邁向數智農業：場景、數據與智能

全球農業正加速向數智驅動范式演進，核心是通過算法重構人類認知范式（如決策模型優化）與物理操作流程（如自主農機系統），實現數據智能與農業場景的深度耦合。這一進程催生了橫跨農學、數據科學、環境科學等的交叉領域——\"農業數據智能\"。《農業大數據學報》設立\"數據智能\"長期專欄，圍繞“場景-數據-智能”的創新三角，匯聚該領域前沿研究與高質量數據，推動農業數智化知識體系發展。專欄將聚焦以下方向：

農業數據的人工智能適配（AI-readyAgriData）

新一代人工智能（artificial intellgence，AI）展現了高度的數據依賴性，凸顯了農業等領域中既有數據正在面臨的人工智能適配（AI-ready）挑戰。專欄關注農業AI-ready 數據集的構建方法與技術，聚焦農業數據（遙感影像、表型組學、環境傳感等）高效標注、多模態對齊、智能適配度評價與規范等議題，促進AI-ready數據集建設與出版，推動滿足大模型訓練需求等智能適配農業領域知識庫的建設。

農業數據處理與分析的智能進化（AIforAgriData）

農業巨系統的開放性與復雜性，催生了高維異構、多模態、超大規模和時空異質數據的系統性涌現，傳統方法面臨維度災難、模態鴻溝和時空耦合建模等多重挑戰，需通過農學機理（例如，作物生長模型）與計算智能的深度耦合，建立“數據-知識-決策”貫通的農業數據智能處理范式。專欄關注農業數據的智能處理與分析，聚焦表型數據高通量采集與邊緣計算、農業時序數據的自適應特征提取、多源異構數據融合與跨模態知識發現等議題，促進農學機理驅動和“數據-模型”協同進化的數據處理與分析智能化演進。

場景驅動的農業數智融合（Scenario-IntelligenceFusion forAgri-Innovation）

人類認知活動的算法替代根本上改變了農業決策的形成與實施，融合日益廣泛深化的人類物理活動機械替代，越來越多的農業場景正在以“數據-知識-行動”的閉環模式成為通向數智農業的一級級臺階。專欄關注農業場景的數智融合創新，聚焦智能育種中的表型-基因關聯挖掘、精準種養中的動態決策系統、農業產業鏈的數字孿生建模等前沿方向，重點征集融合農學機理與數據智能的跨學科研究成果，推動形成可解釋、可復制的農業數智化范式。

智能時代的農業數據治理變革（AgriDataManagementwithinAI）

農業數據治理面臨開放共享與隱私保護、流通效率與權益歸屬、算法權力與倫理約束的三重悖論。人工智能在提供隱私計算和可解釋性工具的同時，也增加了治理復雜度。專欄關注智能時代農業數據的治理挑戰，聚焦農業數據隱私計算、可解釋AI、區塊鏈與數據保護、數據信托與小農戶數據資產化、農業數據的社會化流通和生態化協作等關鍵議題，推動效率與安全兼顧的農業數據治理體系。

開放復雜農業巨系統的數字化表達，超高維多模態大規模農業數據的智能化處理與分析，以及人機混合智能系統賦能的農業場景，正在塑造數智農業并激發一系列跨學科前沿研究。專欄以促進數智農業發展為目標，誠邀全球學者關注農業數據智能理論前沿與實踐邊界，共同推動農業數智化知識系統發展。

主編：周國民

Agriculture is rapidly evolving towards a data amp; 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 machinerysystems）.This enables the deep integration of data， inteligence withinreal-world agricultural scenarios. Such advancements have catalyzed the emergence of an interdisciplinary field spaning agronomy， data science， and environmental science — \"Agricultural Data Intelligence （ADI）\".

The \"Dataamp; Inteligent\" section of The Journal ofAgricultural Big Data invites submissions for its long-term dedicated section，established to advance the frontierof agricultural data intelligence within the evolving paradigmof agriculture.The sectionis designed to foster cuting-edgeresearchand high-qualitydatacontributions in the field， focusing onthe 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 （Al-ready AgriData）

The advent of next-generationartificial inteligence （AI） underscores the critical need for data to be AI-ready， particularly in agriculture where existing datasets facesignificant adaptationchallnges.This sub-theme solicits research on the construction and technical methodologies for AI-ready agricultural datasets.Topics of interest include eficient labelingof agriculturaldata （e.g.，remote sensing imagery，phenomics，environmental sensors），multimodal dataalignment，inteligent adaptabilityevaluation，andstandardization. Contributions thatadvance the development， publication，andutilizationof AI-readydatasets to meet the training demandsof large modelsand build knowledge bases for agricultural intelligence are especially encouraged.

2. Intelligent Evolution of Agricultural Data Processing and Analysis （Al for AgriData）

The complexityandopennessofagricultural mega-systems generate high-dimensional，heterogeneous，multimodal， andspatiotemporally diverse datasets，posing chalenges such as the curse of dimensionality，modal gaps，and spatiotemporal coupling modeling.This direction cals forresearch that integratesagronomic principles （e.g.，crop growth models）withcomputational intellgence to establish aseamless\"data-knowledge-decision\" paradigm.We invite submissions focusing on high-throughput phenotyping data collction and edge computing，adaptive feature extraction from agricultural time-series data，fusion of multi-source heterogeneous data，and cros-modal knowledge discovery. Emphasis is placed on studies that drive the intellgent evolution ofdata processing and analysis through agronomic mechanism-driven and \"data-model\" co-evolution approaches.

3.Scenario-Driven Agricultural Digital-Intelligent Fusion （Scenario-Intelligence Fusion for AgriInnovation）

The algorithmic substitution of human cognitive activities，combined with the mechanical replacement of physical tasks，istransformingagricultural decision-makingand implementation.Thissub-theme exploreshowagricultural scenariosareevolving intodigital-intelligent frameworks through\"data-knowledge-action\"closed loops.Weseek inovative research on phenome-genome assciation mining in intelligent breeding，dynamic decision systems in precision farming，and digital twin modeling across agricultural value chains.Priority willbe given to interdisciplinary studies that integrate agronomic principles with data inteligence，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 eficiency versus rights atribution，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，blockchainfor data protection，data trusts，the asetization of data forsmalholder farmers，and the socialized circulation and ecologicalcollaboration ofagricultural data.Submissions that proposebalanced solutions for efficiency and security in agricultural data governance are highly encouraged.

The \"Data amp; 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-highdimensional multimodal large-scale agricultural data，andthe empowerment of human-machine hybrid inteligent systems inagricultural scenarios.We invite scholars worldwide to contribute to the theoretical frontiers and practical boundares of agricultural dataintellgence，colaboratively advancing the knowledge system of agricultural digitalization and intelligence.Submit your originalresearch to TheJournal ofAgricultural Big Data and join us in this transformative journey.

Editorin chief： ZHOUGuoMin