Work

Biology-aware association for identity-consistent leaf tracking in top-down plant phenotyping

Year: 2026

Type: article

Abstract: Reliable leaf-level trait trajectories require identity-consistent tracking across growth, overlap, and appearance change. We introduce a biology-aware association module combining one appearance cue and three biological/geometric cues: spatial continuity, area-growth plausibility, and lifecycle plausibility. On CanolaTrack, adaptive weighting reduces ID switches from 228 to 126 ± 5 (44.7%; per-plant paired p = 1.6 × 10 −5 , n = 37) and improves HOTA from 88.17 to 89.69 ± 0.10. Difficulty-stratified analysis shows IDSW reductions across all 12 CanolaTrack strata, averaging 40.0% versus Appearance-only and 45.0% versus LeafTrackNet. External association-only Arabidopsis and KOMATSUNA tests identify clean low-displacement GT-box regimes where centroid matching or Fixed bio-constraints are more reliable than zero-shot adaptive-weight transfer. These results provide an operating-envelope recommendation: adaptive weighting is most useful in dense CanolaTrack-like sequences, whereas Fixed bio-constraints or centroid matching are more reliable in clean low-displacement external regimes—where nearest-centroid assignment already resolves most associations, so that even a predictor retrained on the target domain does not surpass it. The limiting factor is thus primarily the association difficulty of the regime rather than dataset size.

Source: Frontiers in Plant Science

DOI: 10.3389/fpls.2026.1881413

Authors: Dongze Zhou, Cong Zhang

Institution: Wuhan Polytechnic University

Language: English

Topic: Smart Agriculture and AI

Subfield: Plant Science

Field: Agricultural and Biological Sciences

Domain: Life Sciences

SDG: Zero hunger

Open Access status: gold

Estimated APC paid: $3,801