Causality and Explainability for Trustworthy Integrated Pest Management
Ilias Tsoumas Vasileios Sitokonstantinou Georgios Giannarakis Evagelia Lampiri Christos Athanassiou Gustau Camps-Valls Charalampos Kontoes Ioannis Athanasiadis
Pesticides serve as a common tool in agricultural pest control but
significantly contribute to the climate crisis. To combat this, Integrated Pest
Management (IPM) stands as a climate-smart alternative. Despite its potential,
IPM faces low adoption rates due to farmers' skepticism about its
effectiveness. To address this challenge, we introduce an advanced data
analysis framework tailored to enhance IPM adoption. Our framework provides i)
robust pest population predictions across diverse environments with invariant
and causal learning, ii) interpretable pest presence predictions using
transparent models, iii) actionable advice through counterfactual explanations
for in-season IPM interventions, iv) field-specific treatment effect
estimations, and v) assessments of the effectiveness of our advice using causal
inference. By incorporating these features, our framework aims to alleviate
skepticism and encourage wider adoption of IPM practices among farmers.