporating new data, and provides information on management in correspondence with climate variability. For the diagnosis validation, images of symptoms confirmed by experts were taken. The neural network model used was 94.6 % accurate. Future work will include the detection of viruses, bacteria and other fungi from images. The tool is a useful and novel application at the service of agri-food safety, serving as a coordinated platform for plant protection technicians and farmers involved in potato cultivation. [ABSTRACT FROM AUTHOR]