[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-5313":3,"related-5313":136},{"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,"primary_topic":32,"secondary_topics":38,"classification":50,"search_phrases":110,"slug":113,"view_count":114,"doi":9,"paper":115,"created_at":135},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。",null,"World Journal of Multidisciplinary Studies","2026-10-16T00:00:00Z","论文",10,false,67,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},12,21,17,9,8,1,"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。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","农业人工智能","农业物联网","温室环境","小气候预测",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"B7","protected-horticulture","设施园艺与植物工厂","\u002Ftopic\u002Fhorticulture\u002Fprotected-horticulture","B",[39,44],{"code":40,"slug":41,"name":42,"path":43,"parent_code":37},"B1","vegetables","蔬菜","\u002Ftopic\u002Fhorticulture\u002Fvegetables",{"code":45,"slug":46,"name":47,"path":48,"parent_code":49},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot","I",{"status":51,"locked":14,"version":52,"method":53,"evidence":54},"classified","2026-10-22.2","rules",{"candidates":55,"agricultural_evidence":58},[56,64,71,80,88,95,103],{"code":45,"score":57,"supported":58,"title":59,"abstract":61,"tags":63},15,true,[60],"IoT",[62,60],"物联网",[29],{"code":33,"score":65,"supported":58,"title":66,"abstract":68,"tags":70},14,[67],"greenhouse",[69,67],"温室",[],{"code":40,"score":20,"supported":58,"title":72,"abstract":73,"tags":79},[],[74,75,76,77,78],"番茄","辣椒","黄瓜","tomato","cucumber",[],{"code":81,"score":82,"supported":58,"title":83,"abstract":84,"tags":87},"I1",7,[],[85,86],"精准农业","precision agriculture",[27],{"code":89,"score":82,"supported":58,"title":90,"abstract":91,"tags":94},"I2",[],[92,93],"深度学习","deep learning",[28],{"code":96,"score":97,"supported":58,"title":98,"abstract":99,"tags":102},"H2",6,[],[100,101],"土壤","soil",[],{"code":104,"score":97,"supported":58,"title":105,"abstract":106,"tags":109},"H3",[],[107,108],"灌溉","irrigation",[],[111,112],"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",0,{"doi":9,"openalex_id":116,"authors":117,"venue":10,"cited_by_count":114,"oa_url":9,"card":128,"direction":132,"ingested_from":134},"W7222909837",[118,120,122,124,126],{"name":119,"orcid":9},"Siman Emmanuel*",{"name":121,"orcid":9},"Edward Onyebueke Agu",{"name":123,"orcid":9},"Musa Gani",{"name":125,"orcid":9},"Oladunjoye John Abiodun",{"name":127,"orcid":9},"Anagu Emmanuel John",{"tldr":129,"method":130,"finding":131,"direction":132,"opportunity":133},"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。","智慧农业 \u002F 