[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3665":3,"related-3665":99},{"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":38,"search_phrases":78,"slug":81,"view_count":21,"doi":82,"paper":83,"created_at":98},3665,"ASAS-NANP SYMPOSIUM: MATHEMATICAL MODELING IN ANIMAL NUTRITION: The Evolution of Large Language Models and Their Impact on Animal Sciences","https:\u002F\u002Fdoi.org\u002F10.1093\u002Fjas\u002Fskag306","The rapid rise of large language models (LLM) is reshaping the scientific landscape, transitioning from early statistical language models to advanced transformer-based architectures capable of synthesizing knowledge across disciplines. While their predictive capacity and scalability have opened new avenues in data analysis, hypothesis generation, and decision support, concerns remain regarding bias, hallucination, reproducibility, and ethical governance. In animal sciences, LLM are gradually applied to challenges in nutrition modeling, animal health, genetic selection, and sustainability. Precision nutrition has benefited from LLM-driven synthesis of feed and metabolic data, enabling individualized feeding strategies and improved resource efficiency. In animal health, domain-specific systems have demonstrated applications in diagnostics and epidemiological monitoring. LLM is augmenting genomic analyses to accelerate marker discovery and breeding optimization, while sustainability efforts employ them to model greenhouse gas emissions, feed additives, and adaptation to climatic stressors. Notably, decision-support platforms demonstrate how domain-specialized LLM can bridge mechanistic knowledge with predictive analytics, enhancing knowledge transfer and empowering livestock producers. However, risks associated with overreliance, recursive reuse of LLM outputs in model development, and pseudo-expertise underscore the critical importance of human oversight. Unlike mechanistic models, which embed biological causality, LLM are entirely data-driven and may confidently propagate errors if trained on ill-conditioned datasets. Responsible use requires transparent reporting, validation, and bias auditing, with domain-specific fine-tuning. Open-source models can enhance reproducibility and trust, but they also raise financial and security concerns. LLM must be guided by transparency, accountability, and fairness to ensure they complement, rather than replace, human expertise. By advancing inquiry and livestock management, LLM hold the potential to support sustainable food production systems if deployed responsibly. Rather than full training, most applications will rely on fine-tuning and augmentation, which are more sustainable and adaptable strategies. This review intends to promote critical literacy and responsible adoption of LLM in research, education, and livestock decision-support systems for animal scientists, nutrition modelers, and extension professionals.","大语言模型（LLM）的迅速崛起正在重塑科学格局，从早期的统计语言模型过渡到能够跨学科综合知识的先进基于Transformer的架构。尽管其预测能力和可扩展性在数据分析、假设生成和决策支持方面开辟了新途径，但围绕偏见、幻觉、可重复性和伦理治理的担忧依然存在。在动物科学领域，LLM正逐步应用于营养建模、动物健康、遗传选择和可持续性等方面的挑战。精准营养已受益于LLM驱动的饲料与代谢数据综合，从而实现个体化饲喂策略并提高资源效率。在动物健康方面，领域专用系统已在诊断和流行病学监测中展现出应用价值。LLM正在增强基因组分析，以加速标记发现和育种优化，而可持续性工作则利用其建模温室气体排放、饲料添加剂及对气候胁迫的适应性。值得注意的是，决策支持平台展示了领域专用LLM如何将机制性知识与预测分析相衔接，促进知识转化并赋能畜牧生产者。然而，过度依赖、在模型开发中递归复用LLM输出以及伪专业知识等风险，凸显了人类监督的至关重要性。与嵌入生物学因果关系的机制性模型不同，LLM完全由数据驱动，若在病态数据集上训练，可能自信地传播错误。负责任的使用需要透明报告、验证和偏见审计，并进行领域专用微调。开源模型可以增强可重复性和信任，但也引发财务和安全方面的担忧。