Employing Canopy Hyperspectral Narrowband Data and Random Forest Algorithm to Differentiate Palmer Amaranth from Colored Cotton - American Journal of Plant Sciences

AJPS > Vol.8 No.12, November 2017

Employing Canopy Hyperspectral Narrowband Data and Random Forest Algorithm to Differentiate Palmer Amaranth from Colored Cotton ()

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DOI: 10.4236/ajps.2017.812219 808 Downloads 1,523 Views Citations

Author(s)

Reginald S. Fletcher^1*, Rickie B. Turley²

Affiliation(s)

¹Crop Production Systems Research Unit, Agricultural Research Service, United States Department of Agriculture, Stoneville, USA.
²Crop Genetics Research Unit, Agricultural Research Service, United States Department of Agriculture, Stoneville, USA.

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.

KEYWORDS

Amaranthus palmeri, Gossypium hirsutum, Cforest, Machine Learning

Share and Cite:

Fletcher, R. and Turley, R. (2017) Employing Canopy Hyperspectral Narrowband Data and Random Forest Algorithm to Differentiate Palmer Amaranth from Colored Cotton. American Journal of Plant Sciences, 8, 3258-3271. doi: 10.4236/ajps.2017.812219.

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