Prediction of Ear Weight, Kernel Weight and Viability in Maize Using Image Analysis

Onurcan Nesrin; Fatih Kahrıman

Makale detayları

Prediction of Ear Weight, Kernel Weight and Viability in Maize Using Image Analysis

Yazarlar:

Onurcan Nesrin | Fatih Kahrıman

Yayıncı:

Çanakkale Onsekiz Mart University

Ana Başlıklar:

Ziraat Mühendisliği

Dergi:

ÇOMÜ Ziraat Fakültesi Dergisi Vol.11 Issue 2, pp.360-367

Belge Tipi:

Makale

Yayın Tarihi:

12/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001306503

pISSN:

2147-8384

Anahtar kelimeler:

Özet:

 In maize breeding studies, it is becoming common to determine the ear and kernel characteristics by image analysis. While current methods focus on measurements that can be obtained directly by image analysis, it has not been adequately addressed whether different parameters such as weight and viability can be estimated using these measurements. This study aimed to determine whether it is possible to estimate the ear weight g, kernel weight g, single kernel weight g and viability 10 status of maize with the help of features area, perimeter, width, length extracted from images of the ear and kernel samples. In this study, 233 ear and 1242 grain samples belonging to 13 maize genotypes were used as material. Digital images of the ear samples were taken with a 5 MP camera and from the kernel samples with a desktop scanner. The ear weight reference data DV1 and the kernel weight reference data DV2 were obtained by weighing each sample on a precision balance. Single kernel reference data DV3 was obtained with the measurements of single kernel weights. Kernel samples underwent paper germination test and reference data DV4 related to viability was created. Regression models were developed by using the features obtained from image analysis area, perimeter, width, height for each reference data set as the predictor variable. As a result of the study, it was seen that the ear weight and kernel weight can be estimated with the help of the parameters extracted from the image analysis. While moderate success was achieved in the determination of single seed weight, it was difficult to determine the viability status based on the morphometric measurements of a single kernel in maize.

 In maize breeding studies, it is becoming common to determine the ear and kernel characteristics by image analysis. While current methods focus on measurements that can be obtained directly by image analysis, it has not been adequately addressed whether different parameters such as weight and viability can be estimated using these measurements. This study aimed to determine whether it is possible to estimate the ear weight g, kernel weight g, single kernel weight g and viability 10 status of maize with the help of features area, perimeter, width, length extracted from images of the ear and kernel samples. In this study, 233 ear and 1242 grain samples belonging to 13 maize genotypes were used as material. Digital images of the ear samples were taken with a 5 MP camera and from the kernel samples with a desktop scanner. The ear weight reference data DV1 and the kernel weight reference data DV2 were obtained by weighing each sample on a precision balance. Single kernel reference data DV3 was obtained with the measurements of single kernel weights. Kernel samples underwent paper germination test and reference data DV4 related to viability was created. Regression models were developed by using the features obtained from image analysis area, perimeter, width, height for each reference data set as the predictor variable. As a result of the study, it was seen that the ear weight and kernel weight can be estimated with the help of the parameters extracted from the image analysis. While moderate success was achieved in the determination of single seed weight, it was difficult to determine the viability status based on the morphometric measurements of a single kernel in maize.

 In maize breeding studies, it is becoming common to determine the ear and kernel characteristics by image analysis. While current methods focus on measurements that can be obtained directly by image analysis, it has not been adequately addressed whether different parameters such as weight and viability can be estimated using these measurements. This study aimed to determine whether it is possible to estimate the ear weight g, kernel weight g, single kernel weight g and viability 10 status of maize with the help of features area, perimeter, width, length extracted from images of the ear and kernel samples. In this study, 233 ear and 1242 grain samples belonging to 13 maize genotypes were used as material. Digital images of the ear samples were taken with a 5 MP camera and from the kernel samples with a desktop scanner. The ear weight reference data DV1 and the kernel weight reference data DV2 were obtained by weighing each sample on a precision balance. Single kernel reference data DV3 was obtained with the measurements of single kernel weights. Kernel samples underwent paper germination test and reference data DV4 related to viability was created. Regression models were developed by using the features obtained from image analysis area, perimeter, width, height for each reference data set as the predictor variable. As a result of the study, it was seen that the ear weight and kernel weight can be estimated with the help of the parameters extracted from the image analysis. While moderate success was achieved in the determination of single seed weight, it was difficult to determine the viability status based on the morphometric measurements of a single kernel in maize.

