Mısırda Koçan ve Tane Ölçümlerinde Kullanılan Görüntü İşleme Yazılımlarından Elde Edilen Sonuçların Karşılaştırılması
Abstract
Mısır ıslahı ve yetiştiriciliğine yönelik araştırmalarda koçan ve tane özelliklerinin belirlenmesine yönelik öçümlere ihtiyaç duyulmaktadır. Geleneksel ölçümler oldukça fazla zaman alan ve fazla işgücü gerektiren yöntemelere dayanmaktadır. Son yıllarda bilgisayar bilimlerinin tarımsal araştırmalarda kullanımı oldukça yaygınlaşmış ve koçan/tane özelliklerinin görüntü analizlerine dayalı olarak belirleyebilen çeşitli alternatifler yazılımlar geliştirilmiştir. Bu çalışmada açıkta ve kontorllü tozlama yöntemleri ile 20 farklı genotipten elde edilen koçan örnekleri kullanılarak iki farklı yazılımın karşılaştırılması amaçlanmıştır. Çalışmada görüntü işleme yazılımı olarak SmartGrain ve Fiji seçenekleri denenmiştir. Koçan uzunluğu (cm), koçan çapı (cm), koçanda tane sayısı (adet), tane çevresi (mm), tane boyu (mm) ile ilgili görüntü işleme yazılımları ile alınan sonuçlar kıyaslanmıştır. Araştırma bulgularına göre mısırda koçan ve tane gelişimini önemli şekilde etkileyen tozlama yöntemlerinin ölçülen özelliklerden bazıları üzerine önemli etkilere sahip olduğu görülmüştür. Kullanılan genotip gruplarına göre tozlama yöntemlerinin etkileri değişkenlik göstermiştir. İncelenen özelliklerden koçanda tane sayısının her iki yazılım ile de mükemmel bir şekilde tespit edilebileceği (r=1), buna karşın tane uzunluğu ve tane çevresi gibi ölçümlerde yazılımlardan elde edilen sonuçlar arasındaki benzerliğin zayıf olduğu dikkat çekmiştir.
Supporting Institution
TÜBİTAK
Project Number
1919B012000636
Thanks
Bu çalışma TÜBİTAK-2209A Üniversite Öğrencileri Araştırma Projeleri Destekleme Programı kapsamında 1919B012000636 nolu proje ile desteklenmiştir. Desteklerinden ötürü TÜBİTAK’a teşekkür ederiz.
References
- Chen, J., Zhang, L., Liu, S., Li, Z., Huang, R., Li, Y., Cheng, H., Li, X., Zhou, B., Wu, S., Chen, W., Wu, J., & Ding, J. (2016). The genetic basis of natural variation in kernel size and related traits using a four-way cross population in maize. PLoS ONE, 11(4): e0153428.
- Graven, L.M., & Carter, P.R. (1990). Seed size/shape and tillage system effect on corn growth and grain yield. Journal of Production Agriculture, 3, 445-452.
- Kahrıman, F., Egesel, C.Ö., Aydın, T., & Subaşı, S. (2015). The role of artificial pollination and pollen effect on ear development and kernel structure of different maize genotypes. Journal of Pollination Ecology, 15, 6-14.
- Kara, B. (2011). Tohum büyüklüğü ve şeklinin mısırın tane verimine ve bazı başak özelliklerine etkisi. Mahsul Araştırmaları,12(3): 680-685.
- Li, T., Qu, J., Tian, X., Lao, Y., Wei, N., Wang, Y., Hao, Y., Zhang, X., Xue, J., & Xu, S. (2020). Identification of ear morphology genes in maize (Zea mays L.) using selective sweeps and association mapping. Front. Genet., 11, 747.
- Liang, X., Wang, K., Huang, C., Zhang, X., Yan, J., & Yang, W., (2016). A high-throughput maize kernel traits scorer based on line-scan imaging. Measurement, 90, 453-460.
- Liu, M., Tan, X., Yang, Y., Liu, P., Zhang, X., Zhang, Y., Wang, L., Hu, Y., Ma, L., Li, Z., Zhang, Y., Zou, C., Lin, H., Gao, S., Lee, M., Lubberstedt, T., Pan, G., & Shen, Y., (2019). Analysis of the genetic architecture of maize kernel size traits by combined linkage and association mapping. Plant Biotechnology Journal, 18, 207-221.
- Makanza, R., Zaman-Allah, M., Cairns, J.E., Eyre, J., Burgueño, J., Pacheco, Á., Diepenbrock, C., Magorokosho, C., Tarekegne, A., Olsen, M., & Prasannai B.M. (2018). High-throughput method for ear phenotyping and kernel weight estimation in maize using ear digital imaging. Biomedcentral (BMC), 4, 2-13.
- Miller, N.D., Haase, N.J., Lee, J., Kaeppler, S.M., Leon, N., Spalding, E.P., 2017. A robust, high-throughput method for computing maize ear, cob, and kernel attributes automatically from images. The Plant Journal, 89:169-178.
- R Core Team, (2019). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.
- Severini, A.D., Borras, L., & Cirilo, A.G. (2011). Counting maize kernels through digital ımage analysis. Crop Science, 51, 2796-2800.
- Tanabata, T., Shibaya, T., Hori, K., Ebana, K., & Yano, M. (2012). SmartGrain:high-throughput phenotyping software for measuring seed shape through image analysis. Plant Physiology, 160:1871-1880.
