[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2652":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":46},2652,"ARTIFICIAL INTELLIGENCE IN HIGHER EDUCATION: TRANSFORMING TEACHING, LEARNING, AND STUDENT ENGAGEMENT","https:\u002F\u002Fdoi.org\u002F10.65725\u002Fijhlt\u002F1\u002F2\u002F001","Efficient irrigation management is essential for sustainable agriculture, particularly in the context of increasing freshwater scarcity and the growing imperative to optimize crop productivity. Conventional irrigation practices rely predominantly on fixed time schedules or manual field assessments, which frequently induce over-irrigation, root-zone nutrient leaching, under-irrigation water stress, and substantial resource inefficiency. This paper proposes an Intelligent Irrigation Management System that integrates Internet of Things (IoT) sensing architectures, multi-parameter environmental telemetry, and supervised machine learning (ML) algorithms to facilitate dynamic, data-driven, and automated irrigation control. The proposed system continuously acquires real-time field data—including soil moisture, ambient temperature, relative humidity, soil temperature, and rainfall—via deployed sensor nodes managed by an ESP32 microcontroller pipeline. The telemetry stream is transmitted through low-power communication channels to a centralized processing engine, where a Random Forest classification model evaluates multidimensional soil-environmental interactions to predict immediate irrigation requirements. The predicted states feed into an automated actuation layer that directly modulates a solenoid-valve and water-pump relay, forming a closed-loop feedback pipeline. Evaluated against traditional threshold-based and schedule-driven approaches, the proposed IoT-ML framework demonstrates superior operational responsiveness, minimizes unnecessary water application, and offers a robust, scalable architectural template for modern precision agriculture.","高效灌溉管理对可持续农业至关重要，尤其是在淡水日益稀缺、优化作物生产力需求不断增长的背景下。传统灌溉实践主要依赖固定时间表或人工田间评估，这常常导致过度灌溉、根区养分淋失、灌溉不足引起的水分胁迫以及严重的资源低效。本文提出了一种智能灌溉管理系统，该系统集成了物联网（IoT）感知架构、多参数环境遥测以及监督式机器学习（ML）算法，以实现动态、数据驱动和自动化的灌溉控制。所提出的系统通过由ESP32微控制器管道管理的部署传感器节点，持续采集实时田间数据——包括土壤湿度、环境温度、相对湿度、土壤温度和降雨量。遥测数据流通过低功耗通信信道传输至集中处理引擎，其中随机森林分类模型评估多维土壤-环境相互作用，以预测即时灌溉需求。预测状态输入自动执行层，直接调节电磁阀和水泵继电器，形成闭环反馈管道。与传统基于阈值和时间表驱动的方法相比，所提出的IoT-ML框架展现出更优的运行响应能力，最大限度地减少了不必要的灌溉用水，并为现代精准农业提供了一种稳健、可扩展的架构模板。",null,"INTERNATIONAL JOURNAL OF HUMANITIES AND LEARNING TECHNOLOGY INNOVATION (IJHLT)","2026-09-15T00:00:00Z","论文",10,false,72,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},16,18,17,13,8,1,"论文提出IoT与随机森林融合的闭环智能灌溉系统，方法完整、数据驱动，对节水农业有参考价值，但标题与摘要主题不符需核实。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","农业物联网","机器学习","智能灌溉","精准农业",0,"10.65725\u002Fijhlt\u002F1\u002F2\u002F001",{"doi":33,"openalex_id":35,"authors":36,"venue":10,"cited_by_count":32,"oa_url":9,"card":39,"direction":43,"ingested_from":45},"W7213277724",[37],{"name":38,"orcid":9},"M. Rathamani",{"tldr":40,"method":41,"finding":42,"direction":43,"opportunity":44},"提出融合物联网传感与随机森林的智能灌溉系统，实现数据驱动的自动灌溉控制。","ESP32传感器节点采集土壤温湿度等数据，随机森林分类预测灌溉需求。","相比传统定时或阈值方法，该系统响应更优、减少不必要灌溉，可扩展性强。","智慧农业 \u002F 农业物联网","可探索多模态数据融合与边缘智能，提升灌溉决策的实时性与泛化能力。","openalex","2026-09-16T23:30:16.194086Z"]