Use of UAV for support of intensive agricultural management decisions: from science to commercial applications

Even in the nascent stage of unmanned aerial vehicles, it was projected that UAV technologies would closely integrate into agriculture activities at a fast pace and become a ubiquitous and low-cost tool for farming operations. Nevertheless, several years later, it is widely recognized that available UAV technology has not yet integrated into agriculture as expected despite multiple UAV platform offerings. In this paper, we discuss several concepts related to the commercial integration of UAV technology for agriculture, along with expected or existing solutions that can further its development and acceptance, especially in the US. Examples from the AggieAir UAV Research Group, at Utah State University, are included to provide a better understanding of the presented concepts.

[1]  S. Running,et al.  A review of remote sensing based actual evapotranspiration estimation , 2016 .

[2]  Mac McKee,et al.  Assessment of optimal irrigation water allocation for pressurized irrigation system using water balance approach, learning machines, and remotely sensed data , 2015 .

[3]  Austin M. Jensen,et al.  Use of high-resolution multispectral imagery from an unmanned aerial vehicle in precision agriculture , 2013, 2013 IEEE International Geoscience and Remote Sensing Symposium - IGARSS.

[4]  L. S. Pereira,et al.  Crop evapotranspiration : guidelines for computing crop water requirements , 1998 .

[5]  D. Reisig,et al.  Aggregation and Association of NDVI, Boll Injury, and Stink Bugs in North Carolina Cotton , 2015, Journal of insect science.

[6]  So Ra Ahn,et al.  Estimation of spatial evapotranspiration using Terra MODIS satellite image and SEBAL model in mixed forest and rice paddy area , 2016 .

[7]  Mac McKee,et al.  Estimating chlorophyll with thermal and broadband multispectral high resolution imagery from an unmanned aerial system using relevance vector machines for precision agriculture , 2015, Int. J. Appl. Earth Obs. Geoinformation.

[8]  W. Kustas,et al.  Utility of an Automated Thermal-Based Approach for Monitoring Evapotranspiration , 2015, Acta Geophysica.

[9]  Dawei Han,et al.  Estimation of land surface temperature from atmospherically corrected LANDSAT TM image using 6S and NCEP global reanalysis product , 2014, Environmental Earth Sciences.

[10]  G. Senay,et al.  Evaluating Landsat 8 evapotranspiration for water use mapping in the Colorado River Basin , 2015 .

[11]  Austin Jensen,et al.  Development of unmanned aerial systems for use in precision agriculture: The AggieAir experience , 2015, 2015 IEEE Conference on Technologies for Sustainability (SusTech).

[12]  Wim G.M. Bastiaanssen,et al.  Spatial evapotranspiration, rainfall and land use data in water accounting - Part 1: Review of the accuracy of the remote sensing data , 2014 .

[13]  Martha C. Anderson,et al.  A Two-Source Time-Integrated Model for Estimating Surface Fluxes Using Thermal Infrared Remote Sensing , 1997 .

[14]  James R. Freemantle,et al.  A comparison of NDVI and MTVI2 for estimating LAI using CHRIS imagery: a case study in wheat , 2008 .

[15]  Richard G. Allen,et al.  Satellite-Based Energy Balance for Mapping Evapotranspiration with Internalized Calibration (METRIC)—Model , 2007 .

[16]  James L. Wright,et al.  Satellite-Based Energy Balance for Mapping Evapotranspiration with Internalized Calibration (METRIC)—Applications , 2007 .

[17]  A. Holtslag,et al.  A remote sensing surface energy balance algorithm for land (SEBAL)-1. Formulation , 1998 .

[18]  Yangquan Chen,et al.  Calibrating thermal imagery from an unmanned aerial system - AggieAir , 2013, 2013 IEEE International Geoscience and Remote Sensing Symposium - IGARSS.

[19]  Steve George FAA unmanned aircraft systems (UAS) - Overview: UAS and the proposed small UAS rule , 2015, 2015 Integrated Communication, Navigation and Surveillance Conference (ICNS).

[20]  Pablo J. Zarco-Tejada,et al.  Hyperspectral indices and model simulation for chlorophyll estimation in open-canopy tree crops , 2004 .

[21]  Olivier Hagolle,et al.  A Software Tool for Atmospheric Correction and Surface Temperature Estimation of Landsat Infrared Thermal Data , 2016, Remote. Sens..

[22]  Leila Hassan-Esfahani,et al.  High Resolution Multi-Spectral Imagery and Learning Machines in Precision Irrigation Water Management , 2015 .

[23]  Hongliang Fang,et al.  Atmospheric correction of Landsat ETM+ land surface imagery. I. Methods , 2001, IEEE Trans. Geosci. Remote. Sens..

[24]  Austin M. Jensen,et al.  Assessment of Surface Soil Moisture Using High-Resolution Multi-Spectral Imagery and Artificial Neural Networks , 2015, Remote. Sens..

[25]  Raj S. Chhikara,et al.  Field size distributions for selected agricultural crops in the United States and Canada , 1986 .

[26]  D. C. Kincaid,et al.  A VARIABLE FLOW RATE SPRINKLER FOR SITE-SPECIFIC IRRIGATION MANAGEMENT , 2004 .

[27]  S. Jones,et al.  A linear physically-based model for remote sensing of soil moisture using short wave infrared bands , 2015 .

[28]  N. Nguyen,et al.  Aerial spraying of wheat: A comparison of conventional low volume with ultra‐low volume spraying , 1984 .

[29]  John R. Miller,et al.  Vegetation stress detection through chlorophyll a + b estimation and fluorescence effects on hyperspectral imagery. , 2002, Journal of environmental quality.

[30]  William P. Kustas,et al.  Mapping Evapotranspiration and Drought at local to Continental Scales using Thermal Remote Sensing , 2008, IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium.

[31]  T. Carlson An Overview of the “Triangle Method” for Estimating Surface Evapotranspiration and Soil Moisture from Satellite Imagery , 2007, Sensors (Basel, Switzerland).

[32]  G. Green Rural Jobs: Making aL iving in the Countryside , 2012 .

[33]  John R. Miller,et al.  Assessing vineyard condition with hyperspectral indices: Leaf and canopy reflectance simulation in a row-structured discontinuous canopy , 2005 .

[34]  Austin Jensen,et al.  Spatial Root Zone Soil Water Content Estimation in Agricultural Lands Using Bayesian‐Based Artificial Neural Networks and High‐ Resolution Visual, NIR, and Thermal Imagery , 2017 .