 Mısır ıslah çalışmalarında, görüntü analizleri ile koçan ve tane özelliklerinin belirlenmesi yaygınlaşmaktadır. Mevcut yöntemler, doğrudan görüntü analizi ile elde edilebilecek ölçümlere odaklanırken, ağırlık ve canlılık gibi farklı parametrelerin bu ölçümler kullanılarak tahmin edilip edilemeyeceği yeterince ele alınmamıştır. Bu çalışmanın amacı, görüntü analizlerinden çıkarılan özellikler alan, çevre, çevre, genişlik, uzunluk kullanılarak mısırda koçan ağırlığı g, tane ağırlığı g, tek tane ağırlığı g ve canlılık 10 durumunun tahminlenip tahminlenemeyeceğinin belirlenmesidir. Çalışmada 13 mısır genotipine ait 233 koçan ve 1242 tane örneği materyal olarak kullanılmıştır. Koçan örneklerinin dijital görüntüleri 5 MP kamera ile, çekirdek örneklerinden ise masaüstü tarayıcı ile alınmıştır. Kulak ağırlığı referans verileri DV1 ve tane ağırlığı referans verileri DV2, her numunenin hassas bir terazide tartılmasıyla elde edilmiştir. Tek tane ağırlıklarının ölçümleri ile tek tohum referans verileri DV3 elde edilmiştir. Tane örnekleri kağıt çimlenme testine tabi tutulmuş ve canlılığa ilişkin referans veriler DV4 oluşturulmuştur. Her bir referans veri seti için görüntü analizinden elde edilen özellikler alan, çevre, genişlik, yükseklik tahminleyici değişken olarak kullanılarak regresyon modelleri geliştirilmiştir. Çalışma sonucunda, görüntü analizinden çıkarılan parametreler yardımıyla koçan ağırlığı ve tane ağırlığı tahmin edilebildiği görülmüştür. Tek tane ağırlığının belirlenmesinde orta düzeyde başarı elde edilirken, mısırda tek taneden alınan morfometrik ölçümlere dayalı canlılık durumunun belirlenmesinin mümkün olmadığı tespit edilmiştir.

Makale detayları

Prediction of Ear Weight, Kernel Weight and Viability in Maize Using Image Analysis

Yazarlar:

Onurcan Nesrin | Fatih Kahrıman

Yayıncı:

Çanakkale Onsekiz Mart University

Ana Başlıklar:

Ziraat Mühendisliği

Dergi:

ÇOMÜ Ziraat Fakültesi Dergisi Vol.11 Issue 2, pp.360-367

Belge Tipi:

Makale

Yayın Tarihi:

12/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001306503

pISSN:

2147-8384

Zea mays

Regression

Morphometric measurementsZea mays

Regresyon

morfometrik ölçümler

Sayı

Makale detayları

Prediction of Ear Weight, Kernel Weight and Viability in Maize Using Image Analysis

Yazarlar: Onurcan Nesrin | Fatih Kahrıman

Yayıncı: Çanakkale Onsekiz Mart University

Ana Başlıklar: Ziraat Mühendisliği

Dergi: ÇOMÜ Ziraat Fakültesi Dergisi Vol.11 Issue 2, pp.360-367

Belge Tipi: Makale

Yayın Tarihi: 12/2023

Belge Dili: İngilizce

Document ID (Org No.): 2001306503

pISSN: 2147-8384

Zea mays

Regression

Morphometric measurementsZea mays

Regresyon

morfometrik ölçümler

Sayı

Yazarlar:

Onurcan Nesrin | Fatih Kahrıman

Yayıncı:

Çanakkale Onsekiz Mart University

Ana Başlıklar:

Ziraat Mühendisliği

Dergi:

ÇOMÜ Ziraat Fakültesi Dergisi Vol.11 Issue 2, pp.360-367

Belge Tipi:

Makale

Yayın Tarihi:

12/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001306503

pISSN:

2147-8384