TITLE:
Testing Leaf Multispectral Reflectance Data as Input into Random Forest to Differentiate Velvetleaf from Soybean
AUTHORS:
Reginald S. Fletcher
KEYWORDS:
Glycine max, Abutilon theophrasti, Machine Learning, Supervised Classification, Ensemble Technique
JOURNAL NAME:
American Journal of Plant Sciences,
Vol.6 No.19,
December
14,
2015
ABSTRACT: Velvetleaf (Abutilon theophrasti Medic.) infestations negatively impact row crop production throughout
the United States and Canada’s eastern provinces. To implement management strategies to
control velvetleaf, managers need tools for differentiating it from crop plants. 5 Band, 7 Band, 8
Band, and 16 Band multispectral datasets simulating LANDSAT 3 plus a blue band, LANDSAT 8,
WorldView 2, and WorldView 3 spectral bands, respectively were tested as input into the random
forest algorithm for velvetleaf soybean [Glycine max L. (Merr.)] discrimination. During two separate
greenhouse experiments in 2014, leaf reflectance measurements were obtained at the vegetative
growth stage of velvetleaf plants and two soybean varieties. The reflectance measurements
were collected with a plant contact probe attached to a hyperspectral spectroradiometer. Leaf
hyperspectral reflectance measurements were convolved to the four multispectral datasets with
computer software. Overall, user’s, and producer’s accuracies and kappa coefficient were employed
to determine classification accuracies. Using the multispectral datasets as input, the random
forest algorithm differentiated velvetleaf from the soybean varieties with accuracies ranging
from 86.7% to 100%. 7 Band, 16 Band, 8 Band, and 5 Band datasets ranked or tied for the highest
accuracies seventeen, sixteen, twelve, and one time, respectively. Kappa coefficients indicated an
almost perfect agreement (i.e., kappa value, 0.81 - 1.0) to substantial agreement (i.e., kappa value,
0.61 - 0.80) between reference data and model predicted classes. This study was the first to demonstrate
the application of the random forest machine learner and leaf multispectral reflectance
data as tools to distinguish velvetleaf from soybean and to identify multispectral band combinations
providing the best accuracies. Findings support further application of the random forest
machine learner along with remotely-sensed multispectral data as tools for velvetleaf soybean
discrimination with future implications for site-specific management of velvetleaf.