A fruit tree feature matching algorithm based on an improved AKAZE method

With the widespread application of image feature matching in orchard automation, enhancing the precision and computational efficiency of current fruit tree feature matching algorithms is crucial. In this paper, we propose a fruit tree feature matching algorithm based on an improved AKAZE method. First, the AKAZE algorithm is employed to perform feature detection and extract feature points from the left and right fruit tree images. The FREAK descriptor is then constructed for the AKAZE feature points detected on the fruit tree images. Subsequently, an improved KNN algorithm is used for preliminary feature matching, followed by the RANSAC algorithm to refine the matching results. Ultimately, the optimal feature matching results for the left and right fruit tree images are obtained. Compared to the SURF, KAZE, AKAZE, and BRISK algorithms, the proposed algorithm demonstrates significant improvements in RMSE, precision, runtime, and RAM usage across experiments involving scale, blur, and rotation matching.