Modern day studies show a high degree of correlation between high yielding crop varieties and plants with upright leaf angles. It is observed that plants with upright leaf angles intercept more light than those without upright leaf angles, leading to a higher rate of photosynthesis. Plant scientists and breeders benefit from tools that can directly measure plant parameters in the field i.e. on-site phenotyping. The estimation of leaf angles by manual means in a field setting is tedious and cumbersome. We mitigate the tedium using a combination of the Mask R-CNN instance segmentation neural network, and Line Segment Transformer (LETR), a vision transformer. The proposed Computer Vision (CV) pipeline is applied on two image datasets, Summer 2015-Ames ULA and Summer 2015- Ames MLA, with a combined total of 1,827 plant images collected in the field using FieldBook, an Android application aimed at on-site phenotyping. The leaf angles estimated by the proposed pipeline on the image datasets are compared to two independent manual measurements using ImageJ, a Java-based image processing program developed at the National Institutes of Health and the Laboratory for Optical and Computational Instrumentation. The results, when compared for similarity using the Cosine Similarity measure, exhibit 0.98 similarity scores on both independent measurements of Summer 2015-Ames ULA and Summer 2015-Ames MLA image datasets, demonstrating the feasibility of the proposed pipeline for on-site measurement of leaf angles.

翻译：现代研究表明，高产量作物品种与具有直立叶片角度的植株之间存在高度相关性。据观察，具有直立叶片角度的植株比非直立叶片角度的植株能截获更多光照，从而导致更高的光合作用速率。植物科学家和育种家受益于能够直接在田间（即现场表型分析）测量植物参数的工具。在田间环境下通过人工方式估计叶片角度既繁琐又费力。我们通过结合Mask R-CNN实例分割神经网络与线段Transformer（LETR）——一种视觉Transformer，来减轻这种繁琐性。所提出的计算机视觉（CV）流程应用于两个图像数据集：Summer 2015-Ames ULA和Summer 2015-Ames MLA，这些数据集共包含1,827张使用FieldBook（一款针对现场表型分析的Android应用程序）在田间采集的植物图像。将所提流程在图像数据集上估计的叶片角度，与使用ImageJ（一款由美国国立卫生研究院和光学与计算仪器实验室开发的基于Java的图像处理程序）进行的两次独立人工测量结果进行比较。使用余弦相似度度量进行相似性比较时，结果显示在Summer 2015-Ames ULA和Summer 2015-Ames MLA图像数据集的两次独立测量上均获得0.98的相似度分数，证明了所提流程用于叶片角度现场测量的可行性。