Optimized phenotyping, the observable characteristics attributed to the interaction between genotype and the environment, using canopy reflectance measurements may increase the efficiency of cultivar development. The objectives of this study were to: (i) assess canopy reflectance as a tool for predicting soybean maturity and seed yield; (ii) identify specific development stages that contribute to maturity and yield estimation; and (iii) test the stability and utility of maturity and yield estimation models across environments. Canopy reflectance, maturity, and seed yield were measured on 20 maturity group (MG) 3 and 20 MG 4 soybean cultivars released from 1923 to 2010. Measurements were conducted on six irrigated and water‐stressed environments in 2011 and 2012. Cultivar, environment, and cultivar by environment sources of variation were all significant for maturity, yield, and reflectance. Maturity estimation models were created using the visible, red edge, and near‐infrared spectrum as well as normalized difference vegetation index (NDVI) and water index ratios. Yield estimation models using the red edge, near‐infrared, and visible NDVI indices explained much of the variation in yield among genotypes. No significant trends were found for canopy reflectance data collected at specific development stages or in different water regimes contributing to more accurate yield estimation; however, later development stages (R5‐R6) were more accurate for maturity estimation due to spectral data identifying senescing vegetation. Performance of canopy reflectance models for maturity and yield accounted for a significant portion of variability among genotypes for maturity in some environments and for seed yield in most environments.

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Plant phenotyping is central to understand causal effects of genotypes and environments on trait expression and is a critical factor in expediting plant breeding. Previously, plant phenotypic traits were quantified using invasive, time-consuming, labor-intensive, costinefficient, and often destructive manual sampling methods that were also prone to observer error. In recent years, photogrammetry and image processing techniques have been introduced to plant phenotyping, but cost efficiency issues remain when combining these two techniques within large-scale plant phenotyping studies. Using these high-throughput techniques in basic plant biology research and agriculture are still in the developmental stages but show great promise for rapid phenotyping, which will materially aid both science and crop improvement efforts. In this study, we introduce an automated high-throughput phenotyping pipeline using affordable imaging systems and image processing algorithms to build 2D mosaicked orthophotos. Chamber-based and ground-level field implementations are used to measure phenotypic traits such as leaf length and rosette area in 2D images. Our automated pipeline has cross-platform capabilities and a degree of instrument independence, making it suitable for various situations.

Conventional phenotyping methods impose a significant bottleneck to the characterization of genotypic and environmental effects on trait expression in plants. In particular, invasive and destructive sampling methods along with manual measurements widely used in conventional studies are labor-intensive, time-consuming, costly, and can lack consistency. These experimental features impede large-scale genetic studies of both crops and wild plant species. Here, we present a high-throughput phenotyping pipeline using photogrammetry and 3D modeling techniques in the model species, Arabidopsis thaliana. We develop novel photogrammetry and computer vision algorithms to quantify 2D and 3D leaf areas for a mapping population of 1050 Arabidopsis thaliana lines, and use 2D areas to analyze plant nastic movements and diurnal cycles. Compared to the 2D leaf areas, 3D leaf areas show an uncorrupted growth trend regardless of plant nastic movement. With optimized algorithms, our pipeline throughput is very computationally efficient for screening a large number of plants. The pipeline not only supports measurement of organ-level growth and development over time, but also enables analysis of whole-plant phenotypes and, thus, identification of genotype-specific performance. Further, the accuracy results evaluating the relationship between physical dimensions and 3D measurements indicate an R 2 = 0.99, and the average 3D area processing time per plant is 0.02 s. Our algorithms provide both high accuracy and throughput in plant phenotyping, thereby, enabling progress in plant genotypic modeling.

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