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Aerial Visual Perception in Smart Farming: Field Study of Wheat Yellow Rust Monitoring

作者:   来源:   发布日期:2020-03-13  浏览次数:

论文信息:Jinya Su, Dewei Yi, Baofeng Su, Zhiwei Mi, Cunjia Liu*, Xiaoping Hu*, Xiangming Xu, Lei Guo, Wenhua Chen. Aerial Visual Perception in Smart Farming: Field Study of Wheat Yellow Rust Monitoring. IEEE Transactions on Industrial Informatics, 2020, 1-8. doi: 10.1109/TII.2020.2979237

中科院大小类1区:计算机-跨学科应用,TOP期刊,IF=7.377

论文摘要:Abstract: Agriculture is facing severe challenges from crop stresses, threatening its sustainable development and food security. This work exploits aerial visual perception for yellow rust disease monitoring, which seamlessly integrates state-of-the-art techniques and algorithms including UAV 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 labelled 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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