[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3262":3,"related-3262":116},{"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":43,"search_phrases":84,"slug":87,"view_count":80,"doi":88,"paper":89,"created_at":115},3262,"Advancing sustainable greenhouse production through organic and integrated systems: effects on the bioactive profile and functional quality of diverse tomato hybrids","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1968194","Sustainable and climate-smart agriculture has emerged in response to the increasingly pronounced effects of climate change, the intensive use of natural resources, and environmental pollution associated with conventional agricultural production. A two-year trial was conducted in Zemun Polje (Serbia) and was set up as a two-factorial design with four replications. The study evaluated the effects of organic and integrated protected production systems on the fruit quality of eight tomato hybrids representing four type - Cherry, Mini\u002FMidi Plum, Cluster, Beef. The analyzed parameters included total polyphenolic content, individual polyphenolic compounds, antioxidant capacity, total antioxidant activity, sugars, and volatile aroma compounds. The results showed differences between the two production systems. In organic production system, the total polyphenol content was 12.2% higher than in integrated production system. Total sugar content was also 4.0% higher, and total antioxidant activity was 15.2% higher in organic production. A total of 74 polyphenolic compounds and 173 volatile aroma compounds were identified. Total polyphenolic content and antioxidant activity were significantly affected by production system and hybrid, with a highly significant interaction between the two factors ( p \u003C 0.01). Significant differences were observed in the sugar composition, particularly for trehalose and melibiose, whose concentrations were higher in organically produced fruits (0.049 vs. 0.016 and 0.613 vs. 0.510 g 100 g −1 , respectively). Trehalose and melibiose showed potential as indicators of organically produced tomatoes. Overall, the results demonstrate that the production system influences the biochemical and nutritional quality of tomato fruits. Organic production may be a suitable option for protected tomato cultivation, as it can contribute to higher levels of selected bioactive compounds and the production of safe, high-quality fruit.","可持续且气候智能型农业的出现，是为了应对气候变化日益显著的影响、自然资源的集约利用以及传统农业生产带来的环境污染。试验在塞尔维亚泽蒙波列（Zemun Polje）进行，为期两年，采用双因素设计，设四次重复。研究评估了有机和综合保护地生产系统对代表四种类型（樱桃番茄、迷你\u002F中李形番茄、串收番茄、牛排番茄）的八个番茄杂交种果实品质的影响。分析参数包括总多酚含量、单体多酚化合物、抗氧化能力、总抗氧化活性、糖类及挥发性香气化合物。结果表明，两种生产系统之间存在差异。在有机生产系统中，总多酚含量比综合生产系统高12.2%，总糖含量高4.0%，总抗氧化活性高15.2%。共鉴定出74种多酚化合物和173种挥发性香气化合物。总多酚含量和抗氧化活性受生产系统和杂交种显著影响，两因素间存在极显著交互作用（p \u003C 0.01）。糖类组成存在显著差异，尤其是海藻糖和棉子糖，其在有机生产果实中浓度更高（分别为0.049 vs. 0.016和0.613 vs. 0.510 g 100 g⁻¹）。海藻糖和棉子糖显示出作为有机生产番茄指示物的潜力。总体而言，结果表明生产系统影响番茄果实的生化和营养品质。