Proceedings of the 3rd International Conference on Smart and Innovative Agriculture (ICoSIA 2022)

<Previous Article In Volume
Next Article In Volume>

Weed and Water Stress Detection Using Drone Video

Authors
Fazeeia Mohammed1, Jade Chattergoon1, Roganci Fontelera1, Omar Mohammed2, Patrick Hosein1, *
1Department of Computer Science, The University of the West Indies, St. Augustine, Trinidad
2The Cropper Foundation, St. James, Trinidad
*Corresponding author. Email: patrick.hosein@sta.uwi.edu
Corresponding Author
Patrick Hosein
Available Online 22 May 2023.
DOI
10.2991/978-94-6463-122-7_45How to use a DOI?
Keywords
AI; Agriculture; CNN; Water stress; Drone; UAV
Abstract

Precision agriculture has greatly improved the quality and quantity of crop yield over the last four decades. However, this approach depends on the availability of sufficient quality data. Determining the amount of weed coverage and crop damage is crucial in crop management. In addition, water stress, which has been exacerbated because of Climate Change, has significantly affected crop yield. All this while population growth is increasing the need for improved food security. We report on the results of a project funded by the National Geographic Society on the application of Artificial Intelligence (AI) to Precision Agriculture. We use AI to investigate weed detection and water-stress estimation on a tropical island. These algorithms are built on data collected with an Unmanned Aerial Vehicle (UAV). We used several Machine Learning models including XG-Boost, Support Vector Machine (SVM), Naive Bayes, Convolutional Neural Networks (CNN), Mobile-Net and Random Forest. Data collected for use with these models is being made available to the public.

Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

<Previous Article In Volume
Next Article In Volume>
Volume Title
Proceedings of the 3rd International Conference on Smart and Innovative Agriculture (ICoSIA 2022)
Series
Advances in Biological Sciences Research
Publication Date
22 May 2023
ISBN
978-94-6463-122-7
ISSN
2468-5747
DOI
10.2991/978-94-6463-122-7_45How to use a DOI?
Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Fazeeia Mohammed
AU  - Jade Chattergoon
AU  - Roganci Fontelera
AU  - Omar Mohammed
AU  - Patrick Hosein
PY  - 2023
DA  - 2023/05/22
TI  - Weed and Water Stress Detection Using Drone Video
BT  - Proceedings of the 3rd International Conference on Smart and Innovative Agriculture (ICoSIA 2022)
PB  - Atlantis Press
SP  - 477
EP  - 486
SN  - 2468-5747
UR  - https://doi.org/10.2991/978-94-6463-122-7_45
DO  - 10.2991/978-94-6463-122-7_45
ID  - Mohammed2023
ER  -