Skip to main content

Advertisement

SpringerLink
Log in

Oil palm yield prediction across blocks from multi-source data using machine learning and deep learning

  • Research
  • Published:
Earth Science Informatics Aims and scope Submit manuscript

Abstract

Crop yield estimates are affected by various factors including weather, nutrients and management practices. Predicting yields on a large scale in a timely and accurate manner by considering these factors is essential for preventing climate risk and ensuring food security, particularly in the light of climate change and the escalation of extreme climatic events. In this study, integrating multi-source data (i.e. satellite-derived vegetation indices (VIs), satellite-derived climatic variables (i.e. land surface temperature (LST) and rainfall precipitation, weather station and field-surveys), we built one multiple linear regression (MLR), three machine learning (XGBoost, support vector regression, and random forest) and one deep learning (deep neural network) models to predict oil palm yield at block-level within the oil palm plantation. Moreover, time-series moving average and backward elimination feature selection technique were implemented at the pre-processing stage. The yield prediction models were developed and tested using MLR, XGBoost, support vector regression (SVR), random forest (RF) and deep neural network (DNN) algorithms. Their model performances were then compared using evaluation metrics and generated the final spatial prediction map based on the best performance. DNN achieved the best model performances for both selected (R2 = 0.91; RMSE = 2.92 t ha− 1; MAE = 2.56 t ha− 1 and MAPE = 0.09 t ha− 1) and full predictors (R2 = 0.76; RMSE of 3.03 t ha− 1; MAE of 2.88 t ha− 1; MAPE of 0.10 t ha− 1). In addition, advanced ensemble machine learning (ML) techniques such as XGBoost may be utilised as a supplementary for oil palm yield prediction at the block level. Among them, MLR recorded the lowest performance. By using backward elimination to identify the most significant predictors, the performance of all models was improved by 5–26% for R2, and that decreased by 3–31% for RMSE, 7–34% for MAE, and 1–15% for MAPE. After backward elimination, the DNN achieved the highest prediction accuracy among the other models, with a 14% increase in R-squared, a 11% decrease in RMSE, a 32% decrease in MAE and a 1% decrease in MAPE. Our study successfully developed efficient and accurate yield prediction models for timely predicting oil palm yield over a large area by integrating data from multiple sources. These would be useful for plantation management estimating oil palm yields to speed up the decision-making process for sustainable production.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

Data availability

In accordance with the research agreement between UPM and FGV, the datasets generated during and/or analysed during the present study are not publicly available and cannot be disclosed to third parties for the time being.

References

  • Abadi M, Barham P, Chen J, Chen Z, Davis A, Dean J, Zheng X (2016) {TensorFlow}: A System for {Large-Scale} Machine Learning. In: 12th USENIX symposium on operating systems design and implementation (OSDI 16), pp 265–283

  • Abubakar A, Ishak MY, Makmom AA (2021) Impacts of and adaptation to climate change on the oil palm in Malaysia: a systematic review. Environ Sci Pollut Res 28(39):54339–54361

    Article  Google Scholar 

  • Agarap AF (2018) Deep learning using Rectified Linear Units (ReLU). 1:2–8. http://arxiv.org/abs/1803.08375. Accessed 20 May 2022

  • Aghighi H, Azadbakht M, Ashourloo D, Shahrabi HS, Radiom S (2018) Machine learning regression techniques for the silage maize yield prediction using Time-Series Images of Landsat 8 OLI. IEEE J Sel Top Appl Earth Obs Remote Sens 11(12):4563–4577. https://doi.org/10.1109/JSTARS.2018.2823361

    Article  Google Scholar 

  • Ahmed A, Ishak MY, Uddin MK, Abd Samad MY, Mukhtar S, Danhassan SS (2021) Effects of some weather parameters on oil palm production in the Peninsular Malaysia. June, pp 1–17. https://doi.org/10.20944/preprints202106.0456.v1

  • Ang Y, Shafri HZM, Bakar SA, Abidin H, Lee YP, Hashim SJ, Che’Ya NN, Hassan MR, Lim HS, Abdullah R (2022) A novel ensemble machine learning and time series approach for oil palm yield prediction using Landsat Time Series Imagery based on NDVI. Geocarto Int 0(0):1–24. https://doi.org/10.1080/10106049.2022.2025920

    Article  Google Scholar 

  • Arab ST, Noguchi R, Matsushita S, Ahamed T (2021) Prediction of grape yields from time-series vegetation indices using satellite remote sensing and a machine-learning approach. Remote Sens Appl Soc Environ 22(November 2020). https://doi.org/10.1016/j.rsase.2021.100485

