Skip to main content
SpringerLink
Log in

Comparison of K-Nearest Neighbor and Support Vector Regression for Predicting Oil Palm Yield

  • Conference paper
  • First Online:
Innovative Technologies in Intelligent Systems and Industrial Applications (CITISIA 2022)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 1029))

  • 200 Accesses

Abstract

With the high demand for oil palm production, implementations of Machine Learning (ML) technologies to provide accurate predictions and recommendations to assist oil palm plantation management tasks have become beneficial, such as in predicting annual oil palm. However, different geographical and meteorological conditions may result in different scales of influence for each variable. In this research, K-Nearest Neighbors (KNN) and Support Vector Regression (SVR) were used in predicting oil palm yield based on data collected in Riau, Indonesia. Pearson’s correlation coefficient was also calculated in selecting the input features for the models, whereas normalization and standardization were used in scaling the data. By setting the minimum absolute correlation threshold to 0.1 and using standardization, both models managed to obtain more than 0.81 R2, with SVR obtaining the overall best performance with 0.8709 R2, 1.372 MAE, and 1.8025 RMSE without hyperparameter fine-tuning. It was also discovered that the oil palm yield in the previous year is the variable with the most influence in estimating oil palm yield in the current year, followed by the number of plants and soil types.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 189.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 249.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Roundtable on Sustainable Palm Oil (RSPO) (2021) Guidance for the 2018 RSPO principles and criteria metrics template. Kuala Lumpur

    Google Scholar 

  2. Zuhdi DAF, Abdullah MF, Suliswanto MSW, Wahyudi ST (2021) The competitiveness of Indonesian crude palm oil in international market. J Ekon Pembang 19:111–124. https://doi.org/10.29259/jep.v19i1.13193

  3. Sheng Goh C, Teong Lee K (2010) Will biofuel projects in Southeast Asia become white elephants? Energy Policy 38:3847–3848. https://doi.org/10.1016/j.enpol.2010.04.009

    Article  Google Scholar 

  4. Germer J, Sauerborn J (2008) Estimation of the impact of oil palm plantation establishment on greenhouse gas balance. Environ Dev Sustain 10:697–716. https://doi.org/10.1007/s10668-006-9080-1

    Article  Google Scholar 

  5. Angelsen A (2010) Policies for reduced deforestation and their impact on agricultural production. Proc Natl Acad Sci 107:19639–19644. https://doi.org/10.1073/pnas.0912014107

    Article  Google Scholar 

  6. Akhter R, Sofi SA (2021) Precision agriculture using IoT data analytics and machine learning. J King Saud Univ—Comput Inf Sci. https://doi.org/10.1016/j.jksuci.2021.05.013

  7. Harsawardana, Rahutomo R, Mahesworo B, Cenggoro TW, Budiarto A, Suparyanto T, Surya Atmaja DB, Samoedro B, Pardamean B (2020) AI-based ripeness grading for oil palm fresh fruit bunch in smart crane grabber. IOP Conf Ser Earth Environ Sci 426:12147. https://doi.org/10.1088/1755-1315/426/1/012147

  8. Herman H, Cenggoro TW, Susanto A, Pardamean B (2021) Deep learning for oil palm fruit ripeness classification with DenseNet. In: International conference on information management and technology (ICIMTech), pp 116–119. https://doi.org/10.1109/ICIMTech53080.2021.9534988

  9. Putra DP, Bimantio MP, Sahfitra AA, Suparyanto T, Pardamean B (2020) Simulation of availability and loss of nutrient elements in land with android-based fertilizing applications. In: International conference on information management and technology (ICIMTech), pp 312–317. https://doi.org/10.1109/ICIMTech50083.2020.9211268

  10. Putra DP, Bimantio P, Suparyanto T, Pardamean B (2021) Expert system for oil palm leaves deficiency to support precision agriculture. In: International conference on information management and technology (ICIMTech), pp 33–36. https://doi.org/10.1109/ICIMTech53080.2021.9535083

  11. Firmansyah E, Pardamean B, Ginting C, Mawandha HG, Putra DP, Suparyanto T (2021) Development of artificial intelligence for variable rate application based oil palm fertilization recommendation system. In: International conference on information management and technology (ICIMTech), pp 6–11. https://doi.org/10.1109/ICIMTech53080.2021.9535082

