High-Throughput Panicle Counting of Wild Rice Accessions for Germplasm Evaluation: An AI-Driven UAV Phenotyping Framework

Abstract

Wild rice (Oryza spp.) harbors abundant genetic variation and represents an important germplasm resource for improving yield-related traits in cultivated rice. Panicle number is a key phenotypic trait for evaluating tillering capacity and yield potential in wild rice. However, existing approaches for acquiring panicle-number phenotypes remain limited by low efficiency, high dependence on manual operation, and cumbersome matching between plant targets and accession identifiers. In this study, we proposed an AI-driven UAV phenotyping framework for high-throughput panicle counting of wild rice accessions for germplasm evaluation. The framework integrates field plant localization, accession identifier binding, flight route planning, plant-by-plant video acquisition, spatiotemporal registration, video slicing, and panicle detection and tracking, enabling structured panicle-number outputs indexed by accession identifier. To address the small scale, loose structure, morphological variation, and wind-induced swaying of wild rice panicles in UAV imagery, a wild rice panicle detection model was constructed, and WRPD-Tracker was developed for cross-frame identity association and non-redundant counting. The wild rice panicle detection model achieved an AP@50 of 91.56%, representing a 6.16-percentage-point improvement over the DEIM baseline, with 3.70 M parameters and 6.55 G FLOPs, while WRPD-Tracker achieved a HOTA of 65.1% and a MOTA of 79.0%, representing a 5.8-percentage-point improvement in HOTA over the baseline tracker. At the final counting level, UAV-based counts were highly consistent with manual ground counts, with an R² of 0.992 and an MAE of 0.37 panicles. This framework enables batch acquisition of panicle-number phenotypes in wild rice and provides quantitative support for germplasm evaluation, panicle-number trait comparison, and subsequent yield-related phenotypic studies.

Plant phenotyping relevance

UAV画像とAIによるイネ穂数の取得・追跡・計数手法を開発し、手動計数と技術検証しており、植物フェノタイピング手法が研究の中心です。

Code and data availability

The paper describes paper-specific UAV imagery datasets (wild rice panicle detection dataset, plant detection dataset, tracking evaluation dataset) and phenotyping models/code, but no public deposit or authors' URL is provided. The only availability statement is 'All data are available from the corresponding authorupon

No evidence-backed public reproduction asset is currently recorded.