Aiming at solving the low-accuracy and slow-speed problem of traditional registration, an improved SIFT-ICP registration method for color point clouds of plant was put forward. Original color point clouds of plant was merged by color images and depth images obtained by Kinect from different perspectives. Firstly, preprocessing was carried out to extract point clouds of plant from original point clouds, in which lots of point clouds of background and noise were involved. Secondly, by making use of depth features and boundary characteristics of plant point clouds, the key points were detected by means of SIFT (Scale invariant feature transforms) algorithm. Thirdly, normal calculation was executed on the key points computed previously, which was revised by adapting the estimation to accelerate the normal estimation process. The normal estimation was determined by the number of surrounding points. For the sparse part of point cloud, the value of adjacent point was reduced, on the contrary, it was increased in the process of normal estimation. Meanwhile, the FPFH (Fast point feature histograms) descriptor was developed to obtain the characteristic vector which contained 33 dimension element for each key point. Fourthly, SAC-IA (Sample consensus-initial alignment) algorithm, an initial registration algorithm, was applied to register plant color point clouds from different perspectives to provide a better spatial mapping relationships for accurate registration. Finally, on the basis of initial registration, the ICP (Iterative closest point) algorithm, which was accelerated by adapting Nanoflann instead of Flann, was used to refine the initial transform matrix inferred by initial registration. Experiments showed that this registration method can improve not only registration speed but also registration accuracy, the average Euclidean distance between corresponding points was below 7mm and registration time-consuming was less than 30s.