Transformers Outperform ConvNets for Root Segmentation: A Systematic Comparison Across Nine Datasets

Smith, A. G.; Lamprinidis, S.; Seethepalli, A.; York, L. M.; Han, E.; Mohl, P.; Boulata, K.; Thorup-Kristensen, K.; Petersen, J.

doi:10.64898/2026.02.18.706562

bioRxiv · 10.64898/2026.02.18.706562

Transformers Outperform ConvNets for Root Segmentation: A Systematic Comparison Across Nine Datasets

Smith, A. G.·Lamprinidis, S.·Seethepalli, A.·York, L. M.·Han, E.·Mohl, P.·Boulata, K.·Thorup-Kristensen, K.·Petersen, J.

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Abstract

Root segmentation is a fundamental yet challenging task in image-based plant phenotyping. We present the first systematic comparison of Transformer and Convolutional Neural Network (ConvNet) architectures for root segmentation, evaluating 21 architectures across nine diverse datasets and comparing pre-trained models to training from scratch. Transformer-based models significantly outperform ConvNets for segmentation accuracy and root-diameter agreement. Pre-training significantly improves mean Dice from 0.623 to 0.666 (p = 3.3 x 10-10). We also find that Transformers benefit more from pre-training than ConvNets, with Dice improvements of +0.072 versus +0.022 (p = 3.7 x 10-4), supporting the hypothesis that fine-tuned Transformers transfer more effectively across large domain gaps. Among evaluated models, MobileSAM achieved the highest Dice score while maintaining computational efficiency. Dataset choice explained far more performance variance (70.9%) than model architecture (6.7%), suggesting that data curation matters more than model selection.

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Smith, A. G., Lamprinidis, S., Seethepalli, A., York, L. M., Han, E., Mohl, P., Boulata, K., Thorup-Kristensen, K., Petersen, J.. 2026-02-19. Transformers Outperform ConvNets for Root Segmentation: A Systematic Comparison Across Nine Datasets. https://doi.org/10.64898/2026.02.18.706562

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