Pest presence prediction using interpretable machine learning

TL;DR

This paper presents an interpretable machine learning model that predicts cotton bollworm presence using weather, vegetation, and trap data, offering insights into key environmental drivers for better farm management.

Contribution

It introduces an explainable boosting machine model for pest prediction that combines multiple data sources and provides interpretability for practical agricultural use.

Findings

Model achieves satisfactory prediction accuracy.

Key environmental drivers identified align with existing literature.

Interpretability enhances trust and potential for operational deployment.

Abstract

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.