Remote sensing big data utilization for paddy growth stages detection

Predicting and estimating the character of big data becomes paramount since it is laborious to deal with big data with conventional models and algorithms. Remote sensing big data from various satellites consist of many large-scale images that are exceptionally complex in terms of their structural, spectral, and textual features. Also, most of them are still in annotated form. Therefore, it is a challenge to explore them for detecting objects on the ground that are beneficial to humans using their sophisticated features. In this paper, we proposed a remote sensing big data for paddy growth stages detection, through multi-temporal analysis with a heuristic algorithm. Information derived from growth stages is very useful to know the needs of water, fertilizer, and crop planting calendar to increase productivity.