Big Data in Agriculture

Aims & Scope

Big Data in Agriculture publishes high-quality research, reviews, and case studies that advance the understanding and application of data-intensive methods in agricultural sciences. The journal aims to provide an international forum for disseminating cutting-edge developments that leverage big data, artificial intelligence, and advanced analytics to address challenges in food production, sustainability, and global food security. Scope of the Journal Precision agriculture and smart farming technologies. Applications of machine learning, AI, and predictive analytics in crop, soil, and livestock systems. Remote sensing, GIS, and sensor networks for agricultural monitoring. Big data approaches to climate change adaptation, risk assessment, and resource optimization. Integration of genomic, phenomic, and bioinformatics data for crop and animal improvement. Data-driven supply chain management, market analysis, and food security forecasting. Ethical, policy, and socio-economic implications of big data in agriculture. By fostering interdisciplinary exchange among agronomists, data scientists, environmental researchers, and policymakers, the journal supports the development of innovative, data-driven solutions for resilient and sustainable agricultural systems.

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Subject Classification