Automatic stitching and segmentation of roots images for the generation of labelled deep learning-ready data

Plant phenotyping is the complete evaluation of complex plant traits such as its growth, development, tolerance or resistance, measured on the basis of quantitative and individual parameters of the plant itself. The evaluation process should be automated and non-destructive, suggesting computer vision as a key enabling technology to perform this task. In this paper, we propose a computer vision software pipeline for the analysis of the roots system of a plant. Two main contributions are provided: first, a deterministic procedure to assemble a roots panorama image starting from multiple shots of a rotating rhizotron; second, the automatic extraction of a binary mask representing the observed roots in the image. Results on more than 20.000 RGB images demonstrate the robustness and feasibility of our approach, reporting 77% median sensitivity and 99% median specificity in the roots segmentation task. This study can be seen as the first step towards the automation of labelled data to be used in complex deep learning architectures devoted to higher level applications, such as the automatic data-driven feature extraction as well as high-throughput applications