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

• URL: http://arxiv.org/abs/2303.10542v1
• Date: Sun, 19 Mar 2023 02:45:53 GMT
• Title: Wheat Head Counting by Estimating a Density Map with Convolutional Neural Networks
• Authors: Hongyu Guo
• Abstract summary: Wheat is one of the most significant crop species with an annual worldwide grain production of 700 million tonnes. In this study, we propose three wheat head counting networks to accurately estimate the wheat head count from an individual 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.
• Score: 15.95772332799123
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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}).

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