Abstract
Palmer amaranth (Amaranthus palmeri S. Wats.) invasion negatively impacts cotton (Gossypium hirsutum L.) production systems throughout the United States. The objective of this study was to evaluate canopy hyperspectral narrowband data as input into the random forest machine learning algorithm to distinguish Palmer amaranth from cotton. The study focused on differentiating the Palmer amaranth from cotton near-isogenic lines with bronze, green, and yellow leaves. A spectroradiometer was used to acquire hyperspectral reflectance measurements of Palmer amaranth and cotton canopies for two separate dates, December 12, 2016, and May 14, 2017. Data were collected from plants that were grown in a greenhouse. The spectral data were aggregated to twenty-four hyperspectral narrowbands proposed for study of vegetation and agriculture crops. Those bands were tested by the conditional inference version of random forest (cforest) to differentiate the Palmer amaranth from cotton. Classifications were binary: Palmer amaranth and cotton bronze, Palmer amaranth and cotton green, and Palmer amaranth and cotton yellow. Classification accuracies were verified with overall, user’s, and producer’s accuracy. For the two dates combined, overall accuracy ranged from 77.8% to 88.9%. The highest overall accuracies were observed for the Palmer amaranth versus the cotton yellow classification (88.9%, December 12, 2016; 83.3%, May 14, 2017). Producer’s and user’s accuracies range was 66.7% to 94.4%. Errors were predominately attributed to cotton being misclassified as Palmer amaranth. The overall results indicated that cforest has moderate to strong potential for differentiating Palmer amaranth from cotton when it used hyperspectral narrowbands known to be useful for vegetation and agricultural surveys as input variables. This research further supports using hyperspectral narrowband data and cforest as decision support tools in cotton production systems.
Highlights
Palmer amaranth, an aggressive and invasive weed, negatively impacts cotton growth and productively throughout the United States
Overall accuracies were greater than 77% with the lowest overall accuracy achieved for Palmer amaranth versus cotton green and Palmer amaranth versus cotton bronze classifications for December 12, 2016 and May 14, 2017, respectively
The greatest overall accuracy was obtained for the Palmer amaranth versus cotton yellow classifications for both dates
Summary
An aggressive and invasive weed, negatively impacts cotton growth and productively throughout the United States. It grows at a rapid rate (i.e., approximately 25 - 50 mm per day), produces several thousand seeds per plant, competes with cotton plants for sunlight and soil nutrients, and reduces cotton yield. Palmer amaranth populations are controlled by chemical and mechanical means. To better implement control strategies for Palmer amaranth invasions in cotton production systems, agriculturalists need tools that can help them differentiate it from cotton. Researchers, consultants, and producers have shown interests in employing remote sensing technologies as weed detection and survey tools in crop production systems
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