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A Multispectral Image Classification Framework for Estimating the Operational Risk of Lethal Wilt in Oil Palm Crops
Alejandro Peña 
,
Alejandro Puerta,
Isis Bonet ,
Fabio Caraffini ,
Mario Gongora,
Ivan Ochoa
,
Alejandro Puerta,
Isis Bonet ,
Fabio Caraffini ,
Mario Gongora,
Ivan Ochoa
Applications of Evolutionary Computation, Pages: 491 - 506
Swansea University Author:
Fabio Caraffini
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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...
| Published in: | Applications of Evolutionary Computation |
|---|---|
| ISBN: | 9783031302282 9783031302299 |
| ISSN: | 0302-9743 1611-3349 |
| Published: |
Cham
Springer Nature Switzerland
2023
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| Online Access: |
Check full text
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa63100 |
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| 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 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. |
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| College: |
Faculty of Science and Engineering |
| Start Page: |
491 |
| End Page: |
506 |