[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1882":3,"related-1882":101},{"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":23,"tags":25,"primary_topic":31,"secondary_topics":37,"classification":44,"search_phrases":67,"slug":70,"view_count":21,"doi":71,"paper":72,"created_at":100},1882,"Continuous nitrate and ammonium monitoring under different VPD conditions in hydroponic cultivation via IoT environmental control and ion sensing","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112400","Continuous nitrate and ammonium monitoring under different VPD conditions in hydroponic cultivation via IoT environmental control and ion sensing。Computers and Electronics in Agriculture","在水培栽培中，通过物联网环境控制与离子传感技术，对不同蒸汽压差（VPD）条件下的硝酸盐和铵态氮进行连续监测。《农业计算机与电子》期刊。",null,"Computers and Electronics in Agriculture","2026-09-07T00:00:00Z","论文",10,false,70,{"impact":17,"substance":18,"depth":19,"authority":17,"freshness":20,"relevant":21,"comment":22},15,20,18,2,1,"研究结合IoT与离子传感监测水培营养液，方法新颖，对精准农业有参考价值，但发表于一年前，时效性低。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","物联网","水培","离子传感","环境调控",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"B7","protected-horticulture","设施园艺与植物工厂","\u002Ftopic\u002Fhorticulture\u002Fprotected-horticulture","B",[38],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot","I",{"status":45,"locked":14,"version":46,"method":47,"evidence":48},"classified","2026-10-22.2","rules",{"candidates":49,"agricultural_evidence":51},[50,56,62],{"code":39,"score":17,"supported":51,"title":52,"abstract":54,"tags":55},true,[53],"IoT",[27,53],[27],{"code":32,"score":57,"supported":51,"title":58,"abstract":60,"tags":61},13,[59],"hydroponic",[59],[28,30],{"code":63,"score":21,"supported":14,"title":64,"abstract":65,"tags":66},"I1",[],[],[26],[68,69],"智慧农业 环境调控 离子传感 物联网","智慧农业 环境调控","智慧农业环境调控离子传感物联网-1882","10.1016\u002Fj.compag.2026.112400",{"doi":71,"openalex_id":73,"authors":74,"venue":10,"cited_by_count":92,"oa_url":9,"card":93,"direction":97,"ingested_from":99},"W7211918988",[75,78,80,82,84,87,89],{"name":76,"orcid":77},"Yi-Min Wu","https:\u002F\u002Forcid.org\u002F0009-0002-2612-0556",{"name":79,"orcid":9},"Wei-Lun Kuo",{"name":81,"orcid":9},"Shao-Yuan Liu",{"name":83,"orcid":9},"Hsing-Ying Chung",{"name":85,"orcid":86},"Chi-Yi Lin","https:\u002F\u002Forcid.org\u002F0000-0002-5631-8806",{"name":88,"orcid":9},"Wei Fang",{"name":90,"orcid":91},"Lin‐Chi Chen","https:\u002F\u002Forcid.org\u002F0000-0001-5947-2019",0,{"tldr":94,"method":95,"finding":96,"direction":97,"opportunity":98},"利用物联网环境控制和离子传感，在水培中连续监测不同VPD条件下的硝酸盐和铵浓度。","物联网环境控制结合离子传感技术，监测不同VPD下的营养液离子浓度。","VPD条件影响水培营养液中硝酸盐和铵的浓度变化，连续监测可揭示其动态。","智慧农业 \u002F 农业物联网","可延伸研究VPD与其他离子（如钾、钙）的关联，或开发基于实时离子数据的营养液智能调控算法。","openalex","2026-09-08T23:30:01.608742Z",{"total":102,"page":21,"page_size":102,"items":103},6,[104,171,270,349,411,464],{"id":105,"title":106,"url":107,"summary":108,"summary_zh":109,"content":9,"source_name":110,"source_url":107,"published_at":111,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":112,"score_detail":113,"sources":117,"tags":119,"primary_topic":123,"secondary_topics":124,"classification":126,"search_phrases":146,"slug":149,"view_count":92,"doi":150,"paper":151,"created_at":170},5237,"Integrasi Framework Laravel dan Android pada Sistem IoT untuk Monitoring Parameter Fisika-Kimia di Greenhouse","https:\u002F\u002Fdoi.org\u002F10.36257\u002Fteknika.v20i3.12040","Pengembangan teknologi pertanian presisi (precision agriculture) pada ekosistem greenhouse menggunakan sistem hidroponik menghadapi tantangan teknis dalam menjaga stabilitas parameter lingkungan mikro secara simultan. Penelitian ini bertujuan untuk mengintegrasikan framework Laravel dan platform Android ke dalam sistem Internet of Things (IoT) untuk memantau parameter fisika-kimia lingkungan, meliputi suhu udara, kelembaban, intensitas cahaya, dan kadar karbon dioksida (CO2), pada budidaya pakcoy (Brassica rapa L.) di greenhouse hidroponik. Fokus utama penelitian ini terletak pada perancangan arsitektur backend RESTful API menggunakan Laravel untuk mengelola penyimpanan basis data riwayat jangka panjang serta menyinkronkan transmisi data dari multi-sensor (DHT22, LDR, dan sensor gas CO2) yang memiliki karakteristik sampling rate berbeda. Di sisi pengguna, aplikasi Android dan antarmuka web dikembangkan sebagai frontend yang mengonsumsi data melalui serialisasi format JSON menggunakan metode polling HTTP GET real-time, serta menyediakan tiga mode kendali aktuator, yaitu manual, otomatis berbasis ambang batas sensor, dan penjadwalan. Pengujian fungsionalitas dan kinerja sistem dilakukan melalui visualisasi antarmuka pada dasbor situs web neliti-sk.darmajaya.ac.id serta evaluasi transmisi data nirkabel. Infrastruktur backend berbasis Laravel terbukti stabil dalam menangani fluktuasi frekuensi data sensor yang kompleks. Berdasarkan evaluasi log data selama 24 jam pengujian kontinu pada rentang 14 Agustus 2026 (00.00–23.00 WIB), sistem berhasil merekam seluruh 24 titik data per jam (100%) tanpa satupun jam yang terlewat. Integrasi ini menghadirkan solusi pemantauan sekaligus pengendalian pertanian presisi yang andal, terintegrasi, dan memiliki skalabilitas tinggi bagi operator greenhouse, meskipun masih memerlukan mekanisme offline logging untuk mengantisipasi gangguan konektivitas atau pemadaman listrik di masa mendatang.","在温室生态系统中利用水培系统开发精准农业（precision agriculture）技术，面临着同时维持微环境参数稳定性的技术挑战。本研究旨在将Laravel框架和Android平台集成到物联网（IoT）系统中，以监测水培温室中小白菜（Brassica rapa L.）栽培环境的物理化学参数，包括空气温度、湿度、光照强度和二氧化碳（CO2）浓度。本研究的主要重点在于设计基于Laravel的RESTful API后端架构，以管理长期历史数据库的存储，并同步具有不同采样率特征的多传感器（DHT22、LDR和CO2气体传感器）的数据传输。在用户端，开发了Android应用和Web界面作为前端，通过HTTP GET实时轮询方法以JSON格式序列化消费数据，并提供三种执行器控制模式，即手动、基于传感器阈值的自动控制和定时调度。系统功能与性能测试通过neliti-sk.darmajaya.ac.id网站仪表板的界面可视化以及无线数据传输评估进行。基于Laravel的后端基础设施在处理复杂的传感器数据频率波动方面表现出稳定性。根据2026年8月14日（00:00–23:00 WIB）连续24小时测试期间的数据日志评估，系统成功记录了每小时全部24个数据点（100%），无一小时遗漏。