Skip to content
DraconiticResearch
Search Sources API
197,768 studies31 sourcesUpdated 16 min ago
Peer-reviewedCC BY 4.0Frontiers in Plant Science

Integrating UAV-based multispectral imagery and machine learning to classify glyphosate-resistant palmer amaranth and assess herbicide program efficacy in soybean

  • Usman Mohammed1
  • Prashant Jha2
  • Vipan Kumar3
  • Bhupesh Dhaka4
  • Shahreen Mirza4
  • Ivan Grijalva5
5 affiliations
  1. 1Research Associate, School of Plant, Environmental and Soil Sciences, Louisiana State University
  2. 2Professor; School of Plant, Environmental and Soil Sciences, Louisiana State University
  3. 3Associate Professor, Soil and Crop Sciences Section, Cornell University
  4. 4Graduate Research Assistant, School of Plant, Environmental and Soil Sciences, Louisiana State University
  5. 5Assistant Professor, Department of Entomology, Louisiana State University
Open at sourcePDFDOICite

Abstract

Palmer amaranth is one of the most troublesome and economically important weeds in soybean production, with widespread occurrence of glyphosate-resistant (GR) populations. This study aimed to map and classify GR Palmer amaranth using UAV-based multispectral imagery and evaluate the effects of herbicide programs [preemergence (PRE) only, postemergence (POST) only, and PRE followed by (fb) POST] on Palmer amaranth coverage (early- and late-season) in soybean field in central Louisiana in 2025. A multispectral drone was used to map GR Palmer amaranth, and images were processed into orthomosaic using Pix4DFields and georeferenced in ArcGIS Pro. Weed classification was performed using pixel-based and object-based approaches implemented in ENVI and Orfeo Toolbox (OTB) within QGIS, employing Random Forest (RF), Multilayer Perceptron (MLP), and Support Vector Machine (SVM) algorithms. Both classification methods exhibited high performance, with RF achieving the highest overall accuracy (99%). UAV-based assessments provided a spatiotemporally efficient method for early-season Palmer amaranth mapping and late-season evaluation of residual weed activity following herbicide applications. UAV-derived Palmer amaranth coverage showed moderate positive correlation with the ground-truth weed density. Herbicide programs significantly reduced UAV-derived Palmer amaranth coverage during early-season (P = 0.0004) and late-season (P = 0.0223) assessments. Among the evaluated programs, PRE fb POST resulted in the greatest reduction in weed coverage (4.3%), compared with PRE only (9.2%) and POST-only (8.8%) programs. In conclusion, integrating UAV-based multispectral imagery with machine learning algorithms, particularly RF, offers a promising, automated approach for classification of GR Palmer amaranth and evaluating herbicide program effectiveness across different crop growth stages.

Related

  1. Advancing herbicide safeners: Integrating structure-guided design, pathway mechanism elucidation, and smart delivery
    Xile Deng, Yudan Wu, Lianyang Bai
    Advanced AgrochemPeer-reviewedCC BY7 citedPlant biologyOpenAlex
  2. Application of spectroscopic and multispectral imaging technologies on the assessment of ready-to-eat pineapple quality: A performance evaluation study of machine learning models generated from two commercial data analytics tools
    Evanthia Manthou, Sergio-Llaneza Lago, Evaggelos Dagres +5
    Computers and Electronics in AgriculturePeer-reviewedLicence not stated38 citedGeneral biologyGeneral chemistryHAL
  3. Integrating multi-environment and path coefficient analysis for the identification of yellow rust-resistant bread wheat (Triticum aestivum L.) genotypes
    Tesfaye Mulugeta, Altaye Tiruneh, Aemiro Bezabih +3
    Scientific ReportsPeer-reviewedCC BY-NC-ND 4.0Full textPlant biologyCrossref
  4. Multi-temporal UAV multispectral phenotyping and machine learning for modeling cotton defoliation dynamics across diverse accessions
    Mingze Li, Qisheng Feng, Xiaofei Li +8
    Plant phenomics (Washington, D.C.)Peer-reviewedCC BYFull textEcologyEurope PMCEurope PMCPMC
  5. UAV-based disease detection in strawberry by multispectral image segmentation invoking deep learning.
    A F Cheshkova, V S Riksen, O V Elkin
    Vavilovskii zhurnal genetiki i selektsiiPeer-reviewedCC BYFull textPlant biologyEurope PMC

Cite

BibTeXRIS
BibTeX
@article{mohammed2026integrating,
  title = {{Integrating UAV-based multispectral imagery and machine learning to classify glyphosate-resistant palmer amaranth and assess herbicide program efficacy in soybean}},
  author = {Usman Mohammed and Prashant Jha and Vipan Kumar and Bhupesh Dhaka and Shahreen Mirza and Ivan Grijalva},
  journal = {Frontiers in Plant Science},
  year = {2026},
  month = oct,
  doi = {10.3389/fpls.2026.1913999},
  url = {https://www.frontiersin.org/articles/10.3389/fpls.2026.1913999/full},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - Integrating UAV-based multispectral imagery and machine learning to classify glyphosate-resistant palmer amaranth and assess herbicide program efficacy in soybean
AU  - Usman Mohammed
AU  - Prashant Jha
AU  - Vipan Kumar
AU  - Bhupesh Dhaka
AU  - Shahreen Mirza
AU  - Ivan Grijalva
PY  - 2026
DA  - 2026/10/26
JO  - Frontiers in Plant Science
DO  - 10.3389/fpls.2026.1913999
UR  - https://www.frontiersin.org/articles/10.3389/fpls.2026.1913999/full
AB  - Palmer amaranth is one of the most troublesome and economically important weeds in soybean production, with widespread occurrence of glyphosate-resistant (GR) populations. This study aimed to map and classify GR Palmer amaranth using UAV-based multispectral imagery and evaluate the effects of herbicide programs [preemergence (PRE) only, postemergence (POST) only, and PRE followed by (fb) POST] on Palmer amaranth coverage (early- and late-season) in soybean field in central Louisiana in 2025. A multispectral drone was used to map GR Palmer amaranth, and images were processed into orthomosaic using Pix4DFields and georeferenced in ArcGIS Pro. Weed classification was performed using pixel-based and object-based approaches implemented in ENVI and Orfeo Toolbox (OTB) within QGIS, employing Random Forest (RF), Multilayer Perceptron (MLP), and Support Vector Machine (SVM) algorithms. Both classification methods exhibited high performance, with RF achieving the highest overall accuracy (99%). UAV-based assessments provided a spatiotemporally efficient method for early-season Palmer amaranth mapping and late-season evaluation of residual weed activity following herbicide applications. UAV-derived Palmer amaranth coverage showed moderate positive correlation with the ground-truth weed density. Herbicide programs significantly reduced UAV-derived Palmer amaranth coverage during early-season (P = 0.0004) and late-season (P = 0.0223) assessments. Among the evaluated programs, PRE fb POST resulted in the greatest reduction in weed coverage (4.3%), compared with PRE only (9.2%) and POST-only (8.8%) programs. In conclusion, integrating UAV-based multispectral imagery with machine learning algorithms, particularly RF, offers a promising, automated approach for classification of GR Palmer amaranth and evaluating herbicide program effectiveness across different crop growth stages.
SN  - 1664-462X
ER  -