Abstract
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.
Original language | English |
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Pages (from-to) | 2242-2249 |
Number of pages | 8 |
Journal | IEEE Transactions on Industrial Informatics |
Volume | 17 |
Issue number | 3 |
Early online date | 9 Mar 2020 |
DOIs | |
Publication status | Published - Mar 2021 |
Bibliographical note
This work was supported by Science and Technology Facilities Council (STFC) under Newton fund with Grant No. ST/N006852/1.Keywords
- deep learning
- multispectral image
- precision agriculture
- semantic segmentation
- U-net
- unmanned aerial vehicle (UAV)
- Deep learning
- U-Net
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Dewei Yi
- School of Natural & Computing Sciences, Computing Science - Senior Lecturer
- Machine Learning
Person: Academic