Design of a Sugarcane Yield and Productivity Prediction Model

Indonesia’s sugar industry has yet to become self-sufficient in sugar production. This is due to the unpredictable and fluctuating relationship between yield (sugar content (%)) and sugarcane productivity (Ton/Ha) in all state-owned and private Indonesian sugar mills. As a result, Indonesia’s domestic sugar consumption is still balanced by sugar imports. This study aimed to identify the main criteria and predict sugarcane yield and productivity using vegetative growth indicators in sugarcane cultivation using data science and a machine learning technique based on SVR and RF. The study found that the essential features for predicting sugarcane yield are clear juice, Pol, purity, Brix, and maturity factor, whereas the number of stems, stem height, stem weight, rainfall, and juring factor are important for predicting sugarcane productivity. The best model to predict sugarcane yield (%) was generated using RF with an average absolute error rate of 0.074% and accuracy in predicting yield with an average absolute percentage error of 0.010% and a sugarcane yield prediction error rate of 0.129%. The best sugarcane productivity prediction model was generated using SVR with an average absolute error rate of 0.051 tons/ha and accuracy in forecasting productivity with an average absolute percentage error of 0.001% and a sugarcane productivity prediction error rate of 0.058 tons/ha. This model may be used to optimize sugar cane cultivation and harvesting times, resulting in increased productivity and yields, which benefits corporate performance and increases national sugar output.

Prediction; Productivity; Random Forest; Sugarcane Yield; Support Vector Regression

Alexandropoulos, SAN, Kotsiantis, SB & Vrahatis, MN 2019, 'Data preprocessing in predictive data mining', The Knowledge Engineering Review, vol. 34, article e1, https://doi.org/10.1017/S026988891800036X

AlSagri, H & Ykhlef, M 2020, 'Quantifying feature importance for detecting depression using random forest', International Journal of Advanced Computer Science and Applications, vol. 11, no. 5, pp. 628-635, https://doi.org/10.14569/IJACSA.2020.0110577

Asrol, M, Marimin, M & Yani, M 2020, 'Business intelligence model construction to improve sugarcane yield for the sustainable sugar industry', Journal of Advanced Research in Dynamical and Control Systems, vol. 12, special issue 6, pp. 109–118, http://dx.doi.org/10.5373/JARDCS/V12SP6/SP20201013

Badan Pusat Statistik (BPS) 2023, Statistik tebu Indonesia 2022, Badan Pusat Statistik, Jakarta

Bocca, FF & Rodrigues, LHA 2016, 'The effect of tuning, feature engineering, and feature selection in data mining applied to rainfed sugarcane yield modelling', Computers and Electronics in Agriculture, vol. 128, pp. 67-76, https://doi.org/10.1016/j.compag.2016.08.015

Cai, J, Luo, J, Wang, S & Yang, S 2018, 'Feature selection in machine learning: A new perspective', Neurocomputing, vol. 300, pp. 70-79, https://doi.org/10.1016/j.neucom.2017.11.077

Canata, TF, Wei, MCF, Maldaner, LF & Molin, JP 2021, 'Sugarcane yield mapping using high-resolution imagery data and machine learning technique', Remote Sensing, vol. 13, no. 2, article 232, https://doi.org/10.3390/rs13020232

Cao, D-S, Liang, Y-Z, Xu, Q-S, Zhang, L-X, Hu, Q-N & Li, H-D 2011, 'Feature importance sampling?based adaptive random forest as a useful tool to screen underlying lead compounds', Journal of Chemometrics, vol. 25, no. 4, pp. 201–207, https://doi.org/10.1002/cem.1375

Charoen-Ung, P & Mittrapiyanuruk, P 2019, 'Sugarcane yield grade prediction using random forest with forward feature selection and hyper-parameter tuning', In: Recent Advances in Information and Communication Technology 2018: Proceedings of the 14th International Conference on Computing and Information Technology (IC2IT 2018), Springer International Publishing, pp. 33–42, https://doi.org/10.1007/978-3-319-93692-5_4

Chen, P, Li, F & Wu, C 2021, 'Research on intrusion detection method based on Pearson correlation coefficient feature selection algorithm', Journal of Physics: Conference Series, IOP Publishing, vol. 1757, no. 1, article 012054, https://doi.org/10.1088/1742-6596/1757/1/012054

Cheng, T, Wang, J & Li, X 2007, 'The support vector machine for nonlinear spatio-temporal regression', In: Proceedings of Geocomputation

Chicco, D, Warrens, MJ & Jurman, G 2021, 'The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation', PeerJ Computer Science, vol. 7, article e623, https://doi.org/10.7717/peerj-cs.623

Criminisi, A, Shotton, J & Konukoglu, E 2012, 'Decision forests: A unified framework for classification, regression, density estimation, manifold learning and semi-supervised learning', Foundations and Trends in Computer Graphics and Vision, vol. 7, no. 2–3, pp. 81–227, http://dx.doi.org/10.1561/0600000035

