Big Data in Agriculture: Acquisition, Processing and Implications

Résumé fourni par la source

The agricultural sector is experiencing a digital revolution centered on big data, driven by the need to feed a growing global population, optimize resources, and address climate change challenges. This chapter examines the role of big data in modern agriculture, focusing on its applications in precision farming, predictive analytics, and resource management. Agricultural big data encompasses diverse information sources, including satellite imagery, IoT sensors, farm machinery data, and market trends, enabling real-time insights and informed decision-making across farming operations. Precision farming leverages this data to customize agricultural practices to specific field conditions, while predictive analytics employs machine learning and AI to forecast weather patterns, pest infestations, and crop yields. The integration of blockchain technology ensures transparency and traceability throughout the agricultural supply chain. However, challenges such as data privacy concerns, IT infrastructure requirements, and data source integration must be addressed through technological innovation and stakeholder collaboration. The chapter explores current applications, implementation challenges, and emerging trends, providing valuable insights for farmers, agronomists, and agricultural stakeholders seeking to harness data-driven approaches for improved farming outcomes.

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