Data-Driven Farming: Harnessing Big Data for Agriculture

Abdullah Mohammad Ghazi Khatib () and Bayan Mohamad Alshaib ()
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Abdullah Mohammad Ghazi Khatib: Damascus University
Bayan Mohamad Alshaib: Damascus University

Chapter Chapter 4 in Transforming Agriculture through Artificial Intelligence for Sustainable Food Systems, 2025, pp 55-72 from Springer

Abstract: Abstract This chapter explores the transformative potential of big data in agriculture, focusing on how data-driven farming practices can enhance productivity, sustainability, and decision-making processes. It examines the various sources of agricultural data, including satellite imagery, IoT sensors, and historical records, and discusses the technologies and methodologies used to collect, process, and analyze this information. The chapter delves into the applications of big data analytics in areas such as yield prediction, pest and disease management, and resource optimization. It also addresses the challenges of data integration, privacy concerns, and the need for standardization in agricultural data management. The role of machine learning and artificial intelligence in extracting actionable insights from vast datasets is highlighted, emphasizing their potential to revolutionize farm management strategies. Finally, the chapter considers the economic and environmental benefits of data-driven farming, as well as the implications for small-scale farmers and global food security. Graphical Abstract Fig. 4.1 Graphical Abstract—Harnessing big data for a sustainable and productive agricultural future Flowchart titled 'Big Data in Agriculture' illustrating the process of leveraging data in modern farming. The diagram begins with multiple data sources such as satellite imagery, IoT sensors, and historical records feeding into a node labeled 'Data Collection & Processing.' From there, the data is analyzed using machine learning and AI techniques in the 'Data Analytics' section, leading to outcomes including 'Yield Prediction,' 'Pest & Disease Management,' and 'Resource Optimization.' These outcomes contribute to enhanced productivity, improved sustainability, and data-driven decision making, ultimately linking to broader economic and environmental benefits and global food security. The chart uses various colors to distinguish different sections for clarity. This graphical abstract illustrates the transformative potential of big data in agriculture. Diverse data sources, including satellite imagery, IoT sensors, and historical records, are integrated and analyzed using advanced techniques like machine learning and AI. This data-driven approach enables applications such as yield prediction, pest and disease management, and resource optimization, leading to enhanced productivity and improved sustainability. Ultimately, big data empowers data-driven decision-making in farm management, contributing to economic and environmental benefits and enhancing global food security.

Keywords: Big data; Data analytics; IoT sensors; Yield prediction; Resource optimization; Machine learning; Data integration; Farm management; Food security (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-981-96-4795-8_4

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DOI: 10.1007/978-981-96-4795-8_4

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