  • Awal MA, Wan Ishak WI, Bockari-Gevao SM (2010) Determination of leaf area index for oil palm plantation using hemispherical photography technique. Pertanika J Sci Technol 18(1):23–32

    Google Scholar 

  • Babu CN, Reddy BE (2014) A moving-average filter-based hybrid ARIMA-ANN model for forecasting time series data. Appl Soft Comput J 23:27–38. https://doi.org/10.1016/j.asoc.2014.05.028

    Article  Google Scholar 

  • Balasundram SK, Memarian H, Khosla R (2013) Estimating oil palm yields using vegetation indices derived from QuickBird. Life Sci J 10(4):851–860

    Google Scholar 

  • Berra EF, Fontana DC, Kuplich TM (2018) Tree age as adjustment factor to NDVI. Rev Árvore 41(3). https://doi.org/10.1590/1806-90882017000300007

  • Bolton DK, Friedl MA (2013) Forecasting crop yield using remotely sensed vegetation indices and crop phenology metrics. Agric For Meteorol 173:74–84. https://doi.org/10.1016/j.agrformet.2013.01.007

    Article  Google Scholar 

  • Bouras EH, Jarlan L, Er-Raki S, Balaghi R, Amazirh A, Richard B, Khabba S (2021) Cereal yield forecasting with satellite drought-based indices, weather data and regional climate indices using machine learning in Morocco. Remote Sens 13(16). https://doi.org/10.3390/rs13163101

  • Breiman L (2001) Random forests. Mach Learn 45(1):5–32

    Article  Google Scholar 

  • Burke M, Lobell DB (2017) Satellite-based assessment of yield variation and its determinants in smallholder African systems. Proc Natl Acad Sci USA 114(9):2189–2194. https://doi.org/10.1073/pnas.1616919114

    Article  Google Scholar 

  • Cao J, Wang H, Li J, Tian Q, Niyogi D (2022) Improving the forecasting of winter wheat yields in Northern China with machine learning–dynamical hybrid subseasonal-to-seasonal ensemble prediction. Remote Sens 14(7):1707

    Article  Google Scholar 

  • Cao J, Zhang Z, Tao F, Zhang L, Luo Y, Zhang J, Han J, Xie J (2021) Integrating multi-source data for rice yield prediction across China using machine learning and deep learning approaches. Agric For Meteorol 297(December 2020):108275. https://doi.org/10.1016/j.agrformet.2020.108275

  • Cedrez CB, Hijmans RJ (2018) Methods for spatial prediction of crop yield potential. Agron J 110(6):2322–2330. https://doi.org/10.2134/agronj2017.11.0664

    Article  Google Scholar 

  • Chandra A, Mitra P, Dubey SK, Ray SS (2019) Machine learning approach for kharif rice yield prediction integrating multi-temporal vegetation indices and weather and non-weather variables. Int Arch Photogramm Remote Sens Spat Inf Sci - ISPRS Arch 42(3/W6):187–194. https://doi.org/10.5194/isprs-archives-XLII-3-W6-187-2019

    Article  Google Scholar 

  • 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(October 2017):338–348. https://doi.org/10.1016/j.compag.2018.06.006

  • Chen T, Guestrin C (2016) Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp 785–794

  • Chen T, He T, Benesty M (2018) XGBoost: eXtreme gradient Boosting. R Package Version 0(71–2):1–4

    Google Scholar 

  • Chlingaryan A, Sukkarieh S, Whelan B (2018) Machine learning approaches for crop yield prediction and nitrogen status estimation in precision agriculture: A review. Comput Electron Agric 151(May):61–69. https://doi.org/10.1016/j.compag.2018.05.012

    Article  Google Scholar 

  • Chong KL, Kanniah KD, Pohl C, Tan KP (2017) A review of remote sensing applications for oil palm studies. Geo-Spatial Inform Sci 20(2):184–200. https://doi.org/10.1080/10095020.2017.1337317

    Article  Google Scholar 

  • Clevers JGPW, Leeuwen HJC, Van Sensing R, Verhoef W (1989) Estimating APAR by means of vegetation indices: a sensitivity analysis. XXIX ISPRS Congress Technical Commission VII: Interpretation of Photographic and Remote Sensing Data, 691–698

  • Diana SR, Dharma G (2019) Estimation the amount of oil palm production using Artificial Neural Network and NDVI SPOT-6 Imagery. Int J Innov Sci Res Technol 4(11):548–554

