Araştırma Makalesi
BibTex RIS Kaynak Göster

A Hybrid Mobile Application for Quality Grade of Tobacco (Nicotiana tabacum L.) Using Correlated Color Temperature

Yıl 2023, Cilt: 10 Sayı: 2, 147 - 153, 31.07.2023

Nurettin Şenyer , Recai Oktaş , Mehmet Serhat Odabas , Dursun Kurt , Eren Karaboğa

https://doi.org/10.19159/tutad.1273405
Cited By: 1

Öz

Concerning tobacco (Nicotiana tabacum L.) producers the size and color of the leaf are important factors in understanding the quality grade of tobacco leaves in the market. The color of tobacco leaves serves as an indicator of quality and is referred to as the maturity index when determining the optimal time for harvesting. In this study, a hybrid mobile application was developed to help determine the harvest time. CoLab was preferred as the backend. Python Imaging Library (Pillow) was used for image processing on the server side. Color correction was performed on the images taken with the help of X-rite. The correlated color temperature (CCT) value of the repaired images was calculated. The CCT values were calculated using the Ohno method. Quality grade (QG) was calculated using the mean CCT value. The data of the images obtained depending on the time were used in the application as a graphic. We present quality grade of tobacco automatically identifying the plant leaves in a given image with the help of the mobile application.

Anahtar Kelimeler

Correlated color temperature tobacco Nicotiana tabacum L. image processing mobile application

