Proceedings of the International Conference on Science Technology and Social Sciences – Physics, Material and Industrial Technology (ICONSTAS-PMIT 2023)

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ORB-Based Homography Computation for Progress Mango Orchard Land Monitoring

Authors
Ardi Mardiana1, *, Dony Susandi2, Yani Syafei3, Trifenaus Prabu Hidayat4, Indah Latifatun Nissa1, Muhammad Dendi Purwanto1
1Department of Informatics, Universitas Majalengka, Majalengka, Indonesia
2Department of Industrial Engineering, Universitas Majalengka, Majalengka, Indonesia
3Department of Industrial Engineering, Universitas Komputer Indonesia, Banding, Indonesia
4Department of Industrial Engineering, Universitas Katolik Indonesia, Atma Jaya, Yogyakarta, Indonesia
*Corresponding author. Email: aim@unma.ac.id
Corresponding Author
Ardi Mardiana
Available Online 29 August 2024.
DOI
10.2991/978-94-6463-500-3_7How to use a DOI?
Keywords
Agricultural Monitoring; Homography Computation; Image Stitching; Mango Buds; ORB Algorithm
Abstract

Indonesia cultivates mangoes across an average land area of 176,000 hectares, yielding 1.4 million tons annually. Researchers have conducted similar studies to enhance mango orchard management techniques. This study uses OpenCV- based image stitching to assess mango orchard areas and provide accurate data to improve effective management. The research involves collecting captured images in extensive agricultural regions and employing techniques such as feature detection, feature matching, computational homography, image warping, and image stitching to gather valuable data. The researchers have implemented the ORB algorithm for feature detection, enabling the identification of matching points among images. Image warping is conducted using the RANSAC algorithm to estimate image geometry. Finally, the research employs flexible camera calibration for precise image registration. This research contributes significantly to the agricultural industry by offering a non-invasive and efficient approach to preparing land for mango tree cultivation.

Copyright
© 2024 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.

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Volume Title
Proceedings of the International Conference on Science Technology and Social Sciences – Physics, Material and Industrial Technology (ICONSTAS-PMIT 2023)
Series
Advances in Engineering Research
Publication Date
29 August 2024
ISBN
978-94-6463-500-3
ISSN
2352-5401
DOI
10.2991/978-94-6463-500-3_7How to use a DOI?
Copyright
© 2024 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  - Ardi Mardiana
AU  - Dony Susandi
AU  - Yani Syafei
AU  - Trifenaus Prabu Hidayat
AU  - Indah Latifatun Nissa
AU  - Muhammad Dendi Purwanto
PY  - 2024
DA  - 2024/08/29
TI  - ORB-Based Homography Computation for Progress Mango Orchard Land Monitoring
BT  - Proceedings of the International Conference on Science Technology and Social Sciences – Physics, Material and Industrial Technology (ICONSTAS-PMIT 2023)
PB  - Atlantis Press
SP  - 63
EP  - 77
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-500-3_7
DO  - 10.2991/978-94-6463-500-3_7
ID  - Mardiana2024
ER  -