Abstract To provide technical support for lodging-resistant wheat breeding, post-disaster assessment, and analysis of factors affecting lodging, the dynamic and accurate extraction of lodging area is particularly important. Most existing methods of monitoring wheat lodging aim to extract the lodging area at a single growth stage, rendering the dynamic monitoring of wheat lodging highly difficult. Thus, this study was aimed at developing a method of estimating wheat lodging at multiple growth stages. For this purpose, nitrogen fertilizers were utilized at different levels to induce different lodging conditions in wheat fields. Unmanned aerial vehicles (UAVs) were used to obtain Red, Green and Blue (RGB) and multispectral images of the field at different wheat-growth stages. Based on these two types of images, a new method combining transfer learning and the DeepLabv3+ network is proposed herein to extract lodging areas at various wheat-growth stages. The proposed method was compared with the commonly used UNet for the extraction of the lodging area. The results show that the proposed method and UNet achieved dice coefficients of 0.82 and 0.75 (early flowering), 0.88 and 0.80 (late flowering), 0.89 and 0.86 (filling stage), 0.90 and 0.87 (early maturity), and 0.90 and 0.88 (late maturity), respectively, using RGB images; further, the proposed method and UNet achieved dice coefficients of 0.91 and 0.51 (early flowering), 0.89 and 0.28 (late flowering), 0.91 and 0.82 (filling stage), 0.93 and 0.76 (early maturity), and 0.92 and 0.56 (late maturity), respectively, at different wheat-growth stages using multispectral image data. Thus, the proposed method can be used to predict lodging at multiple wheat-growth stages, and it outperforms UNet. An effective tool for dynamic monitoring of wheat lodging has been proposed herein.

中文翻译：

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基于迁移学习方法和deeplabv3+网络的小麦栖息地自动提取

摘要 为了为小麦抗倒伏育种、灾后评估、倒伏影响因素分析提供技术支持，动态准确提取倒伏区显得尤为重要。现有的小麦倒伏监测方法大多是提取单一生长阶段的倒伏面积，使得小麦倒伏的动态监测难度很大。因此，本研究旨在开发一种估算小麦在多个生长阶段倒伏的方法。为此，不同水平的氮肥施用以在小麦田中诱导不同的倒伏条件。无人机 (UAV) 用于获取不同小麦生长阶段的田地的红、绿、蓝 (RGB) 和多光谱图像。基于这两种类型的图像，本文提出了一种结合迁移学习和 DeepLabv3+ 网络的新方法来提取小麦不同生长阶段的栖息地。将所提出的方法与常用的UNet进行比较，用于提取住宿区。结果表明，所提出的方法和 UNet 实现了 0.82 和 0.75（早花）、0.88 和 0.80（晚花）、0.89 和 0.86（灌浆期）、0.90 和 0.87（早熟）以及 0.90 和 0.88（早熟）的骰子系数。晚熟），分别使用RGB图像；此外，所提出的方法和 UNet 实现了 0.91 和 0.51（早花）、0.89 和 0.28（晚花）、0.91 和 0.82（灌浆期）、0.93 和 0.76（早熟）以及 0.92 和 0.56（晚熟）的骰子系数。 )，分别在不同的小麦生长阶段使用多光谱图像数据。因此，所提出的方法可用于预测多个小麦生长阶段的倒伏，并且其性能优于 UNet。本文提出了一种有效的小麦倒伏动态监测工具。