Kaynakça

  • Anonymous, 1931. Cie Chromaticity Diagram. (https://en.wikipedia.org/wiki/CIE_1931_color_space), (Accessed: 20.02.2023).
  • Anonymous, 2018. Desktop vs. Mobile vs. Tablet vs. Console Market Share World-Wide. (https://gs.statcounter.com/platform-market-share/ desktop-mobile-tablet), (Accessed: 20.02.2023).
  • Anonymous, 2023. Colour Science for Python. (https:// github.com/colour-science/colour), (Accessed: 20.02.2023).
  • Avila-George, H., Valdez-Morones, T., Perez-Espinosa, H., Acevedo-Ju´arez, B., Castro, W., 2018. Using artificial neural networks for detecting dam- age on tobacco leaves caused by blue mold. Strategies, 9(8): 12-20.
  • Bisong, E., 2019. Building Machine Learning and Deep Learning Models on Google Cloud Platform: A Comprehensive Guide for Beginners. A-Press, New York.
  • Cakir, R., Cebi, U., 2010. The effect of irrigation scheduling and water stress on the maturity and chemical composition of virginia tobacco leaf. Field Crops Research, 119(2-3): 269-276.
  • Changjun, L., Cui, G., Melgosa, M., Ruan, X., Zhang, Y., Ma, L., Xiao, K., Luo, M., 2016. Accurate method for computing correlated color temperature. Optics Express, 24: 14066.
  • Chen, X., Zhao, J., Bi, J., Li, L., 2012. Research of real-time agriculture information collection system base on mobile GIS. 2012 First International Conference on Agro- Geoinformatics (Agro-Geoinformatics), 02-04 August, Shanghai, China, pp. 1-4.
  • Clark, A., 2015. Pillow (Pilfork) Documentation, 2015. (https://buildmedia. readthedocs.org/media/pdf/ pillow/latest/pillow.pdf), (Accessed: 20.02.2023).
  • Glenn, D., 2020. Correlated Color Temperature Isotherms. (https://cran.r-project.org/web/packages/ spacesXYZ/vignettes/isotherms.pdf), (Accessed: 19.02.2023).
  • Hernandez-Andres, J., Lee, R.L., Romero, J., 1999. Calculating correlated color temperatures across the entire gamut of daylight and skylight chromaticities. Applied Optics, 38(27): 5703-5709.
  • Jeefoo, P., 2014. Real-time field survey using android-based interface of mobile GIS. International Conference on Information Science & Applications, 06-09 May, Seoul, South Korea, pp. 1-3.
  • Johannes, A., Picon, A., Alvarez-Gila, A., Echazarra, J., Rodriguez-Vaamonde, S., Navajas, A.D., Ortiz-Barredo, A., 2017. Automatic plant disease diagnosis using mobile capture devices, applied on a wheat use case. Computers and Electronics in Agriculture, 138: 200-209.
  • Kurt, D., 2020. Stability analyses for interpreting genotype by environment interaction of selected oriental tobacco landraces. Turkish Journal of Field Crops, 25(1): 83-91.
  • Kurt, D., 2021. Impacts of environmental variations on quality and chemical contents of oriental tobacco. Contributions to Tobacco & Nicotine Research, 30(1): 50-62.
  • Kurt, D., Yılmaz, G., Kınay, A., 2021. Genotype x environment interactions based on stability parameters of basma type tobacco lines selected for superior characteristics. Journal of Agricultural Sciences, 27(3): 312-320.
  • Li, C., Cui, G., Melgosa, M., Ruan, X., Zhang, Z., Ma, L., Xiao, K., Luo, M.R., 2016. Accurate method for computing correlated color temperature. Optics Express, 24(13): 14066-14078.
  • Liu, S., Zeng, X., Whitty, M., 2020. 3DBunch: A novel IOS-smartphone application to evaluate the number of grape berries per bunch using image analysis techniques. Institute of Electrical and Electronics Engineers, 8: 114663-114674.
  • Manso, G.L., Knidel, H., Krohling, R.A., Ventura, J.A., 2019. A Smartphone Application to Detection and Classification of Coffee Leaf Miner and Coffee Leaf Rust. arXiv Preprint arXiv:1904.00742. (https://arxiv.org/pdf/1904.00742.pdf), (Accessed: 19.02.2023).
  • Mateen, A., Zhu, Q., 2019. Legion based weed extraction from uav imagery. Pakistan Journal of Agricultural Sciences, 56(4): 1045-1052.
  • McCamy, C.S., 1992. Correlated color temperature as an explicit function of chromaticity coordinates. Color Research & Application, 17(2): 142-144.
  • McCamy, C.S., 1993. Correlated color temperature as an explicit function of chromaticity coordinates. Color Research & Application, 18(2): 150-155.
  • Odabas, M.S., Kayhan, G., Ergun, E., Senyer, N., 2016. Using artificial neural network and multiple linear regression for predicting the chlorophyll concentration index of saint john’s wort leaves. Communications in Soil Science and Plant Analysis, 47(2): 237-245.
  • Odabas, M.S., Senyer, N., Kayhan, G., Ergun, E., 2017. Estimation of chlorophyll concentration index at leaves using artificial neural networks. Journal of Circuits, Systems and Computers, 26(02): 1750026.
  • Odabas, M.S., Senyer, N., Kurt, D., 2022. Determination of quality grade of tobacco leaf by image processing on correlated color temperature. Concurrency and Computation Practice and Experience, 11: e7506.
  • Odabas, M.S., Temizel, K.E., Caliskan, O., Senyer, N., Kayhan, G., Ergun, E., 2014. Determination of reflectance values of hypericum’s leaves under stress conditions using adaptive network based fuzzy inference system. Neural Network World, 24(1): 79-82.
  • Ohno, Y., 2014. Practical use and calculation of CCT and DUV. Leukos, 10(1): 47-55.
  • Payne, R., 2019. Developing in flutter. In: R. Payne (Ed.), Beginning App Development with Flutter, Create Cross-Platform Mobile Apps, pp. 9-27.
  • Peksuslu, A., Yılmaz, I., Inal, A., Kartal, H., 2012. Tobacco genotypes of Turkey. Anadolu Journal of Aegean Agricultural Research Institute, 22(2): 82-90.
  • Robertson, A.R., 1968. Computation of correlated color temperature and distribution temperature. The Journal of the Optical Society of America A, 58(11): 1528-1535.
  • Temizel, K.E., Odabas, M.S., Senyer, N., Kayhan, G., Bajwa, S.G., Caliskan, O., Ergun, E., 2014. Comparision of some models for estimation of reflectance of hypericum leaves under stress conditions. Central European Journal of Biology, 9(12): 1226-1234.
  • Tomar, S., 2006. Converting video formats with ffmpeg. Linux Journal, 146: 10-18.
  • Valdez-Morones, T., Espinosa, H.P., George, H.A., Oblitas, J., Castro, J.W., 2018. An Android App for detecting damage on tobacco (Nicotiana tabacum L.) leaves caused by blue mold (Penospora tabacina Adam). 7th International Conference on Software Process Improvement (CIMPS), 17-19 October, Guadalajara, Mexico, pp. 125-129.
  • Wu, J., Xang, S., 2021. Modeling of the bulk tobacco flue-curing process using a deep learning-based method. Institute of Electrical and Electronics Engineers, 9: 140424-140436.
  • Xingzhong, Q., 1987. Formulas for computing correlated color temperature. Color Research & Application, 12(5): 285-287.
  • Xiong, Y., Yu, S., 2019. A novel growth evaluation system for tobacco planting based on image classification. Journal of Advanced Computational Intelligence and Intelligent Informatics, 23(6): 1004-1011.
  • Zhang, F., 2019. High-accuracy method for calculating correlated color temperature with a lookup table based on golden section search. Optik, 193: 163018.
  • Zhang, J., Sokhansanj, S., Wu, S., Fang, R., Yang, W., Winter, P., 1997. A trainable grading system for tobacco leaves. Computers and Electronics in Agriculture, 16(3): 231-244.
  • Zhenbo, L., Ruohao, G., Meng, L., Yaru, C., Guangyao, L., 2020. A review of computer vision technologies for plant phenothyping. Computers and Electronics in Agriculture, 176: 105672.
  • Zhi, R., Gao, M., Liu, Z., Yang, Y., Zheng, Z., Shi, B., 2018. Color chart development by computer vision for flue-cured tobacco leaf. Sensors and Materials, 30(12): 2843-2864.

