  Apple trees being deciduous trees, shed leaves each year which is preceded by
the change in color of leaves from green to yellow (also known as senescence)
during the fall season. The rate and timing of color change are affected by the
number of factors including nitrogen (N) deficiencies. The green color of
leaves is highly dependent on the chlorophyll content, which in turn depends on
the nitrogen concentration in the leaves. The assessment of the leaf color can
give vital information on the nutrient status of the tree. The use of a machine
vision based system to capture and quantify these timings and changes in leaf
color can be a great tool for that purpose.
  \par This study is based on data collected during the fall of 2021 and 2023
at a commercial orchard using a ground-based stereo-vision sensor for five
weeks. The point cloud obtained from the sensor was segmented to get just the
tree in the foreground. The study involved the segmentation of the trees in a
natural background using point cloud data and quantification of the color using
a custom-defined metric, \textit{yellowness index}, varying from $-1$ to $+1$
($-1$ being completely green and $+1$ being completely yellow), which gives the
proportion of yellow leaves on a tree. The performance of K-means based
algorithm and gradient boosting algorithm were compared for \textit{yellowness
index} calculation. The segmentation method proposed in the study was able to
estimate the \textit{yellowness index} on the trees with $R^2 = 0.72$. The
results showed that the metric was able to capture the gradual color transition
from green to yellow over the study duration. It was also observed that the
trees with lower nitrogen showed the color transition to yellow earlier than
the trees with higher nitrogen. The onset of color transition during both years
aligned with the $29^{th}$ week post-full bloom.
