Predicting Cut Rose Stages of Development and Leaf Color Variations by Means of Image Analysis Technique
محورهای موضوعی : مجله گیاهان زینتیMansour Matloobi 1 , Sepide Tahmasebi 2 , Mohamad Reza Dadpour 3
1 - Dep. of Horticulture, University of Tabriz, Tabriz, Iran
2 - University of Tabriz
3 - University of Tabriz
کلید واژه: Chlorophyll, Leaf color, Cut roses, Image analysis, RGB model,
چکیده مقاله :
The monitor and prediction of crop developmental stages, particularly harvest time, play an important role in planning greenhouse cropping programs and timetables by cut rose producers. There have been many scientific reports on the application of image analysis technology in estimating greenhouse crop growth stages. In the present research, we studied leaf color variations over time by taking timely images from four commercial rose cultivars and processing them later using image j software in RGB color space. Results revealed a higher correlation between the leaf color variations and the stages of stem growth in both white color (R2=0.89) and colorful cultivars (R2=0.94). Furthermore, it was determined that there was a significant difference in leaf color components within stem layers in all cultivars. A good correlation was also observed between the leaf total chlorophyll measured directly by spectrophotometric method and the data acquired indirectly from SPAD readings. Among the models fitted to the stem height and color variation data, linear and exponential models performed best. However, some differences were observed between the cultivars. The potential observed in image analysis technique in detecting color differences among the leaf layers and its versatility in non-destructive determination of a link between the leaf color changes and rose stem growth give it the utmost merit and applicability in greenhouses. Developing such a model for other important cultivars of greenhouse roses will make it possible to equip rose greenhouses with several powerful and reliable tools in order to assist the growers to precisely adjust the crop harvest time and accurately plan their operations according to the market demand and policy.
پیشبینی مراحل رشد محصول، بهخصوص زمان برداشت آن نقش بسیار مهمی در برنامهریزی تولیدات گلخانهای دارد. مطالعات فراوانی از کاربرد فنآوری تجزیه و تحلیل تصاویر دیجیتال برای تخمین رفتار رشدی محصول در گلخانه وجود دارد. در مطالعه حاضر تغییرات مشخصات رنگی برگ چهار رقم تجاری گل رز در طول زمان با استفاده از پردازش تصاویر رنگی توسط نرمافزار image j و فضای رنگ RGB مورد بررسی قرار گرفت. نتایج حاصل نشان داد که ارتباط بالایی بین اجزای رنگ برگ و مرحله رشد ساقه در ارقام دارای گلهای سفید رنگ (R2 = 0.986) و ارقام گل رنگی (R2 = 0.94) وجود دارد و همچنین تفاوت معنیداری بین اجزای رنگ در برگهای لایههای مختلف ساقه مشاهده شد. همچنین همبستگی خوبی بین اندازهگیری مستقیم کلروفیل کل توسط روش اسپکتروفتومتری و شاخص کلروفیل به وسیله SPAD بدست آمد. در بین مدلهای بررسی شده معلوم شد مدل خطی و مدل نمایی عملکرد بهتری در ایجاد رابطه منطقی بین دادههای حاصل از ارتفاع ساقه و تغیرات رنگ برگ دارند، هرچند تفاوتهایی در این زمینه بین ارقام مشاهده شد. توانایی روش آنالیز تصویری در تشخیص غیر مخرب تغییرات رنگی در بین لایههای برگی و برقراری یک پیوند معنیدار و منطقی بین تغییرات رشد ساقه ارزشمند و در خور توجه تشخیص داده شد. توسعه این مدل برای سایر ارقام رز گلخانهای مهم میتواند ابزار قوی و قابل اطمینانی در اختیار تولید کنندگان رزهای گلخانهای قرار دهد تا بتوانند به کمک آن برنامههای تولید را تنظیم و زمان برداشت محصول و بازاررسانی را پیشبینی کنند.
Adamsen, F.J., Coffelt, T.A., Nelso, J.M., Barens, E.M. and Robert, C.R. 2000. Method for using images from a color digital camera to estimate flower number. Journal of Crop Science, 40: 704 -709.
Ali, M.M., Al-Ani, A., Eamus, D. and Tan, D.K. 2012. New image processing based technique to determine chlorophyll in plants. American-Eurasian Journal of Agricultural & Environmental Sciences, 12 (10): 1323-1328.
Amaliotis, D., Therios, I. and Karatissiou, M. 2004. Effect of nitrogen fertilization on growth, leaf nutrient concentration and photosynthesis in three peach cultivars. Acta Horticulturae, 449: 36 - 42.
Arnon, D. 1949. Copper enzymes in isolated chloroplasts, polyphenoloxidase in Beta vulgaris. Plant Physiology, 24: 1- 15.
