Elsevier Big Data Analytics in Agriculture: Algorithms and Applications

Big Data Analytics in Agriculture focuses on the quantitative and qualitative assessment of agricultural systems using state‑of‑the‑art technologies to deliver practical improvements in agricultural production. Addressing the challenge of translating data into real‑world applications, the book provides a comprehensive mapping of the entire data lifecycle―from data generation, storage, and curation to processing and implementation. It guides readers through the steps required to produce high‑quality, reliable information that supports effective decision‑making. Following a logical progression, the volume demonstrates how diverse data streams converge into decision‑support systems and how they can be transformed into actionable outcomes, aligned with intelligent, efficient, technologically advanced, economically viable, and politically and culturally sustainable practices. The book further explores the integration of information and communication technologies (ICT) and the Internet of Things (IoT) for managing rural assets and enhancing economic and environmental performance in spatially and temporally variable agricultural environments. Topics covered include big data analytics, data management and processing, and a range of algorithms and applications relevant to agriculture. Subtopics encompass artificial intelligence‑ and machine‑learning‑enabled smart and precision irrigation, disease and pest management, microclimatic forecasting, preventive fertigation and chemigation, data‑driven smart farming through the Internet of Everything (IoE), and supply‑chain analytics for improved farm‑level operations. Introduces readers to the evolution from conventional analytical approaches to large‑scale analytical paradigms through a stepwise, sectioned framework that supports progressive skill development Examines practical considerations surrounding storage architectures, multi-source datasets, scalability constraints, and cost trade‑offs in agricultural computing environments Details algorithm‑centric workflows, including feature construction, model calibration, and comparative performance evaluation across diverse agricultural contexts Showcases a wide range of sector‑specific implementations, covering crop monitoring, irrigation planning, weed control, nutrient stress identification, climate assessment, pest detection, and agri‑food supply‑chain operations in both advanced and resource‑limited settings