    Google Scholar 

  • Dinku T, Funk C, Peterson P, Maidment R, Tadesse T, Gadain H, Ceccato P (2018) Validation of the CHIRPS satellite rainfall estimates over eastern Africa. Q J R Meteorol Soc 144(November 2017):292–312. https://doi.org/10.1002/qj.3244

  • Fairhurst TH, Mutert E (1999) Interpretation and Management of Oil Palm Leaf Analysis Data 13(1):48–51

  • Fashoto S, Mbunge E, Ogunleye G, Van den Burg J (2021) Implementation of machine learning for predicting maize crop yields using multiple linear regression and backward elimination. Malays J Comput 6(1):679. https://doi.org/10.24191/mjoc.v6i1.8822

    Article  Google Scholar 

  • Foster H (2003) Assessment of Oil Palm Fertilizer Requirements. In: Fairhurst, T.H. and Hardter, R., Eds, Oil Palm: Management for Large and Sustainable Yields, Potash and Phosphate Institute (PPI), Potash and Phosphate Institute of Canada (PPIC) and International Potash Institute, 231–257

  • Gitelson AA (2004) Wide dynamic range vegetation index for remote quantification of biophysical characteristics of vegetation. J Plant Physiol 161(2):165–173

    Article  Google Scholar 

  • Gitelson AA, Kaufman YJ, Merzlyak MN (1996) Use of a green channel in remote sensing of global vegetation from EOS- MODIS. Remote Sens Environ 58:289–298

    Article  Google Scholar 

  • Gitelson Anatoly A, Vina A, Arkebauer TJ, Rundquist DC, Keydan G, Leavitt B (2003) Remote estimation of leaf area index and green leaf biomass in maize canopies. Geophys Res Lett 30(5). https://doi.org/10.1029/2002gl016450

  • Gunn SR (1998) Support vector machines for classification and regression. ISIS technical report 14(1):5–16

  • Hartley CWS (1988) The oil palm: world agriculture series, 3rd edn. Longman, London

    Google Scholar 

  • Hilal YY, Yahya A, Ismail WIW, Asha’Ari ZH (2021) Neural networks method in predicting oil palm FFB yields for the Peninsular states of Malaysia. J Oil Palm Res 33(3):400–412. https://doi.org/10.21894/jopr.2020.0105

    Article  Google Scholar 

  • Huete A, Didan K, Miura T, Rodriguez EP, Gao X, Ferreira LG (2002) Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sens Environ 83(1–2):195–213

    Article  Google Scholar 

  • Huete AR (1988) A soil-adjusted vegetation index (SAVI). Remote Sens Environ 25(3):295–309

    Article  Google Scholar 

  • Johnson DM (2014) An assessment of pre- and within-season remotely sensed variables for forecasting corn and soybean yields in the United States. Remote Sens Environ 141:116–128. https://doi.org/10.1016/j.rse.2013.10.027

    Article  Google Scholar 

  • Jordan CF (1969) Derivation of leaf-area index from quality of light on the forest floor. Ecology 50(4):663–666

    Article  Google Scholar 

  • Jurečka F, Lukas V, Hlavinka P, Semerádová D, Žalud Z, Trnka M (2018) Estimating crop yields at the field level using landsat and modis products. Acta Univ Agric et Silvic Mendelianae Brun 66(5):1141–1150. https://doi.org/10.11118/actaun201866051141

    Article  Google Scholar 

  • Kafy AA, Rahman AF, Al Rakib A, Akter KS, Raikwar V, Jahir DMA, Ferdousi J, Kona MA (2021) Assessment and prediction of seasonal land surface temperature change using multi-temporal Landsat images and their impacts on agricultural yields in Rajshahi, Bangladesh. Environ Challenges 4:100147

    Article  Google Scholar 

  • Kee NS, Eng CT, Thamboo S (1968) Nutrient contents of oil palms in Malaya. III. Micronutrient contents in vegetative tissues. Malays Agric J 46:332–391

    Google Scholar 

  • Khaki S, Wang L (2019) Crop yield prediction using deep neural networks. Front Plant Sci 10(May):1–10. https://doi.org/10.3389/fpls.2019.00621

    Article  Google Scholar 

  • 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. Agriculture 11(9):832. https://doi.org/10.3390/agriculture11090832

    Article  Google Scholar 

  • Khor JF, Ling L, Yusop Z, Tan WL, Ling JL, Soo EZX (2021) Impact of El Niño on oil palm yield in Malaysia. Agronomy 11(11). https://doi.org/10.3390/agronomy11112189

