RT Journal Article
SR Electronic
T1 DeepCob: Precise and high-throughput analysis of maize cob geometry using deep learning with an application in genebank phenomics
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2021.03.16.435660
DO 10.1101/2021.03.16.435660
A1 Lydia Kienbaum
A1 Miguel Correa Abondano
A1 Raul Blas
A1 Karl Schmid
YR 2021
UL http://biorxiv.org/content/early/2021/03/20/2021.03.16.435660.abstract
AB Background 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. Various phenotyping approaches were developed to measure maize cob parameters in a high throughput fashion. More recently, deep learning methods like convolutional neural networks (CNN) 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.Results Comparison 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 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 RGB values 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 3,449 images of 2,484 accessions from the maize genebank of Peru to identify phenotypically homogeneous and heterogeneous genebank accessions using multivariate clustering.Conclusions Single Mask R-CNN model and associated analysis pipeline are widely applicable tools for maize cob phenotyping in contexts like genebank phenomics or plant breeding.Competing Interest StatementThe authors have declared no competing interest.AP@[.5: .95]AP@[IoU=0.50:0.95] sometimes also called mAP.CLARAClustering Large ApplicationsRPNRegion Proposal Network