Classification of haploid and diploid maize seeds by using image processing techniques and support vector machines

Altuntaş, Yahya; Kocamaz, Adnan Fatih; Cengiz, Rahime; Esmeray, Mesut

doi:10.1109/siu.2018.8404800

Cited by 20 publications

(14 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…These kinds of systems, like scanning the seeds as visually and by their content, would facilitate the identification of the haploid. Altuntaş et al (2018) used 87 haploid and 326 diploid maize seeds as their dataset. They used the expression of the R1-nj color marker in the embryo and endosperm.…”

Section: Resultsmentioning

confidence: 99%

Development of doubled haploid maize lines by using in vivo haploid technique

Cengiz¹,

Korkut²

2020

biost

Self Cite

View full text Add to dashboard Cite

The doubled haploid technology is now an integral component of modern maize breeding programs. In this study, the maternal haploid induction (gynogenesis) method was used to derive Doubled-Haploid (DH) lines from elite maize germplasm adapted to Turkey. Temperate haploid inducers (RWS, RWK-76, RWS x RWK-76 and WS14) were used as pollinators, and a set of 30 single-crossses (in FAO 650-700 maturity groups) were used as source materials. Putative haploid seeds were selected based on expression of R1-nj anthocyanin color marker. Highest haploid induction rate (20.42%) was recorded by using RWK-76 as inducer line, and the lowest haploid induction rate (17.75%) was obtained through WS14. Putative haploid seeds were germinated and seedlings were treated with 0.06% colchicine + 0.5% dimethylsulfoxide solution. Following transfer of seedlings into the field, 2178 D0 plants were obtained out of a total of 3012 treated haploids. Live plants were from 89% of 2178 seedlings which are planted to the field. Fertile plants were formed 57% of live plants. Inbreeding was succeeded in 31.23% of fertile plants and only 7.8% of inbreeding plants were able to produce seeds. Consequently, 27 doubled haploid lines were developed.

show abstract

Section: Resultsmentioning

confidence: 99%

Development of doubled haploid maize lines by using in vivo haploid technique

Cengiz¹,

Korkut²

2020

biost

Self Cite

View full text Add to dashboard Cite

show abstract

“…As a result of the experiments, it has been claimed that the accuracy rate reached 97% in the optimum waveband of 1367.6-1526.4nm. Altuntaş et al [7] have proposed a method to classify haploid and diploid maize seeds using image processing and classification methods. Firstly, five different features are obtained from each maize seed image.…”

Section: Related Studiesmentioning

confidence: 99%

“…This coloration difference occurring in embryos enables the visual separation of haploid and diploid seeds [7]. This separation is done manually today.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Classification of Haploid and Diploid Maize Seeds based on Pre-Trained Convolutional Neural Networks

Dönmez

2020

Celal Bayar Üniversitesi Fen Bilimleri Dergisi

View full text Add to dashboard Cite

Analysis of agricultural products is an important area that is widely emphasized today. In this context, with the development of technology, computer-aided analysis systems are also being developed. In this study, a system has been proposed for classifying maize seeds as haploid and diploid using pre-trained convolutional neural networks. For this purpose, AlexNet, GoogLeNet, ResNet-18, ResNet-50, and VGG-16 pre-trained models have been used as feature extractors for the haploid and diploid seed classification process. In the first stage, the deep features of haploid and diploid maize seeds have been obtained in these models. The features have been taken from different layers of network architecture. Instead of softmax classifier in the last layer of the network, classifiers based on decision tree, k-nearest neighbor, and support vector machine have been used. According to the classification results with these features, the achievements in network architectures and classifier methods have been observed. The experiments have been carried out on a publicly available dataset consisting of 3000 haploid and diploid maize seed images. The experimental results revealed that the developed classification systems demonstrate a remarkable performance.

show abstract

“…Altuntaş vd. [15] 5 öznitelik ve destek vektör makinesi kullandıkları yöntemle %81,36 doğruluk elde etmişlerdir. Altuntaş vd.…”

Section: Introductionunclassified

Renk Momentleri ve Destek Vektör Makineleri Kullanarak Haploid Mısır Tohumlarının Tanımlanmasında Renk Uzaylarının Sınıflandırma Performansına Etkisinin Karşılaştırılması

Altuntaş

Kocamaz

2019

Fırat Üniversitesi Mühendislik Bilimleri Dergisi

Self Cite

View full text Add to dashboard Cite

Öz: Katlanmış haploid mısır ıslah tekniği gerek ıslah süresini kısaltması, gerekse %100 homozigot hatlar elde edilmesi açısından, geleneksel ıslah yöntemleri ile karşılaştırıldığında önemli avantajlar sağlar. Katlanmış haploid mısır ıslah yönteminde önemli işlerden biri melezleme sonucunda elde edilen haploid ve diploid tohumların ayrıştırılmasıdır. Günümüzde bu işlemin elle yapılıyor olması, emek ve zaman kaybının yanı sıra yüksek sınıflandırma hatalarına neden olmaktadır. Bu çalışmada, haploid mısır tohumlarının tanımlanması için bilgisayar görmesine dayalı bir yöntem önerilmiştir. 3000 mısır tohumundan oluşan veri seti üzerinde yürütülen çalışmada, k-ortalamalar kümeleme yöntemi ile mısır tohum embriyoları bölütlenmiştir. RGB, HSV ve Lab renk uzaylarında her renk kanalı için ilk dört dereceden renk momentleri çıkarılmıştır. Böylece her renk uzayı için 12 öznitelik kullanılmıştır. Elde edilen öznitelikler destek vektör makinesi ile sınıflandırılmıştır. Daha genel sonuçlar elde edebilmek için 10-kat çapraz doğrulama yöntemi 20 kez tekrar edilmiştir. Deneysel sonuçlara göre en iyi performans Lab renk uzayında %87,03 doğruluk, %80,15 duyarlılık ve %93,00 özgünlük değerleriyle ölçülmüştür.Anahtar kelimeler: mısır, haploid tanıma, renk uzayları, renk momentleri, destek vektör makineleri.Abstract: Doubled haploid maize breeding technique provides significant advantages over conventional breeding methods both in terms of shortening breeding time and obtaining 100% homozygous lines. One of the important works in doubled haploid maize breeding method is the separation of haploid and diploid seeds obtained as a result of hybridization. Today, this process is performed by hand, as well as loss of labor and time causes high classification errors. In this study, a computer vision based method for identification of haploid maize seeds was proposed. In the study conducted on the dataset of 3000 maize seeds, seed embryos were segmented by k-means clustering method. In the RGB, HSV and Lab color spaces, the first four degrees of color moments are extracted for each color channel. Thus, 12 features were used for each color space. Obtained features were classified with support vector machine. To obtain more general results, the 10-fold cross-validation method was repeated 20 times. According to the experimental results, the best performance was measured with 87,03% accuracy, 80,15% sensitivity and 93,00% specificity in Lab color space.

show abstract

Classification of haploid and diploid maize seeds by using image processing techniques and support vector machines

Cited by 20 publications

References 5 publications

Development of doubled haploid maize lines by using in vivo haploid technique

Development of doubled haploid maize lines by using in vivo haploid technique

Classification of Haploid and Diploid Maize Seeds based on Pre-Trained Convolutional Neural Networks

Renk Momentleri ve Destek Vektör Makineleri Kullanarak Haploid Mısır Tohumlarının Tanımlanmasında Renk Uzaylarının Sınıflandırma Performansına Etkisinin Karşılaştırılması

Contact Info

Product

Resources

About