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

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dc.contributor.authorPena, Alejandro
dc.contributor.authorPuerta, Alejandro
dc.contributor.authorBonet, Isis
dc.contributor.authorCaraffini, Fabio
dc.contributor.authorOchoa, Ivan
dc.contributor.authorGongora, Mario Augusto
dc.date.acceptance2023-03-03
dc.date.accessioned2023-11-09T09:33:29Z
dc.date.available2023-11-09T09:33:29Z
dc.date.issued2023-04-09
dc.descriptionUnder its remit as a delivery partner of the Newton Fund, the Royal Academy of Engineering has partnered with Promigas, Ecopetrol and Ruta N to enhance engineering teaching, research and innovation outcomes in Colombian universities by building bilateral industry-academia links. One of the projects funded through this scheme seeks to develop an intelligence system to improve the sustainability of oil palm crops through the construction of forecasting maps that integrate adaptive vegetation indices from multispectral aerial views. It brings together researchers from EIA University in Colombia and De Montfort University in the UK in collaboration with Unipalma S.A., a Colombian agricultural company that specialises in the cultivation of oil palm crops.
dc.description.abstractOperational 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.
dc.funderOther external funder (please detail below)
dc.funder.otherRoyal Academy of Engineering
dc.identifier.citationPeña, A., Puerta, A., Bonet, I., Caraffini, F., Gongora, M. and Ochoa, I. (2023) A Multispectral Image Classification Framework for Estimating the Operational Risk of Lethal Wilt in Oil Palm Crops. In International Conference on the Applications of Evolutionary Computation (Part of EvoStar), pp. 491-506. Cham: Springer Nature Switzerland
dc.identifier.doihttps://doi.org/10.1007/978-3-031-30229-9_32
dc.identifier.isbn9783031302299
dc.identifier.isbn9783031302282
dc.identifier.urihttps://hdl.handle.net/2086/23338
dc.language.isoen
dc.peerreviewedYes
dc.projectidSustanability IAPP
dc.publisherSpringer Nature
dc.researchinstituteInstitute of Artificial Intelligence (IAI)
dc.subjectOil Palm and Lethal Wilt
dc.subjectDeep Learning
dc.subjectAugmented Intelligence
dc.subjectConvolutional Neural Network
dc.subjectUnmanned Aerial Vehicles
dc.subjectVegetation Index
dc.subjectSustainability
dc.titleA Multispectral Image Classification Framework for Estimating the Operational Risk of Lethal Wilt in Oil Palm Crops
dc.typeConference

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