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The offshore wind turbine tower, which has been in the marine corrosive environment for a long time, often buckles and collapses under the earthquake records. In order to study the influence of marine corrosion and CFRP reinforcement on the seismic performance of wind turbine tower structures, the horizontal displacement, horizontal acceleration and acceleration change rate of wind turbine towers are studied through numerical simulation and shaking table tests. The results show that the influence of earthquake type on the dynamic response of the wind turbine tower is different. The response values of acceleration and displacement under far-field earthquakes are larger than those of other earthquake types. The increase in PGA has a greater impact on the structural response range in the near-field earthquake. Corrosion defects not only increase the sensitivity of the wind turbine tower structure to seismic response but also have different effects on the location and development of structural plastic hinges. For the structure without corrosion defects, the plastic hinge appears at the connection between the tower and the foundation, while in the corrosion structure, the plastic hinge appears in the corrosion area. Corrosion defects increase the nonlinear development of structures, especially under far-field earthquakes. CFRP reinforcement can effectively reduce the displacement effect of the top of the structure and enhance the seismic performance of the corroded wind turbine tower.

Fusarium head blight (FHB), one of the most important diseases of wheat, mainly occurs in the ear. Given that the severity of the disease cannot be accurately identified, the cost of pesticide application increases every year, and the agricultural ecological environment is also polluted. In this study, a neural network (NN) method was proposed based on the red-green-blue (RGB) image to segment wheat ear and disease spot in the field environment, and then to determine the disease grade. Firstly, a segmentation dataset of single wheat ear was constructed to provide a benchmark for the segmentation of the wheat ear. Secondly, a segmentation model of single wheat ear based on the fully convolutional network (FCN) was established to effectively realize the segmentation of the wheat ear in the field environment. An FHB segmentation algorithm was proposed based on a pulse-coupled neural network (PCNN) with K-means clustering of the improved artificial bee colony (IABC) to segment the diseased spot of wheat ear by automatic optimization of PCNN parameters. Finally, the disease grade was calculated using the ratio of the disease spot to the whole wheat ear. The experimental results show that: (1) the accuracy of the segmentation model for single wheat ear constructed in this study is 0.981. The segmentation time is less than 1 s, indicating that the model can quickly and accurately segment wheat ear in the field environment; (2) the segmentation method of the disease spot performed under each evaluation indicator is improved compared with the traditional segmentation methods, and the accuracy is 0.925 in the disease severity identification. These research results can provide important reference value for grading wheat FHB in the field environment, which also can be beneficial for real-time monitoring of other crops’ diseases under near-Earth remote sensing.