Use of machine learning models-based image analysis for classification of haploid and diploid maize

Abstract

Image analysis is a straightforward and non-destructive technique used to identify haploids/diploids in maize. This study was carried out to characterize haploid/diploid maize kernels based on color space data and to compare the success of classification models developed using different machine learning techniques in maize. In this study, haploid (n=390) and diploid (n=495) kernels obtained by crossing five different donors with a Navajo inducer were used. Kernel images were collected using a standard desktop scanner. After extracting the RGB color space data, it was converted to hue-saturation-value (HSV) and Lab color spaces. Seven combinations of color space datasets were used as predictor variables. Support vector machines (SVM-C), random forest (RF), classification and regression tree (CART) methods were used to develop ML models. The classification success of the models was found between 0.74 and 0.86. The Support Vector Machines model (Accuracy = 0.86) created with RGB+Lab input data was the best.

Keywords:
Kernel classification; image analysis; doubled haploid; machine learning