  Estimating accurate and reliable fruit and vegetable counts from images in
real-world settings, such as orchards, is a challenging problem that has
received significant recent attention. Estimating fruit counts before harvest
provides useful information for logistics planning. While considerable progress
has been made toward fruit detection, estimating the actual counts remains
challenging. In practice, fruits are often clustered together. Therefore,
methods that only detect fruits fail to offer general solutions to estimate
accurate fruit counts. Furthermore, in horticultural studies, rather than a
single yield estimate, finer information such as the distribution of the number
of apples per cluster is desirable. In this work, we formulate fruit counting
from images as a multi-class classification problem and solve it by training a
Convolutional Neural Network. We first evaluate the per-image accuracy of our
method and compare it with a state-of-the-art method based on Gaussian Mixture
Models over four test datasets. Even though the parameters of the Gaussian
Mixture Model-based method are specifically tuned for each dataset, our network
outperforms it in three out of four datasets with a maximum of 94\% accuracy.
Next, we use the method to estimate the yield for two datasets for which we
have ground truth. Our method achieved 96-97\% accuracies. For additional
details please see our video here:
https://www.youtube.com/watch?v=Le0mb5P-SYc}{https://www.youtube.com/watch?v=Le0mb5P-SYc.
