Prediction of sweetness and amino acid content in soybean crops from hyperspectral imagery
Prediction of sweetness and amino acid content in soybean crops from hyperspectral imagery
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DOI：
10.1016/j.isprsjprs.2006.12.002
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发表时间：
2007-05-01
影响因子：
12.7
通讯作者：
Oda, Kunio
中科院分区：
文献类型：
--
作者：
Monteiro, Sildomar Takahashi;Minekawa, Yohei;Oda, Kunio
高光谱图像数据为农作物无损分析提供了强有力的工具。本文研究了一种高光谱图像数据处理方法，用于预测大豆作物的甜度和氨基酸含量。基于人工神经网络的回归模型，以计算水平的蔗糖，葡萄糖，果糖和氮的浓度，这可以与蔬菜的甜度和氨基酸含量。进行性能分析比较使用不同的预处理方法，即，原始反射率，二阶导数，主成分分析得到的回归模型。该方法是使用高分辨率的高光谱数据的波长范围从可见光到近红外线从实验领域的绿色菜大豆收购证明。使用二阶导数转换数据集的非线性回归模型实现了最佳预测。葡萄糖可以更准确地预测，其次是蔗糖，果糖和氮。所提出的方法提供了可能性，提供相对准确的地图预测大豆作物田的化学含量。(C)2006年国际摄影测量和遥感学会。（摄影测量和遥感学会）。Elsevier B.V.出版，保留所有权利。
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. (C) 2006 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved.