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Discrimination of weeds from sugarcane in Louisiana using hyperspectral leaf reflectance data and pigment analysis

Published online by Cambridge University Press:  23 March 2023

Richard M. Johnson Open the ORCID record for Richard M. Johnson [Opens in a new window] ,
Albert J. Orgeron Open the ORCID record for Albert J. Orgeron [Opens in a new window] ,
Douglas J. Spaunhorst Open the ORCID record for Douglas J. Spaunhorst [Opens in a new window] ,
I-Shuo Huang Open the ORCID record for I-Shuo Huang [Opens in a new window]  and
Paul V. Zimba Open the ORCID record for Paul V. Zimba [Opens in a new window]
Richard M. Johnson*
Affiliation:
Research Agronomist, USDA-ARS, Sugarcane Research Unit, Houma, LA, USA
Albert J. Orgeron
Affiliation:
Associate Professor & Resident Coordinator, LSU AgCenter, Baton Rouge, LA, USA
Douglas J. Spaunhorst
Affiliation:
Research Agronomist, USDA-ARS, Sugarcane Research Unit, Houma, LA, USA
I-Shuo Huang
Affiliation:
Postdoctoral Research Associate, Center for Coastal Studies, Texas A&M University Corpus Christi, Corpus Christi, TX, USA; current: Postdoctoral Research Associate, United States Food and Drug Administration, Center for Food Safety and Applied Nutrition, College Park, MD, USA
Paul V. Zimba
Affiliation:
Professor and Director, Center for Coastal Studies, Texas A&M University Corpus Christi, Corpus Christi, TX, USA Research Faculty, Rice Rivers Center, Virginia Commonwealth University, Richmond, VA, USA
*
Corresponding author: Richard M. Johnson; Email: richard.johnson@usda.gov
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Abstract

Controlling weeds is a critically important task in sugarcane production systems. Weeds compete for light, nutrients, and water, and if they are not managed properly can negatively impact sugarcane yields. Accurate detection of weeds versus desired plants was assessed using hyperspectral and pigment analyses. Leaf samples were collected from four commercial Louisiana sugarcane varieties, and nine weed species commonly found in sugarcane fields. Hyperspectral leaf reflectance data (350 to 850 nm) were collected from all samples. Plant pigment (chlorophylls and carotenoids) levels were also determined using high-performance liquid chromatography, and concentrations were determined using authentic standards and leaf area. In all cases, leaf reflectance data successfully differentiated sugarcane from weeds using canonical discrimination analysis. Linear discriminant analysis showed that the accuracy of the classification varied from 67% to 100% for individual sugarcane varieties and weed species. In all cases, sugarcane was not misclassified as a weed. Plant pigment levels exhibited marked differences between sugarcane varieties and weed species with differences in chlorophyll and carotenoid explaining much of the observed variation in reflectance. The ratio of chlorophyll a to chlorophyll b showed significant differences between sugarcane and all weed species. The successful implementation of this technology as either an airborne system to scout and map weeds or a tractor-based system to identify and spray weeds in real-time would offer sugarcane growers a valuable tool for managing their crops. By accurately targeting weeds in sugarcane fields that are emerged and growing, the total amount of herbicide applied could be decreased, resulting in cost savings for the grower and reduced environmental impacts.

Keywords

sugarcaneSaccharum spp.Leaf reflectanceprecision agricultureremote sensingperennialsite-specific weed managementproximal sensing
Type
Research Article
Information
Weed Technology , Volume 37 , Issue 2 , April 2023 , pp. 123 - 131
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Creative Commons
This is a work of the US Government and is not subject to copyright protection within the United States. Published by Cambridge University Press on behalf of the Weed Science Society of America.
Copyright
© United States Department of Agriculture, Agricultural Research Service, 2023

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Footnotes

Associate Editor: Prashant Jha, Iowa State University

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