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

Authors: Johnson, Richard; ORGERON, ALBERT - LSU Agcenter; Spaunhorst, Douglas; ZIMBA, PAUL - Texas A&M University

Submitted to: Weed Technology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 2/16/2023
Publication Date: 5/26/2023
Citation: Johnson, R.M., Orgeron, A., Spaunhorst, D.J., Zimba, P.V. 2023. Discrimination of weeds from sugarcane in Louisiana using hyperspectral leaf reflectance data and pigment analysis. Weed Technology. p.1-9. https://doi.org/10.1017/wet.2023.14.
DOI: https://doi.org/10.1017/wet.2023.14

Interpretive Summary: Controlling weeds in sugarcane fields 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. Weeds were detected in comparison to sugarcane varieties using leaf reflectance measurements and pigment analyses. Leaf samples were collected from four commercial Louisiana sugarcane varieties, and nine weed species commonly found in sugarcane fields. Leaf reflectance data and plant pigment data were collected from all samples. In all cases, leaf reflectance data successfully differentiated sugarcane from weeds. 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 (chlorophylls and carotenoids) levels exhibited marked differences between sugarcane varieties and weed species with differences in chlorophylls and carotenoids explaining much of the observed variation in leaf 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 to manage their crops. By accurately targeting weeds in an emerged, and growing sugarcane field, the total amount of herbicide applied could be decreased, resulting in cost savings for the grower and reduced environmental impacts.

Technical Abstract: Controlling weeds in sugarcane fields 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 machine 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 concentration 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 chlorophylls and carotenoids 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 to manage their crops. By accurately targeting weeds in an emerged, and growing sugarcane field, the total amount of herbicide applied could be decreased, resulting in cost savings for the grower and reduced environmental impacts.