# Wheat Head Counting by Estimating a Density Map with Convolutional Neural Networks

arXiv: https://arxiv.org/abs/2303.10542 · HTML: https://mlsanity.com/paper/2303.10542/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 23 references that are in the map.
- First posted on arXiv 19 Mar 2023.

## Builds on (most cited first, 11 of 11)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Multi-Scale Context Aggregation by Dilated Convolutions](https://mlsanity.com/paper/1511.07122/) (2015)
- [CSRNet: Dilated Convolutional Neural Networks for Understanding the Highly Congested Scenes](https://mlsanity.com/paper/1802.10062/) (2018)
- [Searching for Efficient Multi-Scale Architectures for Dense Image Prediction](https://mlsanity.com/paper/1809.04184/) (2018)
- [Global Wheat Head Detection (GWHD) dataset: a large and diverse dataset of high resolution RGB labelled images to develop and benchmark wheat head detection methods](https://mlsanity.com/paper/2005.02162/) (2020)
- [Counting with Focus for Free](https://mlsanity.com/paper/1903.12206/) (2019)
- [Learning to count with deep object features](https://mlsanity.com/paper/1505.08082/) (2015)
- [Unsupervised Domain Adaptation For Plant Organ Counting](https://mlsanity.com/paper/2009.01081/) (2020)
- [AutoCount: Unsupervised Segmentation and Counting of Organs in Field Images](https://mlsanity.com/paper/2007.09178/) (2020)

## Abstract (arXiv)

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 (WHCNet1, WHCNet2 and WHCNet3) 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 (urlhttps://github.com/hyguozz).