Soil Organic Matter Content Prediction Using Multi-Input Convolutional Neural Network Based on Multi-Source Information Fusion

Li Guo, Qin Gao, Mengyi Zhang, Panting Cheng, Peng He, Lujun Li, Dong Ding, Changcheng Liu, Francis Collins Muga, Masroor Kamal and Jiangtao Qi ()
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Li Guo: College of Biological and Agricultural Engineering, Jilin University, Changchun 130022, China
Qin Gao: College of Biological and Agricultural Engineering, Jilin University, Changchun 130022, China
Mengyi Zhang: College of Biological and Agricultural Engineering, Jilin University, Changchun 130022, China
Panting Cheng: College of Biological and Agricultural Engineering, Jilin University, Changchun 130022, China
Peng He: State Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Harbin 150081, China
Lujun Li: State Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Harbin 150081, China
Dong Ding: Changchun Hyperspectral Imaging Technology Co., Ltd., Changchun 130022, China
Changcheng Liu: Changchun Hyperspectral Imaging Technology Co., Ltd., Changchun 130022, China
Francis Collins Muga: Department of Agricultural and Rural Engineering, University of Venda, Thohoyandou 0950, South Africa
Masroor Kamal: College of Biological and Agricultural Engineering, Jilin University, Changchun 130022, China
Jiangtao Qi: College of Biological and Agricultural Engineering, Jilin University, Changchun 130022, China

Agriculture, 2025, vol. 15, issue 12, 1-24

Abstract: Soil organic matter (SOM) content is a key indicator for assessing soil health, carbon cycling, and soil degradation. Traditional SOM detection methods are complex and time-consuming and do not meet the modern agricultural demand for rapid, non-destructive analysis. While significant progress has been made in spectral inversion for SOM prediction, its accuracy still lags behind traditional chemical methods. This study proposes a novel approach to predict SOM content by integrating spectral, texture, and color features using a three-branch convolutional neural network (3B-CNN). Spectral reflectance data (400–1000 nm) were collected using a portable hyperspectral imaging device. The top 15 spectral bands with the highest correlation were selected from 260 spectral bands using the Correlation Coefficient Method (CCM), Boruta algorithm, and Successive Projections Algorithm (SPA). Compared to other methods, CCM demonstrated superior dimensionality reduction performance, retaining bands highly correlated with SOM, which laid a solid foundation for multi-source data fusion. Additionally, six soil texture features were extracted from soil images taken with a smartphone using the gray-level co-occurrence matrix (GLCM), and twelve color features were obtained through the color histogram. These multi-source features were fused via trilinear pooling. The results showed that the 3B-CNN model, integrating multi-source data, performed exceptionally well in SOM prediction, with an R 2 of 0.87 and an RMSE of 1.68, a 23% improvement in R 2 compared to the 1D-CNN model using only spectral data. Incorporating multi-source data into traditional machine learning models (SVM, RF, and PLS) also improved prediction accuracy, with R 2 improvements ranging from 4% to 11%. This study demonstrates the potential of multi-source data fusion in accurately predicting SOM content, enabling rapid assessment at the field scale and providing a scientific basis for precision fertilization and agricultural management.

Keywords: soil organic matter; soil degradation; spectral data; image features; multi-source data fusion; 3B-CNN (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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