Multilevel threshold segmentation of rice plant images utilizing tuna swarm optimization algorithm incorporating quadratic interpolation and elite swarm genetic operators

Rice plant images exhibit varying texture characteristics across different growth stages and environmental conditions. A suitable image thresholding segmentation method can effectively separate the different feature regions of rice plants for better monitoring of rice growth to improve yield. This paper employs the Sigmoid non-linear weights strategy, Quadratic interpolation strategy, and elite swarm Genetic strategy to enhance distinct stages of the Tuna Swarm Optimization algorithm (TSO) to propose the SQGTSO algorithm, which has better convergence and global optimization capability. 10 CEC2017 benchmark functions are selected to validate the performance of the SQGTSO algorithm, and the experimental results show that the SQGTSO algorithm outperforms the other algorithms in 9 benchmark functions. To assess the feasibility and efficacy of the SQGTSO for multilevel threshold segmentation of rice plant images, this paper selects 8 rice plant images with diverse styles for the design of two sets of comparative experiments. The SQGTSO algorithm is comprehensively benchmarked against seven advanced metaheuristic algorithms and one machine learning method. Under the conditions of threshold levels ranging from 4 to 30, two distinct experiment sets are devised. In each set, Otsu’s method and the MCET method are employed as fitness functions for the metaheuristic algorithms, respectively. The assessment criteria include fitness values, PSNR, SSIM, FSIM and HPSI. Additionally, the Friedman method is utilized for statistical analysis of the five metrics yielded by each algorithm. The experimental findings demonstrate the significant advantages of the SQGTSO method concerning five evaluation metrics and its convergence performance compared to other competitors.