CornPheno: Phenotyping corn ear kernels in the wild via point query transformer.

Abstract

Corn is a globally important economic crop. Certain trait parameters of corn ears kernels per ear are essential indicators for corn breeding. However, acquiring these parameters faces two challenges: i) manual measurement is labor-intensive and error-prone, and ii) vision-based corn phenotyping machines require fixed image capturing environment and are cost-prohibitive. To address these limitations, we introduce CornPheno, a user-friendly, low-end, smartphone-based approach capable of executing corn ear phenotyping in the wild. CornPheno highlights three corn ear parameters: kernels per ear, rows per ear, and kernels per row. Technically, inspired by crowd localization in computer vision, we first extract kernels per ear based on a Corn data-trained Point quEry Transformer (CornPET). CornPET generates interpretable per-kernel point predictions and supports subsequent row detection. To detect rows, we introduce a novel point-based corn row detection approach, termed unicorn, featured by sqUeezed clusteriNg and bI-direCtional pOint seaRchiNg, to phenotype rows per ear and kernels per row. With adaptive geometric modeling, our approach is robust to partial rows, curved rows, and missing kernels. To promote the use of CornPheno, we have integrated it into OpenPheno, a WeChat-based mini-program, and made it open-access for corn breeders. We hope our approach can provide the community with a user-friendly and cost-effective way to facilitate corn breeding.

Plant phenotyping relevance

トウモロコシ穂の粒数・列数などの形質を、スマートフォン画像から抽出する手法とソフトウェアを開発しており、フェノタイピング手法が研究の中心である。

Code and data availability

The paper's CKC-Wild dataset (1,727 annotated corn ear images) and CornPET/unicorn analysis are the paper-specific assets, but the authors state data are available only upon request; no public repository URL for the dataset, code, or trained models is given. X-AnyLabeling is a generic third-party annotation tool, not a

No evidence-backed public reproduction asset is currently recorded.