EarCV: An open‐source, computer vision package for maize ear phenotyping

Abstract

Abstract Fresh market sweet corn (Zea mays L.) is a row crop commercialized as a vegetable, resulting in strict expectations for ear size, color, and shape. Ear phenotyping in breeding programs is typically done manually and can be subjective, time consuming, and unreliable. Computer vision tools have enabled an inexpensive, high‐throughput, and quantitative alternative to phenotyping in agriculture. Here we present a computer vision tool using open‐source Python and OpenCV to measure yield component and quality traits relevant to sweet corn from photographs. This tool increases accuracy and efficiency in phenotyping through high‐throughput, quantitative feature extraction of traits typically measured qualitatively. EarCV worked in variable lighting and background conditions, such as under full sun and shade and against grass and dirt backgrounds. The package compares ears in images taken at varying distances and accurately measures ear length and ear width. It can measure traits that were previously difficult to quantify such as color, tip fill, taper, and curvature. EarCV allows users to phenotype any number of ears, dried or fresh, in any orientation while tolerating some debris and silk noise. The tool can categorize husked ears according to the predefined USDA quality grades based on length and tip fill. We show that the information generated from this computer vision approach can be incorporated into breeding programs by analyzing hybrid ears, capturing heritability of yield component traits, and detecting phenotypic differences between cultivars that conventional yield measurements cannot. Ultimately, computer vision can reduce the cost and resources dedicated to phenotyping in breeding programs.