The Ear Unwrapper: A Maize Ear Image Acquisition Pipeline for Disease Severity Phenotyping

Abstract

Fusarium ear rot (FER) is a common disease in maize caused by the pathogen Fusarium verticillioides. Because of the quantitative nature of the disease, scoring disease severity is difficult and nuanced, relying on various ways to quantify the damage caused by the pathogen. Towards the goal of designing a system with greater objectivity, reproducibility, and accuracy than subjective scores or estimations of the infected area, a system of semi-automated image acquisition and subsequent image analysis was designed. The tool created for image acquisition, “The Ear Unwrapper”, successfully obtained images of the full exterior of maize ears. A set of images produced from The Ear Unwrapper was then used as an example of how machine learning could be used to estimate disease severity from unannotated images. A high correlation (0.74) was found between the methods estimating the area of disease, but low correlations (0.47 and 0.28) were found between the number of infected kernels and the area of disease, indicating how different methods can result in contrasting severity scores. This study provides an example of how a simplified image acquisition tool can be built and incorporated into a machine learning pipeline to measure phenotypes of interest. We also present how the use of machine learning in image analysis can be adapted from open-source software to estimate complex phenotypes such as Fusarium ear rot.

Plant phenotyping relevance

トウモロコシ雌穂の病徴画像取得装置と画像解析・機械学習による病害重症度推定を開発しており、表現型取得法が研究の中心である。

Code and data availability

The authors publicly deposited the codebase for The Ear Unwrapper image acquisition pipeline on GitHub, which is the paper-specific computational/hardware asset for this phenotyping study. The raw phenotype data (Supplemental Table S1) is hosted as an MDPI supplement rather than a separate repository, and the Ilastik/G