Multi-task learning model for agricultural pest detection from crop-plant imagery: A Bayesian approach

Abderraouf Amrani, Dean Diepeveen, David Murray, Michael G. K. Jones, Ferdous Sohel

Abstract: Highlights•This paper presents a Bayesian multi-task learning model for pest detection.•The model estimates size of aphids, which is an indicator of infestation severity.•It also quantifies the predictive uncertainties enabling confident decisions.•It performs thorough audit studies that offer insight for future model refinements.