Machine Learning-Based Multi-Decadal Analysis of Paddy Field Dynamics in the Cauvery Delta, India: Implications for Sustainable Agriculture
DOI:
https://doi.org/10.54386/jam.v28i3.3442Keywords:
Machine learning, Paddy mapping, Land use change, Cauvery Delta, Food security, Sustainable agricultureAbstract
Paddy cultivation forms the backbone of agriculture in the Cauvery Delta of Tamil Nadu, India; however, long-term monitoring of its spatial dynamics remains limited. This study employed multi-temporal Landsat imagery and machine learning techniques to analyse the spatiotemporal dynamics of paddy cultivation in Thanjavur district over a 23-year period (1995–96, 2007–08, and 2018–19), while an additional reference year (2015–16) was used for independent validation. Three machine learning classifiers, namely Random Forest (RF), Gradient Tree Boosting (GTB), and Support Vector Machine (SVM), were evaluated using Recall, Precision, Overall Accuracy (OA), F1 Score, and Kappa Coefficient (KC). The RF classifier achieved the highest classification accuracy during 1995–96 (OA = 0.84, KC = 0.68), 2007–08 (OA = 0.90, KC = 0.81), and 2015–16 (OA = 0.90, KC = 0.80), whereas GTB showed superior performance during 2018–19 (Recall = 0.85, OA = 0.82, F1 Score = 0.83, KC = 0.65). The classification results were validated using independently sourced Ground Control Points (GCPs) for 2015–16 and official Agriculture Department acreage statistics, demonstrating strong agreement with the reference data. The results revealed a 6.72% decline in paddy cultivation area (approximately 228 km²) between 1995–96 and 2018–19, accompanied by an expansion of non-paddy and urban land uses. Spatially, paddy cultivation became increasingly concentrated in the western and central parts of the district along the Cauvery River, primarily due to groundwater depletion, irrigation constraints, rapid urbanisation, and increasing climate variability. The findings demonstrate the effectiveness of ensemble-based machine learning approaches for large-scale agricultural monitoring and provide a transferable framework for assessing long-term land-use changes, thereby supporting food security, climate adaptation, and sustainable water resource management in monsoon-dependent deltaic regions.
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