该集成提供了一种可靠、集成且具有高可扩展性的精准农业监测与控制解决方案，适用于温室操作人员，但未来仍需离线日志记录机制以应对连接中断或停电情况。","TEKNIKA","2026-10-17T00:00:00Z",56,{"impact":114,"substance":19,"depth":115,"authority":102,"freshness":114,"relevant":21,"comment":116},8,16,"论文将Laravel与Android集成用于水培温室多参数监测，方法具体、数据连续24小时完整，但属单点应用研究，公共影响有限，适合作为技术案例而非每日精选头条。",[118],{"name":110,"url":107},[26,27,120,121,122],"精准农业","温室大棚","水培蔬菜",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[125],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"status":45,"locked":14,"version":46,"method":47,"evidence":127},{"candidates":128,"agricultural_evidence":51},[129,134,141],{"code":39,"score":19,"supported":51,"title":130,"abstract":131,"tags":133},[53],[27,132,53],"internet of things",[27],{"code":32,"score":135,"supported":51,"title":136,"abstract":138,"tags":140},14,[137],"greenhouse",[139,137],"温室",[],{"code":63,"score":114,"supported":51,"title":142,"abstract":143,"tags":145},[],[120,144],"precision agriculture",[26,120],[147,148],"Laravel Android 温室物联网","小白菜 水培 温室监测","LaravelAndroid温室物联网-5237","10.36257\u002Fteknika.v20i3.12040",{"doi":150,"openalex_id":152,"authors":153,"venue":110,"cited_by_count":92,"oa_url":164,"card":165,"direction":97,"ingested_from":99},"W7222850582",[154,156,159,162],{"name":155,"orcid":9},"Ramadanzah Ramadanzah",{"name":157,"orcid":158},"Dodi Yudo Setyawan","https:\u002F\u002Forcid.org\u002F0000-0003-2205-7617",{"name":160,"orcid":161},"Nurfiana Nurfiana","https:\u002F\u002Forcid.org\u002F0000-0002-6149-0918",{"name":163,"orcid":9},"Refi Arioen","https:\u002F\u002Fjurnal.polsri.ac.id\u002Findex.php\u002Fteknika\u002Farticle\u002Fdownload\u002F12040\u002F4368",{"tldr":166,"method":167,"finding":168,"direction":97,"opportunity":169},"集成Laravel与Android的IoT系统，实时监测温室水培小白菜的温湿度、光照和CO2。","用Laravel构建RESTful API后端，Android与Web前端通过J","后端稳定处理异构采样率数据，24小时连续测试每小时数据完整记录率100%。","可研究断网或断电下的离线缓存与数据补传机制，提升温室IoT系统鲁棒性。","2026-10-18T23:30:07.964809Z",{"id":172,"title":173,"url":174,"summary":175,"summary_zh":176,"content":9,"source_name":177,"source_url":174,"published_at":178,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":179,"score_detail":180,"sources":186,"tags":188,"primary_topic":193,"secondary_topics":194,"classification":201,"search_phrases":247,"slug":250,"view_count":92,"doi":9,"paper":251,"created_at":269},5313,"Real-Time IoT-Integrated Ensemble LSTM–Bi-LSTM Framework for Greenhouse Microclimate Forecasting","https:\u002F\u002Fwasrpublication.com\u002Findex.php\u002Findex\u002Farticle\u002Fview\u002F512","Accurate prediction of greenhouse microclimates is essential for optimizing crop growth and resource utilization in precision agriculture. The research presents an IoT-integrated ensemble deep learning framework for real-time greenhouse microclimate forecasting using Long Short-Term Memory (LSTM) and Bidirectional Long Short-Term Memory (Bi-LSTM) networks. Environmental variables, including temperature, light intensity, soil moisture, and humidity, were acquired through IoT sensor nodes deployed in a controlled greenhouse environment. A benchmark greenhouse dataset was first used for model development and hyperparameter optimization, while an original IoT-based dataset collected during cucumber, tomato, and pepper cultivation at the Federal University Wukari, Nigeria, was used for validation and real-world deployment. The data were preprocessed through noise filtering, missing value imputation, Z-score-based outlier removal, and Min–Max normalization. A weighted ensemble strategy was then applied to combine LSTM and Bi-LSTM predictions for improved robustness. Experimental results show that the Bi-LSTM model outperformed the LSTM model, achieving R² = 0.9981, MAE = 0.0088, RMSE = 0.0110, and MSE = 0.0001, compared to LSTM results of R² = 0.9956, MAE = 0.0133, RMSE = 0.0167, and MSE = 0.0003. Statistical validation using paired t-test, Wilcoxon signed-rank test, confidence intervals, and multi-run analysis confirmed that the performance improvement is statistically significant and robust. The proposed framework enables accurate real-time microclimate forecasting and intelligent environmental monitoring, providing a scalable solution for precision greenhouse management, improved crop productivity, optimized energy utilization, and efficient irrigation scheduling.","准确预测温室微气候对于精准农业中优化作物生长和资源利用至关重要。本研究提出了一种集成物联网的集成深度学习框架，利用长短期记忆网络（LSTM）和双向长短期记忆网络（Bi-LSTM）进行实时温室微气候预测。温度、光照强度、土壤湿度和湿度等环境变量通过部署在受控温室环境中的物联网传感器节点采集。研究首先使用基准温室数据集进行模型开发和超参数优化，随后使用在尼日利亚武卡里联邦大学黄瓜、番茄和辣椒种植期间采集的原始物联网数据集进行验证和实际部署。数据经过噪声滤波、缺失值插补、基于Z分数的异常值去除和最小—最大归一化预处理。然后采用加权集成策略融合LSTM和Bi-LSTM的预测结果以提高鲁棒性。实验结果表明，Bi-LSTM模型优于LSTM模型，其R² = 0.9981、MAE = 0.0088、RMSE = 0.0110、MSE = 0.0001，而LSTM的结果为R² = 0.9956、MAE = 0.0133、RMSE = 0.0167、MSE = 0.0003。采用配对t检验、Wilcoxon符号秩检验、置信区间和多轮运行分析进行的统计验证证实，性能提升具有统计显著性和鲁棒性。所提出的框架能够实现准确的实时微气候预测和智能环境监测，为精准温室管理、提高作物生产力、优化能源利用和高效灌溉调度提供了可扩展的解决方案。","World Journal of Multidisciplinary Studies","2026-10-16T00:00:00Z",67,{"impact":181,"substance":182,"depth":183,"authority":184,"freshness":114,"relevant":21,"comment":185},12,21,17,9,"方法扎实、验证充分，但属单点应用研究，公共影响有限，适合作为技术前沿条目而非头条精选。",[187],{"name":177,"url":174},[26,189,190,191,192],"农业人工智能","农业物联网","温室环境","小气候预测",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[195,196],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"code":197,"slug":198,"name":199,"path":200,"parent_code":36},"B1","vegetables","蔬菜","\u002Ftopic\u002Fhorticulture\u002Fvegetables",{"status":45,"locked":14,"version":46,"method":47,"evidence":202},{"candidates":203,"agricultural_evidence":51},[204,208,212,221,226,233,240],{"code":39,"score":17,"supported":51,"title":205,"abstract":206,"tags":207},[53],[27,53],[190],{"code":32,"score":135,"supported":51,"title":209,"abstract":210,"tags":211},[137],[139,137],[],{"code":197,"score":184,"supported":51,"title":213,"abstract":214,"tags":220},[],[215,216,217,218,219],"番茄","辣椒","黄瓜","tomato","cucumber",[],{"code":63,"score":222,"supported":51,"title":223,"abstract":224,"tags":225},7,[],[120,144],[26],{"code":227,"score":222,"supported":51,"title":228,"abstract":229,"tags":232},"I2",[],[230,231],"深度学习","deep learning",[189],{"code":234,"score":102,"supported":51,"title":235,"abstract":236,"tags":239},"H2",[],[237,238],"土壤","soil",[],{"code":241,"score":102,"supported":51,"title":242,"abstract":243,"tags":246},"H3",[],[244,245],"灌溉","irrigation",[],[248,249],"温室 小气候 LSTM 预测","尼日利亚 温室 IoT 黄瓜 番茄","温室小气候LSTM预测-5313",{"doi":9,"openalex_id":252,"authors":253,"venue":177,"cited_by_count":92,"oa_url":9,"card":264,"direction":97,"ingested_from":99},"W7222909837",[254,256,258,260,262],{"name":255,"orcid":9},"Siman Emmanuel*",{"name":257,"orcid":9},"Edward Onyebueke Agu",{"name":259,"orcid":9},"Musa Gani",{"name":261,"orcid":9},"Oladunjoye John Abiodun",{"name":263,"orcid":9},"Anagu