利用深度学习工具自动测量水基农药-表面活性剂配方在植物叶片上的覆盖面积
Automatic measurement of coverage area of water-based pesticides-surfactant formulation on plant leaves using deep learning tools
AI总结：
本文提出用深度学习模型自动测量黄瓜叶片润湿面积，以替代接触角测量，并研究了润湿面积随表面活性剂浓度的变化。
摘要：
本文提出了一种高效且定量地研究农药-表面活性剂配方水溶液在植物叶片上递送效果的方法。该方法不再测量接触角，而是将叶片润湿面积作为关键参数。为此，我们训练并测试了一个深度学习模型，通过处理视频画面帧，自动测量黄瓜叶片上被水溶液润湿的面积。我们选定了一个文献中已报道用于其他应用的现有深度学习模型，并将其应用于这一不同任务。本文介绍了测量技术、深度学习模型的部分细节、其训练过程以及图像分割性能。最后，我们报告了润湿面积随农药溶液中表面活性剂浓度变化的测量结果。
英文摘要：
A method to efficiently and quantitatively study the delivery of a pesticide-surfactant formulation in water solution over plants leaves is presented. Instead of measuring the contact angle, the surface of the leaves wet area is used as key parameter. To this goal, a deep learning model has been trained and tested, to automatically measure the surface of area wet with water solution over cucumber leaves, processing the frames of video footage. We have individuated an existing deep learning model, reported in literature for other applications, and we have applied it to this different task. We present the measurement technique, some details of the deep learning model, its training procedure and its image segmentation performance. Finally, we report the results of the wet areas surface measurement as a function of the concentration of a surfactant in the pesticide solution.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
