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Use of deep learning approach on UAV imagery to detect mistletoe infestation

conference contribution
posted on 13.04.2021, 01:38 by Fariza SabrinaFariza Sabrina, Shaleeza Sohail, Sweta Thakur, Salahuddin AzadSalahuddin Azad, Saleh A Wasimi
Mistletoe 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

556

End Page

559

Number of Pages

4

Start Date

05/06/2020

Finish Date

07/06/2020

eISSN

2642-6102

ISSN

2640-821X

ISBN-13

9781728173665

Location

Online

Publisher

IEEE

Place of Publication

Piscataway, NJ

Peer Reviewed

Yes

Open Access

No

External Author Affiliations

The University of Newcastle; King’s Own Institute, NSW

Era Eligible

Yes

Name of Conference

IEEE Region 10 Symposium (TENSYMP 2020)

Parent Title

2020 IEEE Region 10 Symposium, TENSYMP 2020