  • Liakos KG, Busato P, Moshou D, Pearson S, Bochtis D (2018) Machine learning in agriculture: A review. Sensors (Switzerland) 18(8):1–29. https://doi.org/10.3390/s18082674

    Article  Google Scholar 

  • Lindsey C, Sheather S (2010) Variable selection in linear regression. Stata J 10(4):650–669

    Article  Google Scholar 

  • Lobell DB, Di Tommaso S, You C, Djima IY, Burke M, Kilic T (2020) Sight for sorghums: Comparisons of satellite-and ground-based sorghum yield estimates in Mali. Remote Sens 12(1):1–16. https://doi.org/10.3390/RS12010100

    Article  Google Scholar 

  • Matsushita B, Yang W, Chen J, Onda Y, Qiu G (2007) Sensitivity of the Enhanced Vegetation Index (EVI) and Normalized Difference Vegetation Index (NDVI) to topographic effects: A case study in high-density cypress forest. Sensors 7(11):2636–2651. https://doi.org/10.3390/s7112636

    Article  Google Scholar 

  • Meroni M, Waldner F, Seguini L, Kerdiles H, Rembold F (2021) Yield forecasting with machine learning and small data: What gains for grains? Agric For Meteorol 308–309:108555. https://doi.org/10.1016/j.agrformet.2021.108555

  • MPOB (2018) MPOA supports Govt initiative towards fulfilling UN SDGs. https://bepi.mpob.gov.my/news/detail.php?id=26269. Accessed 19 Feb 2022

  • Newton IH, Tariqul Islam AFM, Saiful Islam AKM, Tarekul Islam GM, Tahsin A, Razzaque S (2018) Yield prediction model for potato using Landsat Time Series Images Driven Vegetation Indices. Remote Sens Earth Syst Sci 1(1–2):29–38. https://doi.org/10.1007/s41976-018-0006-0

    Article  Google Scholar 

  • Niazian M, Sadat-Noori SA, Abdipour M (2018) Modeling the seed yield of Ajowan (Trachyspermum ammi L.) using Artificial Neural Network and Multiple Linear Regression Models. Ind Crops Prod 117(November 2017):224–234. https://doi.org/10.1016/j.indcrop.2018.03.013

  • Oikonomidis A, Catal C, Kassahun A (2022) Deep learning for crop yield prediction: a systematic literature review. New Z J Crop Hortic Sci 0(0):1–26. https://doi.org/10.1080/01140671.2022.2032213

    Article  Google Scholar 

  • Ojha N, Merlin O, Suere C, Escorihuela MJ (2021) Extending the spatio-temporal applicability of DISPATCH soil moisture downscaling algorithm: A study case using SMAP, MODIS and Sentinel-3 Data. Front Environ Sci 9(March):1–17. https://doi.org/10.3389/fenvs.2021.555216

    Article  Google Scholar 

  • Orhan O, Ekercin S, Dadaser-Celik F (2014) Use of Landsat land surface temperature and vegetation indices for monitoring drought in the Salt Lake Basin Area, Turkey. Sci World J 2014(Vci). https://doi.org/10.1155/2014/142939

  • Panek E, Gozdowski D, Stępień M, Samborski S, Ruciński D, Buszke B (2020) Within-field relationships between satellite-derived vegetation indices, grain yield and spike number of winter wheat and triticale. Agronomy 10(11):1–18. https://doi.org/10.3390/agronomy10111842

    Article  Google Scholar 

  • Phan P, Chen N, Xu L, Dao DM, Dang D(2021) NDVI variation and yield prediction in growing season: A case study with tea in Tanuyen Vietnam. Atmosphere 12(8). https://doi.org/10.3390/atmos12080962

  • Piekutowska M, Niedbała G, Piskier T, Lenartowicz T, Pilarski K, Wojciechowski T, Pilarska AA, Czechowska-Kosacka A (2021) The application of multiple linear regression and artificial neural network models for yield prediction of very early potato cultivars before harvest. Agronomy 11(5). https://doi.org/10.3390/agronomy11050885

  • Qi J, Chehbouni A, Huete AR, Kerr YH, Sorooshian S (1994) A modified soil adjusted vegetation index. Remote Sens Environ 48(2):119–126

    Article  Google Scholar 

  • 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 

  • Rouse JW, Hass RH, Schell JA, Deering DW(1973) Monitoring vegetation systems in the great plains with ERTS. In: Proceedings of the Third Earth Resources Technology Satellite Symposium, 1, pp 309–317