有机生产可能是保护地番茄栽培的适宜选择，因为它有助于提高特定生物活性化合物的水平并生产安全、优质的果实。",null,"Frontiers in Sustainable Food Systems","2026-09-23T00:00:00Z","论文",10,false,76,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":13,"relevant":21,"comment":22},12,22,18,14,1,"两年四重复试验、74种多酚与173种香气物质数据扎实，但属细分作物品质研究，产业影响有限，可作为专业精选。",[24],{"name":10,"url":6},[26,27,28,29,30],"可持续农业","有机农业","抗氧化活性","设施番茄","番茄品质",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"B1","vegetables","蔬菜","\u002Ftopic\u002Fhorticulture\u002Fvegetables","B",[38],{"code":39,"slug":40,"name":41,"path":42,"parent_code":36},"B7","protected-horticulture","设施园艺与植物工厂","\u002Ftopic\u002Fhorticulture\u002Fprotected-horticulture",{"status":44,"locked":14,"version":45,"method":46,"evidence":47},"classified","2026-10-22.2","rules",{"candidates":48,"agricultural_evidence":51},[49,57,63,71,78],{"code":32,"score":50,"supported":51,"title":52,"abstract":54,"tags":56},15,true,[53],"tomato",[55,53],"番茄",[29],{"code":39,"score":58,"supported":51,"title":59,"abstract":61,"tags":62},8,[60],"greenhouse",[],[],{"code":64,"score":65,"supported":51,"title":66,"abstract":67,"tags":70},"H7",6,[],[68,69],"气候变化","climate change",[],{"code":72,"score":73,"supported":51,"title":74,"abstract":75,"tags":77},"I1",3,[],[76],"smart agriculture",[],{"code":79,"score":80,"supported":14,"title":81,"abstract":82,"tags":83},"H8",2,[],[],[26,27],[85,86],"有机与综合生产系统 番茄品质","Zemun Polje 番茄杂交种","有机与综合生产系统番茄品质-3262","10.3389\u002Ffsufs.2026.1968194",{"doi":88,"openalex_id":90,"authors":91,"venue":10,"cited_by_count":106,"oa_url":6,"card":107,"direction":113,"ingested_from":114},"W7214062089",[92,95,98,101,104],{"name":93,"orcid":94},"Vojin Cvijanović","https:\u002F\u002Forcid.org\u002F0000-0002-1347-952X",{"name":96,"orcid":97},"Marija Bajagić","https:\u002F\u002Forcid.org\u002F0000-0001-9537-8302",{"name":99,"orcid":100},"Biljana Šević","https:\u002F\u002Forcid.org\u002F0000-0003-4486-2860",{"name":102,"orcid":103},"Darko Jakšić","https:\u002F\u002Forcid.org\u002F0000-0002-3186-8932",{"name":105,"orcid":9},"Mladen Petrović",0,{"tldr":108,"method":109,"finding":110,"direction":111,"opportunity":112},"比较有机与综合温室系统对八种番茄杂交种果实品质的影响。","两年双因素试验，测定多酚、抗氧化物、糖和挥发性香气物质。","有机系统多酚、总糖和抗氧化活性更高，海藻糖和蜜二糖可作有机番茄指示物。","农业绿色发展与碳","可探索将海藻糖和蜜二糖作为有机番茄快速无损检测标志物，并开发智能分选模型。","智慧农业 \u002F 农业物联网","openalex","2026-09-23T23:30:06.987783Z",{"total":65,"page":21,"page_size":65,"items":117},[118,169,219,316,358,396],{"id":119,"title":120,"url":121,"summary":122,"summary_zh":9,"content":123,"source_name":124,"source_url":9,"published_at":125,"category":126,"cover_url":9,"hotness":13,"is_selected":14,"score":127,"score_detail":128,"sources":132,"tags":134,"primary_topic":139,"secondary_topics":140,"classification":142,"search_phrases":164,"slug":167,"view_count":80,"doi":9,"paper":9,"created_at":168},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*   [江西组织开展2027年度自然科学基金项目申报工作](https:\u002F\u002Fnews.sciencenet.cn\u002Fhtmlnews\u002F2026\u002F10\u002F572484.shtm)\n\n*   5\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":18,"substance":129,"depth":130,"authority":20,"freshness":65,"relevant":21,"comment":131},21,17,"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。",[133],{"name":124,"url":121},[135,136,137,29,138],"智慧农业","农业人工智能","作物生长模型","温室环控",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[141],{"code":39,"slug":40,"name":41,"path":42,"parent_code":36},{"status":44,"locked":14,"version":45,"method":46,"evidence":143},{"candidates":144,"agricultural_evidence":51},[145,149,155,160],{"code":32,"score":17,"supported":51,"title":146,"abstract":147,"tags":148},[55],[55],[29],{"code":39,"score":150,"supported":51,"title":151,"abstract":153,"tags":154},11,[152],"温室",[152],[],{"code