Predicting In-Season Corn Grain Yield Using Optical Sensors
In-season sensing can account for field variability and improve nitrogen (N) management; however, opportunities exist for refinement. The purpose of this study was to compare different sensors and vegetation indices (VIs) (normalized difference vegetation index (NDVI); normalized difference red edge...
Main Authors: | Camden Oglesby, Amelia A. A. Fox, Gurbir Singh, Jagmandeep Dhillon |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2022-10-01
|
Series: | Agronomy |
Subjects: | |
Online Access: | https://www.mdpi.com/2073-4395/12/10/2402 |
Similar Items
-
Active-Optical Sensors Using Red NDVI Compared to Red Edge NDVI for Prediction of Corn Grain Yield in North Dakota, U.S.A.
by: Lakesh K. Sharma, et al.
Published: (2015-11-01) -
Irrigated corn grain yield prediction in Florida using active sensors and plant height
by: Diego A. H. de S. Leitão, et al.
Published: (2023-10-01) -
Methodology for prediction of corn yield using remote sensing satellite data in Central Mexico
by: Jesús Soria Ruiz, et al.
Published: (2012-02-01) -
Corn Land Extraction Based on Integrating Optical and SAR Remote Sensing Images
by: Haoran Meng, et al.
Published: (2023-02-01) -
A Geographically Weighted Random Forest Approach to Predict Corn Yield in the US Corn Belt
by: Shahid Nawaz Khan, et al.
Published: (2022-06-01)