摘要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 high-lights 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 ker-nels.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.