Innovation in pest management: can the past inform the future?

Area of Science:

• Agricultural Science
• Biotechnology
• Data Science

Background:

• Agricultural innovation historically progressed incrementally, with occasional transformative breakthroughs.
• Modern agriculture relies on advances in mechanization, genetics, and chemistry.
• The Green Revolution exemplifies cumulative innovation driving agricultural productivity.

Purpose of the Study:

• To explore the potential of artificial intelligence (AI) to accelerate agricultural discovery.
• To highlight emerging technologies for enhanced crop management and protection.
• To discuss the integration of diverse datasets for future agricultural advancements.

Main Methods:

• Review of current and emerging technologies in agricultural science.
• Analysis of AI's role in integrating mechanization, genetics, and chemistry data.
• Exploration of gene-editing, RNA interference (RNAi), and non-chemical pest control methods.

Main Results:

• AI is expected to accelerate discovery by integrating vast datasets.
• Autonomous machinery, robotics, and drones will enable precision pest management.
• Gene-editing (CRISPR) and non-chemical methods (RNAi, lasers) offer targeted crop protection.

Conclusions:

• AI integration promises to significantly advance agricultural discovery and efficiency.
• Emerging technologies offer sustainable solutions for pest management and crop resilience.
• The future of agriculture involves synergistic advancements in AI, biotechnology, and precision tools.