  • Sarkar M, Kabir S, Begum RA, Pereira JJ (2020) Impacts of climate change on oil palm production in Malaysia. Environ Sci Pollut Res 27(9):9760–9770

    Article  Google Scholar 

  • Schmidt-Hieber J (2020) Nonparametric regression using deep neural networks with ReLU activation function. Ann Stat 48(4):1875–1897. https://doi.org/10.1214/19-AOS1875

    Article  Google Scholar 

  • Schwalbert RA, Amado T, Corassa G, Pott LP, Prasad PVV, Ciampitti IA (2020) Satellite-based soybean yield forecast: Integrating machine learning and weather data for improving crop yield prediction in southern Brazil. Agric For Meteorol 284(December 2019):107886. https://doi.org/10.1016/j.agrformet.2019.107886

  • Shamshiri RR, Hameed IA, Balasundram SK, Ahmad D, Weltzien C, Yamin M (2018) Fundamental research on unmanned aerial vehicles to support precision agriculture in oil palm plantations. Agric Robot - Fundamentals and Application Intechopen, London 91–116

  • Srivastava AK, Safaei N, Khaki S, Lopez G, Zeng W, Ewert F, Gaiser T, Rahimi J (2022) Winter wheat yield prediction using convolutional neural networks from environmental and phenological data. Sci Rep 12(1):1–14. https://doi.org/10.1038/s41598-022-06249-w

    Article  Google Scholar 

  • Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R (2014) Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res 15(1):1929–1958

    Google Scholar 

  • Tan XJ, Cheor WL, Yeo KS, Leow WZ (2022) Expert systems in oil palm precision agriculture: A decade systematic review. J King Saud Univ - Comput Inf Sci 34(4):1569–1594. https://doi.org/10.1016/j.jksuci.2022.02.006

  • Wang J, Zhou Q, Shang J, Liu C, Zhuang T, Ding J, Xian Y, Zhao L, Wang W, Zhou G, Tan C, Huo Z (2021) UAV-and Machine Learning-Based Retrieval of wheat SPAD values at the overwintering stage for variety screening. Remote Sens 13(24):1–19. https://doi.org/10.3390/rs13245166

    Article  Google Scholar 

  • Xue J, Su B (2017) Significant remote sensing vegetation indices: A review of developments and applications. J Sens 2017. https://doi.org/10.1155/2017/1353691

  • Yin G, Mariethoz G, McCabe MF (2017) Gap-filling of Landsat 7 imagery using the direct sampling method. Remote Sens 9(1):1–20. https://doi.org/10.3390/rs9010012

    Article  Google Scholar 

  • Zarei A, Hasanlou M, Mahdianpari M (2021) A comparison of machine learning models for soil salinity estimation using multi-spectral earth observation data. ISPRS Ann Photogramm Remote Sens Spat Inf Sci 3:257–263

  • Zhang L, Zhang Z, Luo Y, Cao J, Xie R, Li S (2021) Integrating satellite-derived climatic and vegetation indices to predict smallholder maize yield using deep learning. Agric For Meteorol 311(February):108666. https://doi.org/10.1016/j.agrformet.2021.108666

    Article  Google Scholar 

  • Zhang X, Zhang K, Sun Y, Zhao Y, Zhuang H, Ban W, Chen Y, Fu E, Chen S, Liu J, Hao Y (2022) Combining spectral and texture features of UAS-based multispectral images for maize leaf area index estimation. Remote Sens 14(2):1–17. https://doi.org/10.3390/rs14020331

    Article  Google Scholar 

  • Zhao B, Duan A, Ata-Ul-Karim ST, Liu Z, Chen Z, Gong Z, Zhang J, Xiao J, Liu Z, Qin A, Ning D (2018) Exploring new spectral bands and vegetation indices for estimating nitrogen nutrition index of summer maize. Eur J Agron 93(December 2017):113–125. https://doi.org/10.1016/j.eja.2017.12.006

  • Zhao Y, Potgieter AB, Zhang M, Wu B, Hammer GL (2020) Predicting wheat yield at the field scale by combining high-resolution Sentinel-2 satellite imagery and crop modelling. Remote Sens 12(6). https://doi.org/10.3390/rs12061024

Download references

Acknowledgements

We wish to express our gratitude to the Ministry of Higher Education (MOHE), Malaysia for funding through the Long-Term Research Grant Scheme (LRGS) of the Malaysian Research University Network (MRUN). It is categorized under the research programme ‘A Big Data Analytics Platform for Optimizing Oil Palm Yield Via Breeding by Design’ (Grant Number: 203.PKOMP.6770007) as a specific project: ‘Geoinformatics Data for Palm Oil Yield Prediction Using Machine Learning’ (Vote No: 6300268-10801). The team from FGV is also acknowledged for providing their expertise for the research. Also, an appreciation to FGV for the field data provided.

