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Monitoring agriculture growth at different seasons (i.e., Rabi-winter crop, Zaid-summer crop, Kharif-monsoon crop) is an important requirement to understand annual cropping pattern dynamics for food security-related policy and strategy formulation. Cropping pattern is not uniform across a region and hence satellite observation of different time-period over a year is inevitable in such cases, but the associated big-data processing and accuracy requirements makes the problem more pertinent to study. Non-parametric machine learning (ML) algorithms have a higher ability to deal with such information extraction problem related to high-dimensional time-series satellite data, especially to infer multi-growth agriculture patterns. The current study aims to explore one of the popular ML algorithm, i.e., Random Forest (RF), along with two conventional supervised algorithms [i.e., Maximum Likelihood Classifier (MLC) and …