Please use this identifier to cite or link to this item: https://ir.swu.ac.th/jspui/handle/123456789/17352
Full metadata record
DC FieldValueLanguage
dc.contributor.authorYarak K.
dc.contributor.authorWitayangkurn A.
dc.contributor.authorKritiyutanont K.
dc.contributor.authorArunplod C.
dc.contributor.authorShibasaki R.
dc.date.accessioned2022-03-10T13:16:55Z-
dc.date.available2022-03-10T13:16:55Z-
dc.date.issued2021
dc.identifier.issn20770472
dc.identifier.other2-s2.0-85102125527
dc.identifier.urihttps://ir.swu.ac.th/jspui/handle/123456789/17352-
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85102125527&doi=10.3390%2fagriculture11020183&partnerID=40&md5=70d68b9150e952d2fd78b040b164ea4a
dc.description.abstractCombining modern technology and agriculture is an important consideration for the effective management of oil palm trees. In this study, an alternative method for oil palm tree management is proposed by applying high‐resolution imagery, combined with Faster‐RCNN, for automatic detection and health classification of oil palm trees. This study used a total of 4172 bounding boxes of healthy and unhealthy palm trees, constructed from 2000 pixel × 2000 pixel images. Of the total dataset, 90% was used for training and 10% was prepared for testing using Resnet‐50 and VGG‐16. Three techniques were used to assess the models’ performance: model training evaluation, evaluation using visual interpretation, and ground sampling inspections. The study identified three characteristics needed for detection and health classification: crown size, color, and density. The optimal altitude to capture images for detection and classification was de-termined to be 100 m, although the model showed satisfactory performance up to 140 m. For oil palm tree detection, healthy tree identification, and unhealthy tree identification, Resnet‐50 ob-tained F1‐scores of 95.09%, 92.07%, and 86.96%, respectively, with respect to visual interpretation ground truth and 97.67%, 95.30%, and 57.14%, respectively, with respect to ground sampling in-spection ground truth. Resnet‐50 yielded better F1‐scores than VGG‐16 in both evaluations. Therefore, the proposed method is well suited for the effective management of crops. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
dc.languageen
dc.titleOil palm tree detection and health classification on high‐resolution imagery using deep learning
dc.typeArticle
dc.rights.holderScopus
dc.identifier.bibliograpycitationAgriculture (Switzerland). Vol 11, No.2 (2021), p.1-17
dc.identifier.doi10.3390/agriculture11020183
Appears in Collections:Scopus 1983-2021

Files in This Item:
There are no files associated with this item.


Items in SWU repository are protected by copyright, with all rights reserved, unless otherwise indicated.