Model for Training and Predicting the Occurrence of Potato Late Blight Based on an Analysis of Future Weather Conditions

Plant diseases pose a significant challenge to agriculture, leading to serious economic losses and a risk to food security. Predicting and managing diseases such as potato blight requires an analysis of key environmental factors, including temperature, dew point, and humidity, that influence the development of pathogens. The current study uses machine learning to integrate this data for the purpose of early detection of diseases. The use of local weather data from sensors, combined with forecast data from public weather API servers, is a prerequisite for accurate short-term forecasting of adverse events. The results highlight the potential of predictive models to optimize prevention strategies, reduce losses and support sustainable crop management. Machine learning provides powerful tools for analyzing and predicting data related to plant diseases. Combining different approaches allows the creation of more precise and adaptive models for disease management.

@article{Damyanov2025,
  title     = {Model for Training and Predicting the Occurrence of Potato Late Blight Based on an Analysis of Future Weather Conditions},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {2},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Daniel Damyanov and Ivaylo Donchev},
  doi       = {10.14569/IJACSA.2025.0160213},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160213}
}