Deep learning, irrigation enhancement, and agricultural economics for ensuring food security in emerging economies - Aktam U. Burkhanov; Elena G. Popkova; Diana R. Galoyan; Tatul M. Mkrtchyan; Bruno S. Sergi (Global Transitions)
This paper delves into the critical issues of individual health, environmental health, and public health, which are all interconnected in the complex web of food security in emerging countries. Leveraging data from the top 10 countries with the lowest climate index values according to the Numbeo ranking, this article introduces a groundbreaking deep learning algorithm. This algorithm has the potential to revolutionize agricultural productivity and food security in the face of climate change, filling the gap in research on deep learning in agriculture. By enabling intelligent management, this algorithm could boost yields in agriculture, rendering it less dependent on climatic factors and ensuring the effectiveness of digital modernization. Furthermore, we explore the promising benefits of restoring ancient irrigation systems to elevate productivity levels. Our study provides definitive insights into deep learning techniques for yield prediction and productivity enhancement, offering a beacon of hope for the future of food security in emerging economies.