多光谱图像
遥感
计算机科学
人工智能
Rust(编程语言)
RGB颜色模型
精准农业
光谱带
航空影像
高光谱成像
农业
计算机视觉
地理
图像(数学)
考古
程序设计语言
作者
Jinya Su,Dewei Yi,Baofeng Su,Zhiwen Mi,Cunjia Liu,Xiaoping Hu,Xiangming Xu,Lei Guo,Wenhua Chen
出处
期刊:IEEE Transactions on Industrial Informatics
[Institute of Electrical and Electronics Engineers]
日期:2021-03-01
卷期号:17 (3): 2242-2249
被引量:84
标识
DOI:10.1109/tii.2020.2979237
摘要
Agriculture is facing severe challenges from crop stresses, threatening its sustainable development and food security. This article exploits aerial visual perception for yellow rust disease monitoring, which seamlessly integrates state-of-the-art techniques and algorithms, including unmanned aerial vehicle sensing, multispectral imaging, vegetation segmentation, and deep learning U-Net. A field experiment is designed by infecting winter wheat with yellow rust inoculum, on top of which multispectral aerial images are captured by DJI Matrice 100 equipped with RedEdge camera. After image calibration and stitching, multispectral orthomosaic is labeled for system evaluation by inspecting high-resolution RGB images taken by Parrot Anafi Drone. The merits of the developed framework drawing spectral-spatial information concurrently are demonstrated by showing improved performance over purely spectral-based classifier by the classical random forest algorithm. Moreover, various network input band combinations are tested, including three RGB bands and five selected spectral vegetation indices, by sequential forward selection strategy of wrapper algorithm.
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