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In this study, a particle swarm optimization (PSO) algorithm with a negative gradient perturbation and binary tree depth-first strategy (GB-PSO) is proposed. The negative gradient term accelerates particle optimization in the direction of decreasing the objective function value. To calculate the step size of this gradient term more easily, a method based on the ratio was proposed. In addition, a new PSO strategy is also proposed. Each iteration of PSO yields not only the current optimal solution of the group, but also the solution based on the 2-norm maximum. Under the current iteration solution of PSO, these two solutions are the children nodes. In the sense of the binary tree concept, the three solutions constitute the father-son relationship, and the solution generated throughout the entire search process constitutes the binary tree. PSO uses a traceable depth-first strategy to determine the optimal solution. Compared with the linear search strategy adopted by several algorithms, it can fully utilize the useful information obtained during the iterative process, construct a variety of particle swarm search paths, and prevent premature and enhance global optimization. The experimental results show that the algorithm outperforms some state-of-the-art PSO algorithms in terms of search performance.

An automated flower thinning system, when combined with machine vision, has the potential to reduce the labor force, improve efficiency, and lower costs. This combination represents the future of agricultural machinery development. The primary objective of automatic flower thinning is to determine the flowering density of fruit trees under natural light conditions. In this study, we introduce a flower recognition algorithm that uses pixel values as an independent variable to recognize flower categories by constructing a nonlinear regression model. Initially, the RGB pixel values of elements in the training set are extracted. Similar pixel values are clustered together to reduce the amount of computation, and representative elements are selected to construct a nonlinear classification function, known as the regression function. The coefficients in the classifier are determined by transforming the problem into an unconstrained optimization problem using the least square method. The optimal solution is then found as the coefficient value in the classifier. The classification function calculates the function value of the RGB pixel value for each input entity to determine whether it belongs to the flower entity. Finally, the developed algorithm is used to classify the flower graphic elements of the measured pictures, and the efficiency of the algorithm is verified.