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.

翻译：在这项工作中,我们使用了可解释的分类器,即可解释的诱饵机,它使用地球观测植被指数、数字天气预报和昆虫捕捉器,来预测在希腊棉花田出现虫害虫现象。我们的方法的玻璃箱性质为模型的主要驱动因素及其相互作用提供了深刻的洞察力。模型的可解释性增加了我们方法的可信任性,从而增加了我们方法的可信任性,从而增加了其在农业管理业务中快速吸收和基于背景实施的潜力。我们的结果令人满意,通过对全球和地方解释性的分析,驱动因素的重要性与文献一致。