Pest presence prediction using interpretable machine learning

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.

Evaluating Digital Agriculture Recommendations with Causal Inference

“…However, the agricultural sector experiences limited adoption of precision agriculture and smart farming technologies (Gabriel and Gandorfer 2022). This might seem odd at first sight, given the surge of sophisticated digital tools that utilize Artificial Intelligence (AI) techniques and combine remote sensing data with data from Internet of Things (IoT) sensors to offer agricultural information of great detail Nanushi et al 2022;Choumos et al 2022). Yet farmers are skeptical about the effectiveness and actual contribution of these tools to their revenues and daily work (Lowenberg-DeBoer and Erickson 2019; Lioutas, Charatsari, and De Rosa 2021).…”