A locust remote sensing monitoring system based on dynamic model library

Locusts (insect pest) often cause serious loss to crops world-wide. Monitoring the occurrence of locust plagues through remote sensing technology has become a common method. However, with the abundance of remote sensing data resources, the corresponding locust remote sensing monitoring models are also diversified, and the selection of model parameters are more complex. How to manage these models scientifically for better reuse is an important problem that needs to be addressed. In order to solve these problems, we designed and implemented a locust remote sensing monitoring system (LRSMS) based on dynamic model library (DML), which is composed of model file library, model dictionary library, and model library management module. In terms of data to match the remote sensing monitoring models, there is more integration of locust habitat data in LRSMS, such as plantation, soil, which are closely related to the locust plague breeding environment. In addition, to improve the scalability of the system, a data interface is designed for the interaction between the DML and the Structured Query Language (SQL) server database. At the end of the paper, two application cases from China are given, and the results show that the LRSMS can well integrate multi-source data sets and carry out locust plague monitoring research through the dynamic model library. The approaches and methodologies presented in this paper can serve as a reference for those who are interested in remote sensing monitoring applications.

Automated summary

Locusts often cause significant damage to crops worldwide. Remote sensing technology is commonly used to monitor locust plagues. Implementing a locust remote sensing monitoring system based on a dynamic model library has shown to effectively integrate multiple data sources and conduct research on locust plague monitoring.