Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page

Akarshani Amarasinghe

dblp:205/7970 · DBLP profile ↗
← Back
2ranked-venue papers
2as first author
0since 2021 · last 2019
0009-0002-1746-2175ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Computer networks · 2 · 2 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer networks
2 papers
Internet of things and sensor networks · 100%
Interdisciplinary, comprehensive, and emerging computing
1 paper
Environmental and earth informatics · 100%
Artificial intelligence
1 paper
Image recognition and object detection · 100%

Topics — the 4 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Internet of things and sensor networks
wireless sensor network
0.722019
A swarm of crop spraying drones solution for optimising safe pesticide usage in arable lands: poster abstract · SenSys 2019
A Machine Learning Approach for Identifying Mosquito Breeding Sites via Drone Images · SenSys 2017
Environmental and earth informatics › agriculture
precision agriculture
0.412019
A swarm of crop spraying drones solution for optimising safe pesticide usage in arable lands: poster abstract · SenSys 2019
Internet of things and sensor networks › mobile sensing › aerial sensing
aerial sensor network
0.412019
A swarm of crop spraying drones solution for optimising safe pesticide usage in arable lands: poster abstract · SenSys 2019
Internet of things and sensor networks › wireless sensor network
environmental monitoring
0.312017
A Machine Learning Approach for Identifying Mosquito Breeding Sites via Drone Images · SenSys 2017

Methods — techniques the papers use, named apart from their topics

swarm robotics · 0.8machine learning · 0.6
YearPublicationVenuePosition
2019 A swarm of crop spraying drones solution for optimising safe pesticide usage in arable lands: poster abstract
abstract
Pesticides are detrimental to the well-being of all living beings. Inappropriate pesticide usage has been identified as a major health hazard. Therefore, it is important to devise solutions that enable agricultural pest control without excessive use of pesticides. At present, drone technology is being increasingly applied in agriculture through soil and field analysis, planting, crop spraying, crop monitoring, irrigation and health assessment. The primary focus of this ongoing work is to leverage a swarm of drones solution that includes precision agriculture techniques, to efficiently spray pesticides in arable lands with optimal pesticides usage and minimum human intervention.
Akarshani Amarasinghe, Viraj B. Wijesuriya, Dilshan Ganepola, Lakshman Jayaratne
SenSys1
2017 A Machine Learning Approach for Identifying Mosquito Breeding Sites via Drone Images
abstract
Dengue is one of the deadly and fast spreading diseases in Sri Lanka. The female Aedes mosquito is the dengue vector and these mosquitoes breed in clear and non-flowing water. The Public Health Inspectors (PHIs) are tasked with detecting and eliminating such water collection areas.
Akarshani Amarasinghe, Chathura Suduwella, Charith Elvitigala, Lasith Niroshan, Rangana Jayashanka Amaraweera, Kasun Gunawardana, Prabash Kumarasinghe, Kasun De Zoysa, Chamath Keppitiyagama
SenSys1