Research Article | Open Access

Volume 2026 |Article ID 100129 | https://doi.org/10.1016/j.plaphe.2025.100129

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

Xin Li,¹ Pinzhe Li,^1,5 Xinzhe Wang,^2,5 Yaning Zhu,^2,5 Tianqi Hu,¹ Binghui Xu,¹ Yongshuai Zhang,^3,4 Zhiguo Han ,^3,4 and Hao Lu ¹

¹National Key Laboratory of Multispectral Information Intelligent Processing Technology, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, 430074, China
²School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China
³PhenoTrait Technology Co., Ltd., Beijing, 100096, China
⁴MetaPheno Laboratory, Shanghai, 201114, China
⁵These authors contributed equally to this work.

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.