Monitoring winter wheat growth at different heights using aerial imagery

Abstract

Core ideas (3-5 impact statements, 85 char max for each)  Drones flown at 120m can detect differences in winter wheat planting populations over the growing season  Change in NDVI between stages can help find lower populations in the field.  Aerial imagery can be used to make decisions on re-seeding or N applications.

Issue Information

“…However, Boyd et al (2009) reported significantly more early fall coverage for cereal rye in one of two seasons when seeding rates were increased from 80 to 160 lb/ac. Miller and Adkins (2021) also measured greater NDVI with higher seeding rates of winter wheat throughout the growing season.…”

“…Based on Figure 3, fall cover crop growth appeared to occur more rapidly as NDVI values were above 0.8 before 1800 GDD, double the NDVI at 1800 GDD for the other two seasons. Miller and Adkins (2021) becoming established in new areas and preventing seed production. Several management practices are available (for example mechanical control, crop diversity and competition, herbicides, etc.…”

Drone Imagery Provides Valuable Insight on In‐Season Cover Crop Growth

“…Based on Figure 3, fall cover crop growth appeared to occur more rapidly as NDVI values were above 0.8 before 1800 GDD, double the NDVI at 1800 GDD for the other two seasons. Miller and Adkins (2021) also reported high NDVI (0.67 at GS24) for 2019–2020 winter wheat in southern Delaware while NDVI values recorded during previous seasons (2017–2019) were almost half the 2019 values (i.e., 0.32–0.35 at GS24). Increased fall growth likely explains greater overall biomass in the 2019 season compared with the other years, and drone imagery helps indicate when this growth may have occurred.…”

“…(2009) reported significantly more early fall coverage for cereal rye in one of two seasons when seeding rates were increased from 80 to 160 lb/ac. Miller and Adkins (2021) also measured greater NDVI with higher seeding rates of winter wheat throughout the growing season.…”

Estimation of wheat tiller density using remote sensing data and machine learning methods

“…Developments in high-resolution satellite remote sensing are helping this situation: in particular, spectral feature models can be used to estimate the wheat tiller density on a pixel-by-pixel basis. Therefore, the actual number of tillers of winter wheat can be estimated by using high-resolution satellite images acquired in late fall and early winter based on a small number of measured tillers ( Miller and Adkins, 2021 ); maps showing the spatial distribution of the tiller density can then be obtained.…”