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ARS Home » Plains Area » Bushland, Texas » Conservation and Production Research Laboratory » Soil and Water Management Research » Research » Publications at this Location » Publication #364403
Research Project: Precipitation and Irrigation Management to Optimize Profits from Crop Production

Location: Soil and Water Management Research

Title: Crop monitoring from a small unmanned aerial system

Author
item Moorhead, Jerry
item Oshaughnessy, Susan
item RUSH, CHARLES - Texas A&M Agrilife
item MAREK, THOMAS - Texas A&M Agrilife
item Marek, Gary
item HEFLIN, KEVIN - Texas A&M Agrilife
item PORTER, DANA - Texas A&M Agrilife

Submitted to: Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: 7/31/2019
Publication Date: 11/11/2019
Citation: Moorhead, J.E., O'Shaughnessy, S.A., Rush, C.M., Marek, T.H., Marek, G.W., Heflin, K.R., Porter, D.O. 2019. Crop monitoring from a small unmanned aerial system [abstract]. 2019 ASA-SSSA-CSSA Annual Meeting: Embracing the Digital Environment, November 10-13, 2019, San Antonio, Texas. Abstract 85-5.

Interpretive Summary:

Technical Abstract: Monitoring crop growth and health are essential for efficient use of declining water resources. Visual inspection of fields is time consuming and often requires crops to become significantly stressed to be visually observed. By the time crop stress can be seen, growth and yield have already been affected. Non-destructive measurement techniques are available but may not provide adequate spatial coverage. Recent improvements in drones and sensors comprising small unmanned aerial systems (sUAS) have led to greater potential for these instruments in improve crop production and water efficiency. sUAS imagery can be acquired on demand and be used to monitor crop growth and health for stress identification at the beginning of stress occurrence. Early detection can allow for smaller treatment areas at less cost to the producer, which maximizes production and profits. Current, popular crop monitoring metrics include canopy temperature, normalized difference vegetation index (NDVI), and leaf area index (LAI). In this study, imagery from a Micasense Rededge multispectral sensor and Zenmuse XTR thermal sensor were evaluated for potential use in detecting disease and water stress.