农业物联网","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。","openalex","2026-10-19T23:30:13.295236Z",{"total":97,"page":22,"page_size":97,"items":137},[138,219,267,310,351,415],{"id":139,"title":140,"url":141,"summary":142,"summary_zh":143,"content":9,"source_name":144,"source_url":141,"published_at":145,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":146,"score_detail":147,"sources":151,"tags":153,"primary_topic":156,"secondary_topics":157,"classification":163,"search_phrases":192,"slug":195,"view_count":196,"doi":197,"paper":198,"created_at":218},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":17,"substance":148,"depth":149,"authority":13,"freshness":21,"relevant":22,"comment":150},20,16,"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。",[152],{"name":144,"url":141},[27,28,154,29,155],"计算机视觉","温室种植",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[158,162],{"code":89,"slug":159,"name":160,"path":161,"parent_code":49},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"code":45,"slug":46,"name":47,"path":48,"parent_code":49},{"status":51,"locked":14,"version":52,"method":53,"evidence":164},{"candidates":165,"agricultural_evidence":58},[166,172,176,180,184,188],{"code":89,"score":167,"supported":58,"title":168,"abstract":170,"tags":171},19,[169],"computer vision",[92,154,93,169],[28,154],{"code":33,"score":57,"supported":58,"title":173,"abstract":174,"tags":175},[67],[69,67],[155],{"code":45,"score":57,"supported":58,"title":177,"abstract":178,"tags":179},[60],[62,60],[29],{"code":40,"score":97,"supported":58,"title":181,"abstract":182,"tags":183},[],[74,77],[],{"code":96,"score":97,"supported":58,"title":185,"abstract":186,"tags":187},[],[100,101],[],{"code":81,"score":22,"supported":14,"title":189,"abstract":190,"tags":191},[],[],[27],[193,194],"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":197,"openalex_id":199,"authors":200,"venue":144,"cited_by_count":114,"oa_url":212,"card":213,"direction":132,"ingested_from":134},"W7221046739",[201,203,205,207,209],{"name":202,"orcid":9},"HOANG THI TU UYEN",{"name":204,"orcid":9},"NGUYEN HOANH",{"name":206,"orcid":9},"NGO THANH QUYEN",{"name":208,"orcid":9},"Lam Binh Minh",{"name":210,"orcid":211},"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":214,"method":215,"finding":216,"direction":132,"opportunity":217},"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":220,"title":221,"url":222,"summary":223,"summary_zh":9,"content":224,"source_name":225,"source_url":9,"published_at":226,"category":227,"cover_url":9,"hotness":13,"is_selected":14,"score":228,"score_detail":229,"sources":232,"tags":234,"primary_topic":238,"secondary_topics":239,"classification":241,"search_phrases":262,"slug":265,"view_count":254,"doi":9,"paper":9,"created_at":266},4459,"中国农科院环发所构建知识与数据双驱动模型精准预测温室番茄生长态势","https:\u002F\u002Fnews.sciencenet.cn\u002Fhtmlnews\u002F2026\u002F10\u002F572492.shtm","中国农业科学院农业环境与可持续发展研究所设施植物环境工程创新团队以番茄为对象，构建知识与数据双驱动的尺度自适应时序卷积混合模型，根据番茄不同生育期动态调整时序特征提取尺度，结合机理模型合成数据预训练与生理阈值约束，实现番茄不同器官干物质及叶面积指数的精准预测；研究成果发表在Computers and Electronics in Agriculture；仅依靠常规温室环境数据即可无损估算生长态势，对推动温室智能环控、产量预测具重要应用价值。","[![Image 1: 