基于改进粒子群算法的植物冠层图像分割.

Aiming at the problem that the standard particle swarm algorithm is easy to fall into the local optimum which leads to poor image segmentation effect, a hybrid particle swarm optimization algorithm combined with simulated annealing algorithm is used to optimize the threshold selection process of multi-threshold image segmentation. The variance function between Otsu classes is used as the fitness function of the algorithm and simulated annealing algorithm is used to avoid jumping into local optimum. The experiment results show that the algorithm can effectively deal with the problem of complex plant canopy image segmentation, and can improve the image segmentation accuracy while guaranteeing the operation efficiency. It provides a theoretical basis for improving the reliability of plant growth condition evaluation and the accuracy of leaf information, with a strong engineering practicability. [ABSTRACT FROM AUTHOR]