Emmanuel John",{"tldr":265,"method":266,"finding":267,"direction":97,"opportunity":268},"提出物联网集成LSTM与Bi-LSTM加权集成框架，实时预测温室微气候。","IoT传感器采集温光湿数据，经预处理后用LSTM-Bi-LSTM加权集成建模。","Bi-LSTM优于LSTM，R²达0.9981，统计检验证实性能提升显著稳健。","可探索边缘端轻量化部署与多作物跨温室迁移学习，提升实时预测泛化性。","2026-10-19T23:30:13.295236Z",{"id":271,"title":272,"url":273,"summary":274,"summary_zh":275,"content":9,"source_name":276,"source_url":273,"published_at":277,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":278,"score_detail":279,"sources":281,"tags":283,"primary_topic":286,"secondary_topics":287,"classification":293,"search_phrases":322,"slug":325,"view_count":326,"doi":327,"paper":328,"created_at":348},4569,"SMART GREENHOUSE MEASUREMENT AND MONITORING SYSTEM BASED ON IOT AND COMPUTER VISION","https:\u002F\u002Fdoi.org\u002F10.46242\u002Fjstiuh.v83i05.6189","This paper presents a smart greenhouse measurement and monitoring system integrating IoT and computer vision for non-invasive plant growth observation and remote supervision. The proposed architecture combines environmental sensors, STM32-based data acquisition and control modules, a Raspberry Pi 4 with a USB camera, a lightweight deep learning-based stem segmentation module, Firebase Realtime Database, and a web application for monitoring and control. In the computer vision module, tomato plant images are processed directly on the Raspberry Pi to segment stem regions and estimate plant height from calibrated image measurements, enabling edge-based phenotypic monitoring without destructive sampling or manual measurement. Experimental results on a tomato cultivation prototype show that the system can collect temperature, air humidity, soil moisture, and light intensity data in real time while providing automated plant height estimation from RGB images. During the reported period, temperature ranged from 27.8°C to 38.8°C, air humidity from 40.2% to 77.0%, soil moisture from 52.0% to 82.4%, and plant height increased from 65.8 cm to 77.0 cm. The segmentation results demonstrate the feasibility of extracting stem-level structural information under practical greenhouse conditions, including complex background, leaf occlusion, and non-uniform illumination. The prototype confirms the potential of a low-cost smart greenhouse monitoring system and provides a practical basis for future expansion toward richer agronomic indicators, such as stem width, leaf area, canopy coverage, growth rate estimation, and automated environmental control.","本文提出了一种集成物联网与计算机视觉的智能温室测量与监测系统，用于非侵入式植物生长观测与远程监管。所提出的架构结合了环境传感器、基于STM32的数据采集与控制模块、配备USB摄像头的Raspberry Pi 4、基于轻量级深度学习的茎秆分割模块、Firebase实时数据库以及用于监测与控制的Web应用程序。在计算机视觉模块中，番茄植株图像直接在Raspberry Pi上处理，以分割茎秆区域并根据标定后的图像测量值估算株高，从而实现基于边缘的表型监测，无需破坏性采样或人工测量。在番茄栽培原型上的实验结果表明，该系统能够实时采集温度、空气湿度、土壤湿度和光照强度数据，同时基于RGB图像提供自动株高估算。在报告期内，温度范围为27.8°C至38.8°C，空气湿度为40.2%至77.0%，土壤湿度为52.0%至82.4%，株高从65.8 cm增长至77.0 cm。分割结果证明了在实际温室条件下（包括复杂背景、叶片遮挡和非均匀光照）提取茎秆级结构信息的可行性。该原型验证了低成本智能温室监测系统的潜力，并为未来扩展至更丰富的农艺指标（如茎宽、叶面积、冠层覆盖度、生长速率估算和自动化环境控制）提供了实践基础。","Journal of Science and Technology - IUH","2026-10-08T00:00:00Z",66,{"impact":181,"substance":18,"depth":115,"authority":13,"freshness":114,"relevant":21,"comment":280},"低成本IoT与边缘视觉融合的温室监测原型，方法可行、数据翔实，但属细分技术验证，产业影响有限。",[282],{"name":276,"url":273},[26,189,284,190,285],"计算机视觉","温室种植",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[288,292],{"code":227,"slug":289,"name":290,"path":291,"parent_code":43},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"status":45,"locked":14,"version":46,"method":47,"evidence":294},{"candidates":295,"agricultural_evidence":51},[296,302,306,310,314,318],{"code":227,"score":297,"supported":51,"title":298,"abstract":300,"tags":301},19,[299],"computer vision",[230,284,231,299],[189,284],{"code":32,"score":17,"supported":51,"title":303,"abstract":304,"tags":305},[137],[139,137],[285],{"code":39,"score":17,"supported":51,"title":307,"abstract":308,"tags":309},[53],[27,53],[190],{"code":197,"score":102,"supported":51,"title":311,"abstract":312,"tags":313},[],[215,218],[],{"code":234,"score":102,"supported":51,"title":315,"abstract":316,"tags":317},[],[237,238],[],{"code":63,"score":21,"supported":14,"title":319,"abstract":320,"tags":321},[],[],[26],[323,324],"Raspberry Pi 温室监测","番茄 株高 图像识别","RaspberryPi温室监测-4569",4,"10.46242\u002Fjstiuh.v83i05.6189",{"doi":327,"openalex_id":329,"authors":330,"venue":276,"cited_by_count":92,"oa_url":342,"card":343,"direction":97,"ingested_from":99},"W7221046739",[331,333,335,337,339],{"name":332,"orcid":9},"HOANG THI TU UYEN",{"name":334,"orcid":9},"NGUYEN HOANH",{"name":336,"orcid":9},"NGO THANH QUYEN",{"name":338,"orcid":9},"Lam Binh Minh",{"name":340,"orcid":341},"Nguyễn Trung Nhân","https:\u002F\u002Forcid.org\u002F0000-0001-9328-7693","https:\u002F\u002Fjst.iuh.edu.vn\u002Findex.php\u002Fjst-iuh\u002Farticle\u002Fdownload\u002F6189\u002F1131",{"tldr":344,"method":345,"finding":346,"direction":97,"opportunity":347},"集成物联网与计算机视觉的智能温室系统，实现非破坏性植物生长监测与远程管控。","环境传感器、STM32、树莓派4、USB摄像头、轻量茎分割模型、Firebase","原型可实时采集温湿度、土壤水分和光照，并从RGB图像估计株高，验证了边缘表型监测可行性。","可扩展至茎宽、叶面积、冠层覆盖度等更丰富农艺指标，并实现自动化环境控制闭环。","2026-10-10T23:30:11.420750Z",{"id":350,"title":351,"url":352,"summary":353,"summary_zh":354,"content":9,"source_name":355,"source_url":352,"published_at":356,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":357,"score_detail":358,"sources":360,"tags":362,"primary_topic":366,"secondary_topics":367,"classification":369,"search_phrases":387,"slug":390,"view_count":20,"doi":391,"paper":392,"created_at":410},3614,"Integration of digital twin and IoT for virtual greenhouse management","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.sciaf.2026.e03665","The convergence of Digital Twin (DT) and Internet of Things (IoT) technologies represents a key enabler of Agriculture 5.0, providing real-time synchronization between physical and virtual environments. When coupled with Virtual Reality (VR), these technologies enable immersive and interactive systems that enhance intelligent agriculture monitoring, control, and decision-making. This study proposes an advanced and fully integrated VR–DT–IoT framework for virtual greenhouse management, designed not only to simulate and visualize but also to synchronize and control the physical system in real time. The proposed framework achieves seamless cyber-physical fusion by establishing bidirectional communication