A Hybrid Mobile Application for Quality Grade of Tobacco (Nicotiana tabacum L.) Using Correlated Color Temperature

Yıl 2023, Cilt: 10 Sayı: 2, 147 - 153, 31.07.2023

Nurettin Şenyer , Recai Oktaş , Mehmet Serhat Odabas , Dursun Kurt , Eren Karaboğa

https://doi.org/10.19159/tutad.1273405
Cited By: 1

Öz

Concerning tobacco (Nicotiana tabacum L.) producers the size and color of the leaf are important factors in understanding the quality grade of tobacco leaves in the market. The color of tobacco leaves serves as an indicator of quality and is referred to as the maturity index when determining the optimal time for harvesting. In this study, a hybrid mobile application was developed to help determine the harvest time. CoLab was preferred as the backend. Python Imaging Library (Pillow) was used for image processing on the server side. Color correction was performed on the images taken with the help of X-rite. The correlated color temperature (CCT) value of the repaired images was calculated. The CCT values were calculated using the Ohno method. Quality grade (QG) was calculated using the mean CCT value. The data of the images obtained depending on the time were used in the application as a graphic. We present quality grade of tobacco automatically identifying the plant leaves in a given image with the help of the mobile application.

Anahtar Kelimeler

Correlated color temperature Tobacco Nicotiana tabacum L. image processing mobile application

