Jia Shengyao, Li Hongyang, Wang Yanjie, Tong Renyuan, Li Qing
College of Mechanical and Electrical Engineering, China Jiliang University, Hangzhou 310018, China.
College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310014, China.
Sensors (Basel). 2017 Sep 30;17(10):2252. doi: 10.3390/s17102252.
Soil is an important environment for crop growth. Quick and accurately access to soil nutrient content information is a prerequisite for scientific fertilization. In this work, hyperspectral imaging (HSI) technology was applied for the classification of soil types and the measurement of soil total nitrogen (TN) content. A total of 183 soil samples collected from Shangyu City (People's Republic of China), were scanned by a near-infrared hyperspectral imaging system with a wavelength range of 874-1734 nm. The soil samples belonged to three major soil types typical of this area, including paddy soil, red soil and seashore saline soil. The successive projections algorithm (SPA) method was utilized to select effective wavelengths from the full spectrum. Pattern texture features (energy, contrast, homogeneity and entropy) were extracted from the gray-scale images at the effective wavelengths. The support vector machines (SVM) and partial least squares regression (PLSR) methods were used to establish classification and prediction models, respectively. The results showed that by using the combined data sets of effective wavelengths and texture features for modelling an optimal correct classification rate of 91.8%. could be achieved. The soil samples were first classified, then the local models were established for soil TN according to soil types, which achieved better prediction results than the general models. The overall results indicated that hyperspectral imaging technology could be used for soil type classification and soil TN determination, and data fusion combining spectral and image texture information showed advantages for the classification of soil types.
土壤是作物生长的重要环境。快速、准确地获取土壤养分含量信息是科学施肥的前提。在这项工作中，高光谱成像（HSI）技术被应用于土壤类型分类和土壤全氮（TN）含量测定。共采集了183个来自中国上虞市的土壤样本，用波长范围为874 - 1734 nm的近红外高光谱成像系统进行扫描。这些土壤样本属于该地区典型的三种主要土壤类型，包括水稻土、红壤和滨海盐土。采用连续投影算法（SPA）从全光谱中选择有效波长。从有效波长处的灰度图像中提取模式纹理特征（能量、对比度、均匀性和熵）。分别使用支持向量机（SVM）和偏最小二乘回归（PLSR）方法建立分类和预测模型。结果表明，利用有效波长和纹理特征的组合数据集进行建模，可实现91.8%的最佳正确分类率。先对土壤样本进行分类，然后根据土壤类型建立土壤TN的局部模型，其预测结果优于通用模型。总体结果表明，高光谱成像技术可用于土壤类型分类和土壤TN测定，光谱和图像纹理信息相结合的数据融合在土壤类型分类方面具有优势。