":156,"score":80,"supported":14,"title":157,"abstract":158,"tags":159},"I2",[],[],[137,136],{"code":72,"score":21,"supported":14,"title":161,"abstract":162,"tags":163},[],[],[135],[165,166],"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":170,"title":171,"url":172,"summary":173,"summary_zh":9,"content":9,"source_name":174,"source_url":9,"published_at":175,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":176,"score_detail":177,"sources":181,"tags":183,"primary_topic":188,"secondary_topics":189,"classification":191,"search_phrases":206,"slug":209,"view_count":106,"doi":9,"paper":210,"created_at":218},5536,"日光温室番茄全生育期环境特征解析与智能调控决策支持方法","https:\u002F\u002Fpaper.sciencenet.cn\u002Fhtmlpaper\u002F2026\u002F10\u002F20261021145441802156759.shtm","研究解析日光温室番茄全生育期环境特征，发现该类温室气候缓冲能力较强、有效积温与番茄生理需求呈双峰匹配，但相对增益指标揭示单位时间热量供给失衡、深冬热量不足以及光能结构性不足、湿度调控昼夜失衡等问题。作者建议构建温度—光照—湿度耦合的智能闭环控制系统。","Agriculture 2026-10-21","2026-10-21T00:00:00Z",77,{"impact":178,"substance":129,"depth":130,"authority":179,"freshness":13,"relevant":21,"comment":180},16,13,"论文解析日光温室番茄全生育期环境特征并提出温光湿耦合智能调控方案，结论有新意，对设施农业信息化有参考价值。",[182],{"name":174,"url":172},[135,184,185,186,187],"设施农业","环境调控","温室番茄","智能决策",{"code":39,"slug":40,"name":41,"path":42,"parent_code":36},[190],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":44,"locked":14,"version":45,"method":46,"evidence":192},{"candidates":193,"agricultural_evidence":51},[194,198,202],{"code":39,"score":179,"supported":51,"title":195,"abstract":196,"tags":197},[152],[152],[186,185],{"code":32,"score":150,"supported":51,"title":199,"abstract":200,"tags":201},[55],[55],[],{"code":72,"score":21,"supported":14,"title":203,"abstract":204,"tags":205},[],[],[135],[207,208],"日光温室 番茄 环境调控","温室番茄 智能闭环控制","日光温室番茄环境调控-5536",{"doi":9,"openalex_id":9,"authors":211,"venue":9,"cited_by_count":106,"oa_url":9,"card":212,"direction":113,"ingested_from":217},[],{"tldr":213,"method":214,"finding":215,"direction":113,"opportunity":216},"解析日光温室番茄全生育期环境特征并提出智能调控决策支持方法。","基于全生育期环境监测数据，分析积温匹配与相对增益指标。","温室缓冲力强但深冬热量不足、光能结构性不足、湿度昼夜失衡。","可研发温度—光照—湿度耦合的智能闭环控制算法，并验证其对番茄产量与能耗的协同优化效果。","agent","2026-10-22T00:07:10.129353Z",{"id":220,"title":221,"url":222,"summary":223,"summary_zh":224,"content":9,"source_name":225,"source_url":222,"published_at":226,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":227,"score_detail":228,"sources":231,"tags":233,"primary_topic":237,"secondary_topics":238,"classification":246,"search_phrases":293,"slug":296,"view_count":106,"doi":9,"paper":297,"created_at":315},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":17,"substance":129,"depth":130,"authority":229,"freshness":58,"relevant":21,"comment":230},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。",[232],{"name":225,"url":222},[135,136,234,235,236],"农业物联网","温室环境","小气候预测",{"code":39,"slug":40,"name":41,"path":42,"parent_code":36},[239,245],{"code":240,"slug":241,"name":242,"path":243,"parent_code":244},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot","I",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":44,"locked":14,"version":45,"method":46,"evidence":247},{"candidates":248,"agricultural_evidence":51},[249,255,259,266,273,279,286],{"code":240,"score":50,"supported":51,"title":250,"abstract":252,"tags":254},[251],"IoT",[253,251],"物联网",[234],{"code":39,"score":20,"supported":51,"title":256,"abstract":257,"tags":258},[60],[152,60],[],{"code":32,"score":229,"supported":51,"title":260,"abstract":261,"tags":265},[],[55,262,263,53,264],"辣椒","黄瓜","cucumber",[],{"code":72,"score":267,"supported":51,"title":268,"abstract":269,"tags":272},7,[],[270,271],"精准农业","precision