Mr. Chaitanyam Potnuru Profile

Chaitanyam Potnuru

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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.

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