Precise and frequent monitoring of agricultural health and productivity is critical for food security and economic sustainability. When remote sensing is used as a tool to monitor agriculture, the analytics must be reliable and accurate. Deep learning technology has provided highly accurate solutions to geospatial problems for many years. Its use in agricultural applications, however, is relatively new and continues to evolve as more research is conducted.
Download this white paper which:
- Addresses the question, “What types of agricultural remote sensing problems would be best solved using deep learning?”
- Describes how deep learning is superior to traditional machine learning methods for finding spatial patterns in land-use and agriculture regions, at the expense of significantly more training data.
- Explores common applications where deep learning is used in agriculture, the focus being on remote observations from airborne and satellite images as opposed to leaf- or fruit-level observations.