AI-Driven Precision Agriculture: Optimizing Robotic Crop Farming for Sustainable Urban Food Security

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Submitted: Nov 22, 2024
Final Revised: May 12, 2025
Accepted: Jan 17, 2026
Published: Jun 30, 2025
Abstract view: 14
PDF Download: 1
Volumes: Vol. 3 No. 1 (2025) Pages 43-60
DOI: 10.63208/21018-114

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Z. Ruoxuan, Y. Karimov, A. Adebayo, and L. H. . Nam, “AI-Driven Precision Agriculture: Optimizing Robotic Crop Farming for Sustainable Urban Food Security”, CAI, vol. 3, no. 1, pp. 43–60, Jun. 2025, doi: 10.63208/21018-114.

Abstract

Global warming significantly threatens water sustainability, creating an urgent crisis for irrigation systems. As the global population rises, innovative strategies are required to ensure food security amid extreme weather conditions. This paper investigates how artificial intelligence (AI)-powered precision agriculture can transform urban crop farms (CF) to enhance sustainability. We developed a robotic CF prototype utilizing deep reinforcement learning to optimize seeding, irrigation, and maintenance based on real-time sensor data. Experimental results demonstrated a 26% increase in crop yield, a 41% reduction in water consumption, and a 33% decrease in chemical use. Beyond technical efficiency, qualitative assessments through stakeholder interviews revealed that AI-enabled farming strengthens social cohesion and urban well-being. The adoption of such technologies fosters resilient food supplies while minimizing agriculture’s environmental footprint, providing a scalable model for sustainable food production within modern smart cities.

Keywords: Artificial Intelligence Precision Agriculture Urban Food Security Deep Reinforcement Learning Sustainable Irrigation

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