Hyperspectral Technologies for Assessing Seed Germination and Trifloxysulfuron-methyl Response in Amaranthus palmeri (Palmer Amaranth)

Abstract

Weed infestations in agricultural systems constitute a serious challenge to agricultural sustainability and food security worldwide. Amaranthus palmeri S. Watson (Palmer amaranth) is one of the most noxious weeds causing significant yield reductions in various crops. The ability to estimate seed viability and herbicide susceptibility is a key factor in the development of a long-term management strategy, particularly since the misuse of herbicides is driving the evolution of herbicide response in various weed species. The limitations of most herbicide response studies are that they are conducted retrospectively and that they use in vitro destructive methods. Development of a non-destructive method for the prediction of herbicide response could vastly improve the efficacy of herbicide applications and potentially delay the evolution of herbicide resistance. Here, we propose a toolbox based on hyperspectral technologies and data analyses aimed to predict A. palmeri seed germination and response to the herbicide trifloxysulfuron-methyl. Complementary measurement of leaf physiological parameters, namely, photosynthetic rate, stomatal conductence and photosystem II efficiency, was performed to support the spectral analysis. Plant response to the herbicide was compared to image analysis estimates using mean gray value and area fraction variables. Hyperspectral reflectance profiles were used to determine seed germination and to classify herbicide response through examination of plant leaves. Using hyperspectral data, we have successfully distinguished between germinating and non-germinating seeds, hyperspectral classification of seeds showed accuracy of 81.9 and 76.4%, respectively. Sensitive and resistant plants were identified with high degrees of accuracy (88.5 and 90.9%, respectively) from leaf hyperspectral reflectance profiles acquired prior to herbicide application. A correlation between leaf physiological parameters and herbicide response (sensitivity/resistance) was also demonstrated. We demonstrated that hyperspectral reflectance analyses can provide reliable information about seed germination and levels of susceptibility in A. palmeri. The use of reflectance-based analyses can help to better understand the invasiveness of A. palmeri, and thus facilitate the development of targeted control methods. It also has enormous potential for impacting environmental management in that it can be used to prevent ineffective herbicide applications. It also has potential for use in mapping tempo-spatial population dynamics in agro-ecological landscapes.

Using proximal remote sensing in non-invasive phenotyping of invertebrates

“…In this regard, the current study is similar to studies of reflectance-based studies of seed viability [25, 26], ontogeny of blowfly pupae [22], and terminal stress response in insects exposed to killing agents [24]. In addition, we showed how important factors may influence the quality of proximal remote sensing data and that 70% ethanol appears to be the most suitable killing method and solvent for preservation over time of killed specimens.…”

Discrimination of herbicide-resistant kochia with hyperspectral imaging

“…[16][17][18] While we cannot directly compare the results because we studied kochia and they studied Palmer Amaranth, Italian ryegrass, and general weed identification, respectively, all of these studies suggest promise for using hyperspectral imaging in sitespecific weed management. In particular, previous studies showed 91% classification accuracy for Palmer amaranth 16 and an 80% classification accuracy for Italian ryegrass.…”

“…[16][17][18] While we cannot directly compare the results because we studied kochia and they studied Palmer Amaranth, Italian ryegrass, and general weed identification, respectively, all of these studies suggest promise for using hyperspectral imaging in sitespecific weed management. In particular, previous studies showed 91% classification accuracy for Palmer amaranth 16 and an 80% classification accuracy for Italian ryegrass. 17 While our accuracies are lower than those for the Italian ryegrass and for the Palmer amaranth classifications, the results indicate that hyperspectral imaging can be used to identify kochia biotypes, both in the greenhouse and in the field.…”

“…biotypes can be distinguished using differences in reflectance across the near UV, visible, and near IR spectrum in laboratory conditions. 15 Further work using hyperspectral reflectance profiles has been done to predict the herbicide resistance of Palmer amaranth biotypes, 16 differentiate between glyphosate-susceptible and glyphosate-resistant Italian ryegrass, 17 and to detect the injury on crops as a result of dicamba and glyphosate. 18 Weeds also have been distinguished from crops using shape rather than spectral data, 19 but in our application there are no systematic shape differences between herbicide-resistant and herbicide-susceptible kochia plants.…”

Hyperspectral imaging and neural networks to classify herbicide-resistant weeds

“…The process involves a detailed spectral analysis of different weeds (e.g., kochia, marestail, and common lambsquarters) in different crops (e.g., wheat, barley, and sugar beet), aimed at differentiating between herbicide-resistant and -susceptible biotypes of the weed. Other work in this field includes distinguishing between susceptible-and glyphosate-resistant Palmer amaranth biotypes, 14 differentiating between glyphosate-susceptible and glyphosate-resistant Italian ryegrass, 15 and detecting the injury on crops as a result of dicamba and glyphosate. 16 The paper follows this format: Sec.…”