LLM必须以透明性、问责制和公平性为指引，以确保其补充而非取代人类专业知识。通过在研究和畜牧管理中推动探究，LLM在负责任部署的前提下具有支持可持续食品生产系统的潜力。大多数应用将依赖微调和增强，而非完整训练，这是更具可持续性和适应性的策略。本综述旨在促进动物科学家、营养建模人员和推广专业人员在研究、教育和畜牧决策支持系统中对LLM的批判性素养和负责任采用。",null,"Journal of Animal Science","2026-09-25T00:00:00Z","论文",10,false,78,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,20,14,8,1,"核心期刊综述，系统梳理大语言模型在动物营养、健康与育种中的应用与风险，专业深度高但属综述性论文，产业级影响有限。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","农业人工智能","大语言模型","精准营养","动物科学",{"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",[],{"status":39,"locked":14,"version":40,"method":41,"evidence":42},"classified","2026-10-22.2","rules",{"candidates":43,"agricultural_evidence":46},[44,52,59,66,73],{"code":32,"score":45,"supported":46,"title":47,"abstract":48,"tags":51},6,true,[],[49,50],"温室","greenhouse",[],{"code":53,"score":45,"supported":46,"title":54,"abstract":55,"tags":58},"C",[],[56,57],"畜牧","livestock",[],{"code":60,"score":61,"supported":46,"title":62,"abstract":63,"tags":65},"I2",5,[],[64],"decision support",[27,28],{"code":67,"score":68,"supported":46,"title":69,"abstract":70,"tags":72},"C4",3,[],[71],"饲料",[],{"code":74,"score":21,"supported":14,"title":75,"abstract":76,"tags":77},"I1",[],[],[26],[79,80],"大语言模型 动物科学","LLM 精准营养","大语言模型动物科学-3665","10.1093\u002Fjas\u002Fskag306",{"doi":82,"openalex_id":84,"authors":85,"venue":10,"cited_by_count":88,"oa_url":89,"card":90,"direction":96,"ingested_from":97},"W7214347972",[86],{"name":87,"orcid":9},"Luis O Tedeschi",0,"https:\u002F\u002Facademic.oup.com\u002Fjas\u002Fadvance-article-pdf\u002Fdoi\u002F10.1093\u002Fjas\u002Fskag306\u002F71398322\u002Fskag306.pdf",{"tldr":91,"method":92,"finding":93,"direction":94,"opportunity":95},"综述大语言模型在动物科学中的应用、风险与负责任使用路径。","文献综述，梳理LLM在营养、健康、遗传和可持续性中的应用。","LLM可辅助精准营养与决策支持，但需人类监督、验证和领域微调。","农业人工智能与决策模型","可探索面向畜牧生产的领域专用LLM微调与可验证决策支持系统，并建立偏差审计与可复现评估框架。","智慧农业 \u002F 农业物联网","openalex","2026-09-28T23:30:12.831387Z",{"total":45,"page":21,"page_size":45,"items":100},[101,208,288,336,419,462],{"id":102,"title":103,"url":104,"summary":105,"summary_zh":106,"content":9,"source_name":107,"source_url":104,"published_at":108,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":109,"score_detail":110,"sources":116,"tags":118,"primary_topic":122,"secondary_topics":123,"classification":135,"search_phrases":185,"slug":188,"view_count":88,"doi":9,"paper":189,"created_at":207},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":111,"substance":112,"depth":113,"authority":114,"freshness":20,"relevant":21,"comment":115},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。",[117],{"name":107,"url":104},[26,27,119,120,121],"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},[124,130],{"code":125,"slug":126,"name":127,"path":128,"parent_code":129},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot","I",{"code":131,"slug":132,"name":133,"path":134,"parent_code":36},"B1","vegetables","蔬菜","\u002Ftopic\u002Fhorticulture\u002Fvegetables",{"status":39,"locked":14,"version":40,"method":41,"evidence":136},{"candidates":137,"agricultural_evidence":46},[138,145,149,158,165,171,178],{"code":125,"score":139,"supported":46,"title":140,"abstract":142,"tags":144},15,[141],"IoT",[143,141],"物联网",[119],{"code":32,"score":19,"supported":46,"title":146,"abstract":147,"tags":148},[50],[49,50],[],{"code":131,"score":114,"supported":46,"title":150,"abstract":151,"tags":157},[],[152,153,154,155,156],"番茄","辣椒","黄瓜","tomato","cucumber",[],{"code":74,"score":159,"supported":46,"title":160,"abstract":161,"tags":164},7,[],[162,163],"精准农业","precision