林业工程学报2024，Vol.9Issue(6)：152-160,9.DOI:10.13360/j.issn.2096-1359.202409022
零样本深度学习驱动的杨树叶片表型检测方法研究
Poplar leaf phenotyping methods using zero-shot deep learning
摘要
Abstract
Leaf phenotyping is one of the important approaches to perceive the growth status of poplar trees.Morphological structural phenotype information such as leaf color,posture,and texture can provide insights into the stress levels that trees are experiencing.Single-leaf segmentation is essential for calculating and statistically analyzing these phenotypic parameters.At the current stage,various artificial intelligence-based algorithms have been widely applied for leaf segmentation tasks,which can meet the performance requirements.However,conventional deep learning model training requires a large number of manual annotations,restricting its development and application.This article proposed a poplar leaf instance segmentation method that integrated zero-shot deep learning and transfer learning.Poplar seedlings of different varieties were considered and used for experiments.Different irrigation frequencies were applied to obtain trees of different drought levels.Images of these mentioned plant samples were captured for analysis.Leaf objects were retrieved from poplar seedling images using the large vision-language model GroundingDINO to obtain the corresponding bounding boxes.Segment anything model v2(SAM2)was adopted to segment all objects in the input images and obtain corresponding masks.Next,the bounding boxes generated by the GroundingDINO model were used as prompts to assist SAM2 in filtering out masks for"Leaf"category.Then,using transfer learning strategy,the AI generated leaf masks were used as ground-truth to train a lightweight YOLOv8(you look only once version 8)segment model.In addition,an independent test dataset was constructed to evaluate the segmentation accuracy of the models.The average precision using 50％intersection over union threshold(AP50)and mean intersection over union(mIoU)were selected as performance indicators.The results indicated that the combination of GroundingDINO and SAM2(with a weight of approximately 810 MB)based on the text prompt"Leaf"achieved high-performance segmentation of poplar leaves,realizing AP50=0.936,mIoU=0.778.By filtering out bounding boxes with abnormal sizes,the AP50 value was increased to 0.942.The YOLOv8 segment model established by transfer learning possessed a weight file of only 6.5 MB,achieving an AP50 of 0.888,significantly simplifying the model while ensuring its accuracy.In this study,the leaf segmentation models did not require manual annotations,achieving efficient and low-cost segmentation of poplar leaf instances.Finally,the applications of the methods presented on leaf counting and leaf area calculation were conducted.Promising performances could be observed that the average percentage leaf counting error was approximately 6％,and the average percentage error of leaf area was about 12％.关键词
杨树/叶片表型/深度学习/零样本学习/迁移学习Key words
poplar/leaf phenotyping/deep learning/zero-shot learning/transfer learning分类
信息技术与安全科学引用本文复制引用
周磊,张慧春,边黎明..零样本深度学习驱动的杨树叶片表型检测方法研究[J].林业工程学报,2024,9(6):152-160,9.基金项目
国家重点研发计划(2023YFE0123600) （2023YFE0123600）
国家自然科学基金(32171790,32171818,62305166) （32171790,32171818,62305166）
江苏省农业科技自主创新资金项目(CX(23)3126) （CX(23）
江苏省333高层次人才培养工程项目. （）
