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KAT4IA:K-Means Assisted Training for Image Analysis of Field-Grown Plant Phenotypes

Plant Phenomics 2021年第1期3卷 144-155页

作者： Xingche Guo Yumou Qiu Dan Nettleton Cheng-Ting Yeh Zihao Zheng stefan hey Patrick SSchnable Department of Statistics Iowa State UniversityIowaUSA Plant Sciences Institute Iowa State UniversityIowaUSA Department of Agronomy Iowa State UniversityIowaUSA

High-throughput phenotyping enables the efficient collection of plant trait data at *** example involves using imaging systems over key phases of a crop growing *** the resulting images provide rich data for statistical analyses of plant phenotypes,image processing for trait extraction is required as a *** methods for trait extraction are mainly based on supervised learning with human labeled data or semisupervised learning with a mixture of human labeled data and unsupervised ***,preparing a sufficiently large training data is both time and *** describe a self-supervised pipeline(KAT4IA)that uses K-means clustering on greenhouse images to construct training data for extracting and analyzing plant traits from an image-based field phenotyping *** KAT4IA pipeline includes these main steps:self-supervised training set construction,plant segmentation from images of field-grown plants,automatic separation of target plants,calculation of plant traits,and functional curve fitting of the extracted *** deal with the challenge of separating target plants from noisy backgrounds in field images,we describe a novel approach using row-cuts and column-cuts on images segmented by transform domain neural network learning,which utilizes plant pixels identified from greenhouse images to train a segmentation model for field *** approach is efficient and does not require human *** results show that KAT4IA is able to accurately extract plant pixels and estimate plant heights.

关键词： utilize preparing separating

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