For a globally recognized planting breeding organization, manually-recorded
field observation data is crucial for plant breeding decision making. However,
certain phenotypic traits such as plant color, height, kernel counts, etc. can
only be collected during a specific time-window of a crop's growth cycle. Due
to labor-intensive requirements, only a small subset of possible field
observations are recorded each season. To help mitigate this data collection
bottleneck in wheat breeding, we propose a novel deep learning framework to
accurately and efficiently count wheat heads to aid in the gathering of
real-time data for decision making. We call our model WheatNet and show that
our approach is robust and accurate for a wide range of environmental
conditions of the wheat field. WheatNet uses a truncated MobileNetV2 as a
lightweight backbone feature extractor which merges feature maps with different
scales to counter image scale variations. Then, extracted multi-scale features
go to two parallel sub-networks for simultaneous density-based counting and
localization tasks. Our proposed method achieves an MAE and RMSE of 3.85 and
5.19 in our wheat head counting task, respectively, while having significantly
fewer parameters when compared to other state-of-the-art methods. Our
experiments and comparisons with other state-of-the-art methods demonstrate the
superiority and effectiveness of our proposed method.

本文介绍了一种基于深度学习的新型框架 WheatNet，能够快速高效地计算麦田中小麦穗的数量和位置，并在植物育种决策过程中提供实时数据收集的帮助，解决手动记录植物观测数据带来的劳动密集性问题。