All codes and the manual of command line interface and GUI can be found at the GitHub repository: https://github.com/Chimaco37/OpenEar.
Open resource ↗Chimaco37/OpenEar · html-lines:294-325Unverified paper record
OpenEar: an ultra-affordable, high-throughput, and accurate maize ear phenotyping system.
Plant Methods · 8 Feb 2026 · 10.1186/s13007-026-01504-x
Abstract
Crop phenotyping of important agronomic traits in field conditions at single-plant resolution has long been a major bottleneck in both genetic analysis (e.g. large-scale association/linkage analysis) and breeding applications (e.g. genomic prediction/selection). Despite growing interest, ultra-affordable, high-throughput and accurate phenotyping tools for maize ears remain limited. Here, we developed OpenEar, an open source, low-cost phenotyping system that combines a DIY maize ear imaging platform with a deep learning-based end-to-end phenotypic data extraction pipeline. The imaging platform is composed of 3D-printed parts and electronics components easily available from local retailers to perform high-quality 360° surface scanning of maize ears. Our pipeline first employs CNN-based models to identify normally-developed ears suitable for phenotyping, followed by reliable segmentation of ears and ear surface projection images by YOLOv11-based models, from which ten key traits are subsequently extracted. OpenEar demonstrates reliable agreement with manual measurements across a diverse set of ear- and kernel-related traits, including ear length ( R 2 = 0.972), ear diameter ( R 2 = 0.905), ear volume ( R 2 = 0.976), ear weight ( R 2 = 0.878), kernel number ( R 2 = 0.98), kernel row number ( R 2 = 0.888), kernel number per row ( R 2 = 0.852), kernel thickness ( R 2 = 0.705), kernel width ( R 2 = 0.515), and thousand kernel weight ( R 2 = 0.605). A user-friendly graphical interface is developed for manual inspection of ears after computer annotation. Manually annotated ear videos and images are publicly released as a resource for the crop phenomics community. Our study highlights the potential of DIY-based low-cost solutions to make phenotyping more accessible in crop genetic analysis and breeding.
Plant phenotyping relevance
トウモロコシ穂の画像取得・深層学習による形質抽出システムを開発し、手動測定との一致を検証しており、植物フェノタイピング手法が研究の中心です。
abstractwe developed OpenEar, an open source, low-cost phenotyping system that combines a DIY maize ear imaging platform with a deep learning-based end-to-end phenotypic data extraction pipeline.
abstractOpenEar demonstrates reliable agreement with manual measurements across a diverse set of ear- and kernel-related traits
Code and data availability
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