Earbox, an open tool for high-throughput measurement of the spatial organization of maize ears and inference of novel traits.

Abstract

Background Characterizing plant genetic resources and their response to the environment through accurate measurement of relevant traits is crucial to genetics and breeding. Spatial organization of the maize ear provides insights into the response of grain yield to environmental conditions. Current automated methods for phenotyping the maize ear do not capture these spatial features. Results We developed EARBOX, a low-cost, open-source system for automated phenotyping of maize ears. EARBOX integrates open-source technologies for both software and hardware that facilitate its deployment and improvement for specific research questions. The imaging platform consists of a customized box in which ears are repeatedly imaged as they rotate via motorized rollers. With deep learning based on convolutional neural networks, the image analysis algorithm uses a two-step procedure: ear-specific grain masks are first created and subsequently used to extract a range of trait data per ear, including ear shape and dimensions, the number of grains and their spatial organisation, and the distribution of grain dimensions along the ear. The reliability of each trait was validated against ground-truth data from manual measurements. Moreover, EARBOX derives novel traits, inaccessible through conventional methods, especially the distribution of grain dimensions along grain cohorts, relevant for ear morphogenesis, and the distribution of abortion frequency along the ear, relevant for plant response to stress, especially soil water deficit. Conclusions The proposed system provides robust and accurate measurements of maize ear traits including spatial features. Future developments include grain type and colour categorisation. This method opens avenues for high-throughput genetic or functional studies in the context of plant adaptation to a changing environment.

Plant phenotyping relevance

トウモロコシ雌穂の画像取得・深層学習による形質抽出システムを開発し、手動測定との比較で各形質の信頼性を検証しており、植物フェノタイピング手法が研究の中心です。

Code and data availability

The authors' full analysis pipeline (MATLAB GUI plus Python deep-learning code for grain segmentation and phenotypic trait extraction) is explicitly stated to be fully available on a public GitHub repository. The phenotype datasets themselves are only available on request, so they do not qualify as public assets.