  We present an end-to-end computer vision system for mapping yield in an apple
orchard using images captured from a single camera. Our proposed system is
platform independent and does not require any specific lighting conditions. Our
main technical contributions are 1)~a semi-supervised clustering algorithm that
utilizes colors to identify apples and 2)~an unsupervised clustering method
that utilizes spatial properties to estimate fruit counts from apple clusters
having arbitrarily complex geometry. Additionally, we utilize camera motion to
merge the counts across multiple views. We verified the performance of our
algorithms by conducting multiple field trials on three tree rows consisting of
$252$ trees at the University of Minnesota Horticultural Research Center.
Results indicate that the detection method achieves $F_1$-measure $.95 -.97$
for multiple color varieties and lighting conditions. The counting method
achieves an accuracy of $89\%-98\%$. Additionally, we report merged fruit
counts from both sides of the tree rows. Our yield estimation method achieves
an overall accuracy of $91.98\% - 94.81\%$ across different datasets.
