Multivariate Long Short-Term Memory (LSTM) Algorithm for Spatial-Temporal Agricultural Productivity Time Series Forecasting
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Abstract
Accurate forecasting of crop productivity is fundamental to contemporary food security planning, yet conventional predictive models frequently underperform when confronted with the multivariate, spatial, and temporal intricacies inherent in agronomic datasets. This study presents a robust deep learning framework leveraging a multivariate Long Short-Term Memory (LSTM) network to forecast yields of principal food crops. The model was developed using a panel dataset from 12 districts in Chhattisgarh and Madhya Pradesh, India (2010–2017), comprising area, production, and yield observations for multiple competing crops. Rigorous preprocessing protocols included the application of separate StandardScalers to mitigate matrix inversion issues, and the derivation of land-allocation features to capture spatial interactions among crops. A lightweight LSTM architecture stabilized by gradient clipping was employed to enhance convergence and prevent exploding gradients. Empirical results demonstrate that the multivariate LSTM notably outperforms simple baseline estimators by effectively modeling non-linear relationships and district-level yield heterogeneity, attaining an RMSE of 494.70 Kg/ha and an R² of 0.8031. These findings suggest that spatial anthropogenic indicators—particularly the allocation of land across commodities—serve as informative proxies for reliable yield prediction in contexts lacking comprehensive weather-sensor data
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Wati, E. F., Sari, A. P. ., & Tuslaela, T. (2026). Multivariate Long Short-Term Memory (LSTM) Algorithm for Spatial-Temporal Agricultural Productivity Time Series Forecasting. Journal of Intelligent Decision Support System (IDSS), 9(2), 63-70. https://doi.org/10.35335/idss.v9i2.356
References
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Tsalasatul Fitriyah, A., Chamidah, N., & Saifudin, T. (2025). Prediction of Paddy Production in Indonesia Using Semiparametric Time Series Regression Least Square Spline Estimator. Data and Metadata, 4, 527. https://doi.org/10.56294/dm2025527
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Arig Kusuma Jati, Bayu Rizky Utomo, Naufal Hanan Jati Asmara, & Rajnaparamitha Kusumastusi. (2025). Prediksi Hasil Panen untuk Pertanian Menggunakan Model Regresi Machine Learning. SEMINAR NASIONAL AMIKOM SURAKARTA (SEMNASA) 2025, 186–196.
Barreto-Martin, C., Sierra-Parada, R., Calderón-Rivera, D., Jaramillo-Londono, A., & Mesa-Fernández, D. (2021). Spatio-temporal analysis of the hydrological response to land cover changes in the sub-basin of the Chicú river, Colombia. Heliyon, 7(7), e07358. https://doi.org/10.1016/j.heliyon.2021.e07358
Benos, L., Tagarakis, A. C., Dolias, G., Berruto, R., Kateris, D., & Bochtis, D. (2021). Machine Learning in Agriculture: A Comprehensive Updated Review. Sensors, 21(11), 3758. https://doi.org/10.3390/s21113758
Chen, P., Li, Y., Liu, X., Tian, Y., Zhu, Y., Cao, W., & Cao, Q. (2023). Improving yield prediction based on spatio-temporal deep learning approaches for winter wheat: A case study in Jiangsu Province, China. Computers and Electronics in Agriculture, 213, 108201. https://doi.org/10.1016/j.compag.2023.108201
Firdausi, H. M., Utomo, S. B., Rahardi, G. A., & Prasetiyo, D. H. T. (2025). A Multivariate LSTM Approach for Monthly Rice Production Forecasting in East Java. Jurnal Sistem Cerdas, 8(3), 364–374. https://doi.org/10.37396/jsc.v8i3.595
Freitas, J. D., Ponte, C., Bomfim, R., & Caminha, C. (2023). The impact of window size on univariate time series forecasting using machine learning. Anais Do XI Symposium on Knowledge Discovery, Mining and Learning (KDMiLe 2023), 65–72. https://doi.org/10.5753/kdmile.2023.232916
Kaur, A., Goyal, P., Rajhans, R., Agarwal, L., & Goyal, N. (2023). Fusion of multivariate time series meteorological and static soil data for multistage crop yield prediction using multi-head self attention network. Expert Systems with Applications, 226, 120098. https://doi.org/10.1016/j.eswa.2023.120098
Leukel, J., Zimpel, T., & Stumpe, C. (2023). Machine learning technology for early prediction of grain yield at the field scale: A systematic review. Computers and Electronics in Agriculture, 207, 107721. https://doi.org/10.1016/j.compag.2023.107721
