"Data & Intelligent" section call for paper
Agriculture is rapidly evolving towards a data & intelligence-driven paradigm, centered on algorithmization of both human cognition (e.g., by optimizing decision-making models) and physical operational processes (e.g., by autonomous agricultural machinery systems). This enables the deep integration of data, intelligence within real-world agricultural scenarios. Such advancements have catalyzed the emergence of an interdisciplinary field spanning agronomy, data science, and environmental science — "Agricultural Data Intelligence (ADI)".
The "Data & Intelligent" section of The Journal of Agricultural Big Data invites submissions for its long-term dedicated section, established to advance the frontier of agricultural data intelligence within the evolving paradigm of agriculture. The section is designed to foster cutting-edge research and high-quality data contributions in the field, focusing on the innovative "scenario-data-intelligence" triangle, and promote the knowledge development of data and intelligent agriculture.
The section invites papers on the next topics.
1. Agricultural Data’s Artificial Intelligence Adaptation (AI-ready AgriData)
The advent of next-generation artificial intelligence (AI) underscores the critical need for data to be AI-ready, particularly in agriculture where existing datasets face significant adaptation challenges. This sub-theme solicits research on the construction and technical methodologies for AI-ready agricultural datasets. Topics of interest include efficient labeling of agricultural data (e.g., remote sensing imagery, phenomics, environmental sensors), multimodal data alignment, intelligent adaptability evaluation, and standardization. Contributions that advance the development, publication, and utilization of AI-ready datasets to meet the training demands of large models and build knowledge bases for agricultural intelligence are especially encouraged.
2. Intelligent Evolution of Agricultural Data Processing and Analysis (AI for AgriData)
The complexity and openness of agricultural mega-systems generate high-dimensional, heterogeneous, multimodal, and spatiotemporally diverse datasets, posing challenges such as the curse of dimensionality, modal gaps, and spatiotemporal coupling modeling. This direction calls for research that integrates agronomic principles (e.g., crop growth models) with computational intelligence to establish a seamless "data-knowledge-decision" paradigm. We invite submissions focusing on high-throughput phenotyping data collection and edge computing, adaptive feature extraction from agricultural time-series data, fusion of multi-source heterogeneous data, and cross-modal knowledge discovery. Emphasis is placed on studies that drive the intelligent evolution of data processing and analysis through agronomic mechanism-driven and "data-model" co-evolution approaches.
3. Scenario-Driven Agricultural Digital-Intelligent Fusion (Scenario-Intelligence Fusion for Agri-Innovation)
The algorithmic substitution of human cognitive activities, combined with the mechanical replacement of physical tasks, is transforming agricultural decision-making and implementation. This sub-theme explores how agricultural scenarios are evolving into digital-intelligent frameworks through "data-knowledge-action" closed loops. We seek innovative research on phenome-genome association mining in intelligent breeding, dynamic decision systems in precision farming, and digital twin modeling across agricultural value chains. Priority will be given to interdisciplinary studies that integrate agronomic principles with data intelligence, promoting interpretable and replicable paradigms for agricultural digitalization.
4. Agricultural Data Governance Transformation in the Intelligent Era (AgriData Management within AI)
The governance of agricultural data is confronted with paradoxes involving open sharing versus privacy protection, circulation efficiency versus rights attribution, and algorithmic authority versus ethical constraints. The rise of AI introduces tools like privacy computing and explainable AI, yet it also escalates governance complexity. This direction invites research addressing key governance challenges, including privacy-preserving computations, explainable AI in agriculture, blockchain for data protection, data trusts, the assetization of data for smallholder farmers, and the socialized circulation and ecological collaboration of agricultural data. Submissions that propose balanced solutions for efficiency and security in agricultural data governance are highly encouraged.
The "Data & Intelligent" section aims to shape the future of digital agriculture by addressing the digital representation of open complex agricultural mega-systems, the intelligent processing and analysis of ultra-high-dimensional multimodal large-scale agricultural data, and the empowerment of human-machine hybrid intelligent systems in agricultural scenarios. We invite scholars worldwide to contribute to the theoretical frontiers and practical boundaries of agricultural data intelligence, collaboratively advancing the knowledge system of agricultural digitalization and intelligence. Submit your original research to The Journal of Agricultural Big Data and join us in this transformative journey.
