# Assessing the Presence of Pithomyces chartarum in Pastureland Using IoT Sensors and Remote Sensing: The Case Study of Terceira Island (Azores, Portugal)

## TL;DR

This study explores how weather and sensors can help predict and manage a harmful fungus in Azorean pastures that affects cattle health and finances.

## Contribution

The study combines IoT sensors, remote sensing, and meteorological data to predict spore presence and improve risk management for farmers.

## Key findings

- Meteorological data from the past 90 days can predict spore sporulation risk.
- Spectral signatures of grass with and without spores showed no significant differences.
- Predictive models can enhance a web-based alert system for farmers.

## Abstract

Spores from the fungus Pithomyces chartarum are commonly found on Azorean pastures. When consumed by cattle along with the grass, these spores cause health issues in the cattle, resulting in animal suffering and financial losses. For approximately two years, we monitored meteorological parameters using weather stations and collected and analyzed grass samples in a laboratory to control for the presence of spores. The data confirmed a connection between meteorology and sporulation, enabling the prediction of sporulation risk. To detect the presence of spores in pastures rather than predict it, we employed field spectrometry and Sentinel-2 reflectance data to measure the spectral signatures of grass while controlling for spores. Our findings indicate that meteorological variables from the past 90 days can be used to predict sporulation, which can enhance the accuracy of a web-based alert system used by farmers to manage the risk. We did not detect significant differences in spectral signatures between grass with and without spores. These studies contribute to a deeper understanding of P. chartarum sporulation and provide actionable information for managing cattle, ultimately improving animal welfare and reducing financial losses.