PhenoLeaf-TS: A Time-Series Benchmark for Leaf Instance Segmentation, Tracking, and Growth Stage Classification

Image-based high-throughput plant phenotyping leveragesinstance-level understanding of individual leaves growth over experimen-tal time, yet existing datasets lack temporal depth and annotation con-sistency to jointly benchmark segmentation, tracking, and growth stageclassification. We introduce PhenoLeaf-TS, a time-series dataset of 17,082top-down RGB images spanning 21 Arabidopsis thaliana genotypes, to-talling 318 plant replicates, each annotated with colour-coded leaf in-stance masks that maintain consistent identity throughout the growthsequence. We define three benchmark computer vision tasks with stan-dardised protocols and evaluate 21 distinct models: 9 instance segmenta-tion architectures, 6 multi-object trackers, and 6 classification architec-tures. Mask R-CNN with ResNet-50 achieves 73.2 mAP for leaf segmen-tation, ByteTrack reaches 84.1% MOTA for tracking individual leaves insequences, and Swin-T achieves 91.7% accuracy for growth stage classifi-cation. Cross-dataset experiments on CVPPP and Komatsuna show thatfine-tuning from PhenoLeaf-TS weights yields up to +51 mAP over zero-shot transfer. The dataset, the evaluation code, and the trained modelsare publicly accessible at https://pisyntor.github.io/PhenoLeaf-TS.