Testing leaf multispectral reflectance data as input into random forest to differentiate velvetleaf from soybean

Authors: Fletcher, Reginald

Submitted to: American Journal of Plant Sciences
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 12/11/2015
Publication Date: 12/14/2015
Citation: Fletcher, R.S. 2015. Testing leaf multispectral reflectance data as input into random forest to differentiate velvetleaf from soybean. American Journal of Plant Sciences. 6:3193-3204.

Interpretive Summary: Velvetleaf infestations negatively impact cultivated fields, especially those planted to soybeans. To implement management strategies to control velvetleaf, managers need tools for differentiating it from crop plants. An ARS scientist at the USDA-ARS Crop production Systems Research Unit, Stoneville, MS, used light reflectance measurements sensitive to leaf pigment, internal structure, and water content as input into the random forest computer algorithm to distinguish velvetleaf from soybean. Classification accuracies ranged from 86.7% to 100%. Findings support further application of the random forest machine learner along with remotely-sensed light reflectance data as tools for velvetleaf soybean discrimination with future implications for site-specific management of velvetleaf.

Technical 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. 5Band, 7Band, 8Band, and 16Band 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%. 7Band, 16Band, 8Band, and 5Band 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.