摘要
叶绿素含量是评价植物生长状况以及光合作用能力的重要指标。通过叶绿素测定仪实地测定表征紫丁香叶片的叶绿素含量的SPAD(soil plant analysis development)值,利用高光谱图像技术和机器学习算法反演推算紫丁香叶片叶绿素的含量。针对数据采集时噪声信息的干扰、相邻波段间相关性强、冗余信息多的问题,利用空洞卷积去噪自动编码器(Atrous-Convolutional Denoising Auto-Encoder,Atrous-CDAE)将原始高光谱数据由204维减少到51维,并减少噪声干扰。结合1DCNN建立紫丁香叶片叶绿素含量的预测模型,并与原始数据和其他4种数据处理方法进行比较。结果表明:相比于原始高光谱数据和其他数据处理方法,经Atrous-CDAE处理后的数据预测结果最佳,预测集中决定系数R^(2)为0.9723,均方根误差RMSE为1.3266。利用Atrous-CDAE处理的数据与其他经典预测模型组合均取得较优的预测结果,表明Atrous-CDAE可有效地提取数据潜在表征。对其他数据结合本文所提1DCNN模型进行预测,其R^(2)均在0.94以上,RMSE均在2以下,表明该预测模型具有一定的适应性。
Chlorophyll content is an important indicator for evaluating plant growth condition and photosynthesis capacity.The study used a chlorophyll meter to measure the SPAD(soil plant analysis development)value that characterizes the chlorophyll content of lilac leaves and used hyperspectral image technology and the machine learning algorithm inversely calculated the chlorophyll content of lilac leaves.Aiming at the problems of noise information interference,strong correlation between adjacent bands,and redundant information during data collection,this paper took advantage of the Atrous-Convolutional Denoising Auto-Encoder(Atrous-CDAE)to decrease the original hyperspectral data from 204 dimensions to 51 dimensions,and lowered noise interference.Finally,a prediction model of the chlorophyll content of lilac leaves was established based on 1DCNN,and compared with the original data and four other data processing methods.The results indicated that compared with the original hyperspectral data and other data processing methods,the data after Atrous-CDAE showed the best prediction results:with a prediction concentration coefficient(R 2)of 0.9723 and a root mean square error(RMSE)of 1.3266.The combination of the data processed by Atrous-CDAE and other classical prediction models had achieved better prediction results,indicating that Atrous-CDAE can effectively extract the potential representation of the data.Combining other data with the 1DCNN model proposed in this article,the R 2 was above 0.94,and the RMSE was below 2,indicating that the prediction model had a certain degree of adaptability.
作者
高文强
肖志云
Gao Wenqiang;Xiao Zhiyun(College of Electric Power,Inner Mongolia University of Technology,Hohhot,010080,China;Inner Mongolia Key Laboratory of Mechatronic Control,Hohhot,010051,China)
出处
《中国农机化学报》
北大核心
2022年第7期158-166,共9页
Journal of Chinese Agricultural Mechanization
基金
国家自然科学基金项目(61661042)
内蒙古自治区科技计划项目(2021GG0345)
内蒙古自治区自然科学基金项目(2021MS06020)。
关键词
叶绿素含量
高光谱
自动编码器
卷积神经网络
chlorophyll content
hyperspectral
auto encoder
convolutional neural network
