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

Aug 5, 2026

Image analysis serves as a non-destructive approach for identifying haploid and diploid maize kernels. The study characterizes haploid/diploid kernels using color-space information and evaluates classification performance across machine learning models built with different color-space feature sets. Haploid (n=390) and diploid (n=495) kernels obtained using a Navajo inducer were scanned and processed by RGB-to-HSV and RGB-to-Lab transformations. SVM-C, random forest, and CART were compared, achieving accuracies between 0.74 and 0.86, with SVM-C (RGB+Lab) performing best.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages10

Reading Time25 min

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Introduction

Doubled haploid technique and haploid selection

Limits of classical visual classification

Alternative separation methods

Image analysis and color-space features

Machine learning models for seed discrimination

ARTICLE Use of machine learning models-based image analysis for classification of haploid and diploid maize Fatih Kahrıman1*, Abdurrahman Muhammed Güz1 and İpek Pehlivan1 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 Crop Breeding and Applied Biotechnology 23(4): e45322349, 2023 Brazilian Society of Plant Breeding. Printed in Brazil [http://dx.doi.org/10.1590/1984-](http://dx.doi.org/10.1590/1984-)[ ](http://dx.doi.org/10.1590/1984-)70332023v23n4a44 INTRODUCTION The in-vivo doubled haploid technique is one of the techniques proven to develop 100% homozygous lines in maize breeding. There are two types of use in practice for doubling of chromosome numbers of the plants in the in vivo conditions, which are named as “in vivo maternal” and “in vivo paternal”methods (Chaikam et al. 2019) . Both relied on using special genotypes called“inducer” lines. Since the discovery that haploid seeds are formed in donor materials hybridizing with these inducers, numerous inducer lines with different features have been developed (Choe 1959, Chalky 1994, Prigge et al. 2012, Kalinowska et al. 2019, Uliana Trentin et al. 2020) . There are three main steps of in-vivo maternal or paternal doubled haploid techniques in practice. The first step is the hybridization of the donor material with inducer line, and the second step is selection of haploid samples based on the color changes in the seeds or the root of seedlings, and the last step is chemical treatment for chromosome doubling after the selfing plants by growing them under field or greenhouse conditions (Röber et al. 2005, Chidzanga et al. 2017) . Within these steps, separation of haploid samples from the others is one of the most important processes for the success of the technique. In the classical method, *Corresponding author: [E-mail: fkahriman@hotmail.com](E-mail: fkahriman@hotmail.com) ORCID: 0000-0001-6944-0512 Received: 10 May 2023 Accepted: 05 October 2023 Published: 20 October 2023 1 Çanakkale Onsekiz Mart University, Faculty of Agriculture, Barbaros District, Terzioğlu Campus, Prof. Dr. Sevim Buluç St. No:62, 17100 Merkez/Çanakkale, Türkiye Crop Breeding and Applied Biotechnology-23(4): e45322349, 2023 1 F Kahrıman et al. seed classification is performed using the method based on visual phenotypic markers distinguishable by human eyes. However, it takes a long time and its margin of error for classification is high. For this reason, alternative methods that can perform seed discrimination in a more practical way are emphasized. There are numerous approaches to separate haploid and diploid seeds such as stomata measurements (Ribeiro et al. 2022), flow cytometry (Baleroni et al. 2021), near infrared reflectance (NIR) spectroscopy (Jones et al. 2012, Liu et al. 2017, Cui et al. 2019), near infrared transmittance (NIT) spectroscopy (Lin et al. 2017) and image analysis (Veeramani et al. 2018) . Among

FAQs

How does the study use image analysis to distinguish haploid and diploid maize?

Kernel images are collected with a desktop scanner, then RGB data are converted into HSV and Lab color spaces. These color-space datasets are used as predictors to train classification models.

Which machine learning methods are compared in the study?

Support vector machines (SVM-C), random forest (RF), and classification and regression tree (CART) are used to develop models for haploid/diploid classification.

What classification performance is reported, and which model is best?

Model accuracy ranges from 0.74 to 0.86. The best result is achieved by the SVM-C model using RGB+Lab inputs with accuracy of 0.86.

Aug 5, 2026

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