Dr. Neil Hausmann Profile

Dr. Neil Hausmann

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Proceedings Article | 17 September 2021 Presentation + Paper

UAV-based scoring for iron chlorosis in soybean

C. Potnuru, B. Narayanan, L. Ries, B. Floyd, N. Hausmann

Proceedings Volume 11856, 118560Y (2021) https://doi.org/10.1117/12.2604520

KEYWORDS: Visualization, Unmanned aerial vehicles, RGB color model, Image segmentation, Data modeling, Genetics, Machine learning

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Iron chlorosis in soybean is a nutrient deficiency condition with general symptoms of chlorosis (yellowing) of soybean foliage and stunting of the plant, in turn impacting crop yield. Identifying, selecting and advancing varieties offering resistance to iron chlorosis is a critical component of soybean breeding. Genetic characterization of various soybean varieties is carried out using phenotypic measurements that are collected manually. Such measurements are extremely subjective confounded with rater variability, compromising measurement quality. Furthermore, manual data collection is labor intensive and expensive. In this study, we propose an automatic scoring system employing an analytical framework that applies image processing and machine learning (ML) techniques on red-green-blue (RGB) color channel images collected via Unmanned Aerial Vehicle (UAV) for quantifying iron chlorosis severity. Results from the machine learning model indicate that the ML-based scores yielded good correlation with the manual scores. Additionally, ML scores demonstrated higher heritability/repeatability compared to those obtained from the manual scores, suggesting the use of UAV imagery in conjunction with machine learning approaches for field assessments of iron chlorosis, reducing long and tedious manual data collection efforts. Moreover, such approaches provide a scalable and high-throughput scoring system, enabling efficient breeding practices.

Proceedings Article | 14 May 2019 Paper

Improving soybean breeding using UAS measurements of physiological maturity

B. Narayanan, B. Floyd, K. Tu, L. Ries, N. Hausmann

Proceedings Volume 11008, 110080U (2019) https://doi.org/10.1117/12.2519072

KEYWORDS: Unmanned aerial vehicles, RGB color model, Genetics, Remote sensing, Agriculture, Visualization, Sensors, Environmental sensing

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Improving throughput and accuracy of plant phenotyping is core to continued advances in breeding to ensure genetic gain to meet global food demand. Current manual phenotyping requires enormous investments in time, cost, and labor as quantitative values are required for thousands of genetic varieties across different environments. In soybean, a genotype’s maturity governs the geography for which it is adapted and has an impact on yield, which must be controlled for in breeding to realize genetic gain. In this work, we developed and validated a method for highthroughput phenotyping of soybean maturity using high resolution, visual, RGB, imagery collected using an unmanned aerial vehicle (UAV). We illustrate a method to automatically derive maturity date by modeling change through time of a quantitative assessment of canopy greenness on a per plot basis. The efficacy of the analytical framework is compared to the manual scoring system by evaluating phenotypic and genetic correlations and genetic repeatability measures. Analysis of replicated experiments from multiple locations yielded high phenotypic correlations (R² = 0.85 - 0.96) between manual and UAV derived maturity scores. Heritability of the maturity estimates from the proposed remote sensing method is comparable to that of manual scoring. Implementation of this system has allowed for improved scale, cost efficiencies and data quality for soy maturity data collected via UAV remote sensing.

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