agriculture",[135],{"code":156,"score":267,"supported":51,"title":274,"abstract":275,"tags":278},[],[276,277],"深度学习","deep learning",[136],{"code":280,"score":65,"supported":51,"title":281,"abstract":282,"tags":285},"H2",[],[283,284],"土壤","soil",[],{"code":287,"score":65,"supported":51,"title":288,"abstract":289,"tags":292},"H3",[],[290,291],"灌溉","irrigation",[],[294,295],"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":298,"authors":299,"venue":225,"cited_by_count":106,"oa_url":9,"card":310,"direction":113,"ingested_from":114},"W7222909837",[300,302,304,306,308],{"name":301,"orcid":9},"Siman Emmanuel*",{"name":303,"orcid":9},"Edward Onyebueke Agu",{"name":305,"orcid":9},"Musa Gani",{"name":307,"orcid":9},"Oladunjoye John Abiodun",{"name":309,"orcid":9},"Anagu Emmanuel John",{"tldr":311,"method":312,"finding":313,"direction":113,"opportunity":314},"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":317,"title":318,"url":319,"summary":320,"summary_zh":9,"content":321,"source_name":322,"source_url":9,"published_at":125,"category":126,"cover_url":9,"hotness":13,"is_selected":14,"score":323,"score_detail":324,"sources":326,"tags":328,"primary_topic":331,"secondary_topics":332,"classification":334,"search_phrases":353,"slug":356,"view_count":21,"doi":9,"paper":9,"created_at":357},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":18,"substance":129,"depth":130,"authority":20,"freshness":58,"relevant":21,"comment":325},"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。",[327],{"name":322,"url":319},[135,136,186,329,330],"知识数据双驱动","生长预测",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[333],{"code":39,"slug":40,"name":41,"path":42,"parent_code":36},{"status":44,"locked":14,"version":45,"method":46,"evidence":335},{"candidates":336,"agricultural_evidence":51},[337,341,345,349],{"code":32,"score":150,"supported":51,"title":338,"abstract":339,"tags":340},[55],[55],[],{"code":39,"score":229,"supported":51,"title":342,"abstract":343,"tags":344},[152],[],[186],{"code":72,"score":21,"supported":14,"title":346,"abstract":347,"tags":348},[],[],[135],{"code":156,"score":21,"supported":14,"title":350,"abstract":351,"tags":352},[],[],[136],[354,355],"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":359,"title":360,"url":361,"summary":362,"summary_zh":9,"content":9,"source_name":363,"source_url":9,"published_at":364,"category":126,"cover_url":9,"hotness":13,"is_selected":14,"score":323,"score_detail":365,"sources":367,"tags":369,"primary_topic":370,"secondary_topics":371,"classification":373,"search_phrases":392,"slug":394,"view_count":73,"doi":9,"paper":9,"created_at":395},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":19,"substance":18,"depth":19,"authority":50,"freshness":229,"relevant":21,"comment":366},"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。",[368],{"name":363,"url":361},[135,136,184,186,330],{"code":39,"slug":40,"name":41,"path":42,"parent_code":36},[372],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":44,"locked":14,"version":45,"method":46