科学网新闻频道](https:\u002F\u002Fnews.sciencenet.cn\u002Fimages\u002Fnews.jpg)](https:\u002F\u002Fnews.sciencenet.cn\u002F)\n\n[生命科学](https:\u002F\u002Fwww.sciencenet.cn\u002Flife\u002F) | [医学科学](https:\u002F\u002Fwww.sciencenet.cn\u002Fmedicine\u002F) | [化学科学](https:\u002F\u002Fwww.sciencenet.cn\u002Fchemistry\u002F) | [工程材料](https:\u002F\u002Fwww.sciencenet.cn\u002Fmaterial\u002F) | [信息科学](https:\u002F\u002Fwww.sciencenet.cn\u002Finformation\u002F) | [地球科学](https:\u002F\u002Fwww.sciencenet.cn\u002Fearth\u002F) | [数理科学](https:\u002F\u002Fwww.sciencenet.cn\u002Fmathematics\u002F) | [管理综合](https:\u002F\u002Fwww.sciencenet.cn\u002Fpolicy\u002F)[站内规定](https:\u002F\u002Fblog.sciencenet.cn\u002Fblog-45-1064777.html) | [手机版](https:\u002F\u002Fwap.sciencenet.cn\u002F)\n\n[首页](https:\u002F\u002Fwww.sciencenet.cn\u002F) | [新闻](https:\u002F\u002Fnews.sciencenet.cn\u002F) | [博客](https:\u002F\u002Fblog.sciencenet.cn\u002Fblog.php) | [院士](https:\u002F\u002Fnews.sciencenet.cn\u002Fys\u002F) | [人才](https:\u002F\u002Ftalent.sciencenet.cn\u002F) | [会议](https:\u002F\u002Fmeeting.sciencenet.cn\u002F) | [基金·项目](https:\u002F\u002Ffund.sciencenet.cn\u002F) | [论文](https:\u002F\u002Fpaper.sciencenet.cn\u002F) | [绘图](https:\u002F\u002Fvisual-web.sciencenet.cn\u002F) | [视频·直播](https:\u002F\u002Fblog.sciencenet.cn\u002Fblog.php?mod=video) | [小柯机器人](https:\u002F\u002Fpaper.sciencenet.cn\u002FAInews) | [医学科普](https:\u002F\u002Fyxkp.sciencenet.cn\u002F)\n\n作者：李晨,王佳 来源：中国科学报 发布时间：2026\u002F10\u002F4 22:09:23 选择字号：小 中 大 双驱动模型可精准预测温室番茄生长态势 近日，中国农业科学院农业环境与可持续发展研究所设施植物环境工程创新团队构建了知识与数据双驱动模型，实现温室番茄生长的高精度无损预测，相关研究成果发表在《农业计算机与电子学》（_**Computers and Electronics in Agriculture**_）上。 作物生长精准估算是温室智能控制的核心前提。然而，传统机理模型参数获取难、泛化有限，而纯数据驱动模型常因缺乏生理约束致预测失真。因此，如何融合环境时序数据与作物生长规律，实现高精度且生理合理的生长预测仍是关键挑战。 科研团队以番茄为对象，提出一种知识与数据双驱动的尺度自适应时序卷积混合模型。该模型根据番茄不同生育期动态调整时序特征提取尺度，结合机理模型合成数据预训练与生理阈值约束，实现番茄不同器官干物质及叶面积指数的精准预测。该模型依靠常规温室环境数据即可无损估算生长态势，对推动温室智能环控、产量预测具有重要应用价值。 相关论文信息：https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112281 版权声明：凡本网注明“来源：中国科学报、科学网、科学新闻杂志”的所有作品，网站转载，请在正文上方注明来源和作者，且不得对内容作实质性改动；微信公众号、头条号等新媒体平台，转载请联系授权。邮箱：shouquan@stimes.cn。\n\n打印 发E-mail给：\n\n以下评论只代表网友个人观点，不代表科学网观点。\n\n 无标题页 \n\n[![Image 2](https:\u002F\u002Fnews.sciencenet.cn\u002Fimages\u002Fnewcomm.gif)](https:\u002F\u002Fnews.sciencenet.cn\u002Fhtml\u002Fcomment.aspx?id=572492)\n\n相关新闻 相关论文\n*   1\n*   [《科学》（20261001出版）一周论文导读](https:\u002F\u002Fnews.sciencenet.cn\u002Fhtmlnews\u002F2026\u002F10\u002F572487.shtm)\n\n*   2\n*   [2026年度湖南省科学技术奖提名工作启动](https:\u002F\u002Fnews.sciencenet.cn\u002Fhtmlnews\u002F2026\u002F10\u002F572486.shtm)\n\n*   3\n*   [河南中央引导地方科技发展资金项目结项验收结果公示](https:\u002F\u002Fnews.sciencenet.cn\u002Fhtmlnews\u002F2026\u002F10\u002F572485.shtm)\n\n*   4\n*   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[更多>>](https:\u002F\u002Fblog.sciencenet.cn\u002Fblog.php?mod=recommend)\n\n[关于我们](http:\u002F\u002Fwww.sciencenet.cn\u002Faboutus.aspx) | [网站声明](http:\u002F\u002Fwww.sciencenet.cn\u002Fshengming.aspx) | [服务条款](http:\u002F\u002Fwww.sciencenet.cn\u002Ftiaokuang.aspx) | [联系方式](http:\u002F\u002Fwww.sciencenet.cn\u002Fcontact.aspx) | [举报](https:\u002F\u002Fwww.sciencenet.cn\u002Fjubao.aspx) | [中国科学报社](https:\u002F\u002Fstimes.sciencenet.cn\u002F)  \n[京ICP备07017567号-12](http:\u002F\u002Fwww.beian.miit.gov.cn\u002F)互联网新闻信息服务许可证10120230008 京公网安备 11010802032783  \nCopyright @ 2007-2026 中国科学报社 All Rights Reserved  \n地址：北京市海淀区中关村南一条乙三号 电话：010-62580783","科学网 中国农业科学院 2026-10-04","2026-10-04T00:00:00Z","报道",80,{"impact":230,"substance":18,"depth":19,"authority":65,"freshness":97,"relevant":22,"comment":231},22,"国家级科研机构在智慧农业领域的模型方法突破，方法新颖、结论可靠，对温室智能环控与产量预测有应用价值，值得进入每日精选。",[233],{"name":225,"url":222},[27,28,235,236,237],"作物生长模型","设施番茄","温室环控",{"code":40,"slug":41,"name":42,"path":43,"parent_code":37},[240],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":51,"locked":14,"version":52,"method":53,"evidence":242},{"candidates":243,"agricultural_evidence":58},[244,248,253,258],{"code":40,"score":17,"supported":58,"title":245,"abstract":246,"tags":247},[74],[74],[236],{"code":33,"score":249,"supported":58,"title":250,"abstract":251,"tags":252},11,[69],[69],[],{"code":89,"score":254,"supported":14,"title":255,"abstract":256,"tags":257},2,[],[],[235,28],{"code":81,"score":22,"supported":14,"title":259,"abstract":260,"tags":261},[],[],[27],[263,264],"中国农科院环发所 温室番茄 生长预测","知识与数据双驱动 番茄 干物质","中国农科院环发所温室番茄生长预测-4459","2026-10-09T00:04:56.060677Z",{"id":268,"title":269,"url":270,"summary":271,"summary_zh":9,"content":272,"source_name":273,"source_url":9,"published_at":226,"category":227,"cover_url":9,"hotness":13,"is_selected":14,"score":274,"score_detail":275,"sources":277,"tags":279,"primary_topic":283,"secondary_topics":284,"classification":286,"search_phrases":305,"slug":308,"view_count":22,"doi":9,"paper":9,"created_at":309},4259,"中国农科院构建知识与数据双驱动模型精准预测温室番茄生长","https:\u002F\u002Fwap.sciencenet.cn\u002Fmobile.php?cat=T&id=572492&mobile=1&type=detail","中国农业科学院农业环境与可持续发展研究所设施植物环境工程创新团队提出知识与数据双驱动的尺度自适应时序卷积混合模型，根据番茄不同生育期动态调整时序特征提取尺度，结合机理模型合成数据预训练与生理阈值约束，实现番茄不同器官干物质及叶面积指数的精准预测。成果发表在《Computers and Electronics in Agriculture》。","գйũҵѧԺũҵɳչоʩֲﻷ̴Ŷӹ֪ʶ˫ģͣʵҷĸ߾Ԥ⣬оɹڡũҵѧComputers and Electronics in Agricultureϡ\n\n׼ܿƵĺǰᡣȻͳģͲȡѡޣģͳȱԼԤʧ档ˣںϻʱɣʵָ߾Ԥǹؼս","科学网 2026-10-04",82,{"impact":230,"substance":18,"depth":19,"authority":65,"freshness":21,"relevant":22,"comment":276},"国家级科研机构在智慧种植领域提出知识数据双驱动模型，方法新颖、结论可靠，对温室作物精准管理有参考价值。",[278],{"name":273,"url":270},[27,28,280,281,282],"温室番茄","知识数据双驱动","生长预测",{"code":40,"slug":41,"name":42,"path":43,"parent_code":37},[285],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":51,"locked":14,"version":52,"method":53,"evidence":287},{"candidates":288,"agricultural_evidence":58},[289,293,297,301],{"code":40,"score":249,"supported":58,"title":290,"abstract":291,"tags":292},[74],[74],[],{"code":33,"score":20,"supported":58,"title":294,"abstract":295,"tags":296},[69],[],[280],{"code":81,"score":22,"supported":14,"title":298,"abstract":299,"tags":300},[],[],[27],{"code":89,"score":22,"supported":14,"title":302,"abstract":303,"tags":304},[],[],[28],[306,307],"中国农科院 