Advances in the Application of Small Unoccupied Aircraft Systems (sUAS) for High-Throughput Plant Phenotyping

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.

Plant phenotyping relevance

植物フェノタイピングにおけるsUAS・地上プラットフォーム、センサー、画像処理、形質推定精度を体系的にレビューしており、方法論が中心である。

Code and data availability

This is a review article synthesizing 205 prior studies on sUAS-based high-throughput plant phenotyping. The supplied blocks contain no authors' phenotype datasets, images, analysis code, models, or supplements; all measurements and accuracies are cited from prior publications, and no data or code availability/deposit/

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