Performance of Random Forest Group for Basal Stem Rot Disease Classification Caused by Ganoderma boninense in Oil Palm Plantation
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Published:
Dec 1, 2020
Keywords:
basal stem rot disease, oil palm, PLS, PRF
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Abstract
Remote sensing technologies have been used for monitoring basal stem rot (BSR) diseases in oil palm plantations. The classification performance could be improved with machine learning algorithms. The success key of BSR treatment in culture technic treatments to prolong oil palm productivity ages is early detection of BSR disease . The selection of classification models of machine learning algorithms will be affecting accuracy and classification processes time. Random forest (RF) of model classification based on several previous research has a high performance of accuracy for BSR disease classification in the oil palm plantation. Today, there were fifteen models of the variance of the random forest algorithm. The research goals were to compare the accuracy and time consumed for the fitting model from 15 model’s classification of RF group in case of identity and classify the BSR disease in the oil palm plantations. Descriptive analysis was adopted to explain the difference accuracy and time for the fitting model of fifteen model classification. The results showed the highest accuracy and Kappa value (0,914 dan 0,815) was the oblique random forest (ORF) with partial least squares (PLS) method and random ferns (Rferns) was the method with the lowest accuracy and Kappa value (0,657 dan 0,334). The highest time consumed for the fitting model was RF rule-based model (rlb) with 12.993,23 seconds and the shortest time was parallel random forest (PRF) with 13,54 seconds. The accuracy and Kappa values and time consumed for the fitting model as a consideration to selecting model classification of healthy and infected BSR in oil palm plantations.
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Santoso, H. (2020). Performance of Random Forest Group for Basal Stem Rot Disease Classification Caused by Ganoderma boninense in Oil Palm Plantation. Jurnal Penelitian Kelapa Sawit, 28(3), 133-146. https://doi.org/10.22302/iopri.jur.jpks.v28i3.116
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- Indonesian Journal of Oil Palm Research can be accessed freely by anyone (open access) to introduce more journals to the public.
- The results of the research can be used freely with the inclusion of Indonesian Journal of Oil Palm Research as a source of utilization.
References
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Oberweger, M. (2014). Embeddings for Random Ferns Classification. Graz University of Technology, Austria.
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Santoso, H., Tani, H., & Wang, X. (2017). Random Forest classification model of basal stem rot disease caused by Ganoderma boninense in oil palm plantations. International Journal of Remote Sensing, 38(16), 4683–4699. https://doi.org/10.1080/01431161.2017.1331474
Santoso, H., Tani, H., Wang, X., Prasetyo, A. E., & Sonobe, R. (2018). Classifying the severity of basal stem rot disease in oil palm plantations using WorldView-3 imagery and machine learning algorithms. International Journal of Remote Sensing, 00(00), 1–23. https://doi.org/10.1080/01431161.2018.1541368
Seligman, M. (2019). Package “Rborist” extensible, parallelizable implementation of the random forest. Package “Rborist.” Retrieved from https://cran.r-project.org/web/packages/Rborist/Rborist.pdf
Short, N. M. (1982). The landsat tutorial workbook-Basics of satellite remote sensing. Washington, D.C.: National Aeronautics and Space Administration, Scientific and Technical Information Branch.
