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dc.contributor.authorBonet, Isis
dc.contributor.authorCaraffini, Fabio
dc.contributor.authorPena, Alejandro
dc.contributor.authorPuerta, Alejandro
dc.contributor.authorGongora, Mario Augusto
dc.date.accessioned2020-06-09T09:29:47Z
dc.date.available2020-06-09T09:29:47Z
dc.date.issued2020-07
dc.identifier.citationBonet, I., Caraffini, F., Pena, A., Puerta, A., Gongora, M.A. (2020) Oil Palm Detection via Deep Transfer Learning. IEEE World Congress on Computational Intelligence (WCCI), Glasgow, UK., July 2020.en
dc.identifier.urihttps://sites.google.com/site/facaraff/research/rae2018
dc.identifier.urihttps://dora.dmu.ac.uk/handle/2086/19741
dc.description.abstractThis article presents an intelligent system using deep learning algorithms and the transfer learning approach to detect oil palm units in multispectral photographs taken with unmanned aerial vehicles. Two main contributions come from this piece of research. First, a dataset for oil palm units detection is carefully produced and made available online. Although being tailored to the palm detection problem, the latter has general validity and can be used for any classification application. Second, we designed and evaluated a state-of-the-art detection system, which uses a convolutional neural network to extract meaningful features, and a classifier trained with the images from the proposed dataset. Results show outstanding effectiveness with an accuracy peak of 99.5% and a precision of 99.8%. Using different images for validation taken from different altitudes the model reached an accuracy of 97.5% and a precision of 98.3%. Hence, the proposed approach is highly applicable in the field of precision agriculture.en
dc.language.isoenen
dc.publisherIEEEen
dc.subjectClassificationen
dc.subjectConvolutional neural networksen
dc.subjectDeep learningen
dc.subjectOil palmen
dc.subjectMultispectral image processingen
dc.titleOil Palm Detection via Deep Transfer Learningen
dc.typeConferenceen
dc.peerreviewedYesen
dc.funderOther external funder (please detail below)en
dc.projectidIAPP1\100130en
dc.cclicenceCC-BY-NCen
dc.date.acceptance2020-03-20
dc.researchinstituteInstitute of Artificial Intelligence (IAI)en
dc.funder.otherNewton Fund Industry Academia Partnership Programme (IAPP1\100130);en


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