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The plant leaf image is taken as the research target, and the improved particle swarm optimization algorithm is used to optimize the optimal threshold vector of maximum entropy threshold segmentation. Although the traditional maximum entropy segmentation method has many advantages, it also has problems such as slow algorithm operation and insufficient precision. The Improved Particle Swarm Optimization(IPSO) proposed in this paper improves the problem that the traditional particle swarm optimization algorithm is easy to fall into local optimum, and has the characteristics of fast convergence and high precision. Aiming at the problem of blade overlap in plant leaf image segmentation, the RGB image is converted into HSV(Hue, Saturation, Value) image and the V vector is extracted, the fitness function of the maximum entropy thresholding method is optimized by IPSO algorithm. The experiment compares the maximum entropy value with the running time of the algorithm. The results show that the segmentation accuracy and convergence efficiency of the proposed method are greatly improved. It shows that the proposed method is superior to the traditional maximum entropy method. Therefore, the method can segment the plant leaf image more efficiently and has strong practicability in engineering. [ABSTRACT FROM AUTHOR]

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]