Wheat stripe rust poses a serious threat to the quality and yield of wheat crops. Typically, the occurrence data of wheat stripe rust is characterized by small sample sizes, and the current research on severity identification lacks high-precision methods for small sample data. Additionally, the irregular edges of wheat stripe rust lesions make it challenging to draw samples. In this study, we propose a method for wheat stripe rust severity identification that combines SLIC superpixel segmentation and a random forest algorithm. This method first employs SLIC to segment subregions of wheat stripe rust, automatically constructs and augments a dataset of wheat stripe rust samples based on the segmented patches. Then, a random forest model is used to classify the segmented subregion images, achieving fine-grained extraction of wheat stripe rust lesions. By merging the extracted subregion images and using pixel statistics, the percentage of lesion area is calculated, ultimately enabling the identification of the severity of wheat stripe rust. The results show that our method outperforms unsupervised classification algorithms such as watershed segmentation and K-Means clustering in terms of lesion extraction when using the segmented subregion dataset of wheat stripe rust. Compared to the K-Means segmentation method, the mean squared error is reduced by 1.2815, and compared to the watershed segmentation method, it is reduced by 2.0421. When compared to human visual inspection as the ground truth, the perceptual loss for lesion area extraction is 0.064. This method provides a new approach for the intelligent extraction of wheat stripe rust lesion areas and fading green areas, offering important theoretical reference for the precise prevention and control of wheat stripe rust.

Corn thrips do serious harm to the yield and quality of corn. In this paper, the Shandong Province of China was taken as the study area. Based on the data of the occurrence of corn thrips in Shandong Province, a risk regionalization model was established by using eight indicators under four categories of hazard, sensitivity, vulnerability and the disaster prevention and mitigation capacity of diseases and pests on a monthly time scale. Firstly, the cloud model was introduced to determine the weight of each indicator, and then the risk regionalization of the corn thrips disaster in Shandong Province was carried out using the weighted percentage method, the weighted comprehensive evaluation method and the natural disaster risk index method. Finally, combined with the collected data, the disaster prediction of corn thrip occurrence degree was realized based on multiple linear regression, genetic algorithm optimized back-propagation neural network and genetic algorithm optimized support vector machine methods. The results show that: (1) the risk of Corn thrips disaster is mainly concentrated in the central and western parts of Shandong Province. Heze City is a high-risk area. Liaocheng City, Dezhou City, Jinan City and Weifang City are relatively high-risk areas. (2) By comparing the prediction accuracy of the three models, it was determined that the genetic algorithm optimized support vector machine model has the best effect, with an average accuracy of 79.984%, which is 7.013% and 22.745% higher than that of the multiple linear regression and genetic algorithm optimized back-propagation neural network methods, respectively. The results of this study can provide a scientific basis for fine prevention of corn thrips in Shandong Province.