Compressive Sensing and Reconstruction of Crop Growth Environmental Information

Abstract

The crop growth environmental information in farmland such as temperature and humidity has a gentle variation amplitude in a certain time or space domain, and is sparse in the discrete cosine transform domain. Based on compression sensing, we construct the approach to crop growth environmental information monitoring. The reconstruction error for crop growth environmental information is validated through the simulation experiment. By exploiting orthogonal matching pursuit method, it is found that the reconstructed environmental information data are consistent with the measured data.