among IoT sensors, DT models, and VR interfaces. This enables users to trigger actuator commands and observe instant physical feedback directly within the immersive environment. A real-world experimental greenhouse was developed as a testbed to validate the system’s architecture and functionality. IoT sensors continuously monitored environmental parameters with data streams dynamically synchronized to the DT model reproducing greenhouse dynamics. The VR interface provided context-aware interaction and spatial visualization, allowing users to explore live sensor data, analyze trends, and simulate adaptive control strategies in a realistic virtual environment. The experimental validation, conducted with 23 participants across seven training sessions, revealed a marked reduction in task completion time and error rates, along with high usability scores (SUS = 3.84\u002F5), confirming the system’s effectiveness in improving operational performance. The findings demonstrate that the proposed system significantly advances digital agriculture by merging modeling, real-time feedback, and immersive control into a unified human-in-the-loop platform. This research highlights the technological novelty and practical value of integrating DT, IoT, and VR into a single coherent framework, bridging physical and virtual domains.","数字孪生（Digital Twin, DT）与物联网（Internet of Things, IoT）技术的融合是农业5.0的关键使能技术，可实现物理环境与虚拟环境之间的实时同步。当与虚拟现实（Virtual Reality, VR）相结合时，这些技术能够构建沉浸式交互系统，从而增强智能农业的监测、控制与决策能力。本研究提出了一种先进且完全集成的VR–DT–IoT框架，用于虚拟温室管理，该框架不仅能够仿真和可视化，还能够实时同步和控制物理系统。所提出的框架通过建立IoT传感器、DT模型与VR界面之间的双向通信，实现了无缝的信息物理融合。用户可在沉浸式环境中直接触发执行器命令并观察即时物理反馈。本研究搭建了一个真实实验温室作为测试平台，以验证系统架构与功能。IoT传感器持续监测环境参数，数据流动态同步至DT模型，以复现温室动态变化。VR界面提供了情境感知交互与空间可视化功能，使用户能够在逼真的虚拟环境中探索实时传感器数据、分析趋势并仿真自适应控制策略。实验验证共有23名参与者参与，跨越七次培训课程，结果显示任务完成时间和错误率显著降低，同时可用性评分较高（SUS = 3.84\u002F5），证实了该系统在提升操作性能方面的有效性。研究结果表明，所提出的系统通过将建模、实时反馈与沉浸式控制融合为统一的人在回路平台，显著推进了数字农业发展。本研究凸显了将DT、IoT与VR集成到单一连贯框架中的技术创新性与实用价值，弥合了物理域与虚拟域之间的鸿沟。","Scientific African","2026-09-25T00:00:00Z",76,{"impact":115,"substance":182,"depth":19,"authority":57,"freshness":114,"relevant":21,"comment":359},"提出VR-DT-IoT全集成虚拟温室框架并经真实温室与23人实验验证，方法新颖、数据可靠，对智慧农业人机交互控制有参考价值。",[361],{"name":355,"url":352},[26,190,363,364,365],"数字孪生","虚拟现实","温室管理",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[368],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"status":45,"locked":14,"version":46,"method":47,"evidence":370},{"candidates":371,"agricultural_evidence":51},[372,379,383],{"code":63,"score":297,"supported":51,"title":373,"abstract":375,"tags":378},[374],"digital twin",[376,363,377,374],"数字农业","digital agriculture",[363,26],{"code":39,"score":19,"supported":51,"title":380,"abstract":381,"tags":382},[53],[27,132,53],[190],{"code":32,"score":135,"supported":51,"title":384,"abstract":385,"tags":386},[137],[139,137],[],[388,389],"数字孪生 物联网 虚拟温室","VR DT IoT 智慧农业","数字孪生物联网虚拟温室-3614","10.1016\u002Fj.sciaf.2026.e03665",{"doi":391,"openalex_id":393,"authors":394,"venue":355,"cited_by_count":92,"oa_url":404,"card":405,"direction":97,"ingested_from":99},"W7214397681",[395,398,401],{"name":396,"orcid":397},"Hicham Slimani","https:\u002F\u002Forcid.org\u002F0000-0002-7946-4064",{"name":399,"orcid":400},"Abdelilah Jilbab","https:\u002F\u002Forcid.org\u002F0000-0002-1577-9040",{"name":402,"orcid":403},"Jamal El Mhamdi","https:\u002F\u002Forcid.org\u002F0000-0001-8219-3560","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS2468227626004904\u002Fpdf",{"tldr":406,"method":407,"finding":408,"direction":97,"opportunity":409},"提出VR-DT-IoT框架，实现虚拟温室实时同步与沉浸式控制。","构建真实温室试验台，集成IoT传感器、数字孪生模型与VR界面，23人测试。","任务完成时间和错误率显著降低，可用性高（SUS=3.84\u002F5）。","可探索多用户协同VR控制、AI预测性维护及跨温室数字孪生标准化。","2026-09-27T23:30:19.611108Z",{"id":412,"title":413,"url":414,"summary":415,"summary_zh":416,"content":9,"source_name":417,"source_url":414,"published_at":418,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":419,"sources":421,"tags":423,"primary_topic":426,"secondary_topics":427,"classification":428,"search_phrases":444,"slug":447,"view_count":92,"doi":448,"paper":449,"created_at":463},5309,"A Multi-Layer Contextual Model for Managing Data Overload in Smart Hydroponics","https:\u002F\u002Fdoi.org\u002F10.32890\u002Fjict2026.25.4.4","The widespread use of the Internet of Things (IoT) in hydroponic agriculture allows continuous monitoring of environmental conditions and provides real-time information to support crop management. However, the increasing amount of sensor data generated makes the monitoring process more complex as users need to interpret multiple environmental parameters simultaneously. Existing hydroponic monitoring systems focus on data collection and visualization, while the management and information delivery aspects to reduce data overload are still limited. This study proposes a multi-layer contextual model to manage environmental information in an intelligent hydroponic monitoring system. The model consists of five interconnected layers (Power, Sensor, Communication, Database and Visualization), which support the process of acquisition, validation, management and contextual data delivery. To validate the proposed model, a solar-powered IoT hydroponic monitoring system integrated with an Augmented Reality (AR) application was developed based on Situation Awareness Theory. Using the System Usability Scale (SUS), the prototype was assessed by three domain experts. The results achieved an average usability score of 85.83, which reflects excellent usability. The results indicate that the proposed model improves organisation and presentation of environmental information by prioritising contextually relevant data rather than displaying all available sensor readings at once. This enables users to identify important environmental conditions efficiently and supports more effective monitoring. This approach helps to reduce data overload and provides a practical framework that can support the development of IoT-based smart agriculture applications in the future through the integration of anomaly detection techniques and predictive analytics.","物联网（IoT）在水培农业中的广泛使用使得环境条件的持续监测成为可能，并提供实时信息以支持作物管理。然而，传感器数据量的不断增加使监测过程更加复杂，因为用户需要同时解读多个环境参数。现有的水培监测系统侧重于数据采集与可视化，而在减少数据过载的管理与信息传递方面仍然有限。本研究提出一种多层上下文模型，用于管理智能水培监测系统中的环境信息。该模型由五个相互关联的层级（供电、传感器、通信、数据库和可视化）组成，支持数据的采集、验证、管理和上下文传递过程。为验证所提出的模型，基于态势感知理论开发了一套集成了增强现实（AR）应用的太阳能物联网水培监测系统。