摘要
智慧农业作为农业现代化的核心方向,正通过控制科学与信息技术的深度融合推动生产模式变革.本文从农业信息流动全链条切入,系统剖析智慧农业数字化转型中的关键技术挑战:在精准农业领域,复杂环境下多源异构数据的语义冲突(如卫星遥感与田间传感器的尺度割裂)、低价值数据冗余及动态资源适配中的多目标冲突(如产量提升与碳减排矛盾),制约着数据驱动决策的精度与效率;在智能农机领域,非结构化环境下机器人的动态避障与柔性抓取、人机协作中的感知不对称与信任失衡,以及农田多源干扰(如气象突变、通信时延)导致的控制失稳等问题,阻碍着装备的自主作业能力与系统鲁棒性提升.研究表明,突破上述挑战需要构建跨学科理论体系:在精准农业中,通过农业语义编码机制、多模态特征融合算法及动态多目标优化模型,实现数据“去噪–融合–增值”全流程优化与资源高效配置;在智能农机领域,针对人机协作中感知不对称、信任失衡及控制权模糊等核心冲突问题,通过构建跨模态意图互译系统与动态权责分配机制,实现人机协同决策的高效融合,此外借助多模态感知与强化学习提升机器人环境适应性,通过人机意图互译机制与鲁棒控制策略(如H∞与滑模控制融合)解决协作冲突与干扰抑制问题.本文提出的“干扰建模–协同感知–智能决策”技术框架,为智慧农业基础技术研发与产业升级提供了理论指引,对推动农业所有要素的数字化以及整条链路的智能化有着极为重要的意义.
Smart agriculture,as the core direction of agricultural modernization,is reshaping production modes through the deep integration of control science and information technology.This paper examines the full agricultural information chain and systematically analyzes the key technical challenges in the digital transformation of smart agriculture.In the field of precision agriculture,semantic conflicts among multi-source,heterogeneous data,such as scale mismatches between satellite imagery and in-field sensors,generate redundant low-value datasets.Dynamic resource allocation further faces multi-objective dilemmas,e.g.,the trade-off between yield increase and carbon reduction.These factors jointly degrade the accuracy and efficiency of data-driven decisions.In intelligent machinery,robots work in unstructured environments.They must simultaneously avoid dynamic obstacles,perform flexible grasping,and cope with asymmetric human-machine perception,trust deficits,and control instability induced by meteorological shocks or communication delays.These issues limit autonomous operation and system robustness.To address the above challenges,we propose an interdisciplinary framework.For precision agriculture,an agricultural semantic-coding scheme,a multi-modal feature-fusion algorithm,and a dynamic multi-objective optimization model are combined to denoise,fuse,and add value to data while allocating resources efficiently.For intelligent machinery,a cross-modal intention-translation system and a dynamic authority-allocation mechanism are introduced to reconcile perception asymmetry,trust imbalance,and control ambiguity.Multi-modal perception and reinforcement learning enhance environmental adaptability,whereas robust control strategies,such as hybrid H1 and sliding-mode schemes,suppress disturbances and collaborative conflicts.The resulting“disturbance modeling-collaborative perception-intelligent decision”pipeline offers theoretical guidance for developing fundamental technologies and upgrading the smart-agriculture industry,and it promotes the digitization of all agricultural elements as well as end-to-end intelligence.
作者
李鸿一
何兴
王欣
李华青
谢守勇
王进军
Hongyi LI;Xing HE;Xin WANG;Huaqing LI;Shouyong XIE;Jin-Jun WANG(College of Electronic and Information Engineering,Southwest University,Chongqing 400715,China;College of Engineering and Technology,Southwest University,Chongqing 400715,China;College of Plant Protection,Southwest University,Chongqing 400715,China)
出处
《中国科学:信息科学》
2025年第12期3012-3038,共27页
Scientia Sinica(Informationis)
基金
国家自然科学基金(批准号:62433018)
重庆市自然科学基金(批准号:CSTB2023NSCQ-LZX0135)资助项目。
关键词
智慧农业
多源数据融合
人机协作冲突
鲁棒控制策略
smart agriculture
multi-source data fusion
human-machine collaboration conflict
robust control strategies