Kaynakça

  • Anonymous, 1931. Cie Chromaticity Diagram. (https://en.wikipedia.org/wiki/CIE_1931_color_space), (Accessed: 20.02.2023).
  • Anonymous, 2018. Desktop vs. Mobile vs. Tablet vs. Console Market Share World-Wide. (https://gs.statcounter.com/platform-market-share/ desktop-mobile-tablet), (Accessed: 20.02.2023).
  • Anonymous, 2023. Colour Science for Python. (https:// github.com/colour-science/colour), (Accessed: 20.02.2023).
  • Avila-George, H., Valdez-Morones, T., Perez-Espinosa, H., Acevedo-Ju´arez, B., Castro, W., 2018. Using artificial neural networks for detecting dam- age on tobacco leaves caused by blue mold. Strategies, 9(8): 12-20.
  • Bisong, E., 2019. Building Machine Learning and Deep Learning Models on Google Cloud Platform: A Comprehensive Guide for Beginners. A-Press, New York.
  • Cakir, R., Cebi, U., 2010. The effect of irrigation scheduling and water stress on the maturity and chemical composition of virginia tobacco leaf. Field Crops Research, 119(2-3): 269-276.
  • Changjun, L., Cui, G., Melgosa, M., Ruan, X., Zhang, Y., Ma, L., Xiao, K., Luo, M., 2016. Accurate method for computing correlated color temperature. Optics Express, 24: 14066.
  • Chen, X., Zhao, J., Bi, J., Li, L., 2012. Research of real-time agriculture information collection system base on mobile GIS. 2012 First International Conference on Agro- Geoinformatics (Agro-Geoinformatics), 02-04 August, Shanghai, China, pp. 1-4.
  • Clark, A., 2015. Pillow (Pilfork) Documentation, 2015. (https://buildmedia. readthedocs.org/media/pdf/ pillow/latest/pillow.pdf), (Accessed: 20.02.2023).
  • Glenn, D., 2020. Correlated Color Temperature Isotherms. (https://cran.r-project.org/web/packages/ spacesXYZ/vignettes/isotherms.pdf), (Accessed: 19.02.2023).
  • Hernandez-Andres, J., Lee, R.L., Romero, J., 1999. Calculating correlated color temperatures across the entire gamut of daylight and skylight chromaticities. Applied Optics, 38(27): 5703-5709.
  • Jeefoo, P., 2014. Real-time field survey using android-based interface of mobile GIS. International Conference on Information Science & Applications, 06-09 May, Seoul, South Korea, pp. 1-3.
  • Johannes, A., Picon, A., Alvarez-Gila, A., Echazarra, J., Rodriguez-Vaamonde, S., Navajas, A.D., Ortiz-Barredo, A., 2017. Automatic plant disease diagnosis using mobile capture devices, applied on a wheat use case. Computers and Electronics in Agriculture, 138: 200-209.
  • Kurt, D., 2020. Stability analyses for interpreting genotype by environment interaction of selected oriental tobacco landraces. Turkish Journal of Field Crops, 25(1): 83-91.
  • Kurt, D., 2021. Impacts of environmental variations on quality and chemical contents of oriental tobacco. Contributions to Tobacco & Nicotine Research, 30(1): 50-62.
  • Kurt, D., Yılmaz, G., Kınay, A., 2021. Genotype x environment interactions based on stability parameters of basma type tobacco lines selected for superior characteristics. Journal of Agricultural Sciences, 27(3): 312-320.
  • Li, C., Cui, G., Melgosa, M., Ruan, X., Zhang, Z., Ma, L., Xiao, K., Luo, M.R., 2016. Accurate method for computing correlated color temperature. Optics Express, 24(13): 14066-14078.
  • Liu, S., Zeng, X., Whitty, M., 2020. 3DBunch: A novel IOS-smartphone application to evaluate the number of grape berries per bunch using image analysis techniques. Institute of Electrical and Electronics Engineers, 8: 114663-114674.
  • Manso, G.L., Knidel, H., Krohling, R.A., Ventura, J.A., 2019. A Smartphone Application to Detection and Classification of Coffee Leaf Miner and Coffee Leaf Rust. arXiv Preprint arXiv:1904.00742. (https://arxiv.org/pdf/1904.00742.pdf), (Accessed: 19.02.2023).
  • Mateen, A., Zhu, Q., 2019. Legion based weed extraction from uav imagery. Pakistan Journal of Agricultural Sciences, 56(4): 1045-1052.
