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Cereal Crop Ear Counting in Field Conditions Using Zenithal RGB Images

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Predicting micronutrients of wheat using hyperspectral imaging.

Naiyue Hu¹, Wei Li¹, Chenghang Du¹

¹College of Agronomy and Biotechnology, China Agricultural University, Beijing 100193, China.

|November 8, 2020

Summary

Hyperspectral imaging offers a fast, non-destructive method for predicting wheat micronutrient content. This technique accurately estimates key minerals like calcium and zinc in wheat kernels and flour.

Keywords:

Grain nutritional attribute Grain quality PLSR Visible and near-infrared reflectance spectroscopy Wheat flour Wheat grain

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Area of Science:

Agricultural Science
Analytical Chemistry
Food Science

Background:

Micronutrients are crucial for assessing wheat's nutritional quality.
Traditional micronutrient analysis is costly and time-consuming.
Developing rapid, non-destructive methods is essential for wheat quality evaluation.

Purpose of the Study:

To investigate the potential of hyperspectral imaging (HSI) for predicting wheat micronutrient content.
To compare the predictive accuracy of HSI for wheat kernels versus wheat flour.
To establish non-invasive methods for rapid micronutrient assessment in wheat.

Main Methods:

Acquired spectral reflectance data of wheat kernels and flour in the visible and near-infrared (VIS-NIR) range (375–1050 nm).
Quantitatively measured wheat micronutrient contents (Ca, Mg, Mo, Zn).
Developed and validated chemometric models correlating spectral data with measured micronutrient concentrations.

Main Results:

HSI models for wheat kernels accurately predicted Calcium (Ca), Magnesium (Mg), Molybdenum (Mo), and Zinc (Zn) (r² > 0.70).
HSI models for wheat flour showed good predictive capabilities for Mg, Mo, and Zn (r² > 0.60).
Prediction accuracy was higher for wheat kernels compared to wheat flour.

Conclusions:

Hyperspectral imaging is a feasible, non-invasive, and non-destructive tool for predicting wheat micronutrient content.
HSI provides a rapid alternative to traditional methods for wheat quality assessment.
Further research can optimize HSI for broader application in the agri-food industry.