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

Jinya Su, Dewei Yi, Baofeng Su, Zhiwen Mi, Cunjia Liu*, Xiaoping Hu*, Xiangming Xu, Lei Guo, Wen-Hua Chen

*Corresponding author for this work

Research output: Contribution to journalArticle


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.
Original languageEnglish
Number of pages8
JournalIEEE Transactions on Industrial Informatics
Early online date9 Mar 2020
Publication statusE-pub ahead of print - 9 Mar 2020


  • deep learning
  • multispectral image
  • precision agriculture
  • semantic segmentation
  • U-net
  • unmanned aerial vehicle (UAV)

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