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Figure 2. Overflight conducted by manned aircraft on April 11th, 2018. After data was processed, normalized difference vegetation index (NDVI) maps generated for rice (a.) and corn (b.) were used to select sub-areas within each field for UAS and ground data surveying.

Texas A&M advances crop observation and yield prediction through UAS and Satellite data comparison

NASA Harvest partners at Texas A&M are working to evaluate the precision and suitability of rotorcraft-type small unmanned aircraft system (UAS), fixed-wing aircraft (Cessna), and satellite (accessed through the GEOGLAM and Harvest networks) for observing crop stresses and predicting yield of rice (and corn) via integration into a crop simulation model.

Images collected with the manned airplane and small UAS were processed by AgPixel and Texas A&M University – Corpus Christi. Geospatial products were co-registered. Ground data collected were also geo-tagged and incorporated as another layer of information. The team is currently working on finishing processing of UAS and manned aircraft imagery, and extracting crop features to be validated against ground data. Ultimately, remotely acquired crop data will be imported into the APEX simulation model for integration of the different data sources.

News Date
Sep 24, 2018