基于高光谱图像的生菜叶片水分预测研究

为了便于生菜合理施水管理, 力求构建生菜叶片水分检测模型。采集生菜叶片获取高光谱图像并同时测量叶片含水率, 分析高光谱图像寻求生菜叶片水分特征波段, 处理特征波段处的波段图像, 求取生菜叶片水分的图像特征, 并通过相关性分析筛选出其中与水分相关性高的图像特征。由于图像特征之间存在可能的相关性, 利用偏最小二乘PLS提取图像特征的主成分, 作为具有回归预测能力的BP神经网络的输入, 构建PLS-ANN模型。同时分别利用BP神经网络、传统的多元回归方法MLR建模, 采用相同的样本数据分别对三种模型进行预测试验, 结果表明, 发棵期的PLS-ANN网络模型的生菜叶片水分预测平均误差率达到9.323%, 比BP-ANN和MLR预测模型均有了改善。

Abstract

In order to conduct rational management of watering lettuce, the model of detecting lettuce leaves’ moisture was built. First of all, the hyperspectral images of lettuce leaves were acquired and simultaneously the moisture proportions of leaves were measured. Meanwhile, hyperspectral images were analyzed and the characteristic bands of lettuce leaves’ moisture were found. Then the images in characteristic bands were processed and the image features of lettuce leaves’ moisture were computed. The image features highly relevant to moisture were obtained through correlation analysis. Furthermore, due to the possible correlation among image features, the principal components of the images were extracted by principal components analysis and were used as BP neural network’s inputs to establish PCA-ANN model. At the same time, other models were constructed by using BP neural network and traditional MLR (multiple liner regression) method respectively. Prediction examinations of the three models were made based on the same sample data. The experimental results show that the average prediction error of PCA-ANN prediction model of tillering stage reaches 9.323% which is improved compared with BP-ANN and MLR prediction models.

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孙俊, 武小红, 张晓东, 高洪燕. 基于高光谱图像的生菜叶片水分预测研究[J]. 光谱学与光谱分析, 2013, 33(2): 522. SUN Jun, WU Xiao-hong, ZHANG Xiao-dong, GAO Hong-yan. Research on Lettuce Leaves’ Moisture Prediction Based on Hyperspectral Images[J]. Spectroscopy and Spectral Analysis, 2013, 33(2): 522.

基于高光谱图像的生菜叶片水分预测研究

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