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Data-driven vermiculite distribution modelling for UAV-based precision pest management
In recent decades, unmanned aerial vehicles (UAVs) have gained considerable popularity in the agricultural sector, in which UAV-based actuation is used to spray pesticides and release biological control agents. A key challenge in such UAV-based actuation is to account for wind speed and UAV flight p...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9399770/ https://www.ncbi.nlm.nih.gov/pubmed/36035868 http://dx.doi.org/10.3389/frobt.2022.854381 |
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author | Ma , Na Mantri , Anil Bough , Graham Patnaik , Ayush Yadav , Siddhesh Nansen , Christian Kong , Zhaodan |
author_facet | Ma , Na Mantri , Anil Bough , Graham Patnaik , Ayush Yadav , Siddhesh Nansen , Christian Kong , Zhaodan |
author_sort | Ma , Na |
collection | PubMed |
description | In recent decades, unmanned aerial vehicles (UAVs) have gained considerable popularity in the agricultural sector, in which UAV-based actuation is used to spray pesticides and release biological control agents. A key challenge in such UAV-based actuation is to account for wind speed and UAV flight parameters to maximize precision-delivery of pesticides and biological control agents. This paper describes a data-driven framework to predict density distribution patterns of vermiculite dispensed from a hovering UAV as a function of UAV’s movement state, wind condition, and dispenser setting. The model, derived by our proposed learning algorithm, is able to accurately predict the vermiculite distribution pattern evaluated in terms of both training and test data. Our framework and algorithm can be easily translated to other precision pest management problems with different UAVs and dispensers and for difference pesticides and crops. Moreover, our model, due to its simple analytical form, can be incorporated into the design of a controller that can optimize autonomous UAV delivery of desired amount of predatory mites to multiple target locations. |
format | Online Article Text |
id | pubmed-9399770 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-93997702022-08-25 Data-driven vermiculite distribution modelling for UAV-based precision pest management Ma , Na Mantri , Anil Bough , Graham Patnaik , Ayush Yadav , Siddhesh Nansen , Christian Kong , Zhaodan Front Robot AI Robotics and AI In recent decades, unmanned aerial vehicles (UAVs) have gained considerable popularity in the agricultural sector, in which UAV-based actuation is used to spray pesticides and release biological control agents. A key challenge in such UAV-based actuation is to account for wind speed and UAV flight parameters to maximize precision-delivery of pesticides and biological control agents. This paper describes a data-driven framework to predict density distribution patterns of vermiculite dispensed from a hovering UAV as a function of UAV’s movement state, wind condition, and dispenser setting. The model, derived by our proposed learning algorithm, is able to accurately predict the vermiculite distribution pattern evaluated in terms of both training and test data. Our framework and algorithm can be easily translated to other precision pest management problems with different UAVs and dispensers and for difference pesticides and crops. Moreover, our model, due to its simple analytical form, can be incorporated into the design of a controller that can optimize autonomous UAV delivery of desired amount of predatory mites to multiple target locations. Frontiers Media S.A. 2022-08-10 /pmc/articles/PMC9399770/ /pubmed/36035868 http://dx.doi.org/10.3389/frobt.2022.854381 Text en Copyright © 2022 Ma , Mantri , Bough , Patnaik , Yadav , Nansen and Kong . https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Robotics and AI Ma , Na Mantri , Anil Bough , Graham Patnaik , Ayush Yadav , Siddhesh Nansen , Christian Kong , Zhaodan Data-driven vermiculite distribution modelling for UAV-based precision pest management |
title | Data-driven vermiculite distribution modelling for UAV-based precision pest management |
title_full | Data-driven vermiculite distribution modelling for UAV-based precision pest management |
title_fullStr | Data-driven vermiculite distribution modelling for UAV-based precision pest management |
title_full_unstemmed | Data-driven vermiculite distribution modelling for UAV-based precision pest management |
title_short | Data-driven vermiculite distribution modelling for UAV-based precision pest management |
title_sort | data-driven vermiculite distribution modelling for uav-based precision pest management |
topic | Robotics and AI |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9399770/ https://www.ncbi.nlm.nih.gov/pubmed/36035868 http://dx.doi.org/10.3389/frobt.2022.854381 |
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