Abstract

Alfalfa canopy structure reveals useful information for managing this forage crop, but manual measurements are impractical at field-scale. Photogrammetry processing with images from Unmanned Aerial Vehicles (UAVs) can create a field-wide three-dimensional model of the crop canopy. The goal of this study was to determine the appropriate flight parameters for the UAV that would enable reliable generation of canopy models at all stages of alfalfa growth. Flights were conducted over two separate fields on four different dates using three different flight parameters. This provided a total of 24 flights. The flight parameters considered were the following: 30 m altitude with 90° camera gimbal angle, 50 m altitude with 90° camera gimbal angle, and 50 m altitude with 75° camera gimbal angle. A total of 32 three-dimensional canopy models were created using photogrammetry. Images from each of the 24 flights were used to create 24 separate models and images from multiple flights were combined to create an additional eight models. The models were analyzed based on Model Ground Sampling Distance (GSD), Model Root Mean Square Error (RMSE), and camera calibration difference. Of the 32 attempted models, 30 or 94% were judged acceptable. The models were then used to estimate alfalfa yield and the best yield estimates occurred with flights at a 50 m altitude with a 75° camera gimbal angle; therefore, these flight parameters are suggested for the most consistent results.

Document Type

Article

Publication Date

6-25-2021

Notes/Citation Information

Published in Remote Sensing, v. 13, issue 13, 2487.

© 2021 by the authors. Licensee MDPI, Basel, Switzerland.

This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

Digital Object Identifier (DOI)

https://doi.org/10.3390/rs13132487

Funding Information

This work is supported by the Alfalfa and Forage Research Program grant no. 2016-70005-25648 /project accession no. 1010223 from the USDA National Institute of Food and Agriculture.

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