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dc.contributor.authorDadashzadeh, Mojtaba
dc.contributor.authorAbbaspour_Gilandeh, Yousef
dc.contributor.authorMesri Gundoshmian, T.
dc.contributor.authorSabzi, Sajad
dc.contributor.authorHernández-Hernández, José Luis
dc.contributor.authorHernández Hernández, Mario
dc.contributor.authorArribas, Juan
dc.creatorDadashzadeh, Mojtaba;#0000-0003-3790-8672
dc.creatorAbbaspour_Gilandeh, Yousef;#0000-0002-9999-7845
dc.creatorMesri Gundoshmian, T.;#0000-0002-7302-7269
dc.creatorSabzi, Sajad;#0000-0003-2439-5329
dc.creatorHernández-Hernández, José Luis;#0000-0003-0231-2019
dc.creatorHernández Hernández, Mario;#0000-0001-8330-4779
dc.creatorArribas, Juan;#0000-0002-7486-6152
dc.date.accessioned2023-03-23T16:46:16Z
dc.date.available2023-03-23T16:46:16Z
dc.date.issued2020-04
dc.identifier.issndoi:10.3390/plants9050559
dc.identifier.urihttp://ri.uagro.mx/handle/uagro/3528
dc.description.abstractSite-specific weed management and selective application of herbicides as eco-friendly techniques are still challenging tasks to perform, especially for densely cultivated crops, such as rice. This study is aimed at developing a stereo vision system for distinguishing between rice plants and weeds and further discriminating two types of weeds in a rice field by using artificial neural networks (ANNs) and two metaheuristic algorithms. For this purpose, stereo videos were recorded across the rice field and di_erent channels were extracted and decomposed into the constituent frames. Next, upon pre-processing and segmentation of the frames, green plants were extracted out of the background. For accurate discrimination of the rice and weeds, a total of 302 color, shape, and textura features were identified. Two metaheuristic algorithms, namely particle swarm optimization (PSO) and the bee algorithm (BA), were used to optimize the neural network for selecting the most e_ective features and classifying di_erent types of weeds, respectively. Comparing the proposed classification method with the K-nearest neighbors (KNN) classifier, it was found that the proposed ANN-BA classifier reached accuracies of 88.74% and 87.96% for right and left channels, respectively, over the test set. Taking into account either the arithmetic or the geometric means as the basis, the accuracies were increased up to 92.02% and 90.7%, respectively, over the test set. On the other hand, the KNN su_ered from more cases of misclassification, as compared to the proposed ANN-BA classifier, generating an overall accuracy of 76.62% and 85.59% for the classification of the right and left channel data, respectively, and 85.84% and 84.07% for the arithmetic and geometric mean values, respectively.
dc.formatpdf
dc.language.isoeng
dc.publisherPlants
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0
dc.subjectsustainable agriculture
dc.subjectsite-specific management
dc.subjecteco-friendly technique
dc.subjectweed
dc.subjectrice field
dc.subjectmetaheuristic algorithm
dc.subject.classificationINGENIERÍA Y TECNOLOGÍA::CIENCIAS TECNOLÓGICAS::TECNOLOGÍA DE LOS ALIMENTOS
dc.titleWeed Classification for Site-SpecificWeed Management Using an Automated Stereo Computer-Vision Machine-Learning System in Rice Fields
dc.typeArtículo
dc.type.conacytarticle
dc.rights.accesopenAccess
dc.audiencegeneralPublic
dc.identificator7||33||3309
dc.format.digitalOriginBorn digital
dc.thesis.degreelevelDoctorado
dc.thesis.degreenameDoctorado en Innovación y Cultura Digital
dc.thesis.degreegrantorUniversidad Autónoma de Guerrero
dc.thesis.degreedepartmentFacultad de Ingeniería
dc.thesis.degreedisciplineIngeniería y Tecnología
dc.identifier.cvuagro11228


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Except where otherwise noted, this item's license is described as http://creativecommons.org/licenses/by-nc-nd/4.0