Dos Santos Luciano, AC, Picoli, MCA, Duft, DG, Rocha, JV, Leal, MRLV & Le Maire, G 2021, 'Empirical model for forecasting sugarcane yield on a local scale in Brazil using Landsat imagery and random forest algorithm', Computers and Electronics in Agriculture, vol. 184, article 106063, https://doi.org/10.1016/j.compag.2021.106063

ELhadad, R, Tan, YF & Tan, WN 2022, 'Anomaly prediction in electricity consumption using a combination of machine learning techniques', International Journal of Technology, vol. 13, no. 6, pp. 1317–1325, https://doi.org/10.14716/ijtech.v13i6.5931

Erick, Y, Umezuruike, C, Jossy, N & Gusite, B 2023, 'Development of a machine learning regression model for accurate sugarcane crop yield prediction, Jinja–Uganda', Journal of Applied Sciences, Information and Computing, vol. 4, no. 1, pp. 25–33, https://doi.org/10.59568/JASIC-2023-4-1-03

Everingham, Y, Sexton, J, Skocaj, D & Inman-Bamber, G 2016, 'Accurate prediction of sugarcane yield using a random forest algorithm', Agronomy for Sustainable Development, vol. 36, pp. 1–9, https://dx.doi.org/10.1007/s13593-016-0364-z

Gaffar, AWM & Sitanggang, IS 2019, 'Spatial model for predicting sugarcane crop productivity using support vector regression', In: IOP Conference Series: Earth and Environmental Science, IOP Publishing, vol. 335, no. 1, article 012009, https://doi.org/10.1088/1755-1315/335/1/012009

Gregorutti, B, Michel, B & Saint-Pierre, P 2017, 'Correlation and variable importance in random forests', Statistics and Computing, vol. 27, pp. 659–678, https://doi.org/10.1007/s11222-016-9646-1

Hammer, RG, Sentelhas, PC & Mariano, JCQ 2020, 'Sugarcane yield prediction through data mining and crop simulation models', Sugar Tech, vol. 22, no. 2, pp. 216–225, https://doi.org/10.1007/s12355-019-00776-z

Hanka, MKF & Santosa, B 2021, 'Analisis kualitas bahan baku tebu melalui teknik pengklasteran dan klasifikasi kadar gula sebelum giling (studi kasus pabrik gula PT. XYZ)' ('Analysis of the quality of sugar cane raw materials through clustering techniques and classification of sugar content before milling (case study of PT. XYZ sugar factory)), Jurnal Teknik ITS, vol. 10, no. 2, pp. F100–F107

Hyndman, RJ 2014, 'Measuring forecast accuracy', Business Forecasting: Practical Problems and Solutions, pp. 177–183

Indrawanto, C, Purwono, Syakir, M, Siswanto, Soetopo, D, Munarso, SJ, Pitono, J & Rumini, W 2017, Budidaya dan pascapanen tebu, IAARD Press, Jakarta

Jaelani, T, Yamin, M & Mahandari, CP 2022, 'Machine learning untuk prediksi produksi gula nasional' (Machine learning for national sugar production prediction), JMPM (Jurnal Material dan Proses Manufaktur), vol. 6, no. 1, pp. 31–36, https://doi.org/10.18196/jmpm.v6i1.14897

Joshua, SV, Priyadharson, ASM, Kannadasan, R, Khan, AA, Lawanont, W, Khan, FA, Rehman, AU & Ali, MJ 2022, 'Crop yield prediction using machine learning approaches on a wide spectrum', Computers, Materials & Continua, vol. 72, no. 3, pp. 5663–5679, http://dx.doi.org/10.32604/cmc.2022.027178

Jovi?, A, Brki?, K & Bogunovi?, N 2015, 'A review of feature selection methods with applications', In: 2015 38th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), IEEE, pp. 1200–1205, https://doi.org/10.1109/MIPRO.2015.7160458

Lárraga-Altamirano, HR, Hernández-López, DR, Piedad-Rubio, AM & Blanco-Martínez, JR 2024, 'Machine-learning model for estimating sugarcane production at crop level', Journal of Technology and Innovation, pp. 11–28

Mahesh, B 2020, 'Machine learning algorithms – a review', International Journal of Science and Research (IJSR), vol. 9, no. 1, pp. 381–386

Maldaner, LF, De Paula Corrêdo, L, Canata, TF & Molin, JP 2021, 'Predicting the sugarcane yield in real-time by harvester engine parameters and machine learning approaches', Computers and Electronics in Agriculture, vol. 181, article 105945, https://doi.org/10.1016/j.compag.2020.105945

Medar, RA, Rajpurohit, VS & Ambekar, AM 2019, 'Sugarcane crop yield forecasting model using supervised machine learning', International Journal of Intelligent Systems and Applications, vol. 11, no. 8, p. 11-20, https://doi.org/10.5815/ijisa.2019.08.02 

Mohamad, M, Selamat, A, Krejcar, O, Crespo, RG, Herrera-Viedma, E & Fujita, H 2021, 'Enhancing big data feature selection using a hybrid correlation-based feature selection', Electronics, vol. 10, no. 23, article 2984, https://doi.org/10.3390/electronics10232984

