Abstract: Plant leaf is an important organ for photosynthesis and transpiration, and it has a significant effect on plant growth, yield and quality of crops. It is necessary to measure the three dimensional(3D) morphology(3D coordinates of each point on the surface of plant leaves) and illuminance distribution of leaves are important for the study of plant growth. Therefore, in this paper, we proposed a method of measuring the leaf 3D morphology and illuminance distribution synchronization of texture plants based on binocular vision. Firstly, according to the pinhole imaging principle, there is a one-to-one correspondence between 3D spatial coordinate points and image points. The camera calibration is a necessary process when the recovery of information such as angles and distance of spatial points and image points is required. So the intrinsic and external parameters and distortion parameters of the camera were obtained by improved Zhang’s Calibration. For obtaining the 3D spatial coordinate points，two cameras was used to capture the left and right image by the binocular vision principle. To find out the same texture image points of leaves, the digital image correlation technique was used. From the above work we can measure 3D coordinate points of leaf and calculate distance that surface point of leaf to the left camera optical center. For improving the accuracy of 3D coordinate points measurement, the left and right raw format images of chessboard calibration target and leaf were captured by using two same type of color COMS cameras with high resolution and unpacked it to RGB 3-channel images. G channel image brightness was still highest when the illuminance was much lower, so chosen G channel image for calculation. Secondly, in order to achieve the illuminance distribution, according to the applied optics theory and lambert′s law, the image gray values and the illuminance were positively correlated. So an image illuminance measurement equation was developed. Leaf surface illuminance was only related to the distance and image gray values when the camera system parameters were invariant for diffuse reflectance plant leaves. Therefore, when the image gray values and the distance were obtained, the illuminance calculation of plant leaves can be realized. For improving the accuracy of 3D illuminance distribution measurement, converted the RGB format image to HSV format image and segmentation based on hue of image and smoothed. Thirdly, in order to verify the reliability and accuracy of the synchronous measurement method, in this paper, we took Fittonia arundinacea as experiment object. We needed to calibrate before experiment. So 16 left and right chessboard calibration target images under the different postures were captured and used to calculate the intrinsic, external parameters and distortion parameters, and irradiated it with uniform light. One hundred leaf left and right images and the corresponding illuminance data were collected synchronously, then the size of 30×20 pixels of leaf area were selected to calibrate the camera system parameters. For verifying the illuminance equation, captured 120 left and right images of different light intensity and measured illuminance，the illuminance sensor was used to measure the corresponding illuminance. Through the comparative analysis of the illuminance sensor measurement values and the image measurement average values, we found that the correlation was 0.996, the average relative error was less than 6%, which can meet the basic requirements of the application of agricultural information monitoring in intelligent light control. Besides that, it was irradiated with non-uniform light and the image was collected. The original G channel image for 3D morphology measurement was used. The results showed that it was the best when the sub region radius was 57 pixels and the interval of sub region was seven pixels. The illuminance equation was used to measure illuminance distribution of the original G channel image and the hue segmentation and smooth G channel image of leaf synchronously, the latter was better. The experimental results showed that the method can satisfy the synchronous measurement of 3D morphology and illuminance distribution of the leaf.