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Testing Leaf Multispectral Reflectance Data as Input into Random Forest to Differentiate Velvetleaf from Soybean

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DOI: 10.4236/ajps.2015.619311    2,406 Downloads   2,772 Views   Citations


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.

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

Fletcher, R. (2015) Testing Leaf Multispectral Reflectance Data as Input into Random Forest to Differentiate Velvetleaf from Soybean. American Journal of Plant Sciences, 6, 3193-3204. doi: 10.4236/ajps.2015.619311.


[1] Lanini, W.T. and Wertz, B.A. (2015) Velvetleaf. Penn State Extension.
[2] Koger, C.H., Bruce, L.M., Shaw, D.R. and Reddy, K.N. (2003) Wavelet Analysis of Hyperspectral Reflectance Data for Detecting Pitted Morning Glory (Ipomoea lacunosa) in Soybean (Glycine max). Remote Sensing Environment, 86, 108-119.
[3] Smith, A.M. and Blackshaw, R.E. (2003) Weed-Crop Discrimination Using Remote Sensing: A Detached Leaf Experiment. Weed Technology, 17, 811-820.
[4] Yang, C.C., Prasher, S.O. and Goel, P.K. (2004) Differentiation of Crop and Weeds by Decision-Tree Analysis of Multi-Spectral Data. Transactions of the ASAE, 47, 873-879.
[5] Iqbal, J., Owens, P.R. and Ali., I. (2006) Application of Remote Sensing Data to Assess Weed Infestation in Cotton. Agricultural Journal, 1, 186-191.
[6] Gómez-Casero, M.T., Castillejo-González, I.L. and García-Ferrer, A. (2010) Spectral Discrimination of Wild Oat and Canary Grass in Wheat Fields for Less Herbicide Application. Agronomy for Sustainable Development, 30, 689-699.
[7] Nieuwenhuizen, A.T., Hofstee, J.W., van de Zande, J.C., Meuleman, J. and van Henten, E.J. (2010) Classification of Sugar Beet and Volunteer Potato Reflection Spectra with a Neural Network and Statistical Discriminant Analysis to Select Discriminative Wavelengths. Computers Electronics in Agriculture, 73, 146-153.
[8] de Castro, A.I., Jurado-Expósito, M., Gómez-Casero, M.T. and López-Granados, F. (2012) Applying Neural Networks to Hyperspectral and Multispectral Field Data for Discrimination of Cruciferous Weeds in Winter Crops. Science World Journal, Article ID: 630390.
[9] Lamb, D.W. and Brown, R.B. (2001) Remote-Sensing and Mapping of Weeds in Crops. Journal of Agricultural Engneering Research, 78, 117-125.
[10] Goel, P.K., Prasher, S.O., Patel, R.M., Smith, D.L. and Di Tommaso, A. (2002) Use of Airborne Multi-Spectral Imagery for Weed Detection in Field Crops. Transactions of American Society of Agricultural Engineers, 45, 443-449.
[11] Gibson, K.D., Dirks, R., Medlin, C.R. and Johnston, L. (2004) Detection of Weed Species in Soybean Using Multispectral Digital Images. Weed Technology, 18, 742-749.
[12] Gausman, H. (1985) Plant Leaf Optical Properties. Texas Tech Press, Lubbock.
[13] Fernández-Delgado, M., Cernadas, E., Barro, S. and Amorim, D. (2014) Do We Need Hundreds of Classifiers to Solve Real World Classification Problems? Journal of Machine Learning Research, 15, 3133-3181.
[14] Gislason, P.O., Benediktsson, J.A. and Sveinsson, J.R. (2006) Random Forests for Land Cover Classification. Pattern Recognition Letters, 27, 294-300.
[15] Strobl, C., Malley, J. and Tutz, G. (2009) An Introduction to Recursive Partitioning: Rationale, Application and Characteristics of Classification and Regression Trees, Bagging and Random Forests. Psychological Methods, 14, 323-348.
[16] Goldstein, B.A., Polley, E.C. and Briggs, F.B.S. (2011) Random Forest for Genetic Association Studies. Applications in Genetics and Molecular Biology, 10, 1-34.
[17] Ok, A.O., Akar, O. and Gungor, O. (2012) Evaluation of Random Forest Method for Agricultural Crop Classification. European Journal of Remote Sensing, 45, 421-432.
[18] Breiman, L. (2001) Random Forests. Machine Learning, 45, 5-32.
[19] US Geological Survey (2015) Frequently Asked Questions about the Landsat Missions.
[20] Digital Globe (2010) The Benefits of the Eight Spectral Bands of WorldView 2.
[21] Digital Globe (2014) WorldView 3 Data Sheet.
[22] Lehnert, L.W., Meyer, H. and Bendix, J. (2015) Hsdar: Manage, Analyse and Simulate Hyperspectral Data in R. R Package Version 0.3.0.
[23] Hothorn, T., Buehlmann, P., Dudoit, S., Molinaro, A. and van der Laan, M. (2006) Survival Ensembles. Biostatistics, 7, 355-373.
[24] Congalton, R. and Green, K. (2009) Assessing the Accuracy of Remotely Sensed Data: Principles and Practices. 2nd Edition, CRC/Taylor & Francis, Boca Raton, 183 p.
[25] Hothorn, T., Hornik, K. and Zeileis, A. (2006) Unbiased Recursive Partitioning: A Conditional Inference Framework. Journal of Computational and Graphical Statistics, 15, 651-674.
[26] Strobl, C., Boulesteix, A.L., Zeileis, A. and Hothorn, T. (2007) Bias in Random Forest Variable Importance Measures: Illustrations, Sources and a Solution. BMC Bioinformatics, 8, 25.
[27] Strobl, C., Boulesteix, A.L., Kneib, T., Augustin, T. and Zeileis, A. (2008) Conditional Variable Importance for Random Forests. BMC Bioinformatics, 9, 307.

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