采用系统可用性量表（SUS）由三位领域专家对原型进行评估，结果获得平均可用性得分85.83，反映出优异的可用性。结果表明，所提出的模型通过优先呈现上下文相关的数据而非一次性显示所有可用的传感器读数，改善了环境信息的组织与呈现方式。这使用户能够高效识别重要的环境条件，并支持更有效的监测。该方法有助于减少数据过载，并提供了一个实用框架，未来可通过集成异常检测技术和预测分析来支持基于物联网的智慧农业应用的开发。","Journal of Information and Communication Technology","2026-10-18T00:00:00Z",{"impact":181,"substance":18,"depth":115,"authority":57,"freshness":184,"relevant":21,"comment":420},"提出五层情境化模型，并用太阳能物联网与AR原型验证，SUS 85.83，方法新颖、结论可靠，但属单点原型研究，产业影响有限。",[422],{"name":417,"url":414},[26,27,28,424,425],"增强现实","数据过载",{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},[],{"status":45,"locked":14,"version":46,"method":47,"evidence":429},{"candidates":430,"agricultural_evidence":51},[431,435,440],{"code":39,"score":13,"supported":51,"title":432,"abstract":433,"tags":434},[],[27,132,53],[27],{"code":63,"score":222,"supported":51,"title":436,"abstract":437,"tags":439},[],[26,438],"smart agriculture",[26],{"code":32,"score":326,"supported":51,"title":441,"abstract":442,"tags":443},[],[59],[28],[445,446],"智能水培 物联网 监测系统","增强现实 水培 数据过载","智能水培物联网监测系统-5309","10.32890\u002Fjict2026.25.4.4",{"doi":448,"openalex_id":450,"authors":451,"venue":417,"cited_by_count":92,"oa_url":414,"card":458,"direction":97,"ingested_from":99},"W7222865525",[452,455],{"name":453,"orcid":454},"Ruziana Mohamad Rasli","https:\u002F\u002Forcid.org\u002F0009-0006-7187-513X",{"name":456,"orcid":457},"Sobihatun Nur Abdul Salam","https:\u002F\u002Forcid.org\u002F0000-0002-6751-9187",{"tldr":459,"method":460,"finding":461,"direction":97,"opportunity":462},"提出五层情境模型管理水培IoT环境数据，结合AR应用减少数据过载。","五层情境模型+太阳能IoT水培监测系统+AR应用，SUS量表由3位专家评估。","原型SUS均分85.83，情境化优先展示关键数据可有效缓解数据过载。","可延伸至异常检测与预测分析融合，并开展多用户大规模实证验证。","2026-10-19T23:30:07.917393Z",{"id":465,"title":466,"url":467,"summary":468,"summary_zh":9,"content":469,"source_name":470,"source_url":9,"published_at":471,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":472,"score_detail":473,"sources":475,"tags":477,"primary_topic":482,"secondary_topics":483,"classification":485,"search_phrases":510,"slug":513,"view_count":506,"doi":9,"paper":9,"created_at":514},1034,"AI-Enabled IoT-Based Hydroponic Farming with Embedded Automation and Nutrient Prediction","https:\u002F\u002Fwww.mdpi.com\u002F1999-5903\u002F18\u002F9\u002F446","原文标题:AI-Enabled IoT-Based Hydroponic Farming with Embedded Automation and Nutrient Prediction。作者:Jehangir Arshad、Fawad Azeem、Ayesha Butt、Maha Chaudhary、Rana Saad Safdar、M. Kamran Joyo、Izanoordina Ahmad、Prajoona Valsalan、Husham M. Ahmed(COMSATS University Islamabad等)。DOI: 10.3390\u002Ffi18090446。论文构建一个由Raspberry Pi控制、完全监测并持续评估的水培农业模拟器,采用多种传感器、数据管理与处理以及自动化环境调节。该智能水培系统采用双模型机器学习流水线:一个通过图像分析识别植物病害,另一个基于传感器数据评估植物营养水平。两个模型的数据通过云端数字孪生(DT)合并,实现远程访问并提供闭环控制,用于水培环境中的灌溉、营养剂量和所有环境因子管理。该研究展示了将物联网传感、机器学习和数字孪生模拟组合成可运行超物理系统来开发可扩展、数据驱动的精准农业解决方案的能力。","## 1. Introduction and Background\n\nControlled-environment agriculture is moving quickly toward distributed sensing, edge computing, and cloud analytics to reduce manual supervision. However, in practice, many hydroponic platforms treat sensing, vision-based plant health screening, cloud monitoring, and actuator control as separate modules that happen to sit next to each other rather than work together. This separation leaves a gap between collecting real-time data and actually closing the loop on decisions. This work addresses this gap by integrating multi-sensor hydroponic monitoring, RGB image-based disease screening, nutrient-status prediction, actuator control, and Digital Twin visualization within a single operational platform.\n\nMeeting the nutritional needs of a growing global population is hardly easy, and the challenges are not helped by climate change, dwindling freshwater resources, or the gradual loss of arable land [[1](https:\u002F\u002Fwww.mdpi.com\u002F1999-5903\u002F18\u002F9\u002F446#B1-futureinternet-18-00446),[2](https:\u002F\u002Fwww.mdpi.com\u002F1999-5903\u002F18\u002F9\u002F446#B2-futureinternet-18-00446)]. In urban areas, where space is limited and supply chains are extended, traditional farming has difficulty meeting demand. The solution that has emerged as more and more viable is controlled-environment agriculture (CEA): growers can control temperature, humidity, light, and nutrients, and thus grow food reliably and efficiently, irrespective of the outside climate [[3](https:\u002F\u002Fwww.mdpi.com\u002F1999-5903\u002F18\u002F9\u002F446#B3-futureinternet-18-00446),[4](https:\u002F\u002Fwww.mdpi.com\u002F1999-5903\u002F18\u002F9\u002F446#B4-futureinternet-18-00446)]. Hydroponics sits at the center of CEA. Growing plants without soil reduces water consumption significantly and often yields better per square meter than conventional planting. But the problem is that most hydroponic systems continue using manual monitoring and static control rules, causing the systems to be slow to change when problems start [[5](https:\u002F\u002Fwww.mdpi.com\u002F1999-5903\u002F18\u002F9\u002F446#B5-futureinternet-18-00446),[6](https:\u002F\u002Fwww.mdpi.com\u002F1999-5903\u002F18\u002F9\u002F446#B6-futureinternet-18-00446)]. With advancements in recent developments in the fields of IoT, AI, and cloud computing, it is now possible to develop systems that monitor themselves, predict issues, and intervene without human supervision [[7](https:\u002F\u002Fwww.mdpi.com\u002F1999-5903\u002F18\u002F9\u002F446#B7-futureinternet-18-00446),[8](https:\u002F\u002Fwww.mdpi.com\u002F1999-5903\u002F18\u002F9\u002F446#B8-futureinternet-18-00446),[9](https:\u002F\u002Fwww.mdpi.com\u002F1999-5903\u002F18\u002F9\u002F446#B9-futureinternet-18-00446)].