Authors - Amulya Saxena, Pratibha Joshi, Adwitiya Sinha Abstract - Global food security and hunger mitigation is one of the major challenges ahead of us. The global population specifically from underdeveloped countries are quite vulnerable to climate change and its impact in abnormal weather conditions and related bad crop leading to food shortages. In today’s globalised world, where a disruption in food supply chain has its own impact on potentially everyone in the planet is a mounting challenge to surpass. The advent of Artificial Intelligence, specifically Computer Vision techniques prove to be extremely helpful in identifying the data pattern of the images of the cultivated land, its anomalies and is insightful in giving the challenges of farming such as affect of bad weather, bad crop prediction, crop distribution etc. The availability of high-quality geospatial data from the satellites such as Sentinel 1/2, Landsat is extremely helpful for advanced ML techniques to provide timely predictions so that a corrective action can be taken in time. This study focuses on an AI-driven approach that predicts land where Rice will be produced vs. no crop land using satellite optical data and its variates, radar logs, weather data and location information.
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