Accurate crop-type discrimination is vital for effective agricultural planning and sustainability management, especially in regions like sub-Saharan Africa (SSA), where small-scale farming predominates and ground data is scarce. Conducting field surveys in SSA is challenging due to labor and cost constraints, as well as logistical and political barriers. This paper proposes a framework design of cost-effective satellite-based machine learning for crop-type classification in crop growth with limited reference data. So, we have identified the important satellite time-series features and the machine learning model architecture to be used to timely and accurately identify crops in a small and intercropped farms. This study therefore has great role on agricultural data collection at large scale which is one of the ways to accomplish food security advocated by the Sustainable Development Goal 2, zero hunger.