[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3606":3,"related-3606":93},{"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":61,"slug":64,"view_count":65,"doi":66,"paper":67,"created_at":92},3606,"A decision-making method for light regulation of cucumber seedlings considering changes of temperature and CO2 in protected agriculture","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.biosystemseng.2026.104601","Light serves as the primary energy source for photosynthesis and significantly influences plant morphology and biomass accumulation. In protected agricultural systems, light environment parameters (intensity and spectral quality) critically determine crop productivity. A data-driven framework for light optimisation in cucumber seedlings was developed. First, an artificial neural network was established to predict net photosynthetic rate using empirical data spanning diverse environmental regimes. Then, to jointly maximise photosynthetic efficiency and minimise energy consumption, two key innovations were implemented. The U-chord method was employed to derive target light intensities, and a hybrid cubic spline-global Newton algorithm was designed for spectral quality optimisation. These subsystems were fused through support vector regression to construct adaptive decision-making models for real-time light management. The results showed that the artificial neural network model achieved exceptional Pn prediction accuracy with coefficient of determination > 0.95, root mean square error \u003C 1.6 μmol m −2 s −1 , and mean absolute error ≤ 1.2 μmol m −2 s −1 . The target light intensity points effectively delineated the light-limited phase of the Pn–PPFD response from the light-saturated phase. Final decision-making models showed high generalisability (coefficient of determination > 0.97 for both spectral and intensity decision-making models). Simulations quantified the advantages of the proposed strategy. Compared to conventional maximum Pn-oriented strategies, the proposed method reduced lighting energy consumption by 40-46%, while dynamic spectral adjustments enhanced photosynthetic efficiency by 5% relative to static controls. This study effectively improved the efficiency of light energy utilisation, and provided a theoretical method for light regulation in protected agriculture.","光作为光合作用的主要能量来源，显著影响植物形态和生物量积累。在设施农业系统中，光环境参数（光强与光谱质量）对作物生产力具有决定性作用。本研究开发了一种数据驱动的黄瓜幼苗光优化框架。首先，基于涵盖多种环境条件的经验数据，建立了人工神经网络（artificial neural network）以预测净光合速率。随后，为实现光合效率最大化与能耗最小化的协同优化，实施了两项关键创新：采用U弦法（U-chord method）确定目标光强，并设计了三次样条-全局牛顿混合算法（hybrid cubic spline-global Newton algorithm）用于光谱质量优化。通过支持向量回归（support vector regression）融合各子系统，构建了用于实时光管理的自适应决策模型。结果表明，人工神经网络模型实现了优异的净光合速率预测精度，决定系数>0.95，均方根误差\u003C1.6 μmol m⁻² s⁻¹，平均绝对误差≤1.2 μmol m⁻² s⁻¹。目标光强点有效划分了净光合速率-光合光子通量密度响应曲线中的光限制阶段与光饱和阶段。最终决策模型表现出良好的泛化能力（光谱决策模型与光强决策模型的决定系数均>0.97）。仿真量化了所提策略的优势。与传统最大净光合速率导向策略相比，该方法将补光能耗降低了40%~46%，同时动态光谱调节使光合效率较静态对照提高了5%。本研究有效提升了光能利用效率，为设施农业光调控提供了理论方法。",null,"Biosystems 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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","报道",82,{"impact":17,"substance":18,"depth":17,"authority":105,"freshness":20,"relevant":21,"comment":106},15,"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。",[108],{"name":100,"url":98},[26,27,28,110,111],"温室番茄","生长预测",{"code":113,"slug":114,"name":115,"path":116,"parent_code":36},"B7","protected-horticulture","设施园艺与植物工厂","\u002Ftopic\u002Fhorticulture\u002Fprotected-horticulture",[118],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":120},{"candidates":121,"agricultural_evidence":45},[122,128,134,138],{"code":113,"score":123,"supported":45,"title":124,"abstract":126,"tags":127},12,[125],"温室",[125],[110],{"code":32,"score":129,"supported":45,"title":130,"abstract":132,"tags":133},11,[131],"番茄",[131],[],{"code":52,"score":21,"supported":14,"title":135,"abstract":136,"tags":137},[],[],[26],{"code":57,"score":21,"supported":14,"title":139,"abstract":140,"tags":141},[],[],[27],[143,144],"中国农科院环发所 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and Electronics in Agriculture)。","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联合荷兰瓦赫宁根大学在温室番茄生长建模方面取得进展。相关研究成果发表在《农业计算机与电子学（Computers and Electronics in Agriculture）》上。\n\n![Image 1: image.png](https:\u002F\u002Fieda.caas.cn\u002Fimages\u002F2026-09\u002Feacc678d60c145b18d62aa0f87ba78b8.png)\n\n_知识与数据双驱动的SATCN-Informer模型框架_\n\n作物生长精准估算是温室智控的核心前提。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。\n\n研究团队以番茄为对象，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（SATCN-Informer）。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（TOMSIM）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。\n\n该研究得到中央级公益性科研院所基本科研业务费资助。\n\n原文链接：https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112281","中国农业科学院","2026-09-09T00:00:00Z",84,{"impact":18,"substance":301,"depth":17,"authority":105,"freshness":302,"relevant":21,"comment":303},21,8,"国家级科研机构联合瓦赫宁根大学提出知识与数据双驱动的温室番茄生长预测模型，方法新颖、应用价值明确，值得进入每日精选。",[305],{"name":297,"url":294},[26,27,28,110,307],"作物生长模型",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[310],{"code":113,"slug":114,"name":115,"path":116,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":312},{"candidates":313,"agricultural_evidence":45},[314,318,322,326],{"code":32,"score":129,"supported":45,"title":315,"abstract":316,"tags":317},[131],[131],[],{"code":113,"score":20,"supported":45,"title":319,"abstract":320,"tags":321},[125],[],[110],{"code":57,"score":221,"supported":14,"title":323,"abstract":324,"tags":325},[],[],[307,27],{"code":52,"score":21,"supported":14,"title":327,"abstract":328,"tags":329},[],[],[26],[331,332],"作物生长模型 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