Browsing by Author "Miller, Jarrod O."
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Item Assessing relationships of cover crop biomass and nitrogen content to multispectral imagery(Agronomy Journal, 2024-02-29) Miller, Jarrod O.; Shober, Amy L.; Taraila, JamieCover crops provide valuable roles in sustainable agriculture, provided they produce enough biomass. To accurately measure their services to field management, spatial estimates would be useful to producers. This study used multispectral drone imagery to produce maps of normalized difference vegetation index (NDVI), normalized difference red edge index (NDRE), and a digital surface model (DSM) of cover crop plots on sandy, Mid-Atlantic soils. Cover crops included cereal rye (Secale cereale), mixtures of rye and crimson clover (Trifolium incarnatum), and mixtures of rye and hairy vetch (Vicia villosa). Their biomass was sampled in the spring of 2019, 2020, and 2021, dried, weighed, and analyzed for total nitrogen (N) content. Measurements of NDVI became saturated (i.e., reached a linear plateau) at 3.86 Mg biomass ha−1, NDRE at 5.72 Mg biomass ha−1, and the DSM at 5.11 Mg biomass ha−1. The measured N content became saturated at 80.9, 139.1, and 75 kg N ha−1 for NDVI, NDRE, and the DSM, respectively. Based on log transformations, NDVI was a stronger predictor of biomass and N, but not C:N. The NDRE was important for biomass, N, and C:N, while the DSM interactions with cover crop species helped predict both the N content and C:N of cover crop tissues. Accumulated growing degree days was important as an individual variable for biomass and N and as an interaction with cover crop species. Abbreviations DSM digital surface model GDD growing degree days NDRE normalized difference red edge index NDVI normalized difference vegetation indexItem Monitoring winter wheat growth at different heights using aerial imagery(Agronomy Journal, 2021-02-09) Miller, Jarrod O.; Adkins, JamesDrones (unmanned aerial vehicles) provide another system to mount sensors for measuring plant characteristics. For winter wheat (Triticum aestivum) this can include evaluating stands and making nitrogen (N) recommendations. Timing these flights and adequate camera resolution (based on flying height), must be known before applying tasks. This study observed six winter wheat planting populations (222, 297, 371, 445, 494, and 544 seeds m–2) at three different heights above ground level (30, 60, and 120 m) over three growing seasons. Plant populations could be separated at all growth stages and heights flown but were easier to separate right after emergence (GS11). In the spring, additional tillering caused the higher populations (371–544 seeds m–2) to have similar normalized difference vegetative index (NDVI), much like the final yields. Comparing changes in NDVI between flights was also successful in separating high and low planting populations, with inverse relationships in the fall and spring. A random forest classification of tiller counts by NDVI measurements ranked change in NDVI between stages as the most important compared to single flights. As separation and classification was successful at the lowest camera resolution (120 m), it can be possible for scouts to collect whole field imagery for analyses prior to deciding on split N applications.Item Post-harvest drone flights to measure weed growth and yield associations(Agricultural & Environmental Letters, 2022-06-14) Miller, Jarrod O.; Shober, Amy L.; VanGessel, Mark J.Drone flights are often only performed during the growing season, with no data collected once harvest has been completed, although they could be used to measure winter annual weed growth. Using a drone mounted with a multispectral sensor, we flew small plot corn (Zea mays L.) fertility, cover crop, and population studies at black layer and 0–14 d after harvest (DAH). Yields had positive correlations to normalized difference vegetation index (NDVI) at black layer but often had negative correlations to corn yields 0–14 DAH. After harvest, NDVI could be associated with weed growth, and negative correlations to yield could point to reduced corn canopy allowing light to reach late-season weeds. In fertility studies, excess nitrogen appears to increase weed biomass after harvest, which can be easily identified through drone imagery. Flights should be performed after corn harvest as weed growth may provide additional insight into management decisions. Core Ideas: - Corn yields can be correlated to post-harvest weed biomass by using NDVI. - Drone flights efficiently mapped weeds and made correlations to yield and management. - Fall weed control can be prioritized using drone mapping. Abbreviations: DAH days after harvest LAI leaf area index NDVI normalized difference vegetation indexItem Sensor-based measurements of NDVI in small grain and corn fields by tractor, drone, and satellite platforms(Crop and Environment, 2024-02-01) Miller, Jarrod O.; Mondal, Pinki; Sarupria, MananThe use of sensors for variable rate nitrogen (VRN) applications is transitioning from equipment-based to drone and satellite technologies. However, regional algorithms, initially designed for proximal active sensors, require evaluation for compatibility with remotely sensed reflectance and N-rate predictions. This study observed normalized difference vegetation index (NDVI) data from six small grain and two corn fields over three years. We employed three platforms: tractor-mounted active sensors (T-NDVI), passive multispectral drone (D-NDVI), and satellite (S-NDVI) sensors. Averaged NDVI values were extracted from the as-applied equipment polygons. Correlations between NDVI values from the three platforms were positive and strong, with D-NDVI consistently recording the highest values, particularly in areas with lower plant biomass. This was attributed to D-NDVI's lower soil reflectance and its ability to measure the entire biomass within equipment polygons. For small grains, sensors spaced on equipment booms might not capture accurate biomass in poor-growing and low NDVI regions. Regarding VRN, S-NDVI and D-NDVI occasionally aligned with T-NDVI recommendations but often suggested half the active sensor rate. Final yields showed some correlation with landscape variables, irrespective of N application. This finding suggests the potential use of drone or satellite imagery to provide multiple NDVI maps before application, incorporating expected landscape responses and thereby enhancing VRN effectiveness.