High-Throughput Phenotyping for Soybean Iron Deficiency Chlorosis Using an Unmanned Aircraft System: Applications in Breeding, Agronomy, and Genetics

Abstract

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 approach 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. The goal of this study was to use an unmanned aircraft system (UAS) to improve field screening for tolerance to soybean IDC. We achieved high efficiency in collecting data with autonomous UAS flights, greater than 77% accuracy in classifying plots on a 1-5 severity scale, and an average reduction in LSD values across a series of experimental trials. This method is high-throughput, objective, and more precise than traditional ground based visual assessments. The UAS-based system was further used to assess the interactions of IDC and soybean cyst nematode (SCN). A range of treatments were added to change the levels of IDC and SCN stress in a randomized complete block factorial design. Results from the three-year study showed that the treatments independently created IDC and SCN severity symptoms and associated yield differences. Nematode reproduction was significantly impacted by varietal resistance and was not impacted by IDC treatment. An interaction between IDC and SCN treatments was not found for yield, nematode reproduction, or severity symptoms suggesting these stresses act additively. Finally, UAS-based phenotyping was used to assess temporal patterns of iron deficiency chlorosis symptoms in a genome-wide association study. The UAS-based system identified overlapping QTL with the same significance as traditional visual observations indicating that UAS estimates of IDC are useful in soybean genetics research programs. In addition, novel QTL were identified for the rate of IDC recovery. Overall, the efficiency and precision of UAS-based image analysis of IDC can be useful in breeding, agronomic, and genome-wide association studies.