用视觉识别自动判断植物茎部导管水分状态，提升灌溉精准度。
Vision-based Xylem Wetness Classification in Stem Water Potential Determination
- 基于YOLOv8n和ResNet50的视觉模型自动检测茎部并分类导管湿润程度。
- 在20次测量中达到80.98%的准确率，显著提升传统方法效率。
- 适合农业自动化、智能灌溉系统研发人员参考。
水资源常被过度使用，高效管理至关重要。精准农业强调通过茎水势（SWP）分析来更准确判断植物状态，但此类方法通常依赖费力的现场采样。自动化与机器学习可优化流程、提升效果。本研究聚焦于自动化茎部检测与导管湿润度分类，针对广泛使用但操作繁琐的施兰德压差仪（Scholander Pressure Chamber）进行改进。为此，收集并人工标注了视频数据，采用视觉与学习结合的方法实现检测与分类，并探索数据增强与参数调优以确定最优模型。最终在20次SWP测量上端到端评估，基于YOLOv8n的检测与基于ResNet50的分类组合达到80.98%的Top-1准确率，成为最佳方案。
原文摘要 · Abstract (English)
Water is often overused in irrigation, making efficient management of it crucial. Precision Agriculture emphasizes tools like stem water potential (SWP) analysis for better plant status determination. However, such tools often require labor-intensive in-situ sampling. Automation and machine learning can streamline this process and enhance outcomes. This work focused on automating stem detection and xylem wetness classification using the Scholander Pressure Chamber, a widely used but demanding method for SWP measurement. The aim was to refine stem detection and develop computer-vision-based methods to better classify water emergence at the xylem. To this end, we collected and manually annotated video data, applying vision- and learning-based methods for detection and classification. Additionally, we explored data augmentation and fine-tuned parameters to identify the most effective models. The identified best-performing models for stem detection and xylem wetness classification were evaluated end-to-end over 20 SWP measurements. Learning-based stem detection via YOLOv8n combined with ResNet50-based classification achieved a Top-1 accuracy of 80.98%, making it the best-performing approach for xylem wetness classification.
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