Bt Research 2025, Vol.16, No.6, 242-250 http://microbescipublisher.com/index.php/bt 246 5.2 Spatiotemporal tracking models for Bt application timing, location, and dosage Sometimes, it is actually not easy to track exactly how many times Bt is sprayed, where it is sprayed, and in what dosage, especially in the context of large-scale planting. Only after the combination of remote sensing and GIS modeling did these problems become clearer. This type of model usually does not draw a conclusion right away. Instead, it first tracks the changes in vegetation index over a period of time and then uses some machine learning algorithms to identify the "traces" left by pesticide spraying. By combining timelines and maps, the model can help people clearly understand the overall trend of pesticide application events. Another situation that needs attention is that the application effect of the pesticide is not the same in all areas. The model can also indicate which areas may need to be re-sprayed. Overall, such a method is more like matching the use of Bt with the actual situation of crops, which can not only save resources but also reduce environmental pressure (Li et al., 2023; Chen et al., 2025). 5.3 Validation mechanisms between remote sensing results and field data Remote sensing technology is indeed powerful, but to claim 100% accuracy, it still depends on whether there is on-site verification. Many people think that as long as the model can produce results, everything is fine. But that's not the case. From field sampling, sensor measurement to error analysis, all these steps have a direct impact on the reliability of the model. Especially when there is atmospheric interference or significant fluctuations in sensors, ground data serves as the "ruler". By using statistical methods to compare remote sensing results with field observation data, the model can be adjusted in reverse to make it increasingly accurate. In the long run, this mechanism of repeated verification is the core of building a dynamic monitoring system. Precisely because of this, more and more agricultural researchers have begun to combine remote sensing and GIS with greater confidence for the scientific management of Bt pesticides (Rahman et al., 2019). 6 Case Studies: Practical Applications of Remote Sensing-GIS Monitoring Systems for Bt Use 6.1 Case study of Bt crop and pest monitoring integration in Indian cotton regions In some major cotton-growing areas of India, a problem that farmers face is how to quickly know when pests will appear and whether to spray pesticides or not. Not all pests are obvious. Sometimes it's too late to see the leaves being gnawed. In recent years, the local area has gradually introduced a combination of remote sensing and GIS to monitor the growth of genetically modified insect-resistant cotton flowers and the distribution of pests and diseases. By combining satellite images with data collected from the ground, the system can mark the pest outbreak areas and the coverage of Bt pesticide application. This approach not only enhances the reaction speed but also plays a significant role in optimizing the use of reagents. Although a certain technical foundation is required in operation, studies have shown that such spatial integration practices can indeed enhance the effectiveness of pest and disease control (Faisal et al., 2020). 6.2 Spatiotemporal data analysis of Bt spraying in China's Huang-Huai area The Bt spraying in the Huanghuai region is not the same every year. The changes in time and dosage are often related to the weather and the density of pests. This is precisely where remote sensing and GIS come in handy. The research team established a complete dataset for spatio-temporal analysis by combining continuous satellite images with field observations. Not only can it be seen where and when Bt was sprayed, but also this information can be linked to the number of pests and the growth condition of crops to find patterns. In some areas, even after spraying, the effect may not be good due to climate or the return of pest sources. The system can identify these candidate areas for "secondary application" (Figure 2). This approach is quite helpful for improving the efficiency of pesticide spraying and reducing the environmental burden. It can also be regarded as a practical implementation of precision agriculture (Faisal et al., 2020; Saranya et al., 2024). 6.3 GIS visualization and ecological response assessment in Bt corn regions of the U.S. Midwest In the Midwestern United States, Bt corn has been grown for many years, but many people still have doubts about its impact on the ecology. At this point, GIS visualization tools become very valuable. At first, these tools were only used to draw distribution maps of corn planting, and later gradually expanded to analyze ecological
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