Lim, B., Arık, S. Ö., Loeff, N., & Pfister, T. (2021). Temporal Fusion Transformers for interpretable multi-horizon time series forecasting. International Journal of Forecasting, 37(4), 1748–1764. https://doi.org/10.1016/j.ijforecast.2021.03.012
Lokeshwari, M., Jha, G. K., Praveen, K. V., & Bharadwaj, A. (2024). Artificial intelligence for crop yield prediction: a bibliometric analysis. Current Science, 126(10), 1245. https://doi.org/10.18520/cs/v126/i10/1245-1253
Molina Bacca, E. J., Stevanović, M., Bodirsky, B. L., Doelman, J. C., Parsons Chini, L., Volkholz, J., Frieler, K., Reyer, C. P. O., Hurtt, G., Humpenöder, F., Karstens, K., Heinke, J., Müller, C., Dietrich, J. P., Lotze-Campen, H., Stehfest, E., & Popp, A. (2025). Future land-use pattern projections and their differences within the ISIMIP3b framework. Earth System Dynamics, 16(3), 753–801. https://doi.org/10.5194/esd-16-753-2025
Prasetya, F. A. (2024). Analisis Spasial-Temporal Perubahan Penggunaan Lahan Akibat Pembangunan Bandara Internasional Dhoho Kediri Berbasis Data Google Earth. Geodika: Jurnal Kajian Ilmu Dan Pendidikan Geografi, 8(1), 65–74. https://doi.org/10.29408/geodika.v8i1.25731
Rafi, M. A. S., Senyurek, V., Adeli, A., Yanbo, H., Ball, J. E., & Gurbuz, A. C. (2026). Investigation of machine learning based techniques for crop yield estimation of corn and cotton using multi-sensor data fusion. Journal of Agriculture and Food Research, 26, 102676. https://doi.org/10.1016/j.jafr.2026.102676
Raza, A., Hanif, F., & Mohammed, H. A. (2026). Clinical validation of lightweight CNN architectures for reliable multi-class classification of lung cancer using histopathological imaging techniques. Scientific Reports, 16(1), 6512. https://doi.org/10.1038/s41598-026-36652-6
Sari, D. P., Risman, R., Maulana, F., Efrizoni, L., & Rahmaddeni, R. (2025). Model Prediksi Dampak Perubahan Iklim pada Ketahanan Pangan Menggunakan Algoritma Support Vector Machine and K-Nearest Neighbors. MALCOM: Indonesian Journal of Machine Learning and Computer Science, 5(3), 851–861. https://doi.org/10.57152/malcom.v5i3.1975
Seran, M. K. B., Tedy, F., Samane, Ign. P. A. N., Batarius, P., Nani, P. A., & Sinlae, A. A. J. (2024). Analisis Data Pertanian Tanaman Pangan untuk Memprediksi Hasil Panen di Kabupaten Malaka Menggunakan Metode Multiple Linear Regression. KONSTELASI: Konvergensi Teknologi Dan Sistem Informasi, 4(1), 209–221. https://doi.org/10.24002/konstelasi.v4i1.8970
Shawon, S. M., Ema, F. B., Mahi, A. K., Niha, F. L., & Zubair, H. T. (2025). Crop yield prediction using machine learning: An extensive and systematic literature review. Smart Agricultural Technology, 10, 100718. https://doi.org/10.1016/j.atech.2024.100718
Tanjung, M. A., Sari, A. P., & Junaidi, A. (2025). Optimization of LSTM Hyperparameters Using PSO for Forecasting Shallots and Garlic. Bit-Tech, 8(1), 416–426. https://doi.org/10.32877/bt.v8i1.2569
Thesma, V., Rains, G. C., & Velni, J. M. (2025). Cotton Node Count Prediction using Multivariate Time-series Forecasting. IFAC-PapersOnLine, 59(23), 215–220. https://doi.org/10.1016/j.ifacol.2025.11.789
Tsabitah, D. U., Angraini, Y., & Sumertajaya, I. M. (2025). Spatiotemporal Clustering of Key Food Commodity Prices Using Multivariate Time Series. International Journal of Advances in Data and Information Systems, 6(3), 638–652. https://doi.org/10.59395/ijadis.v6i3.1422
Tsalasatul Fitriyah, A., Chamidah, N., & Saifudin, T. (2025). Prediction of Paddy Production in Indonesia Using Semiparametric Time Series Regression Least Square Spline Estimator. Data and Metadata, 4, 527. https://doi.org/10.56294/dm2025527
Xiao, X., Liu, J., Liu, D., Tang, Y., & Zhang, F. (2022). Condition Monitoring of Wind Turbine Main Bearing Based on Multivariate Time Series Forecasting. Energies, 15(5), 1951. https://doi.org/10.3390/en15051951
Xie, W., Zhao, M., Liu, Y., Yang, D., Huang, K., Fan, C., & Wang, Z. (2024). Recent advances in Transformer technology for agriculture: A comprehensive survey. Engineering Applications of Artificial Intelligence, 138, 109412. https://doi.org/10.1016/j.engappai.2024.109412
Zhu, W., Li, X., Peng, J., Sun, R., Wang, Z., Zhang, L., Cao, Z., & Yu, X. (2022). Dynamic and kinetic studies on the oxy-coal combustion using multi-parameter high-speed diagnostics. Applied Energy, 327, 120065. https://doi.org/10.1016/j.apenergy.2022.120065
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