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Enhancing Precision of Crop Farming towards Smart Cities: An Application of Artificial Intelligence

Author

Listed:
  • Abdullah Addas

    (Department of Civil Engineering, College of Engineering, Prince Sattam Bin Abdulaziz University, Alkharj 11942, Saudi Arabia
    Landscape Architecture Department, Faculty of Architecture and Planning, King Abdulaziz University, P.O. Box 8 0210, Jeddah 21589, Saudi Arabia)

  • Muhammad Tahir

    (Computer Software Engineering Department, Sir Syed University of Engineering and Technology, Karachi 75300, Pakistan)

  • Najma Ismat

    (Computer Engineering Department, Sir Syed University of Engineering and Technology, Karachi 75300, Pakistan)

Abstract

Water sustainability will be scarce in the coming decades because of global warming, an alarming situation for irrigation systems. The key requirement for crop production is water, and it also needs to fulfill the requirements of the ever-increasing population around the globe. The changing climate significantly impacts agriculture production due to the extreme weather conditions that prevail in various regions. Since urbanization is increasing worldwide, smart cities must find innovative ways to grow food sustainably within built environments. This paper explores how precision agriculture powered by artificial intelligence (AI) can transform crop farms (CF) to enhance food security, nutrition, and environmental sustainability. We developed a robotic CF prototype that uses deep reinforcement learning to optimize seeding, watering, and crop maintenance in response to real-time sensor data. The system was tested in a simulated CF setting and benchmarked. The results revealed a 26% increase in crop yield, a 41% reduction in water utilization, and a 33% decrease in chemical use. We employed AI-enabled precision farming to improve agriculture’s efficiency, sustainability, and productivity within smart cities. The widespread adoption of such technologies makes food supplies resilient, reduces land, and minimizes agriculture’s environmental footprint. This study also qualitatively assessed the broader implications of AI-enabled precision farming. Interviews with farmers and stakeholders were conducted, which revealed the benefits of the proposed approach. The multidimensional impacts of precision crop farming beyond measurable outcomes emphasize its potential to foster social cohesion and well-being in urban communities.

Suggested Citation

  • Abdullah Addas & Muhammad Tahir & Najma Ismat, 2023. "Enhancing Precision of Crop Farming towards Smart Cities: An Application of Artificial Intelligence," Sustainability, MDPI, vol. 16(1), pages 1-18, December.
    • Handle: RePEc:gam:jsusta:v:16:y:2023:i:1:p:355-:d:1310754
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    References listed on IDEAS

    as
    1. Marwan Albahar, 2023. "A Survey on Deep Learning and Its Impact on Agriculture: Challenges and Opportunities," Agriculture, MDPI, vol. 13(3), pages 1-22, February.
    2. Xinchun Ma & Qixiang Gong & Qingjie Wang & Dijuan Xu & Yinggang Zhou & Guibin Chen & Xinpeng Cao & Longbao Wang, 2022. "Design of an Air Suction Wheel-Hole Single Seed Drill for a Wheat Plot Dibbler," Agriculture, MDPI, vol. 12(10), pages 1-25, October.
    3. Fawad Naseer & Muhammad Nasir Khan & Ali Altalbe, 2023. "Telepresence Robot with DRL Assisted Delay Compensation in IoT-Enabled Sustainable Healthcare Environment," Sustainability, MDPI, vol. 15(4), pages 1-15, February.
    Full references (including those not matched with items on IDEAS)

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