A Deep Learning Approach to Predicting Pithomyces chartarum Sporulation for Livestock Protection

Pithomycotoxicosis is a disease affecting grazing livestock, caused by ingestion of Pithomyces chartarum spores. These spores have been identified in various regions worldwide, including the Azores Archipelago (Portugal) since 1999. The severity of the disease is strongly linked to spore concentration, while spore abundance is known to depend on meteorological conditions. In this study, we develop and evaluate a deep learning-based framework to forecast the sporulation of P. chartarum on Terceira Island (Azores), using historical spore count data together with meteorological and topographic variables. Among 20 neural network architectures tested, a convolutional neural network (CNN) achieved the best performance in classifying high-risk conditions, with an area under the curve (AUC) of 0.81 on the validation set. Feature importance analysis identified mean daily temperature as the most influential variable for sporulation risk, consistent with known favorable conditions for fungal growth. Additionally, the results reveal a marked seasonal pattern in sporulation risk, shaped by short- to mid-term antecedent meteorological conditions. Our findings demonstrate that deep learning models can enhance predictive accuracy and deepen understanding of the environmental drivers of P. chartarum sporulation, thereby improving the performance of existing alert systems for livestock protection.