基于改进UNet3+的粗梗水蕨生长状态智能视觉监测及其GPI综合评估方法OA

Ceratopteris pteridoides,an aquatic fern classified as a second-level nationally protected wild plant,holds significant ecological value.Scientifically monitoring its growth status is crucial for elucidating its endangerment mechanisms and formulating conservation strategies.In this study,computer vision technology was employed to collect and construct a comprehensive image dataset covering its entire growth cycle.Building upon the UNet3+image segmentation model,a Euclidean distance self-attention mechanism was introduced to enhance the model's efficiency in utilizing global contextual information and its ability to retain fine features,thereby improving segmentation accuracy for aquatic plants with complex leaf morphologies and slender stems.Based on the image segmentation results,morphological parameters such as canopy coverage,shape factor,and greenness index were extracted.A comprehensive growth status monitoring index(GPI)is constructed to enable quantitative analysis of the growth status of Ceratopteris pteridoides.The results indicated that the GPI values were less than 0.27 during the seedling stage,between 0.27 and 0.44 during the rapid growth stage,and greater than 0.44 at the maturity stage,demonstrating the effectiveness of GPI in distinguishing growth stages.These findings provide a technical reference for the intelligent monitoring of growth status in other aquatic plants.