Evaluation of the Effect of the Vigor of Soybean Seeds Treated with Micronutrients Using X-ray Fluorescence Spectroscopy and Hyperspectral Imaging

• Published in: Agronomy
• Main Authors: Rafael Mateus Alves, Francisco Guilhien Gomes-Junior, Abimael dos Santos Carmo-Filho, Glória de Freitas Rocha Ribeiro, Carlos Henrique Queiroz Rego, Fernando Henrique Iost-Filho, Pedro Takao Yamamoto
• Format: Article
• Language: English
• Published: MDPI AG 2023-07-01
• Subjects: <i>Glycine max</i>; seed physiological potential; seed coating; nutrient uptake; machine learning
• Online Access: https://www.mdpi.com/2073-4395/13/7/1945

Seed treatment with micronutrients is a crucial strategy for providing early seedling supply during development, and is commonly employed in soybean cultivation. However, responses to micronutrient treatment may vary based on seed vigor levels. Therefore, this study aimed to assess the potential of hyperspectral imaging combined with preprocessing and machine learning, compared to X-ray fluorescence spectroscopy, in evaluating the dynamics of micronutrient uptake during the germination of soybean seeds with varying levels of vigor. Two seed lots with differing levels of vigor were utilized for the analysis. The absorption of micronutrients by the seeds was evaluated using X-ray fluorescence spectroscopy (XRF), microprobe X-ray fluorescence spectroscopy (μ-XRF) and hyperspectral imaging (HSI) in two regions of interest (cotyledons and the embryonic axis). Artificial neural network (ANN), decision tree (DT) and partial least squares–discriminant analysis (PLS-DA) classification models, along with the Savitzky–Golay (SG), standard normal variation (SNV) and multiplicative scatter correction (MSC) methods, were employed to determine seed vigor based on the impact of micronutrient treatment. XRF identified higher concentrations of micronutrients in the treated seeds, with zinc being the predominant element. μ-XRF analysis revealed that a significant proportion of the micronutrients remained adhered to the hilum and seed coat, irrespective of seed vigor. The PLS-DA classification model using spectral data exhibited higher accuracy in classifying soybean seeds with high and low vigor, regardless of seed treatment with micronutrients and the analyzed region.