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Harnessing Precision Agriculture and Artificial Intelligence in Sustainable Farming
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Author(s): Muhammad Safdar (Agricultural Remote Sensing Lab, University of Agriculture, Faisalabad, Pakistan), Wasiq Farooq (Agricultural Remote Sensing Lab, University of Agriculture, Faisalabad, Pakistan), Muhammad Sajid Mehmood (School of Tourism and Planning, Pingdingshan University, Pingdingshan, China), Abdul Rauf (Department of Irrigation and Drainage, University of Agriculture, Faisalabad, Pakistan), Amina Rashid (Department of Agronomy, University of Agriculture, Faisalabad, Pakistan), Kashif Mehmood (Department of Irrigation and Drainage, University of Agriculture, Faisalabad, Pakistan), Muntaha Munir (Institute of Botany, University of the Punjab, Lahore, Pakistan), Nalain E. Muhammad (Department of Irrigation and Drainage, University of Agriculture, Faisalabad, Pakistan), Hafiz Muhammad Bilawal Akram (Department of Agronomy, University of Agriculture, Faisalabad, Pakistan), Hafiz Muhammad Mohsin Raza (Department of Agricultural Sciences, Allama Iqbal Open University, Islamabad, Pakistan), Aisha Nazir (Institute of Botany, University of the Punjab, Lahore, Pakistan)and Hafiz Muhammad Awais (Agricultural Remote Sensing Lab, University of Agriculture, Faisalabad, Pakistan)
Copyright: 2026
Pages: 28
Source title:
Advancing Environmental Research Through Applied GIS and Remote Sensing
Source Author(s)/Editor(s): Jamal Al Karkouri (Ibn Tofail University, Morocco), Adil Moumane (Ibn Tofail University, Morocco), Abdessamad Elmotawakkil (Ibn Tofail University, Morocco)and Mouhcine Batchi (Ibn Tofail University, Morocco)
DOI: 10.4018/979-8-3373-6608-1.ch005
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Abstract
The chapter explores the role of Artificial Intelligence (AI) in revolutionizing precision agriculture, focusing on data-driven, adaptive, and efficient practices. It traces the evolution of AI-integrated digital farming systems, including GNSS, drones, sensors, and IoT technologies. Key AI applications include crop classification, yield forecasting, pest detection, irrigation scheduling, and nutrient management. The chapter also discusses technologies like UAVs, hyperspectral imaging, edge computing, and cloud-based platforms for improved farm-level decision-making. The chapter discusses the potential of AI in large-scale and smallholder farming systems but also highlights challenges like heterogeneous data, economic barriers, limited digital literacy, and low adoption among smallholder farmers. The chapter discusses future opportunities in AI-integrated robotics, blockchain-based supply chains, and big data analytics for climate-resilient agriculture and calls for inclusive policy reforms, capacity-building initiatives, and collaborative innovation for equitable scale.
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