Forestry Canopy Image Segmentation Based on Improved Tuna Swarm Optimization

Forests play a vital role in increasing carbon sequestration the biosphere. In recent years, segmenting forest canopy images order to obtain various plant population parameters has become an essential means assess ecosystem. The objective of image segmentation is separate and extract sky regions from background. This study proposes hybrid method based on improved tuna swarm optimization (ITSO) for forestry segmentation. symmetric cross-entropy introduced perform thresholding by modeling classes as membership functions. achieve optimal thresholds image, entropy-solving procedure arduous time-consuming. resolve this issue, ITSO was adopted search most significant threshold. Meanwhile, Tent chaotic map used initialize according factor. experiment carried out four different types images, with indices (MAE, RVD, IoU, ASD) quantitative analysis. experiment’s results show that ITSO-based outperforms others, making it better way segment canopies.