Web components for late blight (Phytophthora infestans (Mont.) De Bary) and early blight (Alternaria solani Sor.) outbreaks forecast on Solanum tuberosum L. in Cuba under future climate scenarios

The invasive nature of late blight (Phytophthora infestans [Mont.] De Bary) and early blight (Alternaria solani Sor.) has caused important losses in the potato crop, and studies point to climatic variability as one of the most significant causes. The objective of this work was to predict the probability of outbreak of late blight and early blight epiphytotic diseases through weather patterns during the potato harvest season. Future meteorological data for the years 2024 to 2075 obtained from the National Institute of Meteorology in Cuba were used. For the late blight forecasting model development, disease behavior rules and a Random Forest model were used and validated on a small real-filed dataset. For early blight prediction model a framework based on disease behavior rules and phenological age of the crop (P-Days) was developed. The web system was implemented using the Python programming language, the Flask and Bootstrap frameworks, and the necessary libraries, and PyCharm as the development environment. Likewise, the PostgreSQL manager and PgAdmin were used for data management and as a tool for information administration. A web system was obtained that alerts on the probability of outbreak of late blight and early blight in the provinces of Mayabeque, Villa Clara and Ciego de Ávila, important potato-producing regions in Cuba. The forecast for the year 2025 was analyzed and it was found that Ciego de Avila and Mayabeque show a higher number of critical days for each of the diseases. In the month of March, late blight proliferation reached its peak, while early blight outbreak appeared to be more intense in January. The web system is an intelligent tool to guide farmers in making the necessary decisions and prevent an epidemic outbreak.