  12. Rahutomo R, Perbangsa AS, Lie Y, Cenggoro TW, Pardamean B (2019) Artificial intelligence model implementation in web-based application for pineapple object counting. In: International conference on information management and technology (ICIMTech), pp 525–530. https://doi.org/10.1109/ICIMTech.2019.8843741

  13. Purboseno S, Suparyanto T, Hidayat AA, Pardamean B (2021) A hydrodynamic analysis of water system in Dadahup swamp irrigation area. In: 1st international conference on computer science and artificial intelligence (ICCSAI), pp 400–406. https://doi.org/10.1109/ICCSAI53272.2021.9609729

  14. Krisdiarto AW, Julianto E, Wisnubhadra I, Suparyanto T, Sudigyo D, Pardamean B (2021) Design of water information management system in palm oil plantation. In: 1st international conference on computer science and artificial intelligence (ICCSAI), pp 395–399. https://doi.org/10.1109/ICCSAI53272.2021.9609780

  15. Hermantoro S, Suparman S, Ariyanto DS, Rahutomo R, Suparyanto T, Pardamean B (2021) IoT sensors integration for water quality analysis. In: 1st international conference on computer science and artificial intelligence (ICCSAI), pp 361–366. https://doi.org/10.1109/ICCSAI53272.2021.9609707

  16. Firmansyah E, Suparyanto T, Ahmad Hidayat A, Pardamean B (2022) Real-time weed identification using machine learning and image processing in oil palm plantations. IOP Conf Ser Earth Environ Sci 998:12046. https://doi.org/10.1088/1755-1315/998/1/012046

    Article  Google Scholar 

  17. Mawandha HG, Suparyanto T, Pardamean B (2021) Weeds e-Catalog as a tool for identification of weeds in plantation. IOP Conf Ser Earth Environ Sci 794:12113. https://doi.org/10.1088/1755-1315/794/1/012113

    Article  Google Scholar 

  18. Woittiez LS, van Wijk MT, Slingerland M, van Noordwijk M, Giller KE (2017) Yield gaps in oil palm: a quantitative review of contributing factors. Eur J Agron 83:57–77. https://doi.org/10.1016/j.eja.2016.11.002

    Article  Google Scholar 

  19. Hoffmann MP, Donough CR, Cook SE, Fisher MJ, Lim CH, Lim YL, Cock J, Kam SP, Mohanaraj SN, Indrasuara K, Tittinutchanon P, Oberthür T (2017) Yield gap analysis in oil palm: framework development and application in commercial operations in Southeast Asia. Agric Syst 151:12–19. https://doi.org/10.1016/j.agsy.2016.11.005

    Article  Google Scholar 

  20. Soliman T, Lim FKS, Lee JSH, Carrasco LR (2022) Closing oil palm yield gaps among Indonesian smallholders through industry schemes, pruning, weeding and improved seeds. R Soc Open Sci 3:160292. https://doi.org/10.1098/rsos.160292

    Article  Google Scholar 

  21. Chapman R, Cook S, Donough C, Lim YL, Vun Vui Ho P, Lo KW, Oberthür T (2018) Using Bayesian networks to predict future yield functions with data from commercial oil palm plantations: a proof of concept analysis. Comput Electron Agric 151:338–348. https://doi.org/10.1016/j.compag.2018.06.006

  22. Watson-Hernández F, Gómez-Calderón N, da Silva RP (2022) Oil palm yield estimation based on vegetation and humidity indices generated from satellite images and machine learning techniques. https://doi.org/10.3390/agriengineering4010019

  23. Kartika ND, Astika IW, Santosa E (2016) Oil palm yield forecasting based on weather variables using artificial neural network. Indones J Electr Eng Comput Sci 3:626–633

    Google Scholar 

  24. Rodríguez AC, D’Aronco S, Schindler K, Wegner JD (2021) Mapping oil palm density at country scale: an active learning approach. Remote Sens Environ 261:112479. https://doi.org/10.1016/j.rse.2021.112479

    Article  Google Scholar 

  25. Khan N, Kamaruddin MA, Sheikh UU, Yusup Y, Bakht MP (2021) Oil palm and machine learning: reviewing one decade of ideas, innovations, applications, and gaps. https://doi.org/10.3390/agriculture11090832

  26. Rashid M, Bari BS, Yusup Y, Kamaruddin MA, Khan N (2021) A comprehensive review of crop yield prediction using machine learning approaches with special emphasis on palm oil yield prediction. IEEE Access 9:63406–63439. https://doi.org/10.1109/ACCESS.2021.3075159