  • McCamy, C.S., 1992. Correlated color temperature as an explicit function of chromaticity coordinates. Color Research & Application, 17(2): 142-144.
  • McCamy, C.S., 1993. Correlated color temperature as an explicit function of chromaticity coordinates. Color Research & Application, 18(2): 150-155.
  • Odabas, M.S., Kayhan, G., Ergun, E., Senyer, N., 2016. Using artificial neural network and multiple linear regression for predicting the chlorophyll concentration index of saint john’s wort leaves. Communications in Soil Science and Plant Analysis, 47(2): 237-245.
  • Odabas, M.S., Senyer, N., Kayhan, G., Ergun, E., 2017. Estimation of chlorophyll concentration index at leaves using artificial neural networks. Journal of Circuits, Systems and Computers, 26(02): 1750026.
  • Odabas, M.S., Senyer, N., Kurt, D., 2022. Determination of quality grade of tobacco leaf by image processing on correlated color temperature. Concurrency and Computation Practice and Experience, 11: e7506.
  • Odabas, M.S., Temizel, K.E., Caliskan, O., Senyer, N., Kayhan, G., Ergun, E., 2014. Determination of reflectance values of hypericum’s leaves under stress conditions using adaptive network based fuzzy inference system. Neural Network World, 24(1): 79-82.
  • Ohno, Y., 2014. Practical use and calculation of CCT and DUV. Leukos, 10(1): 47-55.
  • Payne, R., 2019. Developing in flutter. In: R. Payne (Ed.), Beginning App Development with Flutter, Create Cross-Platform Mobile Apps, pp. 9-27.
  • Peksuslu, A., Yılmaz, I., Inal, A., Kartal, H., 2012. Tobacco genotypes of Turkey. Anadolu Journal of Aegean Agricultural Research Institute, 22(2): 82-90.
  • Robertson, A.R., 1968. Computation of correlated color temperature and distribution temperature. The Journal of the Optical Society of America A, 58(11): 1528-1535.
  • Temizel, K.E., Odabas, M.S., Senyer, N., Kayhan, G., Bajwa, S.G., Caliskan, O., Ergun, E., 2014. Comparision of some models for estimation of reflectance of hypericum leaves under stress conditions. Central European Journal of Biology, 9(12): 1226-1234.
  • Tomar, S., 2006. Converting video formats with ffmpeg. Linux Journal, 146: 10-18.
  • Valdez-Morones, T., Espinosa, H.P., George, H.A., Oblitas, J., Castro, J.W., 2018. An Android App for detecting damage on tobacco (Nicotiana tabacum L.) leaves caused by blue mold (Penospora tabacina Adam). 7th International Conference on Software Process Improvement (CIMPS), 17-19 October, Guadalajara, Mexico, pp. 125-129.
  • Wu, J., Xang, S., 2021. Modeling of the bulk tobacco flue-curing process using a deep learning-based method. Institute of Electrical and Electronics Engineers, 9: 140424-140436.
  • Xingzhong, Q., 1987. Formulas for computing correlated color temperature. Color Research & Application, 12(5): 285-287.
  • Xiong, Y., Yu, S., 2019. A novel growth evaluation system for tobacco planting based on image classification. Journal of Advanced Computational Intelligence and Intelligent Informatics, 23(6): 1004-1011.
  • Zhang, F., 2019. High-accuracy method for calculating correlated color temperature with a lookup table based on golden section search. Optik, 193: 163018.
  • Zhang, J., Sokhansanj, S., Wu, S., Fang, R., Yang, W., Winter, P., 1997. A trainable grading system for tobacco leaves. Computers and Electronics in Agriculture, 16(3): 231-244.
  • Zhenbo, L., Ruohao, G., Meng, L., Yaru, C., Guangyao, L., 2020. A review of computer vision technologies for plant phenothyping. Computers and Electronics in Agriculture, 176: 105672.
  • Zhi, R., Gao, M., Liu, Z., Yang, Y., Zheng, Z., Shi, B., 2018. Color chart development by computer vision for flue-cured tobacco leaf. Sensors and Materials, 30(12): 2843-2864.
Toplam 40 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Endüstri Bitkileri
Bölüm Araştırma Makalesi / Research Article
Yazarlar