agriculture",[26],{"code":60,"score":159,"supported":46,"title":166,"abstract":167,"tags":170},[],[168,169],"深度学习","deep learning",[27],{"code":172,"score":45,"supported":46,"title":173,"abstract":174,"tags":177},"H2",[],[175,176],"土壤","soil",[],{"code":179,"score":45,"supported":46,"title":180,"abstract":181,"tags":184},"H3",[],[182,183],"灌溉","irrigation",[],[186,187],"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":190,"authors":191,"venue":107,"cited_by_count":88,"oa_url":9,"card":202,"direction":96,"ingested_from":97},"W7222909837",[192,194,196,198,200],{"name":193,"orcid":9},"Siman Emmanuel*",{"name":195,"orcid":9},"Edward Onyebueke Agu",{"name":197,"orcid":9},"Musa Gani",{"name":199,"orcid":9},"Oladunjoye John Abiodun",{"name":201,"orcid":9},"Anagu Emmanuel John",{"tldr":203,"method":204,"finding":205,"direction":96,"opportunity":206},"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":209,"title":210,"url":211,"summary":212,"summary_zh":213,"content":9,"source_name":214,"source_url":211,"published_at":215,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":216,"score_detail":217,"sources":220,"tags":222,"primary_topic":225,"secondary_topics":226,"classification":232,"search_phrases":261,"slug":264,"view_count":265,"doi":266,"paper":267,"created_at":287},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":111,"substance":18,"depth":218,"authority":13,"freshness":20,"relevant":21,"comment":219},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。",[221],{"name":214,"url":211},[26,27,223,119,224],"计算机视觉","温室种植",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[227,231],{"code":60,"slug":228,"name":229,"path":230,"parent_code":129},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"code":125,"slug":126,"name":127,"path":128,"parent_code":129},{"status":39,"locked":14,"version":40,"method":41,"evidence":233},{"candidates":234,"agricultural_evidence":46},[235,241,245,249,253,257],{"code":60,"score":236,"supported":46,"title":237,"abstract":239,"tags":240},19,[238],"computer vision",[168,223,169,238],[27,223],{"code":32,"score":139,"supported":46,"title":242,"abstract":243,"tags":244},[50],[49,50],[224],{"code":125,"score":139,"supported":46,"title":246,"abstract":247,"tags":248},[141],[143,141],[119],{"code":131,"score":45,"supported":46,"title":250,"abstract":251,"tags":252},[],[152,155],[],{"code":172,"score":45,"supported":46,"title":254,"abstract":255,"tags":256},[],[175,176],[],{"code":74,"score":21,"supported":14,"title":258,"abstract":259,"tags":260},[],[],[26],[262,263],"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":266,"openalex_id":268,"authors":269,"venue":214,"cited_by_count":88,"oa_url":281,"card":282,"direction":96,"ingested_from":97},"W7221046739",[270,272,274,276,278],{"name":271,"orcid":9},"HOANG THI TU UYEN",{"name":273,"orcid":9},"NGUYEN HOANH",{"name":275,"orcid":9},"NGO THANH QUYEN",{"name":277,"orcid":9},"Lam Binh