Asefpour, K.V. and Massah, J. 2012. Non-linear growth modeling of greenhouse crops with image textural features analysis. Journal of Applied and Basic Sciences, 3 (1): 197-202.
Berger, B., Parent, B. and Tester, M. 2010. High-throughput shoot imaging to study drought responses. Journal of Experimental Botany, 61: 3519 – 3528.
Berger, B., Regt, B.D. and Tester, M. 2012. High-throughput phenotyping of plant shoots. Springer Science. Methods and Protocols, Methods in Molecular Biology, 918: 978-987.
Buck-Sorlin, G., de Visser, P.H.B., Henke, M., Sarlikioti, V., van der Heijden, G.W.A.M., Marcelis, L.F.M. and Vos, J. 2011. Towards a functional-structural plant model of cut-rose: simulation of light environment, light absorption, photosynthesis and interference with the plant structure. Annals of Botany, 108 (6): 1121-1134.
Brosnan, T. and Sun, D.W. 2004. Improving quality inspection of food products by computer vision - a review. Journal of Food Engineering, 61 (1): 3-16.
Cabrera, R.I. 2004. Evaluating yield and quality of roses with respect to nitrogen fertilization and leaf nitrogen status. XXV International Horticulturae Congress, ISHS Acta Horticulturae, 511: 157 - 170.
Cheng, D.H., Jiang, X.H., Sun, Y. and Wang, J. 2001. Colour image segmentation: Advances and prospects. Pattern Recogn, 34: 2259 – 2281.
Corkidi, G., Balderas-Ruíz, K.A., Taboada, B., Serrano-Carreón, L. and Galindo, E. 2006. Assessing mango anthracnose using a new three-dimensional image analysis technique to quantify lesions on fruit. Journal of Plant Pathology, 55: 250 – 257.
Cui, D., Zhang, Q., Li, M., Hartman, G.L. and Zhao, Y. 2010. Image processing methods for quantitatively detecting soybean rust from multispectral images. Biosystems Engineering, 107: 186 – 193.
Curran, P.J., Dungan, J.L. and Gholz, H.L. 1990. Exploring the relationship between reflectance red edge and Chl content in slash pine. Tree Physiology, 7: 33 – 48.
Dutta Gupta, S., Ibaraki, Y. and Pattanayak, A.K. 2013. Development of a digital image analysis method for real-time estimation of chlorophyll content in micropropagated potato plants. Plant Biotechnology, 7: 91–97.
Easlon, H.M. and Bloom, I.J. 2014. Easy leaf area: automated digital image analysis for rapid and accurate measurement of leaf area. Botanical Society of America, Applications in Plant Sciences, 2 (7): 1 - 4.
French, A., Ubeda-Tomás, S., Holman, T.J., Bennett, M.J. and Pridmore, T. 2009. Highthroughput quantification of root growth using a novel image-analysis tool. Plant Physiology, 150: 1784 – 1795.
Gaddanakeri, S.A., Biradar, D.P., Kambar, N.S. and Nyamgouda, V.B. 2007. Productivity and economics of sugarcane as influenced by leaf colour chart based nitrogen management. Karnataka Journal of Agricultural Sciences, 20 (3): 466 - 468.
Hendry, G.A.F., Houghton, J.D. and Brown, S.B. 1987. The degradation of chlorophyll-A biological enigma. New Phytologist, 107: 255 – 302.
Ibaraki, Y., Yano, Y., Okuhara, H. and Tazuru, M. 2012. Estimation of light intensity distribution on a canopy surface from reflection images. Environmental Control in Biology, 50: 117 – 126.
Iwata, H., Nesumi, H., Ninomiya, S., Takano, Y. and Ukai, Y. 2002. Diallel analysis of leaf shape variations of citrus varieties based on elliptic Fourier descriptors. Breeding Science, 52: 89 – 94.
Keyser, E., Lootens, P., Van Bockstaele, E. and De Rick, J. 2013. Image analysis for QTL mapping of flower color and leaf characteristics in pot azalea (Rhododendron simsii hybrids). Euphytica, 189: 445 – 460.
Kool, M.T.N. 1997. Importance of plant architecture and plant density for rose crop performance. Journal of Horticultural Science, 72: 195 – 203.
Lanaa, M.M., Tijskensa, C. and van Kootena, O. 2006. Effects of storage temperature and stage of ripening on RGB colour aspects of fresh-cut tomato pericarp using video image analysis. Journal of Food Engineering, 77 (4): 871 – 879.
Lee, K.J. and Lee, B.W. 2013. Estimation of rice growth and nitrogen nutrition status using color digital camera image analysis. European Journal of Agronomy, 48: 57 – 65.
Leinauer, B. and Sevostianova, E. 2014. Subsurface-applied tailored water: combining nutrient benefits with efficient turfgrass irrigation. Crop Science, 54: 1926 – 1938.
