Applications, Challenges, and Prospects of Artificial Intelligence in Crop Production

Area of Science:

• Agricultural Science
• Computer Science
• Environmental Science

Background:

• Growing global population, resource constraints, and climate change impact agriculture.
• Ensuring food security and sustainable development are key agricultural challenges.
• Artificial intelligence (AI) presents transformative solutions for traditional agricultural bottlenecks.

Purpose of the Study:

• Systematically review AI applications across five core crop production domains.
• Categorize and analyze key AI technical pathways and implementation challenges.
• Discuss future directions for AI in sustainable crop production.

Main Methods:

• Literature review of AI applications in agriculture.
• Analysis of AI techniques including deep learning, sensor fusion, data-driven methods, and hybrid modeling.
• Examination of challenges in data, technology, implementation, and ethics/policy.

Main Results:

• AI shows technical validation in biotic stress monitoring, soil health management, precision operations, and climate-resilient agriculture.
• Accuracy exceeding 85% achieved in specific applications like pest/disease detection and intelligent spraying.
• Regional data bias, limited model generalization, and the digital divide impede large-scale AI deployment.

Conclusions:

• AI has demonstrated significant potential in enhancing crop production efficiency and sustainability.
• Overcoming challenges related to data, technology, and equitable access is crucial for widespread AI adoption.
• Coordinated efforts in technological innovation and policy are needed to promote inclusive and sustainable AI in agriculture.