Grading and Detecting of Organic Matter in Phaeozem Based on LSVM-Stacking Model Using Hyperspectral Reflectance Data

Zifang Zhang, Zhihua Liu, Qinghe Zhao, Kezhu Tan () and Junlong Fang ()
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Zifang Zhang: Electrical Engineering and Information College, Northeast Agricultural University, Harbin 150030, China
Zhihua Liu: Resources and Environment College, Northeast Agricultural University, Harbin 150030, China
Qinghe Zhao: Electrical Engineering and Information College, Northeast Agricultural University, Harbin 150030, China
Kezhu Tan: Electrical Engineering and Information College, Northeast Agricultural University, Harbin 150030, China
Junlong Fang: Electrical Engineering and Information College, Northeast Agricultural University, Harbin 150030, China

Agriculture, 2025, vol. 15, issue 18, 1-21

Abstract: Phaeozem, which is recognized as one of the world’s most fertile soils, derives much of its productivity from soil organic matter (SOM). Because SOM strongly influences fertility, soil structure, and ecological functions, it is the SOM content that must be rapidly and accurately determined to ensure sustainable soil management. Traditional chemical methods are reliable but time-consuming and labor-intensive, which makes them inadequate for large-scale applications. Hyperspectral reflectance, which is highly sensitive to SOM variations, provides a non-destructive alternative for rapid SOM grading. This study proposes an ensemble learning strategy model based on phaeozem hyperspectral reference data for the rapid grading and detection of SOM content. First, the SOM content of the collected phaeozem samples was determined using the potassium dichromate volumetric method. Next, hyperspectral reflectance data of the phaeozem were collected using a hyperspectral imaging sensor, with a wavelength range of 400–1000 nm. Furthermore, stacking models were constructed by modifying the internal structure, with five classifiers (MLP, SVC, DTree, XGBoost, kNN) as the L1 layer. Then, global optimization was performed using the simulated annealing algorithm. Through comparative analysis, the LSVM-stacking model demonstrated the highest accuracy and generalization capabilities. The results demonstrated that the LSVM-stacking model not only achieved the highest overall accuracy (0.9488 on the independent test set) but also improved the classification accuracy of “Category 1” samples to 1.0. Compared with other models, this framework significantly improved generalization ability and robustness. It is therefore evident that combining hyperspectral reflectance with improved stacking strategies provides a novel and effective approach for the rapid grading and detection of SOM in phaeozem.

Keywords: hyperspectral technology; non-destructive testing; phaeozem; ensemble learning; simulated annealing (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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