温室番茄 生长预测","知识数据双驱动 模型 番茄","中国农科院温室番茄生长预测-4259","2026-10-06T00:05:01.795653Z",{"id":311,"title":312,"url":313,"summary":314,"summary_zh":9,"content":9,"source_name":315,"source_url":9,"published_at":316,"category":227,"cover_url":9,"hotness":13,"is_selected":14,"score":274,"score_detail":317,"sources":320,"tags":322,"primary_topic":324,"secondary_topics":325,"classification":327,"search_phrases":346,"slug":348,"view_count":349,"doi":9,"paper":9,"created_at":350},3935,"知识与数据双驱动赋能温室番茄生长精准预测——中国农科院环发所研究成果发表","https:\u002F\u002Fwww.caas.cn\u002Fxwzx\u002Fkyhd\u002F613bca00d9e84cfeb977251a606af346.htm","近日，中国农业科学院农业环境与可持续发展研究所设施植物环境工程创新团队构建了知识与数据双驱动模型，实现温室番茄生长的高精度无损预测，研究成果发表在《农业计算机与电子学（Computers and Electronics in Agriculture）》上。科研团队提出一种尺度自适应时序卷积混合模型，根据番茄不同生育期动态调整时序特征提取尺度，结合机理模型合成数据预训练与生理阈值约束，实现番茄不同器官干物质及叶面积指数的精准预测。","中国农业科学院·2026年10月","2026-10-01T00:00:00Z",{"impact":318,"substance":230,"depth":318,"authority":57,"freshness":20,"relevant":22,"comment":319},18,"国家级科研机构在核心期刊发表的知识与数据双驱动温室番茄生长预测模型，方法新颖、结论可靠，对智慧农业与设施种植有实质参考价值。",[321],{"name":315,"url":313},[27,28,323,280,282],"设施农业",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[326],{"code":40,"slug":41,"name":42,"path":43,"parent_code":37},{"status":51,"locked":14,"version":52,"method":53,"evidence":328},{"candidates":329,"agricultural_evidence":58},[330,334,338,342],{"code":33,"score":17,"supported":58,"title":331,"abstract":332,"tags":333},[69],[69],[280],{"code":40,"score":249,"supported":58,"title":335,"abstract":336,"tags":337},[74],[74],[],{"code":81,"score":22,"supported":14,"title":339,"abstract":340,"tags":341},[],[],[27],{"code":89,"score":22,"supported":14,"title":343,"abstract":344,"tags":345},[],[],[28],[263,347],"知识与数据双驱动 番茄 模型","中国农科院环发所温室番茄生长预测-3935",3,"2026-10-02T11:53:11.319506Z",{"id":352,"title":353,"url":354,"summary":355,"summary_zh":356,"content":9,"source_name":357,"source_url":354,"published_at":358,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":359,"score_detail":360,"sources":363,"tags":365,"primary_topic":369,"secondary_topics":370,"classification":372,"search_phrases":391,"slug":394,"view_count":254,"doi":395,"paper":396,"created_at":414},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":149,"substance":18,"depth":318,"authority":361,"freshness":21,"relevant":22,"comment":362},13,"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。",[364],{"name":357,"url":354},[27,29,366,367,368],"数字孪生","虚拟现实","温室管理",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[371],{"code":45,"slug":46,"name":47,"path":48,"parent_code":49},{"status":51,"locked":14,"version":52,"method":53,"evidence":373},{"candidates":374,"agricultural_evidence":58},[375,382,387],{"code":81,"score":167,"supported":58,"title":376,"abstract":378,"tags":381},[377],"digital twin",[379,366,380,377],"数字农业","digital