基于改进CNN的猕猴桃根区土壤含水率反演方法  

Inversion method for root soil water content using improved CNN

作　　者：潘时佳 吴津乐 程梅 周敏姑[2] 牛子杰[1] 韩文霆[2] PAN Shijia;WU Jinle;CHENG Mei;ZHOU Mingu;NIU Zijie;HAN Wenting(College of Mechanical and Electronic Engineering,Northwest A&F University,Yangling 712100,China;Institute of Water-saving Agriculture in Arid Areas of China,Northwest A&F University,Yangling 712100,China)

机构地区：[1]西北农林科技大学机械与电子工程学院,杨凌712100 [2]西北农林科技大学中国旱区节水农业研究院,杨凌712100

出　　处：《农业工程学报》2024年第11期85-91,共7页Transactions of the Chinese Society of Agricultural Engineering

基　　金：陕西省重点研发计划项目(2022NY-220);国家自然基金联合基金重点项目(U2243235);“十四五”国家重点研发计划项目(2022YFD1900802)。

摘　　要：为解决无人机遥感领域根据冠层光谱信息对猕猴桃果树根系土壤含水率(root soil water content, RSWC)进行反演时,现有算法对冠层图像信息分析不足的问题,该研究对传统卷积神经网络模型进行改进,提出一种复合视觉卷积回归神经网络(compound visual convolutional regression network, CVCRNet),该网络复合两种不同尺寸卷积层对图像数据进行卷积特征提取,并使用全连接层对卷积特征值进行降维,从而直接以多光谱图像为分析对象对RSWC进行反演,充分利用多光谱图像内所有数据,提升反演精度。研究采集徐香猕猴桃果树果实膨大期(5-9月)冠层多光谱信息和深度40 cm处的RSWC,把基于图像的CVCRNet网络反演方法与基于植被指数的传统反演方法进行对比,CVCRNet训练结果在验证集R^(2)为0.827,RMSE为0.787%,相较于传统方法在验证集R^(2)为0.759,RMSE为0.983%,反演结果相关性有了明显提升,准确率也有得到一定提高。结果表明,改进后的CNN网络能够作为冠层信息反演的重要工具,在冠层复杂的场景下达成良好的土壤数据反演效果。Soil moisture is one of the most crucial indicators to develop the intelligent irrigation in kiwifruit orchards.However,the complex kiwifruit trees with the canopy,varying coverage and substantial shading have posed some challenges on the accurate prediction of the soil moisture content.Current algorithms are still lacking on the canopy image to estimate the root soil water content(RSWC)of kiwifruit trees using canopy spectral information in the field of UAV remote sensing.In this study,a Compound Visual Convolutional Regression Network(CVCRNet)was proposed to combine two sizes of convolutional layers,in order to extract convolutional features from image data.The fully connected layers were used to reduce the dimensionality of convolutional features.Thus,multispectral images were directly analyzed for RSWC inversion.Since there was no pooling layer in the network,all data within multispectral images was fully utilized to enhance the accuracy of inversion.Multispectral images of the canopy and RSWC were collected at a depth of 40 cm during the swelling period(MaySeptember)of Xuxiang kiwifruit trees.The canopy image was processed and normalized to directly served as the input,in order to eliminate the manual feature extraction or complex structural analysis of the fruit tree canopy,as well as the correlation of vegetation indices.Deep convolutional features were extracted from the Red-Green-Near Infrared(RGN)images of kiwifruit tree canopies,in order to train the remote sensing dataset of kiwifruit orchard.The RSWC gradient maps were obtained by cubic spline interpolation.A gradient map of kiwifruit tree distribution was then generated to reflect the actual situation of water control,where the RSWC gradient maps was overlapped with the original ones.As such,the field application of the CVCRNet inversion was realized in this case.Additionally,the performance of RSWC was compared on the vegetation indices and traditional numerical models.A Multilayer Perceptron(MLP)network was introduced to establish a dual-index est

关 键 词：土壤 含水率 多光谱成像 无人机遥感 卷积神经网络 果园管理 

分 类 号：S152.7[农业科学—土壤学] S252[农业科学—农业基础科学]