Sutanto. (2010). REMOTE SENSING RESEARCH: A User’s Perspective. Indonesian Journal of Geography, 42(2), 129–142. https://doi.org/10.22146/indo.j.geog,2287
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Wright, M. N., & Ziegler, A. (2017). Ranger: A fast implementation of random forests for high dimensional data in C++ and R. Journal of Statistical Software, 77(1), 1–17. https://doi.org/10.18637/jss.v077.i01
Zhao, H., Williams, G. J., & Huang, J. Z. (2017). Wsrf: An R package for classification with scalable weighted subspace random forests. Journal of Statistical Software, 77(3). https://doi.org/10.18637/jss.v077.i03
Behmann, J., Mahlein, A.-K., Rumpf, T., Römer, C., & Plümer, L. (2015). A review of advanced machine learning methods for the detection of biotic stress in precision crop protection. Precision Agriculture, 16(3), 239–260. https://doi.org/10.1007/s11119-014-9372-7
Berhane, T. M., Lane, C. R., Wu, Q., Autry, B. C., Anenkhonov, O. A., Chepinoga, V. V, & Liu, H. (2019). Decision-Tree, Rule-Based, and Random Forest Classification of High-Resolution Multispectral Imagery for Wetland Mapping and Inventory. Remote Sens (Basel), 10(4), 580. https://doi.org/10.3390/rs10040580
Breiman, L. (2001). Random forests. Machine Learning, 45, 5–32. https://doi.org/10.1201/9780429469275-8
Chen, J., Li, K., Tang, Z., Bilal, K., Yu, S., Weng, C., & Li, K. (2016). A Parallel Random Forest Algorithm for Big Data in a Spark Cloud Computing Environment. IEEE Transactions on Parallel and Distributed System, 99(1–1), 1–15. https://doi.org/10.1109/TPDS.2016.2603511
Cohen, J. (1960). A coefficient of agreement for nomial scales. Educational and Psychological Measurement, 20(1), 37–46. https://doi.org/10.1177/001316446002000104
Congalton, R. G. (1991). A review of assessing the accuracy of classifications of remotely sensed data. Remote Sensing of Environment, 37(1), 35–46. https://doi.org/10.1016/0034-4257(91)90048-B
Congalton, R. G. (2005). Thematic and Positional Accuracy Assess- ment of Digital Remotely Sensed Data. 2005 Proceedings of the Seventh Annual Forest Inventory and Analysis Symposium, (1993), 149–154.
Crawford, M. M., Tuia, D., & Yang, H. L. (2013). Active Learning: Any Value for Classification of Remotely Sensed Data? PROCEEDINGS OF THE IEEE. PREPRINT DOWNLADED FROM WIKI.EPFL.CK/EO-ADAPT. Retrieved from http://lasig.epfl.ch/
Dhage, S. N., & Raina, C. K. (2016). A review on Machine Learning Techniques. International Journal on Recent and Innovation Trends in Computing and Communication, 4(3), 395–399. Retrieved from http://www.ijritcc.org
Geurts, P., Ernst, D., & Wehenkel, L. (2006). Extremely randomized trees. Mach Learn (). https://doi.org/10.1007/s10994-006-6226-1
Hothorn, T., Hornik, K., & Zeileis, A. (2006). Unbiased recursive partitioning: A conditional inference framework. Journal of Computational and Graphical Statistics, 15(3), 651–674. https://doi.org/10.1198/106186006X133933
Hushiarian, R., Yusof, N. A., & Dutse, S. W. (2013). Detection and control of Ganoderma boninense: strategies and perspectives. SpringerPlus, 2(1), 555. https://doi.org/10.1186/2193-1801-2-555
Izrailev, S. Functions for timing R scripts, as well as implementations of Stack and List structures., 1.0 Package ‘ tictoc ’ Version 1.0 1–8 (2016). Retrieved from https://cran.r-project.org/web/packages/tictoc/tictoc.pdf
Kim, Y. H., Yoo, S. J., Gu, Y. H., Lim, J. H., Han, D., & Baik, S. W. (2014). Crop Pests Prediction Method Using Regression and Machine Learning Technology: Survey. IERI Procedia, 6, 52–56. https://doi.org/10.1016/j.ieri.2014.03.009
Kuhn, M. (2008). Building Predictive Models in R Using the caret Package. Journal Of Statistical Software, 28(5), 1–26. https://doi.org/10.1053/j.sodo.2009.03.002
Kuhn, M. (2016). “caret - Classification and Regression Training” R Package. https://github.com/topepo/caret/. Retrieved from https://github.com/topepo/caret/
Kuhn, M. (2019). The caret Package. Retrieved from http://topepo.github.io/caret/train-models-by-tag.html#random-forest
Kursa, M. B. (2014). rFerns: An implementation of the random ferns method for general-purpose machine learning. Journal of Statistical Software, 61(10), 1–13. https://doi.org/10.18637/jss.v061.i10
Lelong, C. C. D., Roger, J. M., Brégand, S., Dubertret, F., Lanore, M., Sitorus, N. a., … Caliman, J. P. (2010). Evaluation of oil-palm fungal disease infestation with canopy hyperspectral reflectance data. Sensors, 10, 734–747. https://doi.org/10.3390/s100100734
Liaghat, S., Ehsani, R., Mansor, S., Shafri, H. Z. M., Meon, S., Sankaran, S., & Azam, S. H. M. N. (2014). Early detection of basal stem rot disease ( Ganoderma ) in oil palms based on hyperspectral reflectance data using pattern recognition algorithms. International Journal of Remote Sensing, 35(10), 3427–3439. https://doi.org/10.1080/01431161.2014.903353
Menze, B. H., Kelm, B. M., Splitthoff, D. N., Koethe, U., & Hamprecht, F. A. (2011). On oblique random forests. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 6912 LNAI(PART 2), 453–469. https://doi.org/10.1007/978-3-642-23783-6_29
Oberweger, M. (2014). Embeddings for Random Ferns Classification. Graz University of Technology, Austria.
