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Aerial visual perception in smart farming: field study of wheat yellow rust monitoring

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journal contribution
posted on 18.02.2021, 09:16 by Jinya Su, Dewei Yi, Baofeng Su, Zhiwen Mi, Cunjia Liu, Xiaoping Hu, Xiangming Xu, Lei Guo, Wen-Hua Chen
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.

Funding

Enabling wide area persistent remote sensing for agriculture applications by developing and coordinating multiple heterogeneous platforms

Science and Technology Facilities Council

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History

School

  • Aeronautical, Automotive, Chemical and Materials Engineering

Department

  • Aeronautical and Automotive Engineering

Published in

IEEE Transactions on Industrial Informatics

Volume

17

Issue

3

Pages

2242 - 2249

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Version

AM (Accepted Manuscript)

Rights holder

© IEEE

Publisher statement

© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Acceptance date

27/02/2020

Publication date

2020-03-09

Copyright date

2020

ISSN

1551-3203

eISSN

1941-0050

Language

en

Depositor

Prof Wen-Hua Chen. Deposit date: 17 February 2021