Abstract

BackgroundMaize cobs are an important component of crop yield that exhibit a high diversity in size, shape and color in native landraces and modern varieties. Various phenotyping approaches were developed to measure maize cob parameters in a high throughput fashion. More recently, deep learning methods like convolutional neural networks (CNNs) became available and were shown to be highly useful for high-throughput plant phenotyping. We aimed at comparing classical image segmentation with deep learning methods for maize cob image segmentation and phenotyping using a large image dataset of native maize landrace diversity from Peru.ResultsComparison of three image analysis methods showed that a Mask R-CNN trained on a diverse set of maize cob images was highly superior to classical image analysis using the Felzenszwalb-Huttenlocher algorithm and a Window-based CNN due to its robustness to image quality and object segmentation accuracy (r=0.99). We integrated Mask R-CNN into a high-throughput pipeline to segment both maize cobs and rulers in images and perform an automated quantitative analysis of eight phenotypic traits, including diameter, length, ellipticity, asymmetry, aspect ratio and average values of red, green and blue color channels for cob color. Statistical analysis identified key training parameters for efficient iterative model updating. We also show that a small number of 10–20 images is sufficient to update the initial Mask R-CNN model to process new types of cob images. To demonstrate an application of the pipeline we analyzed phenotypic variation in 19,867 maize cobs extracted from 3449 images of 2484 accessions from the maize genebank of Peru to identify phenotypically homogeneous and heterogeneous genebank accessions using multivariate clustering.ConclusionsSingle Mask R-CNN model and associated analysis pipeline are widely applicable tools for maize cob phenotyping in contexts like genebank phenomics or plant breeding.

Highlights

  • Maize cobs are an important component of crop yield that exhibit a high diversity in size, shape and color in native landraces and modern varieties

  • In the context of native maize diversity we demonstrate the usefulness of a convolutional neural networks (CNNs)-based deep learning model implemented in a robust and widely applicable analysis pipeline for recognizing, semantic labeling and automated measurements of maize cobs in RGB images for large scale plant phenotyping

  • Correlations between true and derived values for cob length and diameter show that Mask R-CNN far outperformed the classical Felzenszwalb-Huttenlocher image segmentation algorithm and a windowbased CNN (Window-CNN) (Fig. 1)

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Summary

Introduction

Maize cobs are an important component of crop yield that exhibit a high diversity in size, shape and color in native landraces and modern varieties. High-throughput precision phenotyping of plant traits is rapidly becoming an integral part of plant research, plant breeding, and crop production [4]. This development complements the rapid advances in genomic methods that, when combined with phenotyping, enable rapid, accurate, and efficient analysis of plant traits and Kienbaum et al Plant Methods (2021) 17:91 the interaction of plants with their environment [65]. The phenotyping bottleneck is being addressed by phenomics platforms that integrate high-throughput automated phenotyping with analysis software to obtain accurate measurements of phenotypic traits [28, 46]. High-throughput pipelines with accurate computational analysis will realize the potential of plant phenomics by overcoming the phenotyping bottleneck

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