The automatic compensation effect in a digital camera can cause a bias of the digital number (DN) of the acquired images. This study carried out DN correction using gamma transformation theory combined with a look-up table method. The DN of the acquired image generally bears a nonlinear relationship with the intensity of incident light, which is an inherent characteristic in common commercial digital cameras. The gamma method is a nonlinear operation used to code and decode luminance or tri-stimulus values in video or still image systems. It has been widely used in photography and video productions, but is rarely used in image processing and applications to vegetation parameter determination. The gamma transformation is mainly used to compensate for the properties of human vision by maximizing the use of the bits or bandwidth relative to the perception of light and color. If images are not gamma encoded, they allocate too many bits or too much bandwidth to the darker areas that humans cannot differentiate and too few bits to the brighter areas that humans are sensitive to, and would thus require more bits to maintain the same visual quality. Although the visual quality of RAW image is generally not very appropriate in commercial use, the RAW image is precisely required in the retrieval of vegetation parameters to obtain a better fit with field measurements. Apart from the gamma correction, for the various annual paddy rice conditions we used the vertical gap fraction to obtain the leaf area index (LAI), which we named image LAI, and compared with the field LAI from LAI-2000. The results indicated that the correlation coefficients after the correction procedure were remarkably similar with an R² of 0.71 (P < 0.05), better than for the uncorrected retrieved figures. In the tilling and jointing stages, there was a close approximation between the vertical gap fraction derived LAI and field LAI, but in the booting and heading stages the differences become obvious, reaching a maximum of 0.38. However, in the milk-ripened and maturation stages, the reverse occurred; the field LAI was higher than the vertical gap fraction derived-LAI mainly because the foliage of the rice paddy went yellow. In general, there was a good fit between the retrieved LAI and field LAI, but the estimation accuracy varied depending on the phenology of paddy rice. A better fit between image LAI and LAI 2000 was observed in the tilling and jointing stages, when a larger bias between image LAI and field LAI was observed in the booting and heading stages, while an overestimation in image LAI was observed in the milk-ripened and maturation stages. The commercial digital camera can carry out easy and low cost research given its characteristic properties (ease of use, less costly, and lack of atmospheric effects). These features enable anyone to carry out good estimation work related to vegetation. These factors and many related articles make the digital camera a useful alternative method for assessing vegetation parameters such as vegetation fraction, leaf color, plant type and leaf area index.