Nikhil, UV, Pandiyan, AM, Raja, SP & Stamenkovic, Z 2024, 'Machine learning-based crop yield prediction in South India: Performance analysis of various models', Computers, vol. 13, no. 6, article 137, https://doi.org/10.3390/computers13060137

Noorsaman, A, Amrializzia, D, Zulfikri, H, Revitasari, R & Isambert, A 2023, 'Machine learning algorithms for failure prediction model and operational reliability of onshore gas transmission pipelines', International Journal of Technology, vol. 14, no. 3, pp. 680-689, https://doi.org/10.14716/ijtech.v14i3.6287

Paidipati, KK, Banik, A, Shah, B & Sangwa, NR 2022, 'Forecasting of sugarcane productivity estimation in India – a comparative study with advanced non-parametric regression models', Journal of Algebraic Statistics, vol. 13, no. 2, pp. 760–778

Palanivel, K & Surianarayanan, C 2019, 'An approach for prediction of crop yield using machine learning and big data techniques', International Journal of Computer Engineering and Technology, vol. 10, no. 3, pp. 110–118

Pandey, N, Patnaik, PK & Gupta, S 2020, 'Data pre-processing for machine learning models using python libraries', International Journal of Engineering and Advanced Technology, vol. 9, no. 4, pp. 1995–1999, http://dx.doi.org/10.35940/ijeat.D9057.049420

Panigrahi, B, Kathala, KCR & Sujatha, M 2023, 'A machine learning-based comparative approach to predict the crop yield using supervised learning with regression models', Procedia Computer Science, vol. 218, pp. 2684–2693, https://doi.org/10.1016/j.procs.2023.01.241

Rafiqi, Saidin, OK, Lubis, MY & Ikhsan, E 2021, 'Optimization of sustainable sugar industry towards food security', In: IOP Conference Series: Earth and Environmental Science, IOP Publishing, vol. 782, no. 2, article 032039, https://doi.org/10.1088/1755-1315/782/3/032039 

Respati, E 2022, Outlook komoditas perkebunan tebu 2022 (Sugarcane plantation commodity outlook 2022), Pusat Data dan Sistem Informasi Pertanian, Kementerian Pertanian Republik Indonesia, Indonesia

Sari, M, Berawi, MA, Larasati, SP, Susilowati, SI, Susantono, B & Woodhead, R 2023, 'Developing machine learning model to predict HVAC system of healthy building: A case study in Indonesia', International Journal of Technology, vol. 14, no. 7, pp. 1438–1448, https://doi.org/10.14716/ijtech.v14i7.6682

 Shah, A, Dubey, A, Hemnani, V, Gala, D & Kalbande, DR 2018, 'Smart farming system: Crop yield prediction using regression techniques', In: Proceedings of International Conference on Wireless Communication: ICWiCom 2017, Springer Singapore, pp. 49–56, https://doi.org/10.1007/978-981-10-8339-6_6

Shetty, SA, Padmashree, T, Sagar, BM & Cauvery, NK 2021, 'Performance analysis on machine learning algorithms with deep learning model for crop yield prediction', In: Data Intelligence and Cognitive Informatics: Proceedings of ICDICI 2020, Springer Singapore, pp. 739–750, https://doi.org/10.1007/978-981-15-8530-2_58

Singla, SK, Garg, RD & Dubey, OP 2020, 'Ensemble machine learning methods to estimate the sugarcane yield based on remote sensing information', Revue d'Intelligence Artificielle, vol. 34, no. 6, pp. 731-743, https://doi.org/10.18280/ria.340607

Smola, AJ & Schölkopf, B 2004, 'A tutorial on support vector regression', Statistics and Computing, vol. 14, pp. 199-222, https://doi.org/10.1023/B:STCO.0000035301.49549.88

Som-ard, J, Suwanlee, SR, Pinasu, D, Keawsomsee, S, Kasa, K, Seesanhao, N, Ninsawat, S, Borgogno-Mondino, E & Sarvia, F 2024, 'Evaluating sugarcane yield estimation in Thailand using multi-temporal Sentinel-2 and Landsat data together with machine-learning algorithms', Land, vol. 13, no. 9, article 1481, https://doi.org/10.3390/land13091481

Sulaiman, AA, Arsyad, M, Amiruddin, A, Teshome, TT & Nishanta, B 2023, 'New trends of sugarcane cultivation systems toward sugar production on the free market: A review', AGRIVITA Journal of Agricultural Science, vol. 45, no. 2, pp. 395–406, http://dx.doi.org/10.17503/agrivita.v45i2.4066

Tatachar, AV 2021, 'Comparative assessment of regression models based on model evaluation metrics', International Journal of Innovative Technology and Exploring Engineering, vol. 8, no. 9, pp. 853–860

Van Klompenburg, T, Kassahun, A & Catal, C 2020, 'Crop yield prediction using machine learning: A systematic literature review', Computers and Electronics in Agriculture, vol. 177, article 105709, https://doi.org/10.1016/j.compag.2020.105709

Venkatesh, B & Anuradha, J 2019, 'A review of feature selection and its methods', Cybernetics and Information Technologies, vol. 19, no. 1, pp. 3–26