,"evidence":374},{"candidates":375,"agricultural_evidence":51},[376,380,384,388],{"code":39,"score":17,"supported":51,"title":377,"abstract":378,"tags":379},[152],[152],[186],{"code":32,"score":150,"supported":51,"title":381,"abstract":382,"tags":383},[55],[55],[],{"code":72,"score":21,"supported":14,"title":385,"abstract":386,"tags":387},[],[],[135],{"code":156,"score":21,"supported":14,"title":389,"abstract":390,"tags":391},[],[],[136],[165,393],"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","2026-10-02T11:53:11.319506Z",{"id":397,"title":398,"url":399,"summary":400,"summary_zh":401,"content":9,"source_name":10,"source_url":399,"published_at":402,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":403,"score_detail":404,"sources":406,"tags":408,"primary_topic":413,"secondary_topics":414,"classification":420,"search_phrases":452,"slug":455,"view_count":21,"doi":456,"paper":457,"created_at":480},3814,"Optimizing biogas slurry and biochar co-application to enhance soil microenvironment, mitigate greenhouse gas emissions, and increase tomato yield in greenhouse systems","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1866425","To support agricultural carbon peaking and carbon neutrality goals, this study investigated the uncertainties surrounding greenhouse gas emissions and nutrient leaching associated with biogas slurry utilization. It systematically explored the mechanisms and mitigation pathways of co-applying biogas slurry, biochar, and chemical fertilizers for reducing greenhouse gas emissions and nutrient losses under intensive agricultural management characterized by excessive irrigation and fertilizer application. Under equivalent N, P, and K inputs and consistent irrigation levels, different substitution ratios of biogas slurry for chemical fertilizers were evaluated in a point-source underground irrigation system. Treatments included CF, FR, BS25, BS50, BS75, and BS100, with corresponding biochar treatments CF + C, FR + C, BS25 + C, BS50 + C, BS75 + C, and BS100 + C. Tomato yield, soil physical properties, microbial biomass, microbial gene copy numbers, and soil greenhouse gas emissions were measured. Key influencing factors and optimal management strategies were identified using multivariate statistical analysis and a multi-criteria decision approach. The results indicated that biogas slurry combined with biochar significantly improved the soil microenvironment in greenhouse tomato systems by enhancing soil temperature (ST), soil moisture content (SMC), and water-filled pore space (WFPS), especially during the flowering stage in both spring-summer and autumn-winter periods. Biochar also significantly increased microbial biomass carbon (MBC), while microbial gene copy numbers, including AOA, AOB, and mcrA, showed clear seasonal variation. Regarding greenhouse gas emissions, co-application of biogas slurry and biochar significantly affected CO 2 fluxes, particularly from days 8–119, promoting CO 2 emissions. Biochar effectively suppressed N 2 O emissions, promoted CH 4 emissions during days 1–7, and reduced CH 4 fluxes from days 8–119. The BS75 + C treatment significantly increased tomato yield to 151.34 t ha −1 , reduced global warming potential to 9491.43 kg ha −1 , and lowered greenhouse gas emission intensity. Compared with other treatments, BS75 + C consistently showed superior agronomic and environmental performance. Comprehensive analysis showed that ST, SMC, and MBC mainly influenced CO 2 emissions; ST, AOA, and AOB influenced N 2 O emissions; and mcrA, WFPS, and ST influenced CH 4 emissions. Multi-criteria decision analysis identified BS75 + C as the optimal strategy for maximizing yield while minimizing greenhouse gas emissions and improving biogas slurry utilization efficiency in sustainable greenhouse agriculture.","为支撑农业碳达峰与碳中和目标，本研究探讨了沼液利用过程中温室气体排放与养分淋失的不确定性，系统揭示了在过量灌溉与施肥的集约化农业管理条件下，沼液、生物炭与化肥配施减少温室气体排放和养分损失的机理与调控路径。在等氮、磷、钾投入及一致灌溉水平下，采用点源地下灌溉系统，评估了不同沼液替代化肥比例的效果。处理包括CF、FR、BS25、BS50、BS75和BS100，以及相应的生物炭处理CF + C、FR + C、BS25 + C、BS50 + C、BS75 + C和BS100 + C。测定了番茄产量、土壤物理性质、微生物生物量、微生物基因拷贝数及土壤温室气体排放。通过多元统计分析和多准则决策方法，确定了关键影响因素和最优管理策略。结果表明，沼液配施生物炭通过提高土壤温度(ST)、土壤含水量(SMC)和充水孔隙度(WFPS)，显著改善了温室番茄系统的土壤微环境，尤其在春夏季和秋冬季的开花期。生物炭还显著提高了微生物生物量碳(MBC)，而微生物基因拷贝数(包括AOA、AOB和mcrA)则表现出明显的季节性变化。在温室气体排放方面，沼液与生物炭配施显著影响CO₂通量，尤其在8—119天期间促进CO₂排放。生物炭有效抑制了N₂O排放，在1—7天促进CH₄排放，并在8—119天降低CH₄通量。BS75 + C处理显著提高了番茄产量至151.34 t ha⁻¹，将全球增温潜势降至9491.43 kg ha⁻¹，并降低了温室气体排放强度。与其他处理相比，BS75 + C在农艺和环境性能方面始终表现优越。综合分析表明，ST、SMC和MBC主要影响CO₂排放；ST、AOA和AOB影响N₂O排放；mcrA、WFPS和ST影响CH₄排放。多准则决策分析确定BS75 + C为最优策","2026-09-29T00:00:00Z",78,{"impact":178,"substance":18,"depth":19,"authority":179,"freshness":229,"relevant":21,"comment":405},"该研究通过沼液与生物炭配施实现温室番茄增产与温室气体减排，方法系统、数据扎实，对农业绿色低碳发展具有参考价值。",[407],{"name":10,"url":399},[409,186,410,411,412],"生物炭","农业碳中和","沼液利用","减排增效",{"code":39,"slug":40,"name":41,"path":42,"parent_code":36},[415,419],{"code":280,"slug":284,"name":416,"path":417,"parent_code":418},"土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":44,"locked":14,"version":45,"method":46,"evidence":421},{"candidates":422,"agricultural_evidence":51},[423,427,431,435,442,446],{"code":39,"score":50,"supported":51,"title":424,"abstract":425,"tags":426},[60],[152,60],[186],{"code":280,"score":50,"supported":51,"title":428,"abstract":429,"tags":430},[284],[283,284],[409],{"code":32,"score":20,"supported":51,"title":432,"abstract":433,"tags":434},[53],[55,53],[],{"code":436,"score":65,"supported":51,"title":437,"abstract":438,"tags":441},"F3",[],[439,440],"施肥","fertilizer",[],{"code":287,"score":65,"supported":51,"title":443,"abstract":444,"tags":445},[],[290,291],[],{"code":447,"score":73,"supported":51,"title":448,"abstract":449,"tags":451},"H4",[],[450],"农业碳",[],[453,454],"沼液 生物炭 温室番茄","BS75 C 温室气体减排","沼液生物炭温室番茄-3814","10.3389\u002Ffsufs.2026.1866425",{"doi":456,"openalex_id":458,"authors":459,"venue":10,"cited_by_count":106,"oa_url":399,"card":475,"direction":111,"ingested_from":114},"W7214739741",[460,463,466,468,471,473],{"name":461,"orcid":462},"Qinglin Sa","https:\u002F\u002Forcid.org\u002F0009-0007-5248-566X",{"name":464,"orcid":465},"Jian Zheng","https:\u002F\u002Forcid.org\u002F0009-0003-5723-1950",{"name":467,"orcid":9},"Ke Zhang",{"name":469,"orcid":470},"Yan Wang","https:\u002F\u002Forcid.org\u002F0000-0002-5950-8904",{"name":472,"orcid":9},"Xuqin FU",{"name":474,"orcid":9},"Shikun Sun",{"tldr":476,"method":477,"finding":478,"direction":111,"opportunity":479},"研究沼液与生物炭配施对温室番茄土壤微环境、温室气体排放及产量的影响。","等氮磷钾滴灌试验，设沼液替代化肥比例与生物炭处理，测产量、土壤及气体。","BS75+C处理番茄产量最高达151.34 t\u002Fha，全球增温潜势最低，减排增产效果最优。","可探究不同气候区或土壤类型下沼液生物炭配施的长期固碳减排效应与微生物机制。","2026-09-30T23:30:07.179554Z"]