Design and implementation of remote sensing image-based crop growth monitoring system

Quick and real-time monitoring of crop growth status based on remote sensing can support the decision-making on precision crop management. Based on growth estimating models in wheat and rice established by the authors’ group, a RS image-based monitoring system was developed based on the Microsoft .NET framework using GDAL and GDI+ as information processing methods and EM algorithm for classifying crop growth indices. This system realized the multiple functions as accessing the RS images with common formats, extracting crop information, inverting growth indices, clustering analysis, generating the thematic map and issuing the information with remote sensing technology. Several functions of the system were tested using the RS images at Fangqiang Farm, Jiangsu Province. The results showed that the system could effectively read general remote sensing images, invert the crop growth indices, classify the crop growth information based on the cluster models, interact with users for generating the thematic map of crop growth status, and issue the RS image information rapidly via internet. The present system has overcome the previous weakness that the ordinary users could not directly participate in the process of RS images analysis, and can help to monitor the crop growth condition and provide decision support for precision crop management at regional scale.