- Wu, A., Zhu, J., Yang, Y., Liu, X., Wang, X., Wang, L., Zhang, H., & Chen, J. (2018). Classification of corn kernels grades using image analysis and support vector machine. Advances in Mechanical Engineering, 10(12):1-9.
- Yusuf, C.S., Makate, N., & Jacop, R. (2014). Effect of seed size on germination and early growth of maize. International Journal of Scientific and Research Publications, 4, 1-3.
Comparison of the Results Obtained from Image Processing Software Used in Ear and Kernel Measurements in Maize
Abstract
In studies on maize breeding and cultivation, ear and kernel measurements are needed. Traditional measurements are based on methods that are very time consuming and labor intensive. In recent years, the use of computer science in agricultural research has become widespread and various alternative software has been developed that can determine the ear/kernel characteristics based on image analysis. In this study, it is aimed to compare two different softwares by using ear samples obtained from 20 different genotypes by open and controlled pollination methods. In the study, SmartGrain and Fiji options were tried as image processing software. The results obtained with the image processing software were compared regarding the ear length (cm), the ear diameter (cm), the number of kernels per ear (number), the kernel circumference (mm), the kernel lenght (mm). According to the findings of the research, it has been observed that pollination methods that significantly affect the ear and kernel development in maize have significant effects on some of the measured traits. The effects of pollination methods varied according to the genotype used. It was noted that the number of kernels per cob could be determined perfectly by both software (r=1), but the similarity between the results obtained from the software was weak in measurements such as grain lenght and grain circumference.
Project Number
1919B012000636
References
- Chen, J., Zhang, L., Liu, S., Li, Z., Huang, R., Li, Y., Cheng, H., Li, X., Zhou, B., Wu, S., Chen, W., Wu, J., & Ding, J. (2016). The genetic basis of natural variation in kernel size and related traits using a four-way cross population in maize. PLoS ONE, 11(4): e0153428.
- Graven, L.M., & Carter, P.R. (1990). Seed size/shape and tillage system effect on corn growth and grain yield. Journal of Production Agriculture, 3, 445-452.
- Kahrıman, F., Egesel, C.Ö., Aydın, T., & Subaşı, S. (2015). The role of artificial pollination and pollen effect on ear development and kernel structure of different maize genotypes. Journal of Pollination Ecology, 15, 6-14.
- Kara, B. (2011). Tohum büyüklüğü ve şeklinin mısırın tane verimine ve bazı başak özelliklerine etkisi. Mahsul Araştırmaları,12(3): 680-685.
- Li, T., Qu, J., Tian, X., Lao, Y., Wei, N., Wang, Y., Hao, Y., Zhang, X., Xue, J., & Xu, S. (2020). Identification of ear morphology genes in maize (Zea mays L.) using selective sweeps and association mapping. Front. Genet., 11, 747.
- Liang, X., Wang, K., Huang, C., Zhang, X., Yan, J., & Yang, W., (2016). A high-throughput maize kernel traits scorer based on line-scan imaging. Measurement, 90, 453-460.
- Liu, M., Tan, X., Yang, Y., Liu, P., Zhang, X., Zhang, Y., Wang, L., Hu, Y., Ma, L., Li, Z., Zhang, Y., Zou, C., Lin, H., Gao, S., Lee, M., Lubberstedt, T., Pan, G., & Shen, Y., (2019). Analysis of the genetic architecture of maize kernel size traits by combined linkage and association mapping. Plant Biotechnology Journal, 18, 207-221.
- Makanza, R., Zaman-Allah, M., Cairns, J.E., Eyre, J., Burgueño, J., Pacheco, Á., Diepenbrock, C., Magorokosho, C., Tarekegne, A., Olsen, M., & Prasannai B.M. (2018). High-throughput method for ear phenotyping and kernel weight estimation in maize using ear digital imaging. Biomedcentral (BMC), 4, 2-13.
- Miller, N.D., Haase, N.J., Lee, J., Kaeppler, S.M., Leon, N., Spalding, E.P., 2017. A robust, high-throughput method for computing maize ear, cob, and kernel attributes automatically from images. The Plant Journal, 89:169-178.
- R Core Team, (2019). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.
- Severini, A.D., Borras, L., & Cirilo, A.G. (2011). Counting maize kernels through digital ımage analysis. Crop Science, 51, 2796-2800.
- Tanabata, T., Shibaya, T., Hori, K., Ebana, K., & Yano, M. (2012). SmartGrain:high-throughput phenotyping software for measuring seed shape through image analysis. Plant Physiology, 160:1871-1880.
- Wu, A., Zhu, J., Yang, Y., Liu, X., Wang, X., Wang, L., Zhang, H., & Chen, J. (2018). Classification of corn kernels grades using image analysis and support vector machine. Advances in Mechanical Engineering, 10(12):1-9.
- Yusuf, C.S., Makate, N., & Jacop, R. (2014). Effect of seed size on germination and early growth of maize. International Journal of Scientific and Research Publications, 4, 1-3.
Details
| Primary Language | Turkish |
|---|---|
| Subjects | Engineering |
| Journal Section | Articles |
| Authors | |
| Project Number | 1919B012000636 |
| Early Pub Date | January 30, 2022 |
| Publication Date | January 31, 2022 |
| Published in Issue | Year 2022 Issue: 33 |
