关键词: 小麦渍水胁迫, 渍害调控, 无人机, 多光谱图像, 深度学习, 视觉几何组模型

Abstract:

[Objective] In recent years, global warming and increasingly frequent extreme precipitation events have intensified waterlogging stress in agriculture, establishing it as a major abiotic threat to food security.To achieve scientific and precise control of wheat waterlogging disaster and increase yield, a rapid, accurate, and non-destructive method for identifying wheat waterlogging stress and regulation effect was proposed. [Methods] Field trials of control (CK), waterlogging stress, silicon-fertilizer regulation, and amino-acid regulation were conducted during the jointing-booting and flowering-filling stages of wheat. During the experiments, unmanned aerial vehicle (UAV) multispectral images of the wheat canopy were collected using DJI P4 Multispectral. The multispectral image had five bands, namely blue (B), green (G), red (R), red-edge (RE) and near-infrared (NIR), with reflectivity values of (450±16), (560±16), (650±16), (730±16), and (840±26) nm, respectively. At the end of the experiments, grain yield was measured for each treatment and used to evaluate the effectiveness of the regulation measures. The multispectral images were processed, which included radiometric correction, geometric correction, image alignment, segmentation, etc. Then a dataset was established that includes four categories: CK, waterlogging stress, silicon fertilizer regulation, and amino acid regulation. The stratified sampling was used for dataset partitioning. According to the ratio of 7:1.5:1.5, all original multispectral images of each category were randomly divided into the training, validation and testing set, respectively. Then, the original images were segmented into data blocks of 48×48 pixel size for model training and testing. A wheat waterlogging stress and regulation effect recognition model using visual geometry group (VGG) 21 was established by adding convolutional layers, adjusting the convolutional layers of convolutional block, replacing activation functions, and optimizing model hyperparameters based on VGG19. The VGG21 model included 4 convolutional blocks and the convolutional layers were 3, 3, 6, and 6, respectively. To verify the performance of the VGG21 model, the accuracy, precision, recall and F₁-Score of the ResNet50, Swin-Transformer, VGG19, VGG21, and VGG23 models were compared under the same conditions, and the confusion matrix and Grad-CAM (Gradient-Weighted Class Activation Mapping) were employed to evaluate the CK, waterlogging stress, and silicon fertilizer regulation model. [Results and Discussions] The performance comparison results showed that the VGG model performed better on multispectral image data recognition than the ResNet50 and Swin-Transformer model, and the VGG21 model demonstrated the most favorable overall performance. The comprehensive analysis of model recognition performance showed that the accuracy of the VGG21 model was more than 91% for silicon fertilizer regulation and waterlogging stress. And the precision, recall, and F₁-Score for silicon fertilizer regulation samples reached 96.42%, 89.53%, and 92.85%, respectively. When the silicon fertilizer regulation and stress recognition model was used for amino acid regulation sample identification, the accuracy, precision, recall, F₁-Score were comparable to the amino acid regulation and stress recognition model. A recognition model for CK, waterlogging stress, and silicon fertilizer regulation was constructed using VGG21, and the precision, recall, and F₁-Score for silicon fertilizer regulated samples achieved 95.13%, 88.71%, and 91.80%, respectively. The confusion matrix of the CK, waterlogging stress, and silicon fertilizer regulation model indicated that there was a high proportion of misclassifications between CK and waterlogging stress samples, resulting in a low accuracy of model, which only reached 77.45%. The heatmap analysis results of the CK, waterlogging stress, and silicon fertilizer regulation model indicated that after passing through the fourth convolutional block, the concentrated regions of the output features for misclassified CK and waterlogging stress samples became more dispersed, and even disappear. The CK and waterlogging stress samples with unfocused region heatmap accounted for a large proportion in the dataset, which led to poor recognition of these two types of samples by the regulation model. The comparison between recognition results of VGG21 model and the yield of regulation experiment showed that the silicon fertilizer had a better regulation effect, and the wheat regulated by silicon fertilizer had significant spectral and image features with high recognition accuracy. [Conclusions] The results demonstrated that UAV multispectral imaging technology and VGG21 model were feasible and useful for recognizing waterlogging stress and the regulatory effect of wheat. This technique could provide theory and technical support for integrated waterlogging management of wheat and for refined, intelligent wheat production management. However, the quantitative relationship between the identification results of regulation effect and yield was still unclear. In future research, it will be necessary to quantify the increased yield of regulation measures, and then use statistical methods to quantify the quantitative relationship between the yield-increasing regulation effect and identification indicators of recognition model.

Key words: wheat waterlogging stress, regulation and control of waterlogging, unmanned aerial vehicle, multispectral images, deep learning, visual geometry group model