\n\nSensors for pH, electrical conductivity (EC), total dissolved solids (TDS), ultrasonic sensors, temperature, and humidity are already commercially available. Their data can be used to feed into machine learning models, enabling early detection of nutrient imbalances, optimization of fertigation timings, and identification of environmental anomalies [[10](https:\u002F\u002Fwww.mdpi.com\u002F1999-5903\u002F18\u002F9\u002F446#B10-futureinternet-18-00446),[11](https:\u002F\u002Fwww.mdpi.com\u002F1999-5903\u002F18\u002F9\u002F446#B11-futureinternet-18-00446)]. On the vision side, deep learning has enabled rapid and accurate automatic disease diagnosis of leaf images [[12](https:\u002F\u002Fwww.mdpi.com\u002F1999-5903\u002F18\u002F9\u002F446#B12-futureinternet-18-00446),[13](https:\u002F\u002Fwww.mdpi.com\u002F1999-5903\u002F18\u002F9\u002F446#B13-futureinternet-18-00446),[14](https:\u002F\u002Fwww.mdpi.com\u002F1999-5903\u002F18\u002F9\u002F446#B14-futureinternet-18-00446)]. The Digital Twin adds to this by keeping a current virtual model of the physical system. The real environment is simulated, and operators can run trials on a virtual plant prior to implementation on their own crop [[15](https:\u002F\u002Fwww.mdpi.com\u002F1999-5903\u002F18\u002F9\u002F446#B15-futureinternet-18-00446),[16](https:\u002F\u002Fwww.mdpi.com\u002F1999-5903\u002F18\u002F9\u002F446#B16-futureinternet-18-00446),[17](https:\u002F\u002Fwww.mdpi.com\u002F1999-5903\u002F18\u002F9\u002F446#B17-futureinternet-18-00446)]. All these developments at the individual level, however, do not make a system that integrates sensing, prediction, visualization, and actuation under a single roof commonplace [[4](https:\u002F\u002Fwww.mdpi.com\u002F1999-5903\u002F18\u002F9\u002F446#B4-futureinternet-18-00446),[18](https:\u002F\u002Fwww.mdpi.com\u002F1999-5903\u002F18\u002F9\u002F446#B18-futureinternet-18-00446)]. This paper introduces Hydro Grow, a hydroponic farming system, which combines two different types of AI models, IoT sensors, and an Azure-supported Digital Twin in a functional cyber-physical system. The details of the specific contribution are given below.\n\n*   A dual-stream AI pipeline with computer vision and inference extracted from the sensors.\n\n*   MobileNetV2 for plant disease classification and a decision tree for nutrient deficiency prediction.\n\n*   Cloud-synchronized digital twin with Microsoft Azure Digital Twins for real-time monitoring.\n\n*   Closed-loop actuator control (pumps, dozers, ventilation) with all control based on sensor data.\n\n*   An inbuilt AI chatbot for handling client queries was integrated with a web-based monitoring dashboard.\n\nMost early hydroponic systems were manually operated. Nutrient and pH levels had to be manually analyzed and adjusted, and early automated systems used mechanical timers and simple pumps. These instruments made the work easier but did not add any intelligence to the process, as they operated on a timetable regardless of the conditions in the reservoir. Microcontroller-based systems allow for simple feedback, irrigation, and nutrient timing. They are less reliable than timers but still fundamentally reactive; they might be able to obtain a signal when a threshold is crossed, but they would not be able to predict when a crossing is likely to occur or why it is occurring. This resulted in their being sufficient only for small, stable systems and unsuitable for more complex systems [[19](https:\u002F\u002Fwww.mdpi.com\u002F1999-5903\u002F18\u002F9\u002F446#B19-futureinternet-18-00446)]. IoT has transformed the landscape by making it possible and affordable to continuously monitor multiple parameters [[1](https:\u002F\u002Fwww.mdpi.com\u002F1999-5903\u002F18\u002F9\u002F446#B1-futureinternet-18-00446),[20](https:\u002F\u002Fwww.mdpi.com\u002F1999-5903\u002F18\u002F9\u002F446#B20-futureinternet-18-00446)]. Today, grain growers have far greater real-time access to information about what is happening with their distributed sensor networks. Thus, there is another big space—intelligent interpretation of that data, and that is where the technology lies. Most deployed systems are still rule-based (hard-coded) and have not yet learned models, which means that they cannot adapt to unknown environments or optimize their behavior over time [[21](https:\u002F\u002Fwww.mdpi.com\u002F1999-5903\u002F18\u002F9\u002F446#B21-futureinternet-18-00446),[22](https:\u002F\u002Fwww.mdpi.com\u002F1999-5903\u002F18\u002F9\u002F446#B22-futureinternet-18-00446)].\n\nThe remainder of this paper is organized as follows: [Section 2](https:\u002F\u002Fwww.mdpi.com\u002F1999-5903\u002F18\u002F9\u002F446#sec2-futureinternet-18-00446) reviews the relevant prior work. [Section 3](https:\u002F\u002Fwww.mdpi.com\u002F1999-5903\u002F18\u002F9\u002F446#sec3-futureinternet-18-00446) covers system modeling and simulation. [Section 4](https:\u002F\u002Fwww.mdpi.com\u002F1999-5903\u002F18\u002F9\u002F446#sec4-futureinternet-18-00446) discusses the implementation of hardware and software. [Section 5](https:\u002F\u002Fwww.mdpi.com\u002F1999-5903\u002F18\u002F9\u002F446#sec5-futureinternet-18-00446) presents the evaluation of the model’s performance. [Section 6](https:\u002F\u002Fwww.mdpi.com\u002F1999-5903\u002F18\u002F9\u002F446#sec6-futureinternet-18-00446) provides a comparison of Hydro Grow with other systems. [Section 7](https:\u002F\u002Fwww.mdpi.com\u002F1999-5903\u002F18\u002F9\u002F446#sec7-futureinternet-18-00446) represents the results and discussion, the conclusion is provided in [Section 8](https:\u002F\u002Fwww.mdpi.com\u002F1999-5903\u002F18\u002F9\u002F446#sec8-futureinternet-18-00446).\n\n## 2. Related Work\n\nIoT-based monitoring has become standard in commercial and research hydroponic systems, and monitoring using the Internet of Things (IoT) is becoming commonplace. Platforms based on ESP32 microcontroller electronics have been shown to provide well-defined advantages for nutrient management and decrease manual nutrient monitoring [[9](https:\u002F\u002Fwww.mdpi.com\u002F1999-5903\u002F18\u002F9\u002F446#B9-futureinternet-18-00446),[23](https:\u002F\u002Fwww.mdpi.com\u002F1999-5903\u002F18\u002F9\u002F446#B23-futureinternet-18-00446),[24](https:\u002F\u002Fwww.mdpi.com\u002F1999-5903\u002F18\u002F9\u002F446#B24-futureinternet-18-00446),[25](https:\u002F\u002Fwww.mdpi.com\u002F1999-5903\u002F18\u002F9\u002F446#B25-futureinternet-18-00446)]. These systems have great functionality within their design specifications but come to a halt when the conditions change outside of the designer’s expectations [[6](https:\u002F\u002Fwww.mdpi.com\u002F1999-5903\u002F18\u002F9\u002F446#B6-futureinternet-18-00446),[21](https:\u002F\u002Fwww.mdpi.com\u002F1999-5903\u002F18\u002F9\u002F446#B21-futureinternet-18-00446)]. Machine learning has been used in hydroponics primarily for predictive operations such as predicting water demand, nutrient uptake, and alerting if the operations fall out of the range [[10](https:\u002F\u002Fwww.mdpi.com\u002F1999-5903\u002F18\u002F9\u002F446#B10-futureinternet-18-00446),[22](https:\u002F\u002Fwww.mdpi.com\u002F1999-5903\u002F18\u002F9\u002F446#B22-futureinternet-18-00446),[26](https:\u002F\u002Fwww.mdpi.com\u002F1999-5903\u002F18\u002F9\u002F446#B26-futureinternet-18-00446)]. Structured sensor data have been used to demonstrate a high level of accuracy using decision trees and other hybrid supervised methods [[10](https:\u002F\u002Fwww.mdpi.com\u002F1999-5903\u002F18\u002F9\u002F446#B10-futureinternet-18-00446),[11](https:\u002F\u002Fwww.mdpi.com\u002F1999-5903\u002F18\u002F9\u002F446#B11-futureinternet-18-00446)]. The problem, however, is that most of these models are trained separately and then applied. They are not updated as the crop and environment change and do not provide feedback to actuation systems [[27](https:\u002F\u002Fwww.mdpi.com\u002F1999-5903\u002F18\u002F9\u002F446#B27-futureinternet-18-00446)]. Plant health assessment using computer vision has come a long way. CNN architectures are well-suited to reliably classify diseases from leaf images, for example, MobileNet variants, ResNet, and other lightweight architectures, and transfer learning from ImageNet weights significantly reduces the amount of data required [[14](https:\u002F\u002Fwww.mdpi.com\u002F1999-5903\u002F18\u002F9\u002F446#B14-futureinternet-18-00446),[28](https:\u002F\u002Fwww.mdpi.com\u002F1999-5903\u002F18\u002F9\u002F446#B28-futureinternet-18-00446),[29](https:\u002F\u002Fwww.mdpi.com\u002F1999-5903\u002F18\u002F9\u002F446#B29-futureinternet-18-00446),[30](https:\u002F\u002Fwww.mdpi.com\u002F1999-5903\u002F18\u002F9\u002F446#B30-futureinternet-18-00446),[31](https:\u002F\u002Fwww.mdpi.com\u002F1999-5903\u002F18\u002F9\u002F446#B31-futureinternet-18-00446)]. However, there are complications when using the vision system in the real world; lighting, leaf overlap, and other background clutter can all reduce accuracy in field conditions, which is why the use of sensor-based confirmation makes a good design choice. [Table 1](https:\u002F\u002Fwww.mdpi.com\u002F1999-5903\u002F18\u002F9\u002F446#table_body_display_futureinternet-18-00446-t001) summarizes the gaps identified in each of these research threads.\n\n**Table 1.** Research gaps identified in the related literature.\n\nThe literature has considered digital twin applications in hydroponic and aquaponic systems, with some platforms moving towards complete robot integration, such as DigiHortiRobot [[16](https:\u002F\u002Fwww.mdpi.com\u002F1999-5903\u002F18\u002F9\u002F446#B16-futureinternet-18-00446),[17](https:\u002F\u002Fwww.mdpi.com\u002F1999-5903\u002F18\u002F9\u002F446#B17-futureinternet-18-00446),[32](https:\u002F\u002Fwww.mdpi.com\u002F1999-5903\u002F18\u002F9\u002F446#B32-futureinternet-18-00446),[33](https:\u002F\u002Fwww.mdpi.com\u002F1999-5903\u002F18\u002F9\u002F446#B33-futureinternet-18-00446)]. It is still a challenge to achieve real-time synchronization between physical and virtual environments, and the communication overhead can be substantial. Nevertheless, the idea is intriguing: having a well-maintained twin system that allows the operator to test interventions in a virtual environment before deploying them in the actual system.\n\n### 2.1. Smart and Automated Hydroponic Systems\n\nTransitioning from manual scheduling to sensor-based control has been a gradual process. Most early systems used presets without feedback, and later started to implement feedback based on sensor data using ESP32 microcontrollers [[19](https:\u002F\u002Fwww.mdpi.com\u002F1999-5903\u002F18\u002F9\u002F446#B19-futureinternet-18-00446),[24](https:\u002F\u002Fwww.mdpi.com\u002F1999-5903\u002F18\u002F9\u002F446#B24-futureinternet-18-00446)]. Despite this, many implementations rely on hard-coded thresholds and fall short in regions where the environmental conditions fall outside the threshold ranges for which they are calibrated [[21](https:\u002F\u002Fwww.mdpi.com\u002F1999-5903\u002F18\u002F9\u002F446#B21-futureinternet-18-00446),[25](https:\u002F\u002Fwww.mdpi.com\u002F1999-5903\u002F18\u002F9\u002F446#B25-futureinternet-18-00446)]. Awal et al. [[23](https:\u002F\u002Fwww.mdpi.com\u002F1999-5903\u002F18\u002F9\u002F446#B23-futureinternet-18-00446)] created a house-type hydroponic system for Bangladesh that measures various parameters in real-time without making any prediction, nor do they integrate it with a cloud.\n\n### 2.2. AI and Machine Learning for Hydroponics\n\nNutrient concentration prediction and finding optimum growth conditions have been predicted with reasonable success using random forest, linear regression, and hybrid AI methods [[11](https:\u002F\u002Fwww.mdpi.com\u002F1999-5903\u002F18\u002F9\u002F446#B11-futureinternet-18-00446),[26](https:\u002F\u002Fwww.mdpi.com\u002F1999-5903\u002F18\u002F9\u002F446#B26-futureinternet-18-00446)]. Rahman et al. [[26](https:\u002F\u002Fwww.mdpi.com\u002F1999-5903\u002F18\u002F9\u002F446#B26-futureinternet-18-00446)] developed an IoT system that integrates crop recommendation and nutrient monitoring, demonstrating the potential of IoT systems when using real sensor data as input for ML. The models adopted in the vision part are MobileNet-family models and VGG variants, which have been proven to be well suited for edge deployment due to their small size and efficiency [[14](https:\u002F\u002Fwww.mdpi.com\u002F1999-5903\u002F18\u002F9\u002F446#B14-futureinternet-18-00446),[28](https:\u002F\u002Fwww.mdpi.com\u002F1999-5903\u002F18\u002F9\u002F446#B28-futureinternet-18-00446),[30](https:\u002F\u002Fwww.mdpi.com\u002F1999-5903\u002F18\u002F9\u002F446#B30-futureinternet-18-00446)]. Labeled hydroponic datasets are small, and transfer learning from ImageNet can be used in these cases [[29](https:\u002F\u002Fwww.mdpi.com\u002F1999-5903\u002F18\u002F9\u002F446#B29-futureinternet-18-00446),[31](https:\u002F\u002Fwww.mdpi.com\u002F1999-5903\u002F18\u002F9\u002F446#B31-futureinternet-18-00446)]. The common challenge in all this work is how to link these models to the actuation layer. Most systems produce a prediction and then call it a day and leave it up to the human to decide on the next action to take [[22](https:\u002F\u002Fwww.mdpi.com\u002F1999-5903\u002F18\u002F9\u002F446#B22-futureinternet-18-00446),[27](https:\u002F\u002Fwww.mdpi.com\u002F1999-5903\u002F18\u002F9\u002F446#B27-futureinternet-18-00446)]. Recent