Nurettin Şenyer 0000-0002-2324-9285

Recai Oktaş 0000-0003-3282-3549

Mehmet Serhat Odabas 0000-0002-1863-7566

Dursun Kurt 0000-0001-6697-3954

Eren Karaboğa Bu kişi benim 0009-0005-0516-2222

Yayımlanma Tarihi 31 Temmuz 2023
Yayımlandığı Sayı Yıl 2023 Cilt: 10 Sayı: 2

Kaynak Göster

APA Şenyer, N., Oktaş, R., Odabas, M. S., Kurt, D., vd. (2023). A Hybrid Mobile Application for Quality Grade of Tobacco (Nicotiana tabacum L.) Using Correlated Color Temperature. Türkiye Tarımsal Araştırmalar Dergisi, 10(2), 147-153. https://doi.org/10.19159/tutad.1273405
AMA Şenyer N, Oktaş R, Odabas MS, Kurt D, Karaboğa E. A Hybrid Mobile Application for Quality Grade of Tobacco (Nicotiana tabacum L.) Using Correlated Color Temperature. TÜTAD. Temmuz 2023;10(2):147-153. doi:10.19159/tutad.1273405
Chicago Şenyer, Nurettin, Recai Oktaş, Mehmet Serhat Odabas, Dursun Kurt, ve Eren Karaboğa. “A Hybrid Mobile Application for Quality Grade of Tobacco (Nicotiana Tabacum L.) Using Correlated Color Temperature”. Türkiye Tarımsal Araştırmalar Dergisi 10, sy. 2 (Temmuz 2023): 147-53. https://doi.org/10.19159/tutad.1273405.
EndNote Şenyer N, Oktaş R, Odabas MS, Kurt D, Karaboğa E (01 Temmuz 2023) A Hybrid Mobile Application for Quality Grade of Tobacco (Nicotiana tabacum L.) Using Correlated Color Temperature. Türkiye Tarımsal Araştırmalar Dergisi 10 2 147–153.
IEEE N. Şenyer, R. Oktaş, M. S. Odabas, D. Kurt, ve E. Karaboğa, “A Hybrid Mobile Application for Quality Grade of Tobacco (Nicotiana tabacum L.) Using Correlated Color Temperature”, TÜTAD, c. 10, sy. 2, ss. 147–153, 2023, doi: 10.19159/tutad.1273405.
ISNAD Şenyer, Nurettin vd. “A Hybrid Mobile Application for Quality Grade of Tobacco (Nicotiana Tabacum L.) Using Correlated Color Temperature”. Türkiye Tarımsal Araştırmalar Dergisi 10/2 (Temmuz 2023), 147-153. https://doi.org/10.19159/tutad.1273405.
JAMA Şenyer N, Oktaş R, Odabas MS, Kurt D, Karaboğa E. A Hybrid Mobile Application for Quality Grade of Tobacco (Nicotiana tabacum L.) Using Correlated Color Temperature. TÜTAD. 2023;10:147–153.
MLA Şenyer, Nurettin vd. “A Hybrid Mobile Application for Quality Grade of Tobacco (Nicotiana Tabacum L.) Using Correlated Color Temperature”. Türkiye Tarımsal Araştırmalar Dergisi, c. 10, sy. 2, 2023, ss. 147-53, doi:10.19159/tutad.1273405.
Vancouver Şenyer N, Oktaş R, Odabas MS, Kurt D, Karaboğa E. A Hybrid Mobile Application for Quality Grade of Tobacco (Nicotiana tabacum L.) Using Correlated Color Temperature. TÜTAD. 2023;10(2):147-53.

Cited By

Research on automatic biomass grading and quality assessment technology for tobacco industry based on deep convolutional neural network
Applied Mathematics and Nonlinear Sciences
https://doi.org/10.2478/amns-2024-2590

TARANILAN DİZİNLER

14658    14659     14660   14661  14662  14663  14664        

14665      14667