土壤是农业生产的基础, 通过土壤成分含量的测定分析可了解土壤营养成分供应的丰缺情况, 对农作物的生长有一定的影响, 因此土壤成分含量的检测逐渐成为国内外的研究热点。 多光谱技术利用物体的物理结构和化学成分的不同, 在同一条件下利用不同光的反射对物体进行照射得到对应光谱带上的不同反射率, 然后对获取的光谱数据进行分析, 辨别目标。 近年来, 多光谱技术的应用为土壤成分含量检测提供了一个新思路, 有助于土壤成分含量的精确检测, 有助于实现无损实时在线检测和精准农业。 综述了近6年来国内外多光谱技术在土壤成分指标土壤水、 有机质、 氮磷钾、 重金属及土壤盐分含量检测领域应用的相关文献, 分析了多光谱成像技术的特点, 简述了多光谱技术对土壤成分含量的检测过程, 重点阐述了多光谱技术在土壤成分含量检测中的研究进展, 并展望了多光谱技术在土壤成分含量检测中的未来发展趋势, 提出未来技术发展方向: 机器学习算法的无监督和监督模型能够分析不同实际测量环境下的数据, 减少土壤成分分布不均的光谱数据对建模结果的影响； 多光谱图像与全色图像相结合获取多光谱全色波段, 能够在多光谱土壤成分含量检测中提高预测模型的分析精度和准确度； 多光谱数据预处理过程中采用两种及两种以上算法相结合将使光谱数据处理更加有效。

Soil is the basis of agricultural production. The determination of soil nutrient content is determined by the determination of soil nutrient content, and it has a certain influence on the growth of crops. Therefore, the detection of soil component content has gradually become a research hotspot at home and abroad. Multi-spectral technology utilizes the difference in the physical structure and chemical composition of an object. Under the same conditions, the object is irradiated with different light reflections to obtain different reflectances on the corresponding spectral bands, and then the acquired spectral data is analyzed to identify the target. In recent years, the application of multi-spectral technology has provided a new idea for the detection of soil component content which is helpful for the accurate detection of soil component content, and contributes to the realization of non-destructive real-time online detection technology and precision agriculture. In this paper, the related pieces of literature on the application of multi-spectral techniques in the soil composition index of soil water, organic matter, NPK, heavy metals and soil salinity in the past six years are reviewed. The characteristics of multi-spectral imaging technology are analyzed, and the multi-spectral is briefly described. The detection process of soil composition content by technology focuses on the research progress of multi-spectral technology in soil component content detection, and prospects the future development trend of multi-spectral technology in soil component content detection, and proposes the future technology development direction: machine learning. The unsupervised and supervised model of the algorithm can analyze the data in different actual measurement environments, reduce the influence of spectral data with uneven distribution of soil components on the modeling results; multi-spectral images combined with panchromatic images to obtain multi-spectral panchromatic bands, In the multi-spectral soil component content detection, the accuracy and accuracy of the prediction model are improved. In the multi-spectral data preprocessing process, two or more algorithms are combined to process the spectral data more effectively.