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Crop variables estimation by adaptive neuro-fuzzy inference system using bistatic scatterometer data

机译:利用双站散射仪数据通过自适应神经模糊推理系统估算作物变量

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The aim of present study is to estimate the crop variables by means of high performing technique like adaptive neuro-fuzzy inference system (ANFIS) using the bistatic scatterometer data. An outdoor 4??4 m2 crop bed of rice crop was prepared for performing all the experiments. The bistatic measurements were carried out over the entire growing stages of the rice crop from transplanting to ripening stage at the angular range of 200 to 700 with the steps 50 at both HH- and VV-polarizations in X-band. The ANFIS algorithm was used for the estimation of rice crop variables. The observed bistatic scattering coefficients and crop variables (biomass, leaf area index, plant height and chlorophyll content) were interpolated with the phenological stages of the rice crop. The 80% data sets were used for training while the remaining 20% were kept separately for the testing purposes. The bistatic scattering coefficients were used as the input data sets and the rice crop variables as the target data sets of fuzzy inference system for both the polarizations. The estimated values were found closer to the observed values of rice crop variables that indicate a satisfactory performance of ANFIS algorithm for estimating rice crop variables.
机译:本研究的目的是利用双站散射仪数据通过诸如自适应神经模糊推理系统(ANFIS)的高性能技术估算作物变量。准备进行室外4×4 m2稻谷作物的作物床以进行所有实验。在从移栽到成熟阶段的水稻作物整个生长阶段,以200至700的角度范围进行双基地测量,并在X波段的HH极化和VV极化下进行50步。 ANFIS算法用于估算水稻作物变量。将观测到的双基地散射系数和农作物变量(生物量,叶面积指数,植物高度和叶绿素含量)与水稻作物的物候期进行内插。 80%的数据集用于培训,而其余20%的数据集则分别用于测试目的。双极化的散射系数用作输入数据集,水稻作物变量用作模糊推理系统的目标数据集。发现的估计值更接近稻米作物变量的观测值,表明ANFIS算法在估算稻米作物变量方面具有令人满意的性能。

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