Machine learning-based prediction of rice yield from rain feeding in monsoonal tropical area of Java, Indonesia

Abdul Aziz, . Komariah, Azhari Rizal, Muhammad Rizky Romadhon, Muhamad Mustangin
Received: 06.03.2025 Revised: 06.08.2025 Accepted: 27.08.2025
Retrieved from Vol. 28, No. 8, 2025 Pages: 165-178
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Abstract

Rainfed rice cultivation in monsoonal tropical areas of Java, Indonesia, is challenged by nutrient deficiencies, unpredictable rainfall amounts, and limited agricultural investment, leading to fluctuating yields. The purpose of this study was to develop a precise rice yield prediction model using machine learning tailored to specific toposequences in Central Java. A combination of survey-based field and laboratory methods was employed, integrating climate, soil, socio-economic, and land management variables from 87 targeted sampling points. Machine learning analysis using Bayesian Neural Networks (BNN) demonstrated moderate accuracy with R2 = 0.840 and RMSE = 0.442 overall, but accuracy improved significantly when models were adjusted to elevation-specific categories, achieving R2 values up to 0.999. Lowland paddy field predictions were most influenced by available phosphorus (P), while rainfall, gender, education, and seed variety were key factors in medium-altitude zones; slope, available P, gender, and cropping patterns were dominant in highland areas. Pareto analysis supported the identification of these key yield determinants in each toposequence. The integration of BNN and Pareto approaches enabled the creation of a high-precision, location-specific yield prediction model. This work demonstrated that tailoring machine learning models to elevation-based agroecological zones enhances their performance and practical application. The findings are particularly valuable for agricultural stakeholders including policymakers, extension services, and farmers, who can leverage these predictive insights to optimise rainfed rice management practices and improve productivity under variable climatic conditions

Keywords

agricultural sustainability; Bayesian Neural Network (BNN); food security; Pareto analysis; precision agriculture

