Field identification of weed species and glyphosate-resistant weeds using high resolution imagery in early growing season

Shirzadifar, Alimohammad; Bajwa, Sreekala G.; Nowatzki, John; Bazrafkan, Aliasghar

doi:10.1016/j.biosystemseng.2020.10.001

Cited by 23 publications

(22 citation statements)

References 25 publications

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“…Therefore, it is possible that there were other reliable thermal signatures captured from soybean canopies than weed canopies [64]. Reliable soybean canopy temperature extraction has proven to be possible with spatial resolutions as high as 0.8 m when investigating stomatal closure due to drought conditions [18]. Overall, determining glyphosate-resistant weed species with UAV thermal sensing was not possible in field conditions.…”

Section: Soybean Observation Resultsmentioning

confidence: 99%

“…Prior to herbicide application, there is no significant difference in the visual appearance of resistant and susceptible weeds of the same species that can be noted during scouting [14]. Hyperspectral systems to detect differences between resistant and susceptible biotypes show potential in controlled environments, but their effectiveness is drastically reduced once introduced to field conditions [15]. Lab testing for an accumulation of shikimic acid within plant leaves is one method used to identify glyphosate resistance, but its lack of practicality does not justify its use in large scale applications [16].…”

Section: Introductionmentioning

confidence: 99%

“…Researchers at North Dakota State University (NDSU) pursued an alternative method of herbicide resistance detection that utilized thermal imaging technology [18]. Thermal imaging has shown potential in detecting plant canopies that are experiencing increased levels of stress and reduced rates of photosynthesis [19].…”

Section: Introductionmentioning

confidence: 99%

“…It was reported that glyphosate-susceptible populations of ragweed, waterhemp, and kochia had higher canopy temperatures in comparison to resistant populations after glyphosate [22]. The canopy temperature values were then used to train a support vector machine (SVM) classifier that could identify the weeds that exhibited glyphosate resistance with accuracies exceeding 90% for all three species [18].…”

Section: Introductionmentioning

confidence: 99%

“…Subsequent greenhouse studies were performed with the objective of validating the hypothesis that glyphosate-resistant weeds can be identified using thermal imagery [18]. In these studies, glyphosate was applied to populations of waterhemp, kochia, common ragweed, and redroot pigweed and monitored with hourly image captures from a thermal camera [22].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

UAV-Assisted Thermal Infrared and Multispectral Imaging of Weed Canopies for Glyphosate Resistance Detection

et al. 2021

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The foundation of contemporary weed management practices in many parts of the world is glyphosate. However, dependency on the effectiveness of herbicide practices has led to overuse through continuous growth of crops resistant to a single mode of action. In order to provide a cost-effective weed management strategy that does not promote glyphosate-resistant weed biotypes, differences between resistant and susceptible biotypes have to be identified accurately in the field conditions. Unmanned Aerial Vehicle (UAV)-assisted thermal and multispectral remote sensing has potential for detecting biophysical characteristics of weed biotypes during the growing season, which includes distinguishing glyphosate-susceptible and glyphosate-resistant weed populations based on canopy temperature and deep learning driven weed identification algorithms. The objective of this study was to identify herbicide resistance after glyphosate application in true field conditions by analyzing the UAV-acquired thermal and multispectral response of kochia, waterhemp, redroot pigweed, and common ragweed. The data were processed in ArcGIS for raster classification as well as spectral comparison of glyphosate-resistant and glyphosate-susceptible weeds. The classification accuracy between the sensors and classification methods of maximum likelihood, random trees, and Support Vector Machine (SVM) were compared. The random trees classifier performed the best at 4 days after application (DAA) for kochia with 62.9% accuracy. The maximum likelihood classifier provided the highest performing result out of all classification methods with an accuracy of 75.2%. A commendable classification was made at 8 DAA where the random trees classifier attained an accuracy of 87.2%. However, thermal reflectance measurements as a predictor for glyphosate resistance within weed populations in field condition was unreliable due to its susceptibility to environmental conditions. Normalized Difference Vegetation Index (NDVI) and a composite reflectance of 842 nm, 705 nm, and 740 nm wavelength managed to provide better classification results than thermal in most cases.

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Section: Soybean Observation Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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UAV-Assisted Thermal Infrared and Multispectral Imaging of Weed Canopies for Glyphosate Resistance Detection

et al. 2021

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Early detection, herbicide resistance screening, and integrated management of invasive plant species: a review

Tataridas

Jabran

Kanatas

et al. 2022

Pest Management Science

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Invasive plant species (IPS) are often considered weeds that cause high yield losses in crops, negatively affect the environment, and disrupt certain ecosystem services. The negative impact of IPS on biodiversity is increasing and disturbing native vegetation. The management of plant invasions can be divided in two phases (before and after invasion). Prior to introduction it is crucial to develop the knowledge base (biology, ecology, distribution, impact, management) on IPS, prevention measures and risk assessment. After introduction if eradication fails, the monitoring and the integrated management of IPS are imperative to prevent the naturalization and further dispersal. This review uses two major invasive weed species (Amaranthus palmeri S. Wats. and Solanum elaeagnifolium Cav.) as case studies to propose a framework for early detection, rapid herbicide resistance screening, and integrated management. The holistic framework that is presented exploits recent: (i) novel detection tools, (ii) rapid tests and assays for herbicide resistance, and (iii) biology, ecology, distribution traits, and management tools for the IPS. Farmers, advisors, researchers, and policymakers need briefing on IPS growth dynamics, adaptability rates, and response to conventional and novel treatments to prevent new invasions, eradicate isolated stands, and mitigate the impact of invasive weed species in the long term.

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Parameters of the Phytosociological Survey to Evaluate the Abundance, Distribution, and Diversity of the Weed Community

Souza

Paiva

Zaidan

et al. 2022

Applied Weed and Herbicide Science

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Field identification of weed species and glyphosate-resistant weeds using high resolution imagery in early growing season

Cited by 23 publications

References 25 publications

UAV-Assisted Thermal Infrared and Multispectral Imaging of Weed Canopies for Glyphosate Resistance Detection

UAV-Assisted Thermal Infrared and Multispectral Imaging of Weed Canopies for Glyphosate Resistance Detection

Early detection, herbicide resistance screening, and integrated management of invasive plant species: a review

Parameters of the Phytosociological Survey to Evaluate the Abundance, Distribution, and Diversity of the Weed Community

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