Herbicide efficacy prediction based on object segmentation of glasshouse imagery

Abstract: In this work, we explore the possibility of incorporating deep learning (DL) to propose a solution for the herbicidal efficacy prediction problem based on glasshouse (GH) images. Our approach utilises RGB images of treated and control plant images to perform the analysis and operates in three stages, 1) plant region detection and 2) leaf segmentation, where growth characteristics are inferred about the tested plant, and 3) herbicide activity estimation stage, where these metrics are used to estimate the herbicidal activity in a contrastive manner. The model shows a desirable performance across different species and activity levels, with a mean F1-score of 0.950. These results demonstrate the reliability and promising potential of our framework as a solution for herbicide efficacy prediction based on glasshouse images. We also present a semi-automatic plant labelling approach to address the lack of available public datasets for our target task. While existing works focus on plant detection and phenotyping, to the best of our knowledge, our work is the first to tackle the prediction of herbicide activity from GH images using DL.