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Nevertheless, a lot of farms still use manual control and reactive changes, which result in irregular production and the waste of resources. In a bid to overcome such issues, the current paper presents TwinGrow, a cloud-based AIoT Digital Twin that will help to monitor the environment of mushroom cultivation in real-time and predict as well as control its conditions automatically. The system will have a combination of IoT sensors like DHT22, MQ-135, LDR, and soil moisture sensors and ESP32 microcontroller as the edge controller. Sensor data is sent to AWSiot core, placed in dynamoDB and run on machine learning models with AWS sage maker. Digital Twin developed using AWS TwinMaker is a visualization and predictive environmental will replica of the cultivation chamber. The controlled use of mist generators, fans, and pumps, on a closed-loop system, are controlled by automated applications using AWS Lambda and Node-RED. The efficacy of AIoT-oriented smart agriculture using experimental outcomes indicates better environmental conditions, low water and energy usage, and predictable yields.","蘑菇种植需要对气候条件进行严格调控，包括温度、湿度、二氧化碳（CO₂）浓度、光照强度以及基质含水量。然而，许多农场仍采用人工控制和被动式调节，导致产量不稳定和资源浪费。为解决这些问题，本文提出了TwinGrow，一种基于云的AIoT数字孪生系统，可实时监测蘑菇栽培环境，并自动预测和调控其条件。该系统将DHT22、MQ-135、LDR和土壤湿度传感器等物联网传感器与ESP32微控制器作为边缘控制器相结合。传感器数据发送至AWS IoT Core，存储于DynamoDB，并通过AWS SageMaker上的机器学习模型运行。利用AWS TwinMaker开发的数字孪生是栽培室的可视化与预测性环境副本。在闭环系统中，喷雾发生器、风扇和水泵的受控使用由基于AWS Lambda和Node-RED的自动化应用程序进行管理。面向AIoT的智慧农业的实验结果表明，其有效性体现在环境条件改善、水和能源消耗降低以及产量可预测等方面。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-10T00:00:00Z","论文",false,75,{"impact":584,"substance":585,"depth":586,"authority":587,"freshness":173,"relevant":455,"comment":588},16,21,17,13,"提出基于云AIoT数字孪生的蘑菇种植环境实时监测与自动调控系统，方法新颖、实验结论明确，对智慧农业细分场景有参考价值。",[590],{"name":578,"url":575},[592,593,594,595,311],"智慧农业","农业人工智能","物联网","数字孪生",{"code":309,"slug":310,"name":311,"path":312,"parent_code":271},[],{"status":599,"locked":581,"version":4,"method":600,"evidence":601},"classified","rules",{"candidates":602,"agricultural_evidence":605},[603,611,618,625,630],{"code":16,"score":604,"supported":605,"title":606,"abstract":608,"tags":610},19,true,[607],"digital twin",[592,595,609,607],"smart agriculture",[595,592],{"code":309,"score":612,"supported":605,"title":613,"abstract":615,"tags":617},15,[614],"mushroom",[616,614],"蘑菇",[311],{"code":24,"score":619,"supported":605,"title":620,"abstract":621,"tags":624},7,[],[622,623],"机器学习","machine learning",[593],{"code":48,"score":619,"supported":605,"title":626,"abstract":627,"tags":629},[],[594,628],"IoT",[594],{"code":144,"score":631,"supported":605,"title":632,"abstract":633,"tags":635},6,[],[634,145],"土壤",[],[637,638],"TwinGrow 蘑菇 数字孪生","AIoT 食用菌 智能种植","TwinGrow蘑菇数字孪生-4655","10.5281\u002Fzenodo.23276350",{"doi":640,"openalex_id":642,"authors":643,"venue":578,"cited_by_count":424,"oa_url":575,"card":650,"direction":654,"ingested_from":656},"W7221319111",[644,646,648],{"name":645,"orcid":11},"Katam Reddy Charan Reddy",{"name":647,"orcid":11},"Ajjaguttu Abhishikth Reddy",{"name":649,"orcid":11},"Dr. A. Anbarasi",{"tldr":651,"method":652,"finding":653,"direction":654,"opportunity":655},"提出TwinGrow云AIoT数字孪生系统，实现蘑菇栽培环境实时监测与自动调控。","ESP32与多传感器采集数据，经AWS IoT、SageMaker和TwinMa","系统改善环境条件，降低水耗与能耗，并实现可预测的蘑菇产量。","智慧农业 \u002F 农业物联网","可探索数字孪生与作物生长模型的耦合，实现产量与品质的精准预测及跨品种迁移。","openalex","2026-10-11T23:30:07.397887Z",{"technologies":659,"keywords":665},[660,661,662,663],{"name":593,"count":69},{"name":595,"count":455},{"name":594,"count":455},{"name":664,"count":455},"目标检测",[666,668],{"name":592,"count":667},3,{"name":311,"count":69}]