> In the agricultural industry, precision farming is the most important task that attracts lots of attentions. The health of the plant depends mostly on the amount of water in its access that can be estimated through vegetation indices. These indices can be extracted from satellite images through Image processing algorithms. The objective of this research was to provide an equation for assessment of the quality of the phenological dates of the sugarcane in Degree-Day (DD) which are usually derived using satellite data. Then these calibration equations can be used in the collection of some ground truth data applicable in remote sensing where ever the need arises. A simple way for implementing this task is to develop an algorithm (an equation) with which we can (to a limited extent) quantify the interaction of light (in the RGB region of spectrum) with the plant foliage to have DDs as their outputs. To do this 63 digital photographs were taken in three field campaigns on Sep29, 2006 through Oct1, 2006 from Amirkabir and Dea`bal-Khazaie sugarcane sites located in the south-west of Iran. These photographs included 9 different stages of the sugarcane growth and bare soil. It was found that on the average, the effect of dust on the leaves is an increase in DN values of about 9, 8 and 13 for bands red, green and blue respectively. To find an algorithm for determination of plant phenological date four different methods were used. These were Rectangular Method (RM), Maximum Likelihood Method (MLM), Thresholding Method (TM) and Hybrid Method (HM). To test the ability of different methods in the prediction of plants DDs, three photographs with known DDs and vegetation cover percentage were used. Entering these predicted DDs and true values in the Wilcoxon signed-rank test, the degree of significance level of the predicted value of each method was evaluated. As a result MLM with R2 of 0.987 and TM method with R2 of 0.989 both with significance level of 0.827 were the best methods for estimation of phenological date using broadband digital cameras.
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