Abstract: Oilseed rape has been one of the largest cash crops in the national security of edible oil supply. The yields of oilseed rape can be improved under large-scale production in recent years. The lodging has restricted the high yield, stability, and quality of oilseed crops. Real-time monitoring and evaluation of lodging types are of great significance to the pre-production and variety selection. Unmanned aerial vehicles (UAVs) combined with deep learning can be expected to identify the lodging with the rapid development of artificial intelligence. Among them, the image classification is superior to the oilseed rape lodging, due to the small model size and high accuracy. In this study, an oilseed rape lodging classification was proposed to incorporate multi-scale features and attention mechanisms. The lodging level of oilseed rape was determined at the green and yellow maturity stage. Firstly, an image classification network (NGnet) was designed to identify the lodging of oilseed rape during the pod period. An improved Ghost Bottle Neck structure was then used instead of the traditional convolution. The number of network parameters and computations was reduced greatly because the redundant operation was reduced to maintain the feature expressiveness; Normalization-based attention (NAM) was integrated with the weight factor to represent the important features. The new network was then focused on the interest lodging areas at different lodging levels, leading to suppression of the irrelevant information; A multi-scale fusion strategy was used to fuse attention features at different scales. Multi-scale feature representations were obtained to appropriately weight the fusion of features at different scales, according to the weights of the attention mechanism; The UAV high-altitude remote sensing orthophoto was utilized to construct the rape lodging dataset (RLD). The data was collected from the Yangluo base of the Oilseed Research Institute of the Chinese Academy of Agricultural Sciences in Wuhan City, Hubei Province, China, and the oilseed rape planting area of the Jingzhou Academy of Agricultural Sciences in Shazhou City, Jingzhou City, Hubei Province, China; The dataset consisted of 5 789 images with a resolution of 3 × 255 × 255, of which 4 648 were in the training set, and 1 141 were in the testing set; Lastly, the NGnet network model on the RLD was achieved in recognition accuracy of 85.10%, which was 15.6, 11.92, 7.01, 6.22, 6.08, and 2.37 percentage points higher than that of T2T-VIT, SwinTransformerV2, MobileNetV3, Res2Net, RepVGG, and RepLKNet, respectively. The recognition of the improved model was further evaluated in the task of oilseed rape lodging. The experimental fields of oilseed rape were selected as the validation set at Yangluo and Jingzhou base of the Oilseed Research Institute of the Chinese Academy of Agricultural Sciences in 2021. The average accuracy of the model reached 86.36% and 85.71% in the Yangluo and Jingzhou bases. The improved model was achieved in the high accuracy, small model size, and generalization performance, which fully met the classification of oilseed rape lodging degree in the oilseed rape breeding. In addition, the lodging situation of different plots was also explored in the same field. The lodging resistance of different varieties was better distinguished to visualize. The finding can also provide a strong reference for the UAV RGB images to identify the lodging disaster of rapeseed in the selective breeding of good varieties.