Length Measurement of Potato Leaf using Depth Camera

Abstract

Leaf length is one of important parameters for crop growth estimation. In order to accurately obtain the plant leaf length, this paper proposes a slope linear hypothesis in which the leaf length could be measured in 3-D space with a right-angle model. A binocular stereo vision system was applied with RGB and depth image output. The projection of leaf could be obtained in RGB and depth image. The depth camera is used to obtain one right-angle side to correct the measurement result in RGB image. Four methods were present to compare the extract accuracy in the images. First, RGB images without leaf segmentation were used to extract leaf length (L1→) on the horizontal projection plane. Second, leaf length (L2→) were calculated with the right-angle model correction by depth image based on (L1→). Third, the preprocessing of RGB images was conducted with color image segmentation and morphological operation, then the leaf length (L3→) was extracted. Fourth, the correction leaf length (L4→) were obtained by depth image correction to L3→. Four prediction models were established to analyze L1→, L2→, L3→, and L4→ results. It is indicated that the prediction model based on the L2→ measurement has better performance, in which Rc2 is 0.7178 and Rv2 is 0.833.