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Use of deep learning approach on UAV imagery to detect mistletoe infestation
conference contribution
posted on 2021-04-13, 01:38 authored by Fariza SabrinaFariza Sabrina, Shaleeza Sohail, Sweta Thakur, Salahuddin AzadSalahuddin Azad, Saleh A WasimiMistletoe infestation reduces crop yield and degrades crop quality through depletion of nutrients and moisture from host plants. Timely detection of such infestation is critical for crop growers but a difficult task to perform. Published literature on such research is scarce especially for automated detection of mistletoe infestations, which can assist farmers in taking timely and effective measures. This paper reviews existing literature on mistletoe and other infestation detection through machine learning techniques. Moreover, the paper presents a deep learning-based architecture along with image pre-processing techniques, and a training method that could be used for detection of mistletoe. The experimental studies using the proposed framework are currently in-progress where aerial images of plants are to be taken from UAVs (Unmanned Aerial Vehicles). © 2020 IEEE.
History
Start Page
556End Page
559Number of Pages
4Start Date
2020-06-05Finish Date
2020-06-07eISSN
2642-6102ISSN
2640-821XISBN-13
9781728173665Location
OnlinePublisher
IEEEPlace of Publication
Piscataway, NJPublisher DOI
Full Text URL
Peer Reviewed
- Yes
Open Access
- No
External Author Affiliations
The University of Newcastle; King’s Own Institute, NSWEra Eligible
- Yes
Name of Conference
IEEE Region 10 Symposium (TENSYMP 2020)Parent Title
2020 IEEE Region 10 Symposium, TENSYMP 2020Usage metrics
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