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dc.contributor.authorShirzadifar, Alimohammad
dc.contributor.authorBajwa, Sreekala
dc.contributor.authorNowatzki, John
dc.contributor.authorBazrafkan, Aliasghar
dc.date.accessioned2022-06-21T22:16:45Z
dc.date.available2022-06-21T22:16:45Z
dc.date.issued2020-12
dc.identifier.citationShirzadifar, A., Bajwa, S., Nowatzki, J., & Bazrafkan, A. (2020). Field identification of weed species and glyphosate-resistant weeds using high resolution imagery in early growing season. Biosystems Engineering, 200, 200-214en_US
dc.identifier.issn1537-5110
dc.identifier.urihttps://scholarworks.montana.edu/xmlui/handle/1/16844
dc.description.abstractAccurate weed mapping in early growing season is an essential step in the site-specific weed management (SSWM) system. This study focuses on validating the potential application of high resolution multispectral and thermal UAS images in classification of weed species and glyphosate-resistant weeds at early phenological stages. A field experiment was conducted to evaluate supervised classification methods to identify three-weed species including waterhemp, kochia, and ragweed. The accuracy of six classification algorithms namely Parallelepipeds (P), Mahalanobis Distance (MD), Maximum Likelihood (ML), Spectral Angle Mapper (SAM), Support Vector Machine (SVM) and Decision Tree (DT) implemented at pixel and object- based in weed species classification were evaluated. Thermal infrared imagery was also used to assess the canopy temperature variance within the weed species to identify the glyphosate-resistance status in detected weeds. The object-based algorithms developed with mosaicked imagery effectively classified weed species with the overall accuracy and Kappa coefficient values greater than 86% and 0.77, respectively. The lowest accuracy and Kappa coefficient (67% and 0.58) were observed for pixel-based MD algorithm. The canopy temperature-based classification of susceptible and resistant weeds resulted in the discrimination accuracies of 88%, 93% and 92% in glyphosate-resistant kochia, waterhemp and ragweed, respectively.en_US
dc.language.isoenen_US
dc.publisherElsevier BVen_US
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/en_US
dc.titleField identification of weed species and glyphosate-resistant weeds using high resolution imagery in early growing seasonen_US
dc.typeArticleen_US
mus.citation.extentfirstpage200en_US
mus.citation.extentlastpage214en_US
mus.citation.journaltitleBiosystems Engineeringen_US
mus.citation.volume200en_US
mus.identifier.doi10.1016/j.biosystemseng.2020.10.001en_US
mus.relation.collegeCollege of Agricultureen_US
mus.relation.departmentPlant Sciences & Plant Pathology.en_US
mus.relation.universityMontana State University - Bozemanen_US
mus.data.thumbpage6en_US


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