Validation of Image Based Thermal Sensing Technology for Glyphosate Resistant Weed Identification
Eide, Austin Joshua
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From 2019 to 2020, greenhouse and field research was conducted at North Dakota State University to investigate the canopy temperature response of waterhemp (Amaranthus rudis), kochia (Kochia scoparia), common ragweed (Ambrosia artemisiifolia), horseweed (Conyza canadensis), Palmer amaranth (Amaranthus palmeri), and red root pigweed (Amaranthus retroflexus) after glyphosate application to identify glyphosate resistance. In these experiments, thermal images were captured of randomized glyphosate resistant populations and glyphosate susceptible populations of each weed species. The weed canopies' thermal values were extracted and submitted to statistical testing and various classifiers in an attempt to discriminate between resistant and susceptible populations. Glyphosate resistant horseweed, when collected within greenhouse conditions, was the only biotype reliably classified using significantly cooler temperature signatures than its susceptible counterpart. For field conditions, image based machine learning classifiers using thermal data were outperformed by classifiers made using additional multispectral data, suggesting thermal is not a reliable predictor of glyphosate resistance.