Minh",{"name":279,"orcid":280},"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":283,"method":284,"finding":285,"direction":96,"opportunity":286},"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":289,"title":290,"url":291,"summary":292,"summary_zh":9,"content":293,"source_name":294,"source_url":9,"published_at":295,"category":296,"cover_url":9,"hotness":13,"is_selected":14,"score":297,"score_detail":298,"sources":301,"tags":303,"primary_topic":307,"secondary_topics":308,"classification":310,"search_phrases":331,"slug":334,"view_count":323,"doi":9,"paper":9,"created_at":335},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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2026-10-04","2026-10-04T00:00:00Z","报道",80,{"impact":299,"substance":112,"depth":113,"authority":19,"freshness":45,"relevant":21,"comment":300},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。",[302],{"name":294,"url":291},[26,27,304,305,306],"作物生长模型","设施番茄","温室环控",{"code":131,"slug":132,"name":133,"path":134,"parent_code":36},[309],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":311},{"candidates":312,"agricultural_evidence":46},[313,317,322,327],{"code":131,"score":111,"supported":46,"title":314,"abstract":315,"tags":316},[152],[152],[305],{"code":32,"score":318,"supported":46,"title":319,"abstract":320,"tags":321},11,[49],[49],[],{"code":60,"score":323,"supported":14,"title":324,"abstract":325,"tags":326},2,[],[],[304,27],{"code":74,"score":21,"supported":14,"title":328,"abstract":329,"tags":330},[],[],[26],[332,333],"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":337,"title":338,"url":339,"summary":340,"summary_zh":341,"content":9,"source_name":342,"source_url":339,"published_at":343,"category":12,"cover_url":9,"hotness":13,"is_selected":46,"score":344,"score_detail":345,"sources":347,"tags":349,"primary_topic":351,"secondary_topics":352,"classification":357,"search_phrases":391,"slug":394,"view_count":21,"doi":395,"paper":396,"created_at":418},4371,"Intelligent greenhouse irrigation control via multilayer perceptron neural network climate prediction integrated with fuzzy logic control","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compeleceng.2026.111587","Conventional irrigation methods rely on indoor sensor feedback, which fails to anticipate rapid microclimate fluctuations and often leads to over-irrigation or water stress. Additionally, traditional controllers used in the watering process lack the ability to adjust water flow rate dynamically, resulting in inefficient water usage and energy waste. Intelligent Greenhouse Irrigation Management (IGIM) remains a critical challenge for achieving sustainable, high-yield greenhouse agriculture. Precise irrigation management is especially important for crops like tomatoes, where both timing and quantity directly influence productivity and energy consumption. To address these challenges, this paper proposes a hybrid intelligent system that integrates a Multi-Layer Perceptron Artificial Neural Network (MLP-ANN) with an Adaptive Fuzzy Logic Controller (AFLC). The MLP-ANN forecasts key indoor variables such as temperature, humidity, and soil moisture, achieving R² values of 0.999. The Root Mean Square Errors of 0.266 °C, 0.254%, and 0.808%, respectively. These predictions directly feed into the AFLC, which employs an adaptive rule base to dynamically adjust irrigation flow rates in real time (0–18 LPH), significantly enhancing water-use efficiency and reducing waste to save energy consumption in the watering process. The key novelty of this work is the direct integration of MLP-predicted microclimate variables into the antecedent of adaptive fuzzy rules, enabling the controller to anticipate and respond to climate variations before they affect crop conditions. Unlike conventional reactive or static rule-based systems, the proposed MLP-AFLC framework creates a closed-loop coupling between forecasting and actuation, allowing predictive, rather than threshold-based, irrigation decisions. By integrating predictive intelligence with adaptive control, the proposed MLP-AFLC framework provides a robust, autonomous, and sustainable solution for greenhouse management, advancing precision agriculture in highly dynamic environments.","传统灌溉方法依赖室内传感器反馈，无法预判微气候的快速波动，常导致过度灌溉或水分胁迫。