High-resolution image dataset for the automatic classification of phenological stage and identification of racemes in Urochloa spp. hybrids.

Abstract

Urochloa grasses are widely used forages in the Neotropics and are gaining importance in other regions due to their role in meeting the increasing global demand for sustainable agricultural practices. High-throughput phenotyping (HTP) is important for accelerating Urochloa breeding programs focused on improving forage and seed yield. While RGB imaging has been used for HTP of vegetative traits, the assessment of phenological stages and seed yield using image analysis remains unexplored in this genus. This work presents a dataset of 2,400 high-resolution RGB images of 200 Urochloa hybrid genotypes, captured over seven months and covering both vegetative and reproductive stages. Images were manually labelled as vegetative or reproductive, and a subset of 255 reproductive stage images were annotated to identify 22,340 individual racemes. This dataset enables the development of machine learning and deep learning models for automated phenological stage classification and raceme identification, facilitating HTP and accelerated breeding of Urochloa spp. hybrids with high seed yield potential.

Plant phenotyping relevance

植物のフェノロジー段階と穂状花序を画像から識別するための高解像度データセットであり、植物表現型取得・抽出を中心とする研究。

Code and data availability

The paper is itself a data descriptor whose core asset is a public Harvard Dataverse dataset of 2,400 RGB images of Urochloa hybrids with phenological stage labels and COCO-format raceme polygon annotations, directly reproducing the paper's phenotyping data.