  1. Bal, S.K., Sandeep, V.M., Kumar, P.V., Rao, A.V.M.S., Pramod, V.P., Manikandan, N., Rao, C.S., Singh, N.P., & Bhaskar, S. (2022). Assessing impact of dry spells on the principal rainfed crops in major dryland regions of India. Agricultural and Forest Meteorology, 313, article number 108768. doi: 10.1016/j.agrformet.2021.108768.
  2. Bazrafshan, O., Ehteram, M., Moshizi, Z.G., & Jamshidi, S. (2022). Evaluation and uncertainty assessment of wheat yield prediction by multilayer perceptron model with bayesian and copula bayesian approaches. Agricultural Water Management, 273, article number 107881. doi: 10.1016/J.AGWAT.2022.107881.
  3. Bremner, J.M., & Mulvaney, C.S. (1982). Nitrogen – total. In A.L. Page (Ed.), Methods of soil analysis: Part 2. Chemical and microbiological properties (595-624). Madison, WI: American Society of Agronomy and Soil Science Society of America. doi: 10.2134/agronmonogr9.2.2ed.c31.
  4. Dewi, W.S., Romadhon, M.R., Amalina, D.D., & Aziz, A. (2022). Paddy soil quality assessment to sustaining food security. IOP Conference Series: Earth and Environmental Science, 1107, article number 012051. doi: 10.1088/1755-1315/1107/1/012051.
  5. Din, N. M. U., Assad, A., Dar, R. A., Rasool, M., Sabha, S. U., Majeed, T., Islam, Z. U., Gulzar, W., & Yaseen, A. (2024). RiceNet: A deep convolutional neural network approach for classification of rice varieties. Expert Systems with Applications, 235, 121214. https://doi.org/10.1016/j.eswa.2023.121214
  6. ESRI. (2022). GIS software for mapping and spatial analytics. Retrieved from https://www.esri.com/en-us/home
  7. He, Z.C., Huo, S.L., Li, E., Cheng, H.T., & Zhang, L.M. (2022). Data-driven approach to characterize and optimize properties of carbon fiber non-woven composite materials. Composite Structures, 297, article number 115961. doi: 10.1016/j.compstruct.2022.115961.
  8. Hengl, T., Nussbaum, M., Wright, M.N., Heuvelink, G.B.M., & Gräler, B. (2018). Random forest as a generic framework for predictive modeling of spatial and spatio-temporal variables. PeerJ, 9, article number e11088.
  9. Indonesian Agricultural Assembly and Modernization Agency. (2005). Chemical analysis of soil, plants, water, and fertilizers. Retrieved from https://repository.pertanian.go.id/server/api/core/bitstreams/77f52e6b-6a13-48bc-96d1-d6a35025d793/content
  10. Jackson, M.L. (1973). Soil chemical analysis. Prentice Hall of India.
  11. Jeong, S., Ko, J., & Yeom, J.M. (2022). Predicting rice yield at pixel scale through synthetic use of crop and deep learning models with satellite data in South and North Korea. Science of The Total Environment, 802, article number 149726. doi: 10.1016/j.scitotenv.2021.149726.
  12. Kadir, M.K.A., Ayob, M.Z., & Miniappan, N. (2015). Wheat yield prediction: Artificial neural network based approach. In 2014 4th International conference on engineering technology and technopreneuship (ICE2T) (pp. 161-165). Kuala Lumpur: IEEE. doi: 10.1109/ICE2T.2014.7006239.
  13. Liu, S., Wang, X., Liu, M., & Zhu, J. (2017). Towards better analysis of machine learning models: A visual analytics perspective. Visual Informatics, 1(1), 48-56. doi: 10.1016/j.visinf.2017.01.006.
  14. Moeletsi, M.E., Walker, S., & Hamandawana, H. (2013). Comparison of the Hargreaves and Samani equation and the Thornthwaite equation for estimating dekadal evapotranspiration in the Free State Province, South Africa. Physics and Chemistry of the Earth, 66, 4-15. doi: 10.1016/j.pce.2013.08.003.
  15. Mou, L., Liang, L., Gao, Z., & Wang, X. (2022). A multi-scale anomaly detection framework for retinal OCT images based on the Bayesian neural network. Biomedical Signal Processing and Control, 75, article number 103619. doi: 10.1016/j.bspc.2022.103619.
  16. Olsen, S.R., Cole, C.V., Watanabe, F.S., & Dean, L.A. (1954). Estimation of available phosphorus in soils by extraction with sodium bicarbonate (USDA Circular No. 939). Washington: U.S. Government Printing Office.
  17. Pant, J., Pant, R.P., Singh, M.K., Singh, D.P., & Pant, H. (2021). Analysis of agricultural crop yield prediction using statistical techniques of machine learning. Materials Today: Proceedings, 46, 10922-10926. doi: 10.1016/j.matpr.2021.01.948.
  18. Paudel, D., Boogaard, H., de Wit, A., Janssen, S., Osinga, S., Pylianidis, C., & Athanasiadis, I.N. (2021). Machine learning for large-scale crop yield forecasting. Agricultural Systems, 187, article number 103016. doi: 10.1016/j.agsy.2020.103016.
  19. Paudel, D., Boogaard, H., de Wit, A., van der Velde, M., Claverie, M., Nisini, L., Janssen, S., Osinga, S., & Athanasiadis, I.N. (2022). Machine learning for regional crop yield forecasting in Europe. Field Crops Research, 276, article number 108377. doi: 10.1016/j.fcr.2021.108377.
  20. Qian, J., Zhao, H., Wang, X., Wang, T., Feng, Z., Cao, C., Li, X., & Zhang, A. (2025). Declining suitability for conversion of drylands to paddy fields in Northeast China: Impact of future climate and socio-economic changes. Geography and Sustainability, 6(1), article number 100199. doi: 10.1016/j.geosus.2024.05.004.
  21. Supriyadi, S., Ustiatik, R., Mukti, B., Minardi, S., Widijanto, H., & Sakti, M.B.G. (2022). Soil quality status under Hazton’s paddy farming: A case study in Banyumas Regency, Indonesia. SAINS TANAH – Journal of Soil Science and Agroclimatology, 19(2), 123-131. doi: 10.20961/STJSSA.V19I2.58375.
  22. Takeda, N., López-Galvis, L., Pineda, D., Castilla, A., Takahashi, T., Fukuda, S., & Okada, K. (2019). Evaluation of water dynamics of contour-levee irrigation system in sloped rice fields in Colombia. Agricultural Water Management, 217, 107-118. doi: 10.1016/J.AGWAT.2019.02.032.
  23. Thomas, G.W. (1982). Exchangeable cations. In A.L. Page (Ed.), Methods of soil analysis: Part 2. Chemical and microbiological properties (pp. 159-165). Madison, WI: American Society of Agronomy and Soil Science Society of America. doi: 10.2134/agronmonogr9.2.2ed.c9.
  24. Tu, T., et al. (2023). Methods and experiments for collecting information and constructing models of bottom-layer contours in paddy fields. Computers and Electronics in Agriculture, 207, article number 107719. doi:10.1016/j.compag.2023.107719.
  25. Van Klompenburg, T., Kassahun, A., & Catal, C. (2020). Crop yield prediction using machine learning: A systematic literature review. Computers and Electronics in Agriculture, 177, article number 105709. doi: 10.1016/j.compag.2020.105709.
  26. Velmurugan, P., Kannagi, A., & Varsha, M. (2023). Superior fuzzy enumeration crop prediction algorithm for big data agriculture applications. Materials Today: Proceedings, 81(2), 112-117. doi: 10.1016/j.matpr.2021.02.578.
  27. Wadoux, A.M. J.-C., Minasny, B., & McBratney, A.B. (2020). Machine learning for digital soil mapping: Applications, challenges and suggested solutions. Earth-Science Reviews, 210, article number 103359. doi: 10.1016/j.earscirev.2020.103359.
  28. Wahyunto, et al. (2016). Technical guidelines for land suitability assessment for strategic agricultural commodities at a semi-detailed level on a scale of 1:50,000. Retrieved from https://repository.pertanian.go.id/handle/123456789/20342.
  29. Walkley, A., & Black, I.A. (1934). An examination of the Degtjareff method for determining soil organic matter, and a proposed modification of the chromic acid titration method. Soil Science, 37(1), 29-38. doi: 10.1097/00010694-193401000-00003.
  30. Yue, X., Li, Z., Li, H., Wang, F., & Jin, S. (2022). Multi-temporal variations in surface albedo on Urumqi glacier No.1 in Tien Shan, under arid and semi-arid environment. Remote Sensing, 14(4), article number 808. doi: 10.3390/rs14040808.
  31. Zhang, J., Jing, X., Song, X., Zhang, T., Duan, W., & Su, J. (2023). Hyperspectral estimation of wheat stripe rust using fractional order differential equations and Gaussian process methods. Computers and Electronics in Agriculture, 206, 107671. https://doi.org/10.1016/j.compag.2023.107671
Aziz, A., Komariah, Rizal, A., Romadhon, M.R., & Mustangin, M. (2025). Machine learning-based prediction of rice yield from rain feeding in monsoonal tropical area of Java, Indonesia. Scientific Horizons, 28(8), 165-178. https://doi.org/10.48077/scihor8.2025.165