此外，灌溉过程中使用的传统控制器缺乏动态调节流速的能力，造成水资源利用效率低下和能源浪费。智能温室灌溉管理（IGIM）仍是实现可持续、高产温室农业的关键挑战。对于番茄等作物而言，精准灌溉管理尤为重要，因为灌溉时机和水量直接影响产量和能耗。为解决这些挑战，本文提出了一种将多层感知器人工神经网络（MLP-ANN）与自适应模糊逻辑控制器（AFLC）相集成的混合智能系统。MLP-ANN预测温度、湿度和土壤湿度等关键室内变量，R²值达到0.999，均方根误差分别为0.266 °C、0.254%和0.808%。这些预测结果直接输入AFLC，后者采用自适应规则库实时动态调节灌溉流速（0–18 LPH），显著提高水资源利用效率并减少浪费，从而在灌溉过程中节约能耗。本工作的关键创新在于将MLP预测的微气候变量直接集成到自适应模糊规则的前件中，使控制器能够在气候变化影响作物状况之前进行预判和响应。与传统反应式或静态规则系统不同，所提出的MLP-AFLC框架在预测与执行之间建立了闭环耦合，实现了基于预测而非基于阈值的灌溉决策。通过将预测智能与自适应控制相结合，所提出的MLP-AFLC框架为温室管理提供了一种稳健、自主且可持续的解决方案，推动了高度动态环境下的精准农业发展。","Computers & Electrical Engineering","2026-10-06T00:00:00Z",81,{"impact":17,"substance":299,"depth":17,"authority":19,"freshness":114,"relevant":21,"comment":346},"提出MLP-ANN与自适应模糊控制融合的温室预测性灌溉框架，R²达0.999，方法新颖且数据可靠，对精准农业节水节能有实质参考价值。",[348],{"name":342,"url":339},[26,27,350,162,224],"智能灌溉",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[353],{"code":179,"slug":183,"name":354,"path":355,"parent_code":356},"水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",{"status":39,"locked":14,"version":40,"method":41,"evidence":358},{"candidates":359,"agricultural_evidence":46},[360,365,369,373,377,381,387],{"code":179,"score":17,"supported":46,"title":361,"abstract":362,"tags":364},[183],[182,363,183],"水资源",[350],{"code":32,"score":139,"supported":46,"title":366,"abstract":367,"tags":368},[50],[49,50],[224],{"code":74,"score":20,"supported":46,"title":370,"abstract":371,"tags":372},[],[162,163],[26,162],{"code":172,"score":45,"supported":46,"title":374,"abstract":375,"tags":376},[],[175,176],[],{"code":131,"score":68,"supported":46,"title":378,"abstract":379,"tags":380},[],[152],[],{"code":382,"score":68,"supported":46,"title":383,"abstract":384,"tags":386},"H7",[],[385],"气候变化",[],{"code":60,"score":21,"supported":14,"title":388,"abstract":389,"tags":390},[],[],[27],[392,393],"MLP-AFLC 温室灌溉","神经网络 模糊控制 灌溉","MLP-AFLC温室灌溉-4371","10.1016\u002Fj.compeleceng.2026.111587",{"doi":395,"openalex_id":397,"authors":398,"venue":342,"cited_by_count":88,"oa_url":339,"card":413,"direction":94,"ingested_from":97},"W7220746994",[399,402,405,407,410],{"name":400,"orcid":401},"Jamel Riahi","https:\u002F\u002Forcid.org\u002F0009-0006-1434-4367",{"name":403,"orcid":404},"Hamza Nasri","https:\u002F\u002Forcid.org\u002F0009-0008-7343-4199",{"name":406,"orcid":9},"Abdelkader Mami",{"name":408,"orcid":409},"Mario Carpentieri","https:\u002F\u002Forcid.org\u002F0000-0001-5165-5873",{"name":411,"orcid":412},"Silvano Vergura","https:\u002F\u002Forcid.org\u002F0000-0002-0826-9237",{"tldr":414,"method":415,"finding":416,"direction":96,"opportunity":417},"提出MLP-ANN预测温室微气候并驱动自适应模糊控制器，实现预测性智能灌溉。","MLP神经网络预测温湿度与土壤水分，结合自适应模糊逻辑控制流量。","预测R²达0.999，模糊控制器动态调节0-18 