Funding

This research is supported by the Ministry of Higher Education (MOHE), Malaysia.

Author information

Authors and Affiliations

  1. Department of Civil Engineering and Geospatial Information Science Research Centre (GISRC), Faculty of Engineering, Universiti Putra Malaysia (UPM), 43400, Serdang, Selangor, Malaysia

    Yuhao Ang & Helmi Zulhaidi Mohd Shafri

  2. Geoinformatics Unit, FGV R&D Sdn Bhd, FGV Innovation Centre, PT23417, Lengkuk Teknologi, 71760, Bandar Enstek, Negeri Sembilan, Malaysia

    Yang Ping Lee, Shahrul Azman Bakar, Haryati Abidin & Mohd Na’aim Samad

  3. FGV Agri Services Sdn Bhd, Level 9, West, Wisma FGV, Jalan Raja Laut, 50350, Kuala Lumpur, Malaysia

    Mohd Umar Ubaydah Mohd Junaidi

  4. Department of Computer and Communication Systems Engineering, Faculty of Engineering, Universiti Putra Malaysia, 43400, Serdang, Selangor, Malaysia

    Shaiful Jahari Hashim

  5. Department of Agriculture Technology, Faculty of Agriculture, Universiti Putra Malaysia, 43400, Serdang, Selangor, Malaysia

    Nik Norasma Che’Ya

  6. Department of Mechanical and Manufacturing Engineering, Faculty of Engineering, Universiti Putra Malaysia, 43400, Serdang, Selangor, Malaysia

    Mohd Roshdi Hassan

  7. School of Physics, Universiti Sains Malaysia (USM), 11800, Gelugor, Penang, Malaysia

    Hwee San Lim

  8. School of Computer Sciences, Universiti Sains Malaysia (USM), 11800, Gelugor, Penang, Malaysia

    Rosni Abdullah

  9. School of Industrial Technology, Universiti Sains Malaysia (USM), 11800, Gelugor, Penang, Malaysia

    Yusri Yusup & Syahidah Akmal Muhammad

  10. School of Management, Universiti Sains Malaysia (USM), 11800, Gelugor, Penang, Malaysia

    Sin Yin Teh

Authors
  1. Yuhao Ang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Helmi Zulhaidi Mohd Shafri
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yang Ping Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Shahrul Azman Bakar
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Haryati Abidin
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Mohd Umar Ubaydah Mohd Junaidi
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Shaiful Jahari Hashim
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Nik Norasma Che’Ya
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Mohd Roshdi Hassan
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Hwee San Lim
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Rosni Abdullah
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Yusri Yusup
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Syahidah Akmal Muhammad
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Sin Yin Teh
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Mohd Na’aim Samad
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

YA analysed and interpreted remote sensing data regarding oil palm yield prediction along with other pertinent aspects using machine learning models, and drafted the manuscript. HZMS is the main supervisor who supervised the research and reviewed the manuscript. YPL commented on previous version of the manuscript. The historical yield and agronomic information were provided by SAB and MUUMJ. HSL and RA participated in the management of the research and checked the manuscript for clarity. TSY proofread the manuscript. HA, SJH, NNCY, MRH, YY, SAM, and MNS were also involved in the management of the research and checking the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Helmi Zulhaidi Mohd Shafri.

Ethics declarations

The manuscript is original, has not been published before, and is not currently being considered for publication elsewhere.

Conflict of interest

The authors declare that there is no conflict of interests regarding the publication of this article.

Additional information

Communicated by H. Babaie.

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

ESM 1

(DOCX 150 KB)

ESM 2

(DOCX 2.43 MB)

ESM 3

(DOCX 14.8 KB)

ESM 4

(DOCX 2.40 MB)

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ang, Y., Shafri, H.Z.M., Lee, Y.P. et al. Oil palm yield prediction across blocks from multi-source data using machine learning and deep learning. Earth Sci Inform 15, 2349–2367 (2022). https://doi.org/10.1007/s12145-022-00882-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12145-022-00882-9

Keywords

Search

Navigation