Li, Y., Chen, D., Walker, C.N. and Angus, J.F. 2010. Estimating the nitrogen status of crops using a digital camera. Field Crops Research, 118: 221–227.
Liu, J. and Pattey, E. 2010. Retrieval of leaf area index from top-of-canopy digital photography over agricultural crops. Agricultural and Forest Meteorologyis, 150: 1485 – 1490.
Lobet, G., Pagès, L. and Draye, X. 2011. A novel image-analysis toolbox enabling quantitative analysis of root system architecture. Plant Physiology, 157: 29 – 39.
Lopez, V.N., Sasaki, Y., Nakano, K., Mejía-Muñoz, J.M. and Romanchik Kriuchkova, E. 2011. Detection of powdery mildew disease on rose using image processing with open CV. Journal Revista Chapingo, Serie Horticultura , 17 (2): 151-160.
Martin, V., Moisan, S., Paris, B. and Nicolas, O. 2009. Towards a video camera network for early pest detection in greenhouses. ENDURE International Conference France.
Mendoza, F., Dejmek, P. and Jose, M.A. 2013. Calibrated color measurements of agricultural foods using image analysis. Postharvest Biology and Technology, 41 (3): 285 - 295.
Murakami, P.F., Hitchcock, M.R., van den Berg, A.K. and Schaberg, P.G. 2005. An instructional guide for computer-based leaf color analysis. General Technical Report NE-327.
Noordam, J., Hemming, C., van Heerde, C., Golbach, F., van Soest, R. and Wekking, E. 2005. Automated rose cutting in greenhouses with 3d vision and robotics: Analysis of 3D vision techniques for stem detection. Acta Horticulturae, 691: 885-892.
Pasian, C.C. and Leith, J.H. 1996. Prediction of rose shoot development: Model validation for ‘Cara Mia’ and extension to the cultivars ‘Royalty’ and ‘Sonia’. Scientia Horticulturae, 66: 117-124
Porra, R.J., Thompson, W.A. and Kriedmann, P.E. 1989. Determination of accurate extinction coefficients and simultaneous equations for assaying chlorophylls a and b extracted with four different solvents: Verification of the concentration of chlorophyll standards by atomic absorption spectroscopy. Biochimica et Biophysica Acta, 975: 384 – 394.
Pydipati, R., Burks, T.F. and Lee, W.S. 2006. Identification of citrus disease using color texture features and discriminate analysis. Computers and Electronics in Agriculture, 52: 49 - 59.
Schmitzer, V., Veberic, R., Osterc, G. and Stampar, F. 2009. Changes in the phenolic concentration during flower development of rose ‘Korcrisett’. American Society for Horticultural Science, 134: 491 – 496.
Shimomura, N., Inamoto, K., Doi, M., Sakai, E. and Imanishi, H. 2003. Cut flower productivity and leaf area index of photosynthesizing shoots evaluated by image analysis in “Arching” roses. Japanese Society for Horticultural Science, 72: 131–133.
Stutte, G.W. 1990. Analysis of video images using an interactive image capture and analysis system. HortScience, 25: 695 - 697.
Townsend, A.M. and McIntosh, M.S. 1993. Variation among full-sib progenies of red maple in growth, autumn leaf color, and leafhopper injury. Journal of Environmental Horticulture, 11: 72 - 75.
Wang, Y., Wang, D., Zhang, G. and Wang, J. 2013. Estimating nitrogen status of rice using the image segmentation of G-R thresholding method. Field Crops, 149: 33 – 39.
Wijekoon, C.P., Goodwin, P.H. and Hsiang, T. 2008. Quantifying fungal infection of plant leaves by digital image analysis using Scion Image software. Journal of Microbiological Methods, 74: 94–101.
Wiwart, M., Fordonski, G., Zuk-Golaszewska, K. and Suchowisska, E. 2009. Early diagnostics of macronutrient deficiencies in three legume species by color image analysis. Computers and Electronics in Agriculture, 65: 125 – 132.
Yadav, S.P., Ibaraki, Y. and Dutta Gupta, S. 2010. Estimation of the chlorophyll content of micropropagated potato plants using RGB based image analysis. Plant Cell Tissue And Organ Culture, 100: 183 –188.
Yoshioka, Y., Iwata, H., Ohsawa, R. and Ninomiya, S. 2004. Quantitative evaluation of flower colour pattern by image analysis and principal component analysis of Primula sieboldii E. Morren. Euphytica, 139: 179 – 186.
Yuzhu, H., Xiaomei, W. and Shuyao, S. 2011. Nitrogen determination in pepper (Capsicum frutescens L.) plants by color image analysis (RGB). African Journal of Biotechnology, 77: 17737 – 17741.
Zhenjiang, M. 2000. Zernike moment-based image shape analysis and its application. Pattern Recognition Letters, 21 (2): 169 –177.