LPH流量，显著节水节能。","可探索多作物多环境迁移学习与边缘部署，降低预测模型算力需求。","2026-10-07T23:30:43.848308Z",{"id":420,"title":421,"url":422,"summary":423,"summary_zh":9,"content":424,"source_name":425,"source_url":9,"published_at":295,"category":296,"cover_url":9,"hotness":13,"is_selected":14,"score":426,"score_detail":427,"sources":429,"tags":431,"primary_topic":435,"secondary_topics":436,"classification":438,"search_phrases":457,"slug":460,"view_count":21,"doi":9,"paper":9,"created_at":461},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":299,"substance":112,"depth":113,"authority":19,"freshness":20,"relevant":21,"comment":428},"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。",[430],{"name":425,"url":422},[26,27,432,433,434],"温室番茄","知识数据双驱动","生长预测",{"code":131,"slug":132,"name":133,"path":134,"parent_code":36},[437],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":439},{"candidates":440,"agricultural_evidence":46},[441,445,449,453],{"code":131,"score":318,"supported":46,"title":442,"abstract":443,"tags":444},[152],[152],[],{"code":32,"score":114,"supported":46,"title":446,"abstract":447,"tags":448},[49],[],[432],{"code":74,"score":21,"supported":14,"title":450,"abstract":451,"tags":452},[],[],[26],{"code":60,"score":21,"supported":14,"title":454,"abstract":455,"tags":456},[],[],[27],[458,459],"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":463,"title":464,"url":465,"summary":466,"summary_zh":467,"content":9,"source_name":468,"source_url":465,"published_at":469,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":470,"sources":472,"tags":474,"primary_topic":478,"secondary_topics":479,"classification":480,"search_phrases":495,"slug":498,"view_count":265,"doi":499,"paper":500,"created_at":524},3976,"Model-based optimization of dynamic photon allocation for maximizing biomass production under a fixed total light integral in vertical farming","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112489","Model-based optimization of dynamic photon allocation for maximizing biomass production under a fixed total light integral in vertical farming。Computers and Electronics in Agriculture","基于模型的动态光子分配优化，以在垂直农业固定总光积分下最大化生物量生产。","Computers and Electronics in Agriculture","2026-10-01T00:00:00Z",{"impact":218,"substance":112,"depth":17,"authority":19,"freshness":114,"relevant":21,"comment":471},"核心期刊论文，提出固定光积分下动态光子分配模型优化方法，对垂直农业光环境精准调控有参考价值，但属细分领域方法研究，产业影响有限。",[473],{"name":468,"url":465},[26,27,475,476,477],"垂直农业","作物模型","光环境调控",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":481},{"candidates":482,"agricultural_evidence":46},[483,487,491],{"code":32,"score":265,"supported":46,"title":484,"abstract":485,"tags":486},[],[475],[475],{"code":60,"score":323,"supported":14,"title":488,"abstract":489,"tags":490},[],[],[476,27],{"code":74,"score":21,"supported":14,"title":492,"abstract":493,"tags":494},[],[],[26],[496,497],"垂直农业 光积分 生物量","动态光子分配 模型优化","垂直农业光积分生物量-3976","10.1016\u002Fj.compag.2026.112489",{"doi":499,"openalex_id":501,"authors":502,"venue":468,"cited_by_count":88,"oa_url":9,"card":519,"direction":94,"ingested_from":97},"W7215104938",[503,506,509,511,514,516],{"name":504,"orcid":505},"Eunjeong Lim","https:\u002F\u002Forcid.org\u002F0009-0008-2621-8602",{"name":507,"orcid":508},"Da-Seul Choi","https:\u002F\u002Forcid.org\u002F0009-0008-3239-9817",{"name":510,"orcid":9},"Hyeonjee Kim",{"name":512,"orcid":513},"Taewon Moon","https:\u002F\u002Forcid.org\u002F0000-0002-1593-7870",{"name":515,"orcid":9},"Myung-min Oh",{"name":517,"orcid":518},"Tae In Ahn","https:\u002F\u002Forcid.org\u002F0000-0001-5939-7450",{"tldr":520,"method":521,"finding":522,"direction":94,"opportunity":523},"在固定总光照积分下，用模型优化垂直农业中动态光子分配以最大化生物量。","基于作物生长模型的优化方法，模拟不同光子分配策略。","动态光子分配策略可提升固定光积分下的生物量产量。","可结合实时传感与作物状态反馈，发展自适应光配方优化，并验证多作物多场景泛化性。","2026-10-02T23:30:02.603545Z"]