Application of an ECAM‐ConvNeXt Model With Multichannel Spectrogram Based on Vis–NIR for Soil Property Prediction

ABSTRACT Vis–NIR spectroscopy is increasingly widely used for soil property analysis due to its rapid, cost‐effective, and nondestructive advantages. In particular, deep learning models perform very well when working with large sample data. In this study, we propose a deep learning model based on three‐channel ECAM‐ConvNeXt. Firstly, the method applies three window functions, Bartlett, Gaussian, and Blackman, in the short‐time Fourier transform to convert a one‐dimensional spectral sequence signal into three different two‐dimensional spectrograms. Next, we perform multichannel feature fusion and use the resulting triple‐channel spectrograms as model inputs. This method fully preserves the temporal information and spectral characteristics of the spectral sequence, thereby improving the performance of the model. Secondly, this study introduces the Efficient Channel Attention Module in the ConvNeXt model. This module combines the advantages of the Convolutional Block Attention Module and Efficient Channel Attention Network, further enhancing the expressive ability of the network by highlighting useful information and suppressing irrelevant information. Finally, we also validate the effectiveness of multichannel inputs by deep learning models (AlexNet18, ResNet50, MobileNet‐V3, EfficientNet, VIT) and compare them with existing techniques reported in the literature. The results indicate that the root‐mean‐square error (RMSE) of the TriCH‐ECAM‐ConvNeXt model in predicting soil nitrogen content (N (g/kg)), organic carbon content (OC (g/kg)), cation exchange capacity (CEC (cmol(+)/kg)), pH, clay content (%), and sand content (%) was reduced to 0.9847, 19.7347, 6.3380, 0.3812, 5.1537, and 12.9706, respectively, and the coefficient of determination ( R 2 ) increased to 0.9307, 0.9544, 0.7999, 0.9206, 0.8493, and 0.7526, respectively.