  The success of modern farming and plant breeding relies on accurate and
efficient collection of data. For a commercial organization that manages large
amounts of crops, collecting accurate and consistent data is a bottleneck. Due
to limited time and labor, accurately phenotyping crops to record color, head
count, height, weight, etc. is severely limited. However, this information,
combined with other genetic and environmental factors, is vital for developing
new superior crop species that help feed the world's growing population. Recent
advances in machine learning, in particular deep learning, have shown promise
in mitigating this bottleneck. In this paper, we propose a novel deep learning
method for counting on-ear corn kernels in-field to aid in the gathering of
real-time data and, ultimately, to improve decision making to maximize yield.
We name this approach DeepCorn, and show that this framework is robust under
various conditions. DeepCorn estimates the density of corn kernels in an image
of corn ears and predicts the number of kernels based on the estimated density
map. DeepCorn uses a truncated VGG-16 as a backbone for feature extraction and
merges feature maps from multiple scales of the network to make it robust
against image scale variations. We also adopt a semi-supervised learning
approach to further improve the performance of our proposed method. Our
proposed method achieves the MAE and RMSE of 41.36 and 60.27 in the corn kernel
counting task, respectively. Our experimental results demonstrate the
superiority and effectiveness of our proposed method compared to other
state-of-the-art methods.
