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

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...