Abstract
Deep learning is characterized by its strong ability of data feature extraction. This method can provide unique advantages when applying it to visible and near-infrared spectroscopy for predicting soil organic matter (SOM) content in those cases where the SOM content is negatively correlated with the spectral reflectance of soil. This study relied on the SOM content data of 248 red soil samples and their spectral reflectance data of 400–2450 nm in Fengxin County, Jiangxi Province (China) to meet three objectives. First, a multilayer perceptron and two convolutional neural networks (LeNet5 and DenseNet10) were used to predict the SOM content based on spectral variation and variable selection, and the outcomes were compared with that from the traditional back-propagation neural network (BPN). Second, the four methods were applied to full-spectrum modeling to test the difference to selected feature variables. Finally, the potential of direct modeling was evaluated using spectral reflectance data without any spectral variation. The results of prediction accuracy showed that deep learning performed better at predicting the SOM content than did the traditional BPN. Based on full-spectrum data, deep learning was able to obtain more feature information, thus achieving better and more stable results (i.e., similar average accuracy and far lower standard deviation) than those obtained through variable selection. DenseNet achieved the best prediction result, with a coefficient of determination (R2) = 0.892 ± 0.004 and a ratio of performance to deviation (RPD) = 3.053 ± 0.056 in validation. Based on DenseNet, the application of spectral reflectance data (without spectral variation) produced robust results for application-level purposes (validation R2 = 0.853 ± 0.007 and validation RPD = 2.639 ± 0.056). In conclusion, deep learning provides an effective approach to predict the SOM content by visible and near-infrared spectroscopy and DenseNet is a promising method for reducing the amount of data preprocessing.
Highlights
Soil organic matter (SOM) content, a key indicator of soil fertility, substantially impacts the physicochemical properties and quality of soil; the SOM content must be considered in scientific fertilization
E multilayer perceptron (MLP) entails a structural evolution of the back-propagation neural network (BPN). e results obtained with 203 bands showed that the MLP has a stronger ability to fit data fitting ability because its artificial neural network with multiple hidden layers has an excellent feature learning ability and the learned features are essential for data characterization. e drawback of shallow structure algorithms is that their ability to represent complex functions is limited in the case of finite samples and computational units, hindering the generalization ability for complex problems
E number of parameters for DenseNet10 (43,273) was far lower than that of LeNet5 (108,941). e advanced architecture of the convolutional neural network (CNN) gave an absolute improvement in the prediction accuracy with fewer parameters
Summary
Soil organic matter (SOM) content, a key indicator of soil fertility, substantially impacts the physicochemical properties and quality of soil; the SOM content must be considered in scientific fertilization. Visible (VIS, 400– 780 nm) and near-infrared (NIR, 780–2526 nm) spectroscopy is a convenient and efficient technique for quickly and inexpensively monitoring SOM [1], since spectral reflectance of soil is negatively correlated with the SOM content and the SOM content could be obtained from measured soil reflectance spectrum [2, 3]. Computational Intelligence and Neuroscience of the SOM content based on hyperspectral data. Shi et al [6] were able to use PLSR to predict the SOM content from the Chinese VIS-NIR spectral library. By learning the deep nonlinear network structure, a complex function approximation is realized using the BP algorithm. E obtained results indicate how the deep learning machine should change its internal parameters to discover the complex structure of larger data sets, demonstrating the powerful ability to learn the essential features of a data set from a smaller sample set [16] By learning the deep nonlinear network structure, a complex function approximation is realized using the BP algorithm. e obtained results indicate how the deep learning machine should change its internal parameters to discover the complex structure of larger data sets, demonstrating the powerful ability to learn the essential features of a data set from a smaller sample set [16]
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