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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 #389915

Research Project: Molecular Genetic and Proximal Sensing Analyses of Abiotic Stress Response and Oil Production Pathways in Cotton, Oilseeds, and Other Industrial and Biofuel Crops

Location: Plant Physiology and Genetics Research

Title: Advances in the application of small unoccupied aircraft systems (sUAS) for high-throughput plant phenotyping

item AYANKOJO, IBUKUN - University Of Florida
item Thorp, Kelly
item Thompson, Alison

Submitted to: Remote Sensing
Publication Type: Review Article
Publication Acceptance Date: 5/15/2023
Publication Date: 5/18/2023
Citation: Ayankojo, I.T., Thorp, K.R., Thompson, A.L. 2023. Advances in the application of small unoccupied aircraft systems (sUAS) for high-throughput plant phenotyping. Remote Sensing. 15(10). Article 2623.

Interpretive Summary: sUAS technology has received increasing attention from researchers to determine and evaluate important crop traits associated with growth and productivity under different growing conditions (limited water and nutrients, heat stress, salinity stress, disease pressure, etc.). High throughput plant phenotyping studies typically rely on imaging sensors such as RGB, thermal infrared, hyperspectral and multispectral cameras, and LiDAR for predicting crop traits or phenotypes. The availability of a wide range of these imaging sensors or cameras and image processing pipelines that allows for the combination of multiple sensors (multi-sensor data fusion) have enhanced the level of accuracy of trait evaluation and prediction of crop performance from both sUAS and proximal platforms compared to the manual measurements.

Technical Abstract: High-throughput plant phenotyping (HTPP) involves the application of modern information technologies to evaluate the effects of genetics, environment, and management on the expression of plant traits in plant breeding programs. In recent years, HTPP has been advanced via sensors mounted on terrestrial vehicles and small unoccupied aircraft systems (sUAS) to estimate plant phenotypes in several crops. Previous reviews have summarized these recent advances, but the accuracy of estimation across traits, platforms, crops, and sensors has not been fully established. Therefore, the objectives of this review were to (1) identify the advantages and limitations of terrestrial and sUAS platforms for HTPP, (2) summarize the different imaging techniques and image processing methods used for HTPP, (3) describe individual plant traits that have been quantified using sUAS, (4) summarize the different imaging techniques and image processing methods used for HTPP, and (5) compare the accuracy of estimation among traits, platforms, crops, and sensors. A literature survey was conducted using the Web of ScienceTM Core Collection Database (THOMSON REUTERSTM) to retrieve articles focused on HTPP research. A total of 205 articles were obtained and reviewed using the Google search engine. Based on the information gathered from the literature, in terms of flexibility and ease of operation, sUAS technology is a more practical and cost-effective solution for rapid HTPP at field scale level (>2 ha) compared to terrestrial platforms. Of all the various plant traits or phenotypes, plant growth traits (height, LAI, canopy cover, etc.) were studied most often, while RGB and multispectral sensors were most often deployed aboard sUAS in HTPP research. Sensor performance for estimating crop traits tended to vary according to the chosen platform and crop trait of interest. Regardless of sensor type, the prediction accuracies for crop trait extraction (across multiple crops) were similar for both sUAS and terrestrial platforms; however, yield prediction from sUAS platforms was more accurate compared to terrestrial phenotyping platforms. This review presents a useful guide for researchers in the HTPP community on appropriately matching their traits of interest with the most suitable sensor and platform.