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.
翻译:对于一个全球公认的种植育种组织来说,人工记录的实地观察数据对于植物育种的决策至关重要。然而,某些植物特征,如植物颜色、高度、内核计数等,只能在作物生长周期的特定时空窗口中收集。由于劳动密集型的要求,每个季节只记录少量可能的实地观测。为了帮助减轻这种数据收集在小麦育种方面的瓶颈,我们提议了一个新的深层次学习框架,以便准确和有效地计算小麦头头目,以协助收集实时决策数据。我们称为惠特网的模型,表明我们的方法对小麦田广泛的环境条件来说是稳健和准确的。惠特网使用特速的移动网络2作为轻质骨干特征提取器,将地貌地图与不同尺度合并,以抵消图像规模的变化。然后,为同时进行密度计和本地化任务,我们拟议的方法在小麦头任务中取得了3.85和5.19的MAE和RME。同时,我们提出的方法在进行其他优势性实验时,分别以远小麦头任务中以远小麦标的状态和最高性方法展示了我们的其他方法。