Realizing the high-precision monitoring of wheat stripe rust over a large area is of great significance in ensuring the safety of wheat production. Existing studies have mostly focused on the fusion of multi-source data and the construction of key monitoring features to improve the accuracy of disease monitoring, with less consideration for the regional distribution characteristics of the disease. In this study, based on the occurrence and spatial distribution patterns of wheat stripe rust in the experimental area, we constructed a multi-source monitoring feature set, then utilized geographical detectors for feature selection that integrates the spatial-distribution differences of the disease. The research results show that the optimal monitoring feature set selected by the geographical detectors has a higher monitoring accuracy. Based on the Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and Support Vector (SVM) models, the disease monitoring results demonstrate that the monitoring feature set constructed in this study has an overall accuracy in its disease monitoring that is 3.2%, 2.7%, and 4.3% higher, respectively, than that of the ReliefF method, with Kappa coefficient higher by 0.064, 0.044, and 0.087, respectively. Furthermore, the optimal monitoring feature set obtained by the geographical detectors method exhibits a higher stability, and the spatial distribution of wheat stripe rust in the monitoring results generated by the different models demonstrates good consistency. In contrast, the features selected by the ReliefF method exhibit significant spatial-distribution differences in the wheat stripe rust among the different monitoring results, indicating poor stability and consistency. Overall, incorporating information on disease spatial-distribution differences in stripe-rust monitoring can improve the accuracy and stability of disease monitoring, and it can provide data and methodological support for regional stripe-rust detection and accurate preventions.

Grasshoppers can greatly interfere with agriculture and husbandry, and they will breed and grow rapidly in suitable habitats. Therefore, it is necessary to extract the distribution of the grasshopper potential habitat (GPH), analyze the spatial-temporal characteristics of the GPH, and detect the different effects of key environmental factors in the meadow and typical steppe. To achieve the goal, this study took the two steppe types of Xilingol (the Inner Mongolia Autonomous Region of China) as the research object and coupled them with the MaxEnt and multisource remote sensing data to establish a model. First, the environmental factors, including meteorological, vegetation, topographic, and soil factors, that affect the developmental stages of grasshoppers were obtained. Secondly, the GPH associated with meadow and typical steppes from 2018 to 2022 were extracted based on the MaxEnt model. Then, the spatial-temporal characteristics of the GPHs were analyzed. Finally, the effects of the habitat factors in two steppe types were explored. The results demonstrated that the most suitable and moderately suitable areas were distributed mainly in the southern part of the meadow steppe and the eastern and southern parts of the typical steppe. Additionally, most areas in the town of Gaorihan, Honggeergaole, Jirengaole, as well as the border of Wulanhalage and Haoretugaole became more suitable for grasshoppers from 2018 to 2022. This paper also found that the soil temperature in the egg stage, the vegetation type, the soil type, and the precipitation amount in the nymph stage were significant factors both in the meadow and typical steppes. The slope and precipitation in the egg stage played more important roles in the typical steppe, whereas the aspect had a greater contribution to the meadow steppe. These findings can provide a methodical guide for grasshopper control and management and for further ensuring the security of agriculture and husbandry.

The fall armyworm (Spodoptera frugiperda) (J. E. Smith) is a widespread, polyphagous, and highly destructive agricultural pest. Global climate change may facilitate its spread to new suitable areas, thereby increasing threats to host plants. Consequently, predicting the potential suitable distribution for the fall armyworm and its host plants under current and future climate scenarios is crucial for assessing its outbreak risks and formulating control strategies. This study, based on remote sensing assimilation data and plant protection survey data, utilized machine learning methods (RF, CatBoost, XGBoost, LightGBM) to construct potential distribution prediction models for the fall armyworm and its 120 host plants. Hyperparameter methods and stacking ensemble method (SEL) were introduced to optimize the models. The results showed that SEL demonstrated optimal performance in predicting the suitable distribution for the fall armyworm, with an AUC of 0.971 ± 0.012 and a TSS of 0.824 ± 0.047. Additionally, LightGBM and SEL showed optimal performance in predicting the suitable distribution for 47 and 30 host plants, respectively. Overlay analysis suggests that the overlap areas and interaction links between the suitable areas for the fall armyworm and its host plants will generally increase in the future, with the most significant rise under the RCP8.5 climate scenario, indicating that the threat to host plants will further intensify due to climate change. The findings of this study provide data support for planning and implementing global and intercontinental long-term pest management measures aimed at mitigating the impact of the fall armyworm on global food production.