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A Multispectral Image Classification Framework for Estimating the Operational Risk of Lethal Wilt in Oil Palm Crops

Alejandro Peña Orcid Logo, Alejandro Puerta, Isis Bonet Orcid Logo, Fabio Caraffini Orcid Logo, Mario Gongora, Ivan Ochoa Orcid Logo

Applications of Evolutionary Computation, Pages: 491 - 506

Swansea University Author: Fabio Caraffini Orcid Logo

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DOI (Published version): 10.1007/978-3-031-30229-9_32

Abstract

Operational risk is the risk associated with business operations in an organisation. With respect to agricultural crops, in particular, operational risk is a fundamental concept to establish differentiated coverage and to seek protection against different risks. For cultivation, these risks are rela...

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Published in: Applications of Evolutionary Computation
ISBN: 9783031302282 9783031302299
ISSN: 0302-9743 1611-3349
Published: Cham Springer Nature Switzerland 2023
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URI: https://cronfa.swan.ac.uk/Record/cronfa63100
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first_indexed 2023-04-09T08:35:18Z
last_indexed 2023-04-11T03:21:56Z
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spelling 2023-04-10T15:15:39.1759939 v2 63100 2023-04-09 A Multispectral Image Classification Framework for Estimating the Operational Risk of Lethal Wilt in Oil Palm Crops d0b8d4e63d512d4d67a02a23dd20dfdb 0000-0001-9199-7368 Fabio Caraffini Fabio Caraffini true false 2023-04-09 SCS Operational risk is the risk associated with business operations in an organisation. With respect to agricultural crops, in particular, operational risk is a fundamental concept to establish differentiated coverage and to seek protection against different risks. For cultivation, these risks are related to the agricultural business process and to external risk events. An operational risk assessment allows one to identify the limits of environmental and financial sustainability. Specifically, in oil palm cultivation, the characterisation of the associated risk remains a challenge from a technological perspective. To advance in this direction, researchers have used different technologies, including spectral aerial images, unmanned aerial vehicles to construct a vegetation index, intelligent augmented platforms for real-time monitoring, and adaptive fuzzy models to estimate operational risk. In line with these technological developments, in this article we propose a framework for the estimation of the risk assessment associated with the disease of Lethal Wilt (LW) in oil palm plantations. Although our purpose is not to predict lethal wilt, since the framework starts from the result of a prediction model, a model to detect LW in an early stage is used for the demonstration. For the implementation of the prediction model, we use a novel deep learning system based on two neural networks. This refers to a case study conducted at UNIPALMAS. We show that the suitability of our system aims to evaluate operational risks of LW with a confidence level of 99.9% and for a period of 6 months. Book chapter Applications of Evolutionary Computation 491 506 Springer Nature Switzerland Cham 9783031302282 9783031302299 0302-9743 1611-3349 1 1 2023 2023-01-01 10.1007/978-3-031-30229-9_32 http://dx.doi.org/10.1007/978-3-031-30229-9_32 COLLEGE NANME Computer Science COLLEGE CODE SCS Swansea University Not Required Royal Academy of Engineering, Newton Fund Industry Academia Partnership Programme (IAPP1\100130) 2023-04-10T15:15:39.1759939 2023-04-09T08:01:21.1517962 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Alejandro Peña 0000-0001-9441-9280 1 Alejandro Puerta 2 Isis Bonet 0000-0002-3031-2334 3 Fabio Caraffini 0000-0001-9199-7368 4 Mario Gongora 5 Ivan Ochoa 0000-0003-1651-3831 6
title A Multispectral Image Classification Framework for Estimating the Operational Risk of Lethal Wilt in Oil Palm Crops
spellingShingle A Multispectral Image Classification Framework for Estimating the Operational Risk of Lethal Wilt in Oil Palm Crops
Fabio Caraffini
title_short A Multispectral Image Classification Framework for Estimating the Operational Risk of Lethal Wilt in Oil Palm Crops
title_full A Multispectral Image Classification Framework for Estimating the Operational Risk of Lethal Wilt in Oil Palm Crops
title_fullStr A Multispectral Image Classification Framework for Estimating the Operational Risk of Lethal Wilt in Oil Palm Crops
title_full_unstemmed A Multispectral Image Classification Framework for Estimating the Operational Risk of Lethal Wilt in Oil Palm Crops
title_sort A Multispectral Image Classification Framework for Estimating the Operational Risk of Lethal Wilt in Oil Palm Crops
author_id_str_mv d0b8d4e63d512d4d67a02a23dd20dfdb
author_id_fullname_str_mv d0b8d4e63d512d4d67a02a23dd20dfdb_***_Fabio Caraffini
author Fabio Caraffini
author2 Alejandro Peña
Alejandro Puerta
Isis Bonet
Fabio Caraffini
Mario Gongora
Ivan Ochoa
format Book chapter
container_title Applications of Evolutionary Computation
container_start_page 491
publishDate 2023
institution Swansea University
isbn 9783031302282
9783031302299
issn 0302-9743
1611-3349
doi_str_mv 10.1007/978-3-031-30229-9_32
publisher Springer Nature Switzerland
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Mathematics and Computer Science - Computer Science{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Computer Science
url http://dx.doi.org/10.1007/978-3-031-30229-9_32
document_store_str 0
active_str 0
description Operational risk is the risk associated with business operations in an organisation. With respect to agricultural crops, in particular, operational risk is a fundamental concept to establish differentiated coverage and to seek protection against different risks. For cultivation, these risks are related to the agricultural business process and to external risk events. An operational risk assessment allows one to identify the limits of environmental and financial sustainability. Specifically, in oil palm cultivation, the characterisation of the associated risk remains a challenge from a technological perspective. To advance in this direction, researchers have used different technologies, including spectral aerial images, unmanned aerial vehicles to construct a vegetation index, intelligent augmented platforms for real-time monitoring, and adaptive fuzzy models to estimate operational risk. In line with these technological developments, in this article we propose a framework for the estimation of the risk assessment associated with the disease of Lethal Wilt (LW) in oil palm plantations. Although our purpose is not to predict lethal wilt, since the framework starts from the result of a prediction model, a model to detect LW in an early stage is used for the demonstration. For the implementation of the prediction model, we use a novel deep learning system based on two neural networks. This refers to a case study conducted at UNIPALMAS. We show that the suitability of our system aims to evaluate operational risks of LW with a confidence level of 99.9% and for a period of 6 months.
published_date 2023-01-01T04:23:36Z
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