Predicting Cut Rose Stages of Development and Leaf Color Variations by Means of Image Analysis Technique
الموضوعات : 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
الکلمات المفتاحية: Chlorophyll, Leaf color, Cut roses, Image analysis, RGB model,
ملخص المقالة :
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.
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