Machine Learning Based Approach for Weed Detection in Chilli Field Using RGB Images

Nahina Islam; Md Mamunur Rashid; Santoso Wibowo; Saleh Wasimi; Ahsan Morshed; Chengyuan Xu; Steven Moore

doi:10.1007/978-3-030-70665-4_119

Machine Learning Based Approach for Weed Detection in Chilli Field Using RGB Images

Nahina Islam, Md Mamunur Rashid, Santoso Wibowo, Saleh Wasimi, Ahsan Morshed, Chengyuan Xu, Steven Moore

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

14 Citations (Scopus)

Abstract

Smart farming has become imperative these days due to competition, and use of Unmanned Aerial Vehicle (UAV) imagery is becoming an integral part of the process. Machine learning techniques have been successfully applied to capture UAV imagery of various spectral bands to identify weed infestations. Identification of weeds in chilli crop is a challenging task. In this paper, RGB images captured by drones have been used to detect weed in chilli field. This task has been addressed through orthomasaicking of images, feature extraction, labelling of images to train machine learning algorithms, and use of unsupervised learning with random forest for classification. MATLAB has been used for all computations and out-of-bag accuracy achieved for identifying weeds is 96 %.

Original language	English
Title of host publication	Lecture Notes on Data Engineering and Communications Technologies
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	1097-1105
Number of pages	9
DOIs	https://doi.org/10.1007/978-3-030-70665-4_119
Publication status	Published - 2021
Externally published	Yes

Publication series

Name	Lecture Notes on Data Engineering and Communications Technologies
Volume	88
ISSN (Print)	2367-4512
ISSN (Electronic)	2367-4520

Bibliographical note

Funding Information:
Acknowledgement. This research is partially funded by research RSH/5339, funded by Central Queensland University, Australia.

Publisher Copyright:
© 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Access to Document

10.1007/978-3-030-70665-4_119

Cite this

Islam, N., Rashid, M. M., Wibowo, S., Wasimi, S., Morshed, A., Xu, C., & Moore, S. (2021). Machine Learning Based Approach for Weed Detection in Chilli Field Using RGB Images. In Lecture Notes on Data Engineering and Communications Technologies (pp. 1097-1105). (Lecture Notes on Data Engineering and Communications Technologies; Vol. 88). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-70665-4_119

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abstract = "Smart farming has become imperative these days due to competition, and use of Unmanned Aerial Vehicle (UAV) imagery is becoming an integral part of the process. Machine learning techniques have been successfully applied to capture UAV imagery of various spectral bands to identify weed infestations. Identification of weeds in chilli crop is a challenging task. In this paper, RGB images captured by drones have been used to detect weed in chilli field. This task has been addressed through orthomasaicking of images, feature extraction, labelling of images to train machine learning algorithms, and use of unsupervised learning with random forest for classification. MATLAB has been used for all computations and out-of-bag accuracy achieved for identifying weeds is 96 %.",

keywords = "Image processing, Machine learning, Random Forest, Smart farming, Unmanned aerial vehicle, Weed detection",

author = "Nahina Islam and Rashid, {Md Mamunur} and Santoso Wibowo and Saleh Wasimi and Ahsan Morshed and Chengyuan Xu and Steven Moore",

note = "Funding Information: Acknowledgement. This research is partially funded by research RSH/5339, funded by Central Queensland University, Australia. Publisher Copyright: {\textcopyright} 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.",

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language = "English",

series = "Lecture Notes on Data Engineering and Communications Technologies",

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Islam, N, Rashid, MM, Wibowo, S, Wasimi, S, Morshed, A, Xu, C & Moore, S 2021, Machine Learning Based Approach for Weed Detection in Chilli Field Using RGB Images. in Lecture Notes on Data Engineering and Communications Technologies. Lecture Notes on Data Engineering and Communications Technologies, vol. 88, Springer Science and Business Media Deutschland GmbH, pp. 1097-1105. https://doi.org/10.1007/978-3-030-70665-4_119

Machine Learning Based Approach for Weed Detection in Chilli Field Using RGB Images. / Islam, Nahina; Rashid, Md Mamunur; Wibowo, Santoso et al.
Lecture Notes on Data Engineering and Communications Technologies. Springer Science and Business Media Deutschland GmbH, 2021. p. 1097-1105 (Lecture Notes on Data Engineering and Communications Technologies; Vol. 88).

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

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AU - Wibowo, Santoso

AU - Wasimi, Saleh

AU - Morshed, Ahsan

AU - Xu, Chengyuan

AU - Moore, Steven

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Islam N, Rashid MM, Wibowo S, Wasimi S, Morshed A, Xu C et al. Machine Learning Based Approach for Weed Detection in Chilli Field Using RGB Images. In Lecture Notes on Data Engineering and Communications Technologies. Springer Science and Business Media Deutschland GmbH. 2021. p. 1097-1105. (Lecture Notes on Data Engineering and Communications Technologies). doi: 10.1007/978-3-030-70665-4_119

Machine Learning Based Approach for Weed Detection in Chilli Field Using RGB Images

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