agricultural vision research is moving beyond image-level classification toward fine-grained segmentation and multimodal reasoning; Yang et al. proposed PestScope, an exclusion-aware large multimodal model for fine-grained agricultural pest segmentation, showing that agricultural diagnosis benefits from models that can localize visually similar stress or pest patterns [[34](https:\u002F\u002Fwww.mdpi.com\u002F1999-5903\u002F18\u002F9\u002F446#B34-futureinternet-18-00446)]. The present work instead adopts MobileNetV2 for lightweight image-level disease screening on the Raspberry Pi 4B, prioritizing edge deployability over pixel-level segmentation. Feature-fusion strategies have also shown value in other sensor-driven automation tasks: Wang et al. improved seam extraction for teachless welding robots using a multichannel feature-fusion network, demonstrating that combining complementary visual features can improve perception reliability under challenging imaging conditions [[35](https:\u002F\u002Fwww.mdpi.com\u002F1999-5903\u002F18\u002F9\u002F446#B35-futureinternet-18-00446)]. This supports the design choice of combining visual plant-health screening with sensor-based nutrient inference rather than relying on a single sensing stream.\n\nTaken together, the shortfall in the literature is not a lack of sensors or models. Recent hydroponic systems increasingly combine IoT monitoring with cloud dashboards; however, most still rely on threshold-based actuation or evaluate machine learning models separately from the control layer. What is missing is an experimental validation of a unified architecture in which sensing, edge inference, cloud synchronization, visualization, and actuator response are tested together as one closed-loop system, rather than as components verified in isolation. Hybrid CNN-Transformer architectures have also been applied to high-resolution agricultural remote-sensing tasks; for example, Xie et al. used a boundary-guided CNN-Transformer network for cropland parcel mapping, reflecting the wider movement toward hybrid visual architectures in agricultural image interpretation [[36](https:\u002F\u002Fwww.mdpi.com\u002F1999-5903\u002F18\u002F9\u002F446#B36-futureinternet-18-00446)]. However, such models are computationally heavier than MobileNetV2 and less suitable for the Raspberry Pi-based edge deployment targeted in this work.\n\n## 3. Materials and Methods\n\n### 3.1. Conceptual Framework\n\nIn this system, the physical growing system employed is deep water culture (DWC), in which the root system hangs in the solution, and the solution is supplied with oxygen and nutrients [[1](https:\u002F\u002Fwww.mdpi.com\u002F1999-5903\u002F18\u002F9\u002F446#B1-futureinternet-18-00446),[5](https:\u002F\u002Fwww.mdpi.com\u002F1999-5903\u002F18\u002F9\u002F446#B5-futureinternet-18-00446)]. Water moves and is oxygenated by an air pump, and stagnant water results in anaerobic conditions that adversely affect the roots and yield. DWC is a suitable application for an automated system because of the relatively low changes in the system chemistry, allowing the sensor to settle before the actuator reacts to the change in the water quality. It consists of five layers: real-time IoT monitoring, edge AI inference, cloud-based analytics, digital twin synchronization, and a human-supervised web interface [[7](https:\u002F\u002Fwww.mdpi.com\u002F1999-5903\u002F18\u002F9\u002F446#B7-futureinternet-18-00446),[15](https:\u002F\u002Fwww.mdpi.com\u002F1999-5903\u002F18\u002F9\u002F446#B15-futureinternet-18-00446),[17](https:\u002F\u002Fwww.mdpi.com\u002F1999-5903\u002F18\u002F9\u002F446#B17-futureinternet-18-00446)]. These layers are loosely coupled, such that a drop to the cloud does not eliminate the use of local sensing or edge inference. Five sensors were used to measure the important environmental variables. The pH sensor determines the acidity of the nutrient solution, which directly influences mineral absorption [[10](https:\u002F\u002Fwww.mdpi.com\u002F1999-5903\u002F18\u002F9\u002F446#B10-futureinternet-18-00446),[25](https:\u002F\u002Fwww.mdpi.com\u002F1999-5903\u002F18\u002F9\u002F446#B25-futureinternet-18-00446)]. The total dissolved mineral concentration was measured using an EC\u002FTDS sensor. The DHT22 is a sensor that measures the temperature and air humidity around the plant canopy of a tree. The water level of the reservoir was monitored using an ultrasonic sensor. Images of the leaves were captured using a Raspberry Pi Camera Module V2 for visual disease screening [[12](https:\u002F\u002Fwww.mdpi.com\u002F1999-5903\u002F18\u002F9\u002F446#B12-futureinternet-18-00446),[14](https:\u002F\u002Fwww.mdpi.com\u002F1999-5903\u002F18\u002F9\u002F446#B14-futureinternet-18-00446)].\n\n### 3.2. Four-Layer System Architecture\n\nThe main architecture is a four-layer pyramid to separate sensing, edge control, cloud services, and user-facing applications, as presented in [Figure 1](https:\u002F\u002Fwww.mdpi.com\u002F1999-5903\u002F18\u002F9\u002F446#fig_body_display_futureinternet-18-00446-f001). These concerns should be kept separate to allow the system to be debugged, updated, and scaled without redesign [[7](https:\u002F\u002Fwww.mdpi.com\u002F1999-5903\u002F18\u002F9\u002F446#B7-futureinternet-18-00446),[8](https:\u002F\u002Fwww.mdpi.com\u002F1999-5903\u002F18\u002F9\u002F446#B8-futureinternet-18-00446)]. The sensing layer is a data-collection layer. Analog sensors are connected to the ADC circuits on the Raspberry Pi 4B, and digital sensors are connected through I2C or GPIO. No analysis is performed here.\n\n**Figure 1.** Four-layered architecture of the proposed AI-enhanced intelligent hydroponic farming.\n\nThe control layer is installed on the Raspberry Pi 4B. It receives sensor data, checks against target ranges, and activates the relay-controlled actuators, which include the water pump, nutrient dispensers, and LED grow lights when deviations appear. The system includes the sensing layer (pH, EC, and ultrasonic imaging), control layer (logical controller, microcontroller, supervisory control, and AI controller), processing layer (cloud server, AI services, AI services control, and artificial intelligence unit), and user layer (mobile application). 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