基于深度稀疏学习的土壤近红外光谱分析预测模型
发布时间：2018-07-29 07:36
【摘要】：提出一种基于深度稀疏学习的土壤近红外光谱分析预测模型。首先,使用稀疏特征学习方法对土壤近红外光谱数据进行约简,实现土壤近红外光谱内容的稀疏表示;然后采用径向基函数神经网络以稀疏表示特征系数为输入,以所测土壤成分为输出,分别建立土壤有机质、速效磷、速效钾的非线性预测模型。结果表明用该模型预测土壤有机质的含量是可行的,但对土壤速效磷和速效钾含量的预测还需对模型做进一步的优化。
[Abstract]:A soil near infrared spectroscopy (NIR) analysis and prediction model based on deep sparse learning is proposed. Firstly, the sparse feature learning method is used to reduce the soil near infrared spectral data to realize the sparse representation of the soil near infrared spectrum, and then the sparse representation feature coefficient is used as the input of the radial basis function neural network. The nonlinear prediction models of soil organic matter, available phosphorus and available potassium were established by using the measured soil composition as the output. The results showed that it was feasible to predict the content of soil organic matter by using this model, but the prediction of soil available phosphorus and potassium content needed to be further optimized.
【作者单位】： 中国科学院合肥智能机械研究所;
【基金】：中国科学院科技服务网络计划(KFJ-EW-STS-069) 国家自然科学基金(31671586)资助项目~~
【分类号】：S151.9;O657.33;TP183
[Abstract]:A soil near infrared spectroscopy (NIR) analysis and prediction model based on deep sparse learning is proposed. Firstly, the sparse feature learning method is used to reduce the soil near infrared spectral data to realize the sparse representation of the soil near infrared spectrum, and then the sparse representation feature coefficient is used as the input of the radial basis function neural network. The nonlinear prediction models of soil organic matter, available phosphorus and available potassium were established by using the measured soil composition as the output. The results showed that it was feasible to predict the content of soil organic matter by using this model, but the prediction of soil available phosphorus and potassium content needed to be further optimized.
【作者单位】： 中国科学院合肥智能机械研究所;
【基金】：中国科学院科技服务网络计划(KFJ-EW-STS-069) 国家自然科学基金(31671586)资助项目~~
【分类号】：S151.9;O657.33;TP183
【相似文献】
相关期刊论文 前10条
1 李勇,魏益民,王锋;影响近红外光谱分析结果准确性的因素[J];核农学报;2005年03期
2 褚小立,袁洪福,陆婉珍;基础数据准确性对近红外光谱分析结果的影响[J];光谱学与光谱分析;2005年06期
3 杨皓e，
本文编号：2151973
本文链接：https://www.wllwen.com/kejilunwen/zidonghuakongzhilunwen/2151973.html
