土壤和作物的高光谱成像:综述

C. Vairavan, B. M. Kamble, A. G. Durgude, Snehal R. Ingle, K. Pugazenthi
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引用次数: 0

摘要

遥感是精密技术之一,可用于监测和评估土壤、作物和水等目标区域或对象。高光谱成像(HSI),又称成像光谱学或高光谱遥感,是光谱学和成像系统的结合技术,用于感测区域或物体的光谱信息。它使用多个不同的光学波段来捕捉物体的图像,这些波段覆盖电磁波谱(350-2500 nm)的大范围。高光谱波段是连续的、窄的、有传染性的,包含成百上千个数字。高光谱遥感对于收集农业规划和精准农业所需的最新精确信息尤为重要。高光谱遥感技术是利用高光谱传感器来分析土壤的物理(容重、质地、含水量)、化学(pH 值、EC 值、SOC 值、宏养分和微养分)、生物(SOM)特性,并帮助对不同作物品种进行分类、识别病虫害、评估作物产量和植物的水分胁迫。土壤的光谱反射率受其特性的影响,如矿物成分(铁氧化物)、有机质、土壤水分和质地。例如,如果土壤中的有机物较少,光谱反射率就会较高。土壤分子的化学键与电磁波谱相互作用,产生不同的反射模式。但是,高光谱成像采集的数据量大,需要大量存储,而且寻找最合适的高光谱图像分类算法也是一项具有挑战性的任务。因此,这些问题应在未来得到解决,同时需要国家土壤光谱库来校准模型,这有助于高效利用高光谱成像技术。
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Hyperspectral Imaging of Soil and Crop: A Review
The remote sensing is one of the precision technologies, can be used to monitor and assess the target area or object such as soil, crop, and water. Hyperspectral imaging (HSI), also known as imaging spectrometry or hyperspectral remote sensing, is a combined technique of spectroscopy and imaging system for sensing spectral information of an area or object. It involves capturing images of an object using multiple distinct optical bands that cover a wide range of the electromagnetic spectrum (350-2500 nm). The hyperspectral bands are continuous, narrow, and contagious and contain hundreds and thousands of numbers. Hyperspectral remote sensing is particularly valuable for gathering precise and up-to-date information necessary for agricultural planning and precision farming. HSI technology is the employment of hyperspectral sensors aids in analyzing soil physical (bulk density, texture, water content), chemical (pH, EC, SOC, and macro and micro nutrients), biological (SOM) properties and helps to categorize different crop varieties, identify pests and diseases, and assess crop yield and water stress in plants. The spectral reflectance of soil is affected by its properties such as mineral composition (Fe oxides), organic matter, soil moisture, and texture. For example, the spectral reflectance will be more if soil has less organic matter. The chemical bonds of soil molecules interact with the electromagnetic spectrum, and produce distinct pattern of reflectance. But the data collected from hyperspectral imaging are required big storage due to its large amount of data and finding the most appropriate hyperspectral image classification algorithm is a challenging task. So, these problems should be solved in future and national soil spectral library is needed for calibration of models which helps for efficient use of hyperspectral imaging technology.
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