从众包技术获得的照片中识别可耕地土壤

Q4 Agricultural and Biological Sciences
E. Prudnikova, I. Savin, G. Vindeker
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引用次数: 0

摘要

这项研究的重点是使用众包技术获得的照片进行可耕地土壤操作库存的可能性。该研究的目的是使用HandHeld-2光谱辐射计在325–1075 nm范围内测量试验地块耕地开放表面的光谱反射率,以及用传统相机拍摄的照片中的图像。测试地点位于图拉、莫斯科和特维尔地区。试验地块的土壤为草皮灰化土、灰色森林和浸出黑钙土。基于对表面照片和使用光谱辐射计获得的信息的分析,计算了RGB、YMC和HSI颜色系统中的一组光谱参数及其比值(45个参数)。这些参数用于使用分类树分离分析的土壤类型。基于验证结果的分类准确率在63–100%之间。同时,HSI和YMC颜色系统的参数比RGB颜色系统的数据量更大。建立的分类规则稍后可以用于从使用众包技术收集的图像中确定土壤的分类位置。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Recognition of arable soils from photographs obtained as part of crowdsourcing technologies
The study focuses on the possibilities of using photographs obtained using crowdsourcing technologies for the operational inventory of arable soils. The object of the study is the spectral reflectance of the open surface of arable soils of the test plots, measured using a HandHeld-2 spectroradiometer operating in the range of 325–1 075 nm, and their image in photographs taken with conventional cameras. Test sites are located in the Tula, Moscow and Tver regions. The soils of the test plots are sod-podzolic, gray forest, and leached chernozems. Based on the analysis of photographs of the surface and information obtained using a spectroradiometer, a set of spectral parameters in the RGB, YMC and HSI color systems, as well as their ratios (45 parameters), was calculated. These parameters were used to separate the analyzed soil types using classification trees. The accuracy of classification based on the results of validation varies from 63–100%. At the same time, the parameters of the HSI and YMC color systems turned out to be more informative than the parameters of the RGB color system. The established classification rules can later be used to determine the classification position of soils from images collected using crowdsourcing technologies.
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来源期刊
CiteScore
0.90
自引率
0.00%
发文量
15
审稿时长
8 weeks
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