Share Email Print
cover

Proceedings Paper • new

Experimental approach to detect water stress in ornamental plants using sUAS-imagery
Author(s): Ana I. de Castro; Joe Mari Maja; Jim Owen; James Robbins; Jose M. Peña
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

Efficiency in irrigation management is crucial to optimize water use in agriculture. A good irrigation strategy requires accurate and reliable measurements of crop water status that provide dynamic data and timely spatial information. However, this is not feasible with time-consuming manual measurements, which are also prone to cumulative errors due to subjective estimations. Ornamental horticulture crops offer challenges for applying small unmanned aircraft systems (sUAS) technology due to the relatively small area of production and its diversity of plant species. sUAS can operate on demand at low flight height and to carry a wide range of sensors allows capturing the variation of plant traits over time, making it a timely alternative to ground-based data collection in nursery systems. This research evaluated the potential of sUAS-based images to estimate crop water status under three different irrigation regimes. sUAS-imagery of experimental plots was acquired in August 2017 using several multispectral sensors. Container-grown ornamental plants used in the study were Cornus, Hydrangea, Spiraea, Buddleia and Physocarpus. An algorithm based on the object-based image analysis (OBIA) paradigm was applied to retrieve spectral information from each individual plant. Preliminary one-way analysis of variance (ANOVA) identified water stressed and non-stressed plants from data of each study sensor, although spectral separation was higher when information from the sensors was combined. Our results revealed the potential of the sUAS to monitor water status in container-grown ornamental plants, although further analysis is needed to explore vegetation indices and data analysis algorithms.

Paper Details

Date Published: 21 May 2018
PDF: 11 pages
Proc. SPIE 10664, Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping III, 106640N (21 May 2018); doi: 10.1117/12.2304739
Show Author Affiliations
Ana I. de Castro, Institute for Sustainable Architecture, CSIC (Spain)
Joe Mari Maja, Clemson Univ. (United States)
Jim Owen, Virginia Polytechnic Institute and State Univ. (United States)
James Robbins, Univ. of Arkansas (United States)
Jose M. Peña, Institute of Agricultural Sciences, CSIC (Spain)


Published in SPIE Proceedings Vol. 10664:
Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping III
J. Alex Thomasson; Mac McKee; Robert J. Moorhead, Editor(s)

© SPIE. Terms of Use
Back to Top