Analysis of Tea Plantation Suitability Using Geostatistical and Machine Learning Techniques: A Case of Darjeeling Himalaya, India

This study aimed to identify suitable sites for tea cultivation using both random forest and logistic regression models. The utilized 2770 sample points map the plantation suitability zones (TPSZs), considering 12 important conditioning factors, such as temperature, rainfall, elevation, slope, soil depth, drainability, electrical conductivity, base saturation, texture, pH, normalized difference vegetation index (NDVI), land use cover (LULC). data were ArcGIS 10.2 models calibrated 70% of total data, while remaining 30% used validation. final TPSZ was classified into four different categories: highly zones, moderately marginally not-suitable zones. revealed that (RF) model more precise than model, with areas under curve (AUCs) 85.2% 83.3%, respectively. results indicated well-drained a pH range between 5.6 6.0 is ideal farming, highlighting importance climate properties in cultivation. Furthermore, emphasized need balance economic environmental considerations when expansion. findings this provide insights site selection can aid farmers, policymakers, other stakeholders making informed decisions regarding