Prediction of sweetness and amino acid content in soybean crops from hyperspectral imagery

• Published in: ISPRS journal of photogrammetry and remote sensing Vol. 62; no. 1; pp. 2 - 12
• Main Authors: Monteiro, Sildomar Takahashi, Minekawa, Yohei, Kosugi, Yukio, Akazawa, Tsuneya, Oda, Kunio
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01.05.2007; Elsevier Science
• Subjects: Agriculture; Animal, plant and microbial ecology; Applied geophysics; Biological and medical sciences; Earth sciences; Earth, ocean, space; Exact sciences and technology; Fundamental and applied biological sciences. Psychology; General aspects. Techniques; Hyperspectral image; Internal geophysics; Modeling; Neural networks; Spatial prediction; Teledetection and vegetation maps; Spatial prediction; Agriculture; Hyperspectral image; Neural networks; Modeling; experimental studies; wavelength; models; maps; reflectance; nitrogen; amino acids; neural networks; data processing; accuracy; Image; principal components analysis; agriculture; regression; imagery; prediction; high resolution
• ISSN: 0924-2716; 1872-8235
• DOI: 10.1016/j.isprsjprs.2006.12.002

Hyperspectral image data provides a powerful tool for non-destructive crop analysis. This paper investigates a hyperspectral image data-processing method to predict the sweetness and amino acid content of soybean crops. Regression models based on artificial neural networks were developed in order to calculate the level of sucrose, glucose, fructose, and nitrogen concentrations, which can be related to the sweetness and amino acid content of vegetables. A performance analysis was conducted comparing regression models obtained using different preprocessing methods, namely, raw reflectance, second derivative, and principal components analysis. This method is demonstrated using high-resolution hyperspectral data of wavelengths ranging from the visible to the near infrared acquired from an experimental field of green vegetable soybeans. The best predictions were achieved using a nonlinear regression model of the second derivative transformed dataset. Glucose could be predicted with greater accuracy, followed by sucrose, fructose and nitrogen. The proposed method provides the possibility to provide relatively accurate maps predicting the chemical content of soybean crop fields.