Soybean iron deficiency chlorosis high-throughput phenotyping using an unmanned aircraft system

Abstract

Background Iron deficiency chlorosis (IDC) is an abiotic stress in soybean [Glycine max (L.) Merr.] that causes significant yield reductions. Symptoms of IDC include interveinal chlorosis and stunting of the plant. While there are management practices that can overcome these drastic yield losses, the preferred way to manage IDC is growing tolerant soybean varieties. To develop varieties tolerant to IDC, breeders may easily phenotype up to thousands of candidate soybean lines every year for severity of symptoms related to IDC, a task traditionally done with a 1–5 visual rating scale. The visual rating scale is subjective and, because it is time consuming and laborious, can typically only be accomplished once or twice during a growing season. Results The goal of this study was to use an unmanned aircraft system (UAS) to improve field screening for tolerance to soybean IDC. During the summer of 2017, 3386 plots were visually scored for IDC stress on two different dates. In addition, images were captured with a DJI Inspire 1 platform equipped with a modified dual camera system which simultaneously captures digital red, green, blue images as well as red, green, near infrared (NIR) images. A pipeline was created for image capture, orthomosaic generation, processing, and analysis. Plant and soil classification was achieved using unsupervised classification resulting in 95% overall classification accuracy. Within the plant classified canopy, the green, yellow, and brown plant pixels were classified and used as features for random forest and neural network models. Overall, the random forest and neural network models achieved similar misclassification rates and classification accuracy, which ranged from 68 to 77% across rating dates. All 36 trials in the field were analyzed using a linear model for both visual score and UAS predicted values on both dates. In 32 of the 36 tests on date 1 and 33 of 36 trials on date 2, the LSD associated with UAS image-based IDC scores was lower than the LSD associated with visual scores, indicating the image-based scores provided more precise measurements of IDC severity. Conclusions Overall, the UAS was able to capture differences in IDC stress and may be used for evaluations of candidate breeding lines in a soybean breeding program. This system was both more efficient and precise than traditional scoring methods.

Correction to: Soybean iron deficiency chlorosis high throughput phenotyping using an unmanned aircraft system

“…In the original article [1], under the subheading “Image data processing”, last paragraph, last sentence that reads as “The least …… data collection” was incorrectly published. The correct sentence should read as “Least-significant differences (P < 0.20) were calculated for all 36 trials on both ground-based and UAS-image based scores for both dates of data collection.” The original article has been corrected.…”

Review on unmanned aerial vehicles, remote sensors, imagery processing, and their applications in agriculture

“…Disease severity ratings have been obtained by use of pixel color ratios (tis-sue coloration), spectral indices, and machine learning protocols with varying degrees of accuracy. Additionally, phenotyping characteristics such as tolerance to iron deficiency chlorosis have also been examined using tissue coloration (Dobbels & Lorenz, 2019). Furthermore, the use of UAV technology to successfully capture high-throughput phenotyping data for conducting genome-wide association studies (GWAS) show promise for enhancing breeding programs.…”

Development and Validation of a Phenotyping Computational Workflow to Predict the Biomass Yield of a Large Perennial Ryegrass Breeding Field Trial

“…Ortho-mosaic processing have five main steps; including image alignment, image geo-referencing, building dense point clouds, ortho-mosaics, and vegetative indices map, were followed for image processing. For detailed processing steps, please refer to following previous studies (Khan et al, 2018;Dobbels and Lorenz, 2019). Ortho-mosaic images were generated and stored as geo-referenced TIFF files.…”