[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2148":3},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":24,"tags":26,"view_count":32,"doi":33,"paper":34,"created_at":52},2148,"AI-Driven Autonomous Decision Intelligence for Smart Food Manufacturing Cyber-Physical Systems","https:\u002F\u002Fdoi.org\u002F10.68104\u002Fijasit.v1.i3.24","The food manufacturing industry is undergoing a rapid transformation through the adoption of Industry 4.0 technologies, which include Cyber Physical System (CPS), Artificial Intelligence (AI) and Industrial Internet of Things (IIOT). Despite of these developments, modern food manufacturers continue to face challenges which are related to quality consistency, food safety assurance, process optimization and real-time decision making. The large amount of data generated by numerous sensors and production systems often remain underutilized because of the limited intelligent decision-making capability of the system. Simultaneously, the manufacturers identify production inefficiencies, resource wastage, increased operational cost and difficulties in maintaining a uniform product quality. To address these challenges, Autonomous System, Artificial Intelligence (AI) and Decision Intelligence are introduced as transformative technologies which are capable of converting a raw data into an actionable insight. This process includes by integrating the Machine Learning, Computer Vision and Predictive Analytics enables real-time monitoring, predictive maintenance and adaptive process control. Therefore, these capabilities support a data-driven decision making, improve the resource utilization and enhance food-safety and sustainability. Furthermore, the autonomous system can dynamically respond to the changing production conditions enabling a flexible manufacturing operation. This paper explores the role of AI-driven autonomous system and decision intelligence in advancing smart food manufacturing CPS. It discusses all the key enabling technologies, implementation challenges and industrial application while highlighting their potential to transform traditional manufacturing environments into an intelligent and a self-optimizing system. This paper aims to provide an insight into the development of efficient, reliable and a sustainable next-generation food manufacturing ecosystem","食品制造业正通过采用工业4.0技术经历快速转型，这些技术包括信息物理系统（CPS）、人工智能（AI）和工业物联网（IIOT）。尽管取得了这些进展，现代食品制造商仍持续面临与质量一致性、食品安全保障、工艺优化和实时决策相关的挑战。大量由众多传感器和生产系统产生的数据往往未得到充分利用，原因在于系统的智能决策能力有限。同时，制造商还面临生产效率低下、资源浪费、运营成本增加以及难以维持产品品质均一等问题。为应对这些挑战，自主系统、人工智能（AI）和决策智能被引入作为变革性技术，能够将原始数据转化为可执行的洞察。这一过程通过集成机器学习、计算机视觉和预测分析，实现实时监控、预测性维护和自适应过程控制。因此，这些能力支持数据驱动的决策，改善资源利用，并增强食品安全性与可持续性。此外，自主系统能够动态响应不断变化的生产条件，实现柔性制造运行。本文探讨了AI驱动的自主系统和决策智能在推进智能食品制造信息物理系统中的作用。文中讨论了所有关键使能技术、实施挑战和工业应用，同时强调其将传统制造环境转变为智能且自我优化系统的潜力。本文旨在为开发高效、可靠且可持续的下一代食品制造生态系统提供洞见。",null,"International Journal of Applied Smart Interdisciplinary Technologies","2026-09-09T00:00:00Z","论文",10,false,74,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},18,20,16,12,8,1,"论文系统梳理AI与决策智能在食品制造信息物理系统中的应用，方法框架清晰但偏综述，对智慧农业与食品加工数字化有参考价值。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","农业人工智能","食品智能制造","工业互联网","决策智能",0,"10.68104\u002Fijasit.v1.i3.24",{"doi":33,"openalex_id":35,"authors":36,"venue":10,"cited_by_count":32,"oa_url":43,"card":44,"direction":50,"ingested_from":51},"W7212007209",[37,39,41],{"name":38,"orcid":9},"Shajahan Basheer",{"name":40,"orcid":9},"Anusha A",{"name":42,"orcid":9},"Likitha N","https:\u002F\u002Fijasit.org\u002Findex.php\u002Fhome\u002Farticle\u002Fdownload\u002F24\u002F21",{"tldr":45,"method":46,"finding":47,"direction":48,"opportunity":49},"综述AI驱动的自主决策智能在智能食品制造信息物理系统中的应用与挑战。","综述机器学习、计算机视觉与预测分析在食品制造CPS中的集成应用。","AI与决策智能可将传感数据转化为可执行洞察，实现实时监控、预测维护与自适应控制。","农业人工智能与决策模型","食品制造CPS中多源异构数据融合与自主决策闭环验证尚缺，可切入可解释AI与实时优化研究。","智慧农业 \u002F 农业物联网","openalex","2026-09-11T23:30:11.968935Z"]