agriculture",[366,27],{"code":45,"score":318,"supported":58,"title":383,"abstract":384,"tags":386},[60],[62,385,60],"internet of things",[29],{"code":33,"score":65,"supported":58,"title":388,"abstract":389,"tags":390},[67],[69,67],[],[392,393],"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":395,"openalex_id":397,"authors":398,"venue":357,"cited_by_count":114,"oa_url":408,"card":409,"direction":132,"ingested_from":134},"W7214397681",[399,402,405],{"name":400,"orcid":401},"Hicham Slimani","https:\u002F\u002Forcid.org\u002F0000-0002-7946-4064",{"name":403,"orcid":404},"Abdelilah Jilbab","https:\u002F\u002Forcid.org\u002F0000-0002-1577-9040",{"name":406,"orcid":407},"Jamal El Mhamdi","https:\u002F\u002Forcid.org\u002F0000-0001-8219-3560","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS2468227626004904\u002Fpdf",{"tldr":410,"method":411,"finding":412,"direction":132,"opportunity":413},"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":416,"title":417,"url":418,"summary":419,"summary_zh":9,"content":9,"source_name":420,"source_url":9,"published_at":421,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":422,"score_detail":423,"sources":425,"tags":427,"primary_topic":429,"secondary_topics":430,"classification":432,"search_phrases":451,"slug":454,"view_count":22,"doi":9,"paper":455,"created_at":464},2215,"中国农业大学马韫韬教授团队:攻克温室黄瓜冠层点云分割难题 助力设施数字化精准管理","https:\u002F\u002Fh.mffb.com.cn\u002Fa\u002F569056.html","马韫韬教授团队构建Cuc-HiSeg分割框架,通过尺度变换、角度旋转、边缘扰动等策略扩充数据集,显著缓解三维标注样本稀缺难题,使深度学习模型分割F1值提升5.6%-6.4%;研发结合形态学优化与K-Means++二次分割的改进区域生长算法,有效克服叶片重叠与点云缺失干扰,叶片实例分割F1值最高达95.0%,在结果期复杂场景下仍保持93.9%的高精度。该方法在苗期、开花初期、结果期均表现出优异的稳定性与适应性,可高效完成日光温室黄瓜冠层点云的高精度解析。","农业信息网 2026年9月8日","2026-09-08T00:00:00Z",78,{"impact":318,"substance":230,"depth":318,"authority":65,"freshness":97,"relevant":22,"comment":424},"中国农大团队提出黄瓜冠层点云分割新框架，F1值提升显著且多生育期稳定，属设施农业数字化领域有实质增量的技术突破。",[426],{"name":420,"url":418},[27,28,323,76,428],"点云分割",{"code":40,"slug":41,"name":42,"path":43,"parent_code":37},[431],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":51,"locked":14,"version":52,"method":53,"evidence":433},{"candidates":434,"agricultural_evidence":58},[435,439,443,447],{"code":40,"score":17,"supported":58,"title":436,"abstract":437,"tags":438},[76],[76],[76],{"code":33,"score":249,"supported":58,"title":440,"abstract":441,"tags":442},[69],[69],[],{"code":89,"score":196,"supported":58,"title":444,"abstract":445,"tags":446},[],[92],[28],{"code":81,"score":22,"supported":14,"title":448,"abstract":449,"tags":450},[],[],[27],[452,453],"农业人工智能 智慧农业 点云分割 设施农业","农业人工智能 智慧农业","农业人工智能智慧农业点云分割设施农业-2215",{"doi":9,"openalex_id":9,"authors":456,"venue":9,"cited_by_count":114,"oa_url":9,"card":457,"direction":461,"ingested_from":463},[],{"tldr":458,"method":459,"finding":460,"direction":461,"opportunity":462},"构建Cuc-HiSeg框架，实现温室黄瓜冠层点云高精度叶片实例分割。","数据增强扩充点云样本，结合改进区域生长与K-Means++二次分割。","叶片分割F1最高95.0%，结果期复杂场景仍达93.9%，各生育期稳定。","农业遥感与作物表型","可迁移至其他设施作物冠层解析，并探索点云分割与生长量、产量性状的自动关联。","agent","2026-09-12T00:06:40.512918Z"]