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.
Highlights
IntroductionTransportation surveillance [2], aircraft detection [3], smart health [4], industrial inspection [5])
Visual perception is to interpret the environment by the light reflected by the objects via image analysis [1] and is finding a wide range of applications in smart society (e.g.transportation surveillance [2], aircraft detection [3], smart health [4], industrial inspection [5])
This work proposes an automated monitoring framework for yellow rust disease in winter wheat by seamlessly integrating deep learning algorithms and multispectral aerial images collected by a small Unmanned Aerial Vehicle (UAV) at an experimental wheat field
Summary
Transportation surveillance [2], aircraft detection [3], smart health [4], industrial inspection [5]) Following this line of thought, this work aims to exploit aerial visual perception in smart farming to tackle the grand challenge facing modern agriculture: feeding a growing world population with an ageing structure while protecting the environment. This is achieved by developing a disease monitoring framework for precision stress management. It is estimated that yield loss caused by yellow rust disease is at least 5.5 million tons per year at a global level
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
