  Wheat is one of the most significant crop species with an annual worldwide
grain production of 700 million tonnes. Assessing the production of wheat
spikes can help us measure the grain production. Thus, detecting and
characterizing spikes from images of wheat fields is an essential component in
a wheat breeding process. In this study, we propose three wheat head counting
networks (WHCNet\_1, WHCNet\_2 and WHCNet\_3) to accurately estimate the wheat
head count from an individual image and construct high quality density map,
which illustrates the distribution of wheat heads in the image. The WHCNets are
composed of two major components: a convolutional neural network (CNN) as the
front-end for wheat head image feature extraction and a CNN with skip
connections for the back-end to generate high-quality density maps. The dataset
used in this study is the Global Wheat Head Detection (GWHD) dataset, which is
a large, diverse, and well-labelled dataset of wheat images and built by a
joint international collaborative effort. We compare our methods with CSRNet, a
deep learning method which developed for highly congested scenes understanding
and performing accurate count estimation as well as presenting high quality
density maps. By taking the advantage of the skip connections between CNN
layers, WHCNets integrate features from low CNN layers to high CNN layers,
thus, the output density maps have both high spatial resolution and detailed
representations of the input images. The experiments showed that our methods
outperformed CSRNet in terms of the evaluation metrics, mean absolute error
(MAE) and the root mean squared error (RMSE) with smaller model sizes. The code
has been deposited on GitHub (\url{https://github.com/hyguozz}).
