Big data analytics for seasonal crop patterns: integrating machine learning techniques

Authors

DOI:

https://doi.org/10.15587/1729-4061.2024.315066

Keywords:

seasonal crop patterns, random forest, grid search, SHAP, LIME, cluster analysis, predictive model, climate variability, local agricultural, model accuracy

Abstract

This study addresses the challenge of predicting rice growing season lengths, crucial for agricultural planning in tropical regions. Climate variability and season timing create uncertainties in decision-making, and while machine learning is widely used in agriculture, a gap persists in integrating spatial-temporal data for accurate season length prediction and region-specific pattern analysis influenced by rainfall. Using a combination of Random Forest algorithms with hyperparameter optimization (grid search), and clustering techniques such as PCA, K-Means, and Hierarchical Clustering, this study analyzes key features such as the start of the season (SOS), end of the season (EOS), and their significance indicators (sig_sos and sig_eos). The findings reveal a strong correlation (0.98) between SOS and EOS, with an optimal growing season ranging from day 93 to day 207 (113.82 days). The Random Forest model, optimized with Grid Search, achieved a MSE of 28.9474 and an R2 of 0.8636, showing an outstanding predictive result. SHAP and LIME analyses identified sos and eos as the most influential predictors, while cluster analysis highlighted three distinct growing season groups characterized by variations in rainfall and seasonal stability. These results underscore the importance of understanding localized agricultural conditions and provide actionable insights for optimizing planting schedules, resource allocation, and climate adaptation strategies. By integrating advanced machine learning techniques with spatial-temporal data, this study establishes a foundation for improving agricultural resilience and sustainability in the face of climate variability

Author Biographies

Roni Yunis, Universitas Mikroskil

Assistant Professor

Department of Information Systems

Arwin Halim, Universitas Mikroskil

Assistant Professor

Department of Informatics

Irpan Adiputra Pardosi, Universitas Mikroskil

Assistant Professor

Department of Informatics

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Big data analytics for seasonal crop patterns: integrating machine learning techniques

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Published

2024-12-27

How to Cite

Yunis, R., Halim, A., & Pardosi, I. A. (2024). Big data analytics for seasonal crop patterns: integrating machine learning techniques. Eastern-European Journal of Enterprise Technologies, 6(4 (132), 46–56. https://doi.org/10.15587/1729-4061.2024.315066

Issue

Vol. 6 No. 4 (132) (2024): Mathematics and Cybernetics - applied aspects

Section

Mathematics and Cybernetics - applied aspects

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Copyright (c) 2024 Roni Yunis, Arwin Halim, Irpan Adiputra Pardosi

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