Calibrating Corn Color from Aerial Photographs to Predict Sidedress Nitrogen Need

By P. Scharf, John A. Lory. 2002

140 citations recorded by the source corpus.

Subfields: Ecology, Environmental Engineering

View Paper: https://www.semanticscholar.org/paper/8c23012259148e87fa5b1145d36be744ed50b308

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Ecology

1. R. P. Sripada
2. A. D. Meijer
3. J. G. White
4. R. Heiniger
5. P. Scharf

In Environmental Engineering

1. R. P. Sripada
2. A. D. Meijer
3. J. G. White
4. R. Heiniger
5. P. Scharf

Citations in the corpus

• Chlorophyll meter readings can predict nitrogen need and yield response of corn in the north-central USA (2006, background): “There is evidence that EONR may change little if at all during the planting to V7 period (e.g., Scharf and Lory, 2002 ), and also that large yield response to N can be obtained throughout the vegetative period and at least into the early reproductive period (Scharf et al., 2002; Binder et al., 2000”
• Sensor‐Based Nitrogen Applications Out‐Performed Producer‐Chosen Rates for Corn in On‐Farm Demonstrations (2011, result): “Th is is consistent with other calibration research relating EONR values to spectral measurements of corn (Scharf and Lory, 2002; Sripada et al., 2005; Scharf et al., 2006a; Dellinger et al., 2008), which has generally found an average EONR of 30 to 45 kg N ha–1 when the relative spectral value = 1.”
• Aerial Color Infrared Photography for Determining Early In‐Season Nitrogen Requirements in Corn (2005, methodology): “ Scharf and Lory (2002) had greater success using corn color in the form of Rel G from aerial color images compared with CIR images to predict optimum sidedress N in corn at the V6–V7 stage.”
• Acquisition of NIR-Green-Blue Digital Photographs from Unmanned Aircraft for Crop Monitoring (2010, methodology): “Currently, these methods are being re-assessed for analyzing within-field spatial variability for agricultural precision management, because aerial imagery may be acquired quickly during critical periods of rapid crop growth [2-7].”
• A visible band index for remote sensing leaf chlorophyll content at the canopy scale (2013, background): “The requirement of TGI for high LAI or canopy closure may e eliminated with the use of very-high-spatial-resolution sensors <10-mm pixels), such as digital cameras, because only pixels that re pure vegetation need to be analyzed (Scharf and Lory, 2002).”