- Title
- Use of Deep Learning Approach on UAV imagery to Detect Mistletoe Infestation
- Creator
- Sabrina, Fariza; Sohail, Shaleeza; Thakur, Sweta; Azad, Salahuddin; Wasimi, Saleh
- Relation
- 2020 IEEE Region 10 Symposium (TENSYMP). Proceedings of the 2020 IEEE Region 10 Symposium (TENSYMP) (Dakha, Bangladesh 5-7 June, 2020) p. 556-559
- Publisher Link
- http://dx.doi.org/10.1109/TENSYMP50017.2020.9230971
- Publisher
- Institute of Electrical and Electronics Engineers (IEEE)
- Resource Type
- conference paper
- Date
- 2020
- Description
- 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).
- Subject
- mistletoe infestation; deep learning; agriculture; machine learning techniques
- Identifier
- http://hdl.handle.net/1959.13/1440824
- Identifier
- uon:41234
- Identifier
- ISBN:9781728173665
- Language
- eng
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