Omer, G., Mutanga, O., Abdel-Rahman, E. M., & Adam, E. (2016). Empirical prediction of leaf area index (LAI) of endangered tree species in intact and fragmented indigenous forests ecosystems using WorldView-2 data and two robust machine learning algorithms. Remote Sensing, 8(4), 1–26. https://doi.org/10.3390/rs8040324
Paterson, R. R. M. (2007). Ganoderma disease of oil palm-A white rot perspective necessary for integrated control. Crop Protection, 26(9), 1369–1376. https://doi.org/10.1016/j.cropro.2006.11.009
Poona, N., van Niekerk, A., & Ismail, R. (2016). Investigating the utility of oblique tree-based ensembles for the classification of hyperspectral data. Sensors (Switzerland), 16(11). https://doi.org/10.3390/s16111918
Priwiratama, H., Prasetyo, A. E., & Susanto, A. (2014). Pengendalian Penyakit Busuk Pangkal Batang Kelapa Sawit secara Kultur Teknis. Jurnal Fitopatologi Indonesia, 10(51), 1–7. https://doi.org/10.14692/jfi.10.1.1
Santoso, H., Gunawan, T., Jatmiko, R. H., Darmosarkoro, W., & Minasny, B. (2011). Mapping and identifying basal stem rot disease in oil palms in North Sumatra with QuickBird imagery. Precision Agriculture, 12(2), 233–248. https://doi.org/10.1007/s11119-010-9172-7
Santoso, H., Tani, H., & Wang, X. (2017). Random Forest classification model of basal stem rot disease caused by Ganoderma boninense in oil palm plantations. International Journal of Remote Sensing, 38(16), 4683–4699. https://doi.org/10.1080/01431161.2017.1331474
Santoso, H., Tani, H., Wang, X., Prasetyo, A. E., & Sonobe, R. (2018). Classifying the severity of basal stem rot disease in oil palm plantations using WorldView-3 imagery and machine learning algorithms. International Journal of Remote Sensing, 00(00), 1–23. https://doi.org/10.1080/01431161.2018.1541368
Seligman, M. (2019). Package “Rborist” extensible, parallelizable implementation of the random forest. Package “Rborist.” Retrieved from https://cran.r-project.org/web/packages/Rborist/Rborist.pdf
Short, N. M. (1982). The landsat tutorial workbook-Basics of satellite remote sensing. Washington, D.C.: National Aeronautics and Space Administration, Scientific and Technical Information Branch.
Sutanto. (2010). REMOTE SENSING RESEARCH: A User’s Perspective. Indonesian Journal of Geography, 42(2), 129–142. https://doi.org/10.22146/indo.j.geog,2287
Uhlmann, S., Kiranyaz, S., & Gabbouj, M. (2014). Semi-Supervised Learning for Ill-Posed Polarimetric SAR Classification. Remote Sens, 6, 4801–4830. https://doi.org/10.3390/rs6064801
Viera, A. J., & Garrett, J. M. (2005). Understanding interobserver agreement: The kappa statistic. Family Medicine, 37(5), 360–363. https://doi.org/Vol. 37, No. 5
Wieland, M., & Pittore, M. (2014). Performance Evaluation of Machine Learning Algorithms for Urban Pattern Recognition from Multi - spectral Satellite Images. Remote Sens, 6, 2912–2939. https://doi.org/10.3390/rs6042912
Wright, M. N., & Ziegler, A. (2017). Ranger: A fast implementation of random forests for high dimensional data in C++ and R. Journal of Statistical Software, 77(1), 1–17. https://doi.org/10.18637/jss.v077.i01
Zhao, H., Williams, G. J., & Huang, J. Z. (2017). Wsrf: An R package for classification with scalable weighted subspace random forests. Journal of Statistical Software, 77(3). https://doi.org/10.18637/jss.v077.i03