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Classification and Recognition of Soybean Quality Based on Hyperspectral Imaging and Random Forest Methods

文献类型： 外文期刊

作者： Chen, Man 1 ; Chang, Zhichang 1 ; Jin, Chengqian 1 ; Cheng, Gong 1 ; Wang, Shiguo 3 ; Ni, Youliang 1 ;

作者机构： 1.Minist Agr & Rural Affairs, Nanjing Inst Agr Mechanizat, Nanjing 210014, Peoples R China

2.Natl Digital Agr Equipment South China Intelligenc, Nanjing 210014, Peoples R China

3.Xinjiang Acad Agr & Reclamat Sci, Inst Mech Equipment, Shihezi 832000, Peoples R China

关键词： soybean; hyperspectral imaging; feature extraction; random forest; classification and identification

期刊名称：SENSORS （ 影响因子：3.5； 五年影响因子：3.7 ）

ISSN：

年卷期： 2025 年 25 卷 5 期

页码：

收录情况： SCI

摘要： To achieve the rapid and accurate classification and identification of soybean components, this study selected soybeans harvested by the 4LZ-1.5 soybean combine harvester as the research subject. Hyperspectral images of soybean samples were collected using the Pika L spectrometer, and spectral information was extracted from the regions of interest (ROI) in the images. Eight preprocessing methods, including baseline correction (BC), moving average (MA), Savitzky-Golay derivative (SGD), normalization, standard normal variate transformation (SNV), multiplicative scatter correction (MSC), first derivative (DS), and Savitzky-Golay smoothing (SGS), were applied to the raw spectral data to eliminate irrelevant information. Feature wavelengths were selected using the successive projections algorithm (SPA) and the competitive adaptive reweighted sampling (CARS) algorithm to reduce spectral redundancy and enhance model detection performance, retaining eight and ten feature wavelengths, respectively. Subsequently, a random forest (RF) model was developed for soybean component classification. The model parameters were optimized using particle swarm optimization (PSO) and differential evolution (DE) algorithms to improve performance. Experimental results showed that the RF classification model based on SPA-BC preprocessed spectra and DE-tuned parameters achieved an optimal prediction accuracy of 1.0000 during training. This study demonstrates the feasibility of using hyperspectral imaging technology for the rapid and accurate detection of soybean components, providing technical support for the assessment of breakage and impurity levels during soybean harvesting and storage processes. It also offers a reference for the development of future machine-harvested soybean breakage and impurity detection systems.

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