Data-Driven Knowledge Agriculture: A Paradigm Shift for Enhancing Farm Productivity & Global Food Security

Aprajita Srivastava, Dr. H.O. Srivastava
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Abstract

Data-driven Knowledge agriculture using mechanized intelligent computer-based monitoring and control systems and complex Software for machine learning and visualization for predicting a variety of parameters such as future food requirements, resource planning for higher yield,and supply chain is the future of farming.This needs to be urgently adopted by the world farming community to provide food to the growing world population, remove hunger, and at the same time sustain planet resources by judicious uses of input such as water, fertilizer, pesticide etc., as envisioned by Sustainable Development Goals 2030. This paper discusses data-driven technology for identifying trends and other insights for making in formed decisions for enhanced productivity and profitability, through market research and evaluating customer needs and sentiments.
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数据驱动的知识农业:提高农业生产力和全球粮食安全的范式转变
数据驱动的知识农业使用机械化的基于计算机的智能监控系统和复杂的软件进行机器学习和可视化,以预测各种参数,如未来的食物需求,更高产量的资源规划和供应链,这是农业的未来。这需要世界农业界紧急采取行动,为不断增长的世界人口提供粮食,消除饥饿,同时按照2030年可持续发展目标的设想,通过明智地使用水、化肥、农药等投入物来维持地球资源。本文讨论了数据驱动技术,通过市场研究和评估客户需求和情绪,确定趋势和其他见解,以制定提高生产力和盈利能力的决策。
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