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.

翻译：灌溉中常存在水资源过度使用问题，因此实现高效的水资源管理至关重要。精准农业强调采用茎水势分析等工具以更准确地评估植物状态。然而，此类工具通常需要耗费大量人力进行原位采样。自动化技术与机器学习能够优化这一流程并提升测量效果。本研究致力于利用Scholander压力室（一种广泛使用但操作要求较高的茎水势测量方法）实现茎干自动检测与木质部湿度分类。研究目标在于改进茎干检测方法，并开发基于计算机视觉的技术以更精准地分类木质部水分渗出状态。为此，我们采集并人工标注了视频数据，应用基于视觉与学习的方法进行检测与分类。此外，我们探索了数据增强技术并微调参数以确定最优模型。针对茎干检测与木质部湿度分类筛选出的最佳性能模型，在超过20次茎水势测量中进行了端到端评估。基于YOLOv8n的学习式茎干检测结合ResNet50分类模型取得了80.98%的Top-1准确率，成为木质部湿度分类任务中性能最优的方法。