Food Security: 3D Dynamic Display and Early Warning Platform Construction and Security Strategy

Area of Science:

• Agricultural Science
• Data Science
• National Security

Background:

• Food security is vital for national security and economic stability.
• The COVID-19 pandemic has exacerbated global food security concerns.
• Machine learning offers promising solutions for food security challenges.

Purpose of the Study:

• To develop a 3D dynamic visualization method for multidimensional agricultural spatial-temporal data.
• To construct a food security early warning system using machine learning.
• To analyze China's food security policies.

Main Methods:

• A 3D dynamic visualization analysis method based on self-organizing maps for multidimensional agricultural data.
• Development of a food security early warning platform utilizing a Support Vector Regression (SVR) model.
• Analysis of China's medium and long-term food security policies.

Main Results:

• The 3D visualization method enables multi-angle data presentation and deep mining.
• The SVR-based food security early warning platform demonstrated effectiveness from 2007 to 2016.
• The study provides insights into China's food security policy alignment with modernization.

Conclusions:

• Enhancing food supply stability, reliability, and sustainability is essential.
• Proactive measures are needed to maintain control over national food security.
• A robust national food security system aligned with modernization goals must be established.