  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.
