Zhang Haowen, He Qinghai, Yang Chongshan, Lu Min, Liu Zhongyuan, Zhang Xiaojia, Li Xiaoli, Dong Chunwang
Tea Research Institute, Shandong Academy of Agricultural Sciences, Jinan 250100, China.
Shandong Academy of Agricultural Machinery Science, Jinan 250100, China.
Sensors (Basel). 2023 Dec 7;23(24):9684. doi: 10.3390/s23249684.
Soil organic matter is an important component that reflects soil fertility and promotes plant growth. The soil of typical Chinese tea plantations was used as the research object in this work, and by combining soil hyperspectral data and image texture characteristics, a quantitative prediction model of soil organic matter based on machine vision and hyperspectral imaging technology was built. Three methods, standard normalized variate (SNV), multisource scattering correction (MSC), and smoothing, were first used to preprocess the spectra. After that, random frog (RF), variable combination population analysis (VCPA), and variable combination population analysis and iterative retained information variable (VCPA-IRIV) algorithms were used to extract the characteristic bands. Finally, the quantitative prediction model of nonlinear support vector regression (SVR) and linear partial least squares regression (PLSR) for soil organic matter was established by combining nine color features and five texture features of hyperspectral images. The outcomes demonstrate that, in comparison to single spectral data, fusion data may greatly increase the performance of the prediction model, with MSC + VCPA-IRIV + SVR (RC = 0.995, RP = 0.986, RPD = 8.155) being the optimal approach combination. This work offers excellent justification for more investigation into nondestructive methods for determining the amount of organic matter in soil.
土壤有机质是反映土壤肥力并促进植物生长的重要组成部分。本研究以典型中国茶园土壤为研究对象，通过结合土壤高光谱数据和图像纹理特征，构建了基于机器视觉和高光谱成像技术的土壤有机质定量预测模型。首先采用标准归一化变量变换（SNV）、多源散射校正（MSC）和平滑三种方法对光谱进行预处理。之后，使用随机蛙跳（RF）、变量组合群体分析（VCPA）以及变量组合群体分析与迭代保留信息变量（VCPA - IRIV）算法提取特征波段。最后，结合高光谱图像的九个颜色特征和五个纹理特征，建立了土壤有机质的非线性支持向量回归（SVR）和线性偏最小二乘回归（PLSR）定量预测模型。结果表明，与单光谱数据相比，融合数据可显著提高预测模型的性能，其中MSC + VCPA - IRIV + SVR（相关系数RC = 0.995，预测精度RP = 0.986，相对预测偏差RPD = 8.155）为最优方法组合。本研究为进一步探索土壤有机质含量无损检测方法提供了有力依据。
