Additional file 1 of A two-step registration-classification approach to automated segmentation of multimodal images for high-throughput greenhouse plant phenotyping

Additional file 1: Table S1. Summary of evaluation of 288 case-scenario models for plant image segmentation against ground truth data. 288 case-scenarios result from training of eight classification models (i.e. bayes, da, glm, gpr, linmod, svm, svmreg, net) for three different plant types (arabidopsis, wheat and maize), two different camera views (top/side view), three image modalities (FLU, VIS and FLU+VIS) and three plant developmental stages including: I—juvenile/small, II—mid-stage and III—adult/large shoots, as well as their combinations, i.e. I + II, I + III, II + III, I + II + III.