Automatic extraction of wheat lodging area based on transfer learning method and deeplabv3+ network

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.