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ARS Home » Pacific West Area » Maricopa, Arizona » U.S. Arid Land Agricultural Research Center » Plant Physiology and Genetics Research » Research » Publications at this Location » Publication #345719

Research Project: Genetic Improvement and Phenotyping of Cotton, Bioenergy and Other Industrial Crops

Location: Plant Physiology and Genetics Research

Title: Deploying a proximal sensing cart to identify drought-adaptive traits in upland cotton for high-throughput phenotyping

Author
item Thompson, Alison
item Thorp, Kelly
item ANDRADE-SANCHEZ, PEDRO - University Of Arizona
item Conley, Matthew
item HEUN, JOHN - University Of Arizona
item Dyer, John
item White, Jeffrey

Submitted to: Frontiers in Plant Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 4/3/2018
Publication Date: 4/23/2018
Citation: Thompson, A.L., Thorp, K.R., Andrade-Sanchez, P., Conley, M.M., Heun, J.T., Dyer, J.M., White, J.W. 2018. Deploying a proximal sensing cart to identify drought-adaptive traits in upland cotton for high-throughput phenotyping. Frontiers in Plant Science. 9:507. https://doi.org/10.3389/fpls.2018.00507.
DOI: https://doi.org/10.3389/fpls.2018.00507

Interpretive Summary: Field-based high-throughput phenotyping (FB-HTP) is a novel approach to characterize complex traits in large plant populations using proximal and remote sensing or imaging. The power of high-throughput phenotyping (HTP) is its ability to characterize plant traits for large populations in both time and space, which improves monitoring of dynamic genetic responses to environmental conditions. Proximal Sensing Carts (PSCs) are a low cost platform for breeding programs that aim to explore HTP options using proximal sensors or cameras. As PSCs are typically lightweight, narrow wheeled and relatively small, concerns for soil compaction with repeated sampling are minimal, and are easy to transport to multiple fields. The primary goal of the present study was to develop a novel PSC with specific deployment protocol for crop improvement research, using cotton as a test case. The PSC and sensor package developed in this study was able to identify three breeding lines with improved drought tolerant characteristics compared to a local control cultivar.

Technical Abstract: Field-based high-throughput phenotyping is an emerging approach to characterize difficult, time-sensitive plant traits in relevant growing conditions. Proximal sensing carts have been developed as an alternative platform to more costly high-clearance tractors for phenotyping dynamic traits in the field. A proximal sensing cart and deployment protocol were developed to phenotype drought tolerant traits in the field. The cart sensor package included an infrared thermometer, ultrasonic transducer, spectral reflectance sensor, weather station, and RGB cameras. The cart was evaluated on 35 upland cotton (Gossypium hirsutum L.) breeding lines and cultivars grown in 2017 at Maricopa, Arizona. Experimental plots were grown under well-watered and water-limited conditions using a (0,1) alpha lattice design and evaluated in June and July. Total collection time of the 0.87-ha field averaged 2-h 27-min and produced 50.7MB and 45.7GB of data from the sensors and RGB cameras respectively. Measurements of canopy temperature, crop water stress index, canopy height, normalized difference vegetative index (NDVI), and leaf area index (LAI) were found to be significantly different between breeding lines and the water treatment interaction. Broad-sense heritability (H2) estimates ranged from 0.097 to 0.574 across all phenotypes and collections. A significant positive correlation was found between the RGB derived canopy cover and manually counted established plants (r=0.747, P-value=0.033). Three breeding lines were found to have improved drought adaptive traits compared to a local adapted cultivar using the cart derived phenotypes. These results indicate the cart, sensor package, and deployment protocol can measure multiple traits rapidly and accurately to characterize complex plant traits under drought conditions.