Random forest and leaf multispectral reflectance data to differentiate three soybean varieties from two pigweeds

Accurate weed identification is a prerequisite for implementing site-specific weed management in crop production. Palmer amaranth (Amaranthus palmeri S. Wats.) and redroot pigweed (Amaranthus retroflexus L.) are two common pigweeds that reduce soybean [Glycine max (L.) Men.] yields in the southeastern United States. The objective of this study was to evaluate leaf multispectral reflectance data as input into the random forest machine learning algorithm to differentiate three soybean varieties (Progeny 4928, Progeny 5160, and Progeny 5460) from Palmer amaranth and redroot pigweed. Leaf reflectance measurements of soybean, Palmer amaranth, and redroot pigweed plants grown in a greenhouse were collected with a plant contact probe attached to a hyperspectral spectroradiometer. Data were obtained at the vegetative growth stage of the plants on two dates, June 30, 2014, and September 17, 2014. The hyperspectral data were aggregated to sixteen multispectral bands (viz. coastal, blue, green, yellow, red, red-edge, near-infrared 1 and 2, and shortwave-infrared 1-8) mimicking those recorded by the WorldView-3 satellite sensor. Classifications were binary, meaning one soybean variety versus one weed tested per classification. Random forest classification accuracies were determined with a confusion matrix, incorporating user's, producer's, and overall accuracies and the kappa coefficient. User's, producer's, and overall accuracies of the soybean weed classifications ranged from 93.8% to 100%. Kappa results (values of 0.93-0.97) indicated an excellent agreement between the classes predicted by the models and the actual reference data. Shortwave-infrared bands were ranked the most important variables for distinguishing the pigweeds from the soybean varieties. These results suggest that random forest and leaf multispectral reflectance data could be used as tools to differentiate soybean from two pig weeds with a potential application of this technology in site-specific weed management programs. Published by Elsevier B.V.