    Article  Google Scholar 

  27. Hilal YY, Yahya A, Ismail WIW, Asha’ari ZH (2020) Neural networks method in predicting oil palm FFB yields for the Peninsular States of Malaysia. J Oil Palm Res

    Google Scholar 

  28. Awad M, Khanna R (2015) Support vector regression. In: Awad M, Khanna R (eds) Efficient learning machines: theories, concepts, and applications for engineers and system designers. Apress, Berkeley, CA, pp 67–80. https://doi.org/10.1007/978-1-4302-5990-9_4

  29. Ray S (2019) A quick review of machine learning algorithms. In: International conference on machine learning, big data, cloud and parallel computing (COMITCon), pp 35–39. https://doi.org/10.1109/COMITCon.2019.8862451

  30. Hopf K, Reifenrath S (2021) Filter methods for feature selection in supervised machine learning applications—review and benchmark. https://doi.org/10.48550/arxiv.2111.12140

  31. Pearson K, Henrici OMFE (1896) VII. Mathematical contributions to the theory of evolution.—III. Regression, heredity, and panmixia. Phil Trans R Soc Lond A 187:253–318. https://doi.org/10.1098/rsta.1896.0007

  32. Hassan MA, Yang M, Rasheed A, Yang G, Reynolds M, Xia X, Xiao Y, He Z (2019) A rapid monitoring of NDVI across the wheat growth cycle for grain yield prediction using a multi-spectral UAV platform. Plant Sci 282:95–103. https://doi.org/10.1016/j.plantsci.2018.10.022

    Article  Google Scholar 

  33. Wang J, Xu J, Peng Y, Wang H, Shen J (2020) Prediction of forest unit volume based on hybrid feature selection and ensemble learning. Evol Intell 13:21–32. https://doi.org/10.1007/s12065-019-00219-4

    Article  Google Scholar 

  34. Cannas B, Fanni A, Pintus M, Sechi GM (2002) Neural network models to forecast hydrological risk, vol 1. In: Proceedings of the 2002 international joint conference on neural networks. IJCNN’02 (Cat. No.02CH37290), pp 423–426. https://doi.org/10.1109/IJCNN.2002.1005509

  35. Rajurkar MP, Kothyari UC, Chaube UC (2002) Artificial neural networks for daily rainfall—runoff modelling. Hydrol Sci J 47:865–877. https://doi.org/10.1080/02626660209492996

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Bioinformatics and Data Science Research Center, Bina Nusantara University, Jakarta, 11480, Indonesia

    Bens Pardamean, Teddy Suparyanto, Gokma Sahat Tua Sinaga & Gregorius Natanael Elwirehardja

  2. Computer Science Department, BINUS Graduate Program—Master of Computer Science Program, Bina Nusantara University, Jakarta, 11480, Indonesia

    Bens Pardamean

  3. Faculty of Agriculture, Institute of Agriculture STIPER, Yogyakarta, 55282, Indonesia

    Erick Firmansyah, Candra Ginting, Hangger Gahara Mawandha & Dian Pratama Putra

Authors
  1. Bens Pardamean
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Teddy Suparyanto
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Gokma Sahat Tua Sinaga
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Gregorius Natanael Elwirehardja
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Erick Firmansyah
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Candra Ginting
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Hangger Gahara Mawandha
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Dian Pratama Putra
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Gregorius Natanael Elwirehardja .

Editor information

Editors and Affiliations

  1. School of Engineering, Macquarie University, Sydney, NSW, Australia

    Subhas Chandra Mukhopadhyay

  2. Faculty of Science, Taylor’s University, Selangor, Malaysia

    S.M. Namal Arosha Senanayake

  3. Charles Sturt University, Bathurst, NSW, Australia

    P.W. Chandana Withana

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Pardamean, B. et al. (2023). Comparison of K-Nearest Neighbor and Support Vector Regression for Predicting Oil Palm Yield. In: Mukhopadhyay, S.C., Senanayake, S.N.A., Withana, P.C. (eds) Innovative Technologies in Intelligent Systems and Industrial Applications. CITISIA 2022. Lecture Notes in Electrical Engineering, vol 1029. Springer, Cham. https://doi.org/10.1007/978-3-031-29078-7_3

Download citation

Publish with us

Policies and ethics

Search

Navigation