  Helicoverpa Armigera, or cotton bollworm, is a serious insect pest of cotton
crops that threatens the yield and the quality of lint. The timely knowledge of
the presence of the insects in the field is crucial for effective farm
interventions. Meteo-climatic and vegetation conditions have been identified as
key drivers of crop pest abundance. In this work, we applied an interpretable
classifier, i.e., Explainable Boosting Machine, which uses earth observation
vegetation indices, numerical weather predictions and insect trap catches to
predict the onset of bollworm harmfulness in cotton fields in Greece. The
glass-box nature of our approach provides significant insight on the main
drivers of the model and the interactions among them. Model interpretability
adds to the trustworthiness of our approach and therefore its potential for
rapid uptake and context-based implementation in operational farm management
scenarios. Our results are satisfactory and the importance of drivers, through
our analysis on global and local explainability, is in accordance with the
literature.
