ArticlePlant methods2021
Remotely sensed estimation of total iron content in soil with harmonic analysis and BP neural network.
Article in Plant methods, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- The fusion of vegetation indices increases the accuracy of cotton leaf area prediction.Frontiers in plant science · 2024Article
- Multi-dimensional variables and feature parameter selection for aboveground biomass estimation of potato based on UAV multispectral imagery.Frontiers in plant science · 2022Article
- Recognition of soybean pods and yield prediction based on improved deep learning model.Frontiers in plant science · 2022Article
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8 authors.
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Abstract
backgroundThe estimation of total iron content at the regional scale is of much significance as iron deficiency has become a routine problem for many crops.
methodsIn this study, a novel method for estimating total iron content in soil (TICS) was proposed using harmonic analysis (HA) and back propagation (BP) neural network model. Several data preprocessing methods of first derivative (FD), wavelet packet transform (WPT), and HA were conducted to improve the correlation between the soil spectra and TICS. The principal component analysis (PCA) was exploited to obtained three kinds of characteristic variables (FD, WPT-FD, and WPT-FD-HA) for TICS estimation. Furthermore, the estimated accuracy of three BP models based on these variables was compared.
resultsThe results showed that the BP models of different soil types based on WPT-FD-HA had better estimation accuracy, with the highest R
conclusionThe HA method and BP neural network combined with WPT and FD have great potential in TICS estimation under the conditions of single soil and mixed soil. This method can be expected to be applied to the prediction of crop biochemical parameters.
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