2022/05/17 by Lorenzo Cerrone, Cerrone, Lorenzo, Athul Vijayan +7
Agricultural and Biological Sciences · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Leaf Properties and Growth Measurement #Machine Learning (cs.LG) #Smart Agriculture and AI
paper · pdf · doi:10.48550/arxiv.2205.08166
openalex publication_date 2022/05/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Classifying all cells in an organ is a relevant and difficult problem from plant developmental biology. We here abstract the problem into a new benchmark for node classification in a geo-referenced graph. Solving it requires learning the spatial layout of the organ including symmetries. To allow the convenient testing of new geometrical learning methods, the benchmark of Arabidopsis thaliana ovules is made available as a PyTorch data loader, along with a large number of precomputed features. Finally, we benchmark eight recent graph neural network architectures, finding that DeeperGCN currently works best on this problem.