[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2278":3,"related-2278":104},{"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":70,"slug":73,"view_count":74,"doi":75,"paper":76,"created_at":103},2278,"StrawberryGRO-E: A techno-economic analytic modeling framework for strawberry production in plant factories","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112405","Plant factories with artificial lighting (PFALs) offer a transformative approach to food security, yet their economic viability is constrained by high production expenditures and persistent yield heterogeneity. Despite uniform macro-environmental controls, endogenous genetic rhythms and micro-environmental fluctuations induce asynchronous inflorescence overlap which acts as a critical biological driver of output variation. This study aimed to quantify dynamic reproductive load and determine how inflorescence management affected physiological performance, resource efficiency, and economic outcomes in PFAL strawberry production. The Simultaneous Active Inflorescences (SAI) index was developed as a quantitative metric of dynamic reproductive load, and physiological assessments were conducted to characterize its effects on source-sink competition. These relationships were then incorporated into the StrawberryGRO-E techno-economic analytic modeling framework to evaluate three management strategies defined by maximum SAI thresholds: Conservative ( SA I max = 1), Moderate ( SA I max = 2), and Intensive ( SA I max = 3). Results demonstrated that despite individual physiological penalties associated with higher reproductive loads, the Intensive strategy recorded the lowest specific energy consumption of 83.7 kWh\u002Fkg, enhancing system-level energy efficiency by diluting fixed costs. However, economic performance was highly market-dependent. Moderate management strategy maximized net profit (1115.55 CNY\u002Fm 2 \u002Fyear) in quality-sensitive premium markets, whereas Intensive management strategy was most effective in volume-driven mass markets. Sensitivity analyses further identified profitability crossover points under fluctuating electricity prices and market volatility, with the Moderate strategy showing stronger resilience in quality-sensitive markets. This research confirms that inflorescence management, tailored to market positioning and resource expenditures, is essential for sustained annual profitability in PFALs.","人工光植物工厂（PFALs）为粮食安全提供了一种变革性途径，但其经济可行性受到高生产成本和持续产量异质性的制约。尽管宏观环境控制保持一致，内源性遗传节律和微环境波动仍会导致花序异步重叠，而后者是产量变异的关键生物驱动因素。本研究旨在量化动态生殖负荷，并确定花序管理如何影响PFAL草莓生产中的生理表现、资源利用效率和经济产出。研究开发了同步活跃花序（SAI）指数，作为动态生殖负荷的定量指标，并通过生理评估表征其对源库竞争的影响。随后，这些关系被纳入StrawberryGRO-E技术经济分析建模框架，用以评估以最大SAI阈值为依据的三种管理策略：保守型（SAI max = 1）、适度型（SAI max = 2）和密集型（SAI max = 3）。结果表明，尽管较高的生殖负荷会带来个体生理代价，密集型策略仍实现了最低的单位能耗83.7 kWh\u002Fkg，通过摊薄固定成本提升了系统级能源效率。然而，经济表现高度依赖市场。适度型管理策略在质量敏感型高端市场中实现了净利润最大化（1115.55 CNY\u002Fm²\u002F年），而密集型管理策略在销量驱动的大众市场中最为有效。敏感性分析进一步识别了在电价波动和市场波动条件下的盈利交叉点，其中适度型策略在质量敏感型市场中表现出更强的韧性。本研究证实，根据市场定位和资源投入来定制花序管理，对于PFAL实现持续年度盈利至关重要。",null,"Computers and Electronics in Agriculture","2026-09-12T00:00:00Z","论文",10,false,81,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,22,14,9,1,"核心期刊论文，提出植物工厂草莓花序管理与技术经济分析框架，方法新颖、数据扎实，对设施农业盈利模式有参考价值。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","设施农业","植物工厂","草莓种植","技术经济分析",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"B1","vegetables","蔬菜","\u002Ftopic\u002Fhorticulture\u002Fvegetables","B",[],{"status":39,"locked":14,"version":40,"method":41,"evidence":42},"classified","2026-10-22.2","rules",{"candidates":43,"agricultural_evidence":45},[44,51,59,65],{"code":32,"score":19,"supported":45,"title":46,"abstract":48,"tags":50},true,[47],"strawberry",[49,47],"草莓",[],{"code":52,"score":53,"supported":45,"title":54,"abstract":55,"tags":58},"A8",6,[],[56,57],"粮食安全","food security",[],{"code":60,"score":61,"supported":45,"title":62,"abstract":63,"tags":64},"B7",4,[],[28],[28],{"code":66,"score":21,"supported":14,"title":67,"abstract":68,"tags":69},"I1",[],[],[26],[71,72],"技术经济分析 智慧农业 植物工厂 草莓种植","技术经济分析 智慧农业","技术经济分析智慧农业植物工厂草莓种植-2278",2,"10.1016\u002Fj.compag.2026.112405",{"doi":75,"openalex_id":77,"authors":78,"venue":10,"cited_by_count":95,"oa_url":6,"card":96,"direction":100,"ingested_from":102},"W7212359284",[79,81,83,85,88,90,92],{"name":80,"orcid":9},"Rongmei Fu",{"name":82,"orcid":9},"Wei Liu",{"name":84,"orcid":9},"Fulin Xia",{"name":86,"orcid":87},"Renhai Zhong","https:\u002F\u002Forcid.org\u002F0000-0001-8922-0138",{"name":89,"orcid":9},"K.C. Ting",{"name":91,"orcid":9},"Guichao Hua",{"name":93,"orcid":94},"Tao Lin","https:\u002F\u002Forcid.org\u002F0000-0001-9721-5363",0,{"tldr":97,"method":98,"finding":99,"direction":100,"opportunity":101},"构建草莓植物工厂技术经济模型，量化花序管理对生理、能效与利润的影响。","提出同时活跃花序指数SAI，结合生理评估与StrawberryGRO-E技术经济","集约策略能耗最低83.7 kWh\u002Fkg，但中等策略在优质市场净利最高。","农业人工智能与决策模型","可扩展SAI指数至其他果菜，耦合实时传感与市场动态优化决策。","openalex","2026-09-13T23:30:01.554192Z",{"total":53,"page":21,"page_size":53,"items":105},[106,155,212,258,300,362],{"id":107,"title":108,"url":109,"summary":110,"summary_zh":9,"content":111,"source_name":112,"source_url":9,"published_at":113,"category":114,"cover_url":9,"hotness":13,"is_selected":14,"score":115,"score_detail":116,"sources":120,"tags":122,"primary_topic":126,"secondary_topics":127,"classification":128,"search_phrases":150,"slug":153,"view_count":61,"doi":9,"paper":9,"created_at":154},2379,"上海蔬菜越夏生产攻坚战:种源与设施双线突破 上海市农科院两项成果获2025年市科技进步二等奖","https:\u002F\u002Fwww.toutiao.com\u002Farticle\u002F7684223283671171603\u002F","上海市农科院两项成果同获2025年上海市科技进步奖二等奖：朱红芳团队的耐热耐湿宜机化青菜种质创新与新品种选育、丁小涛团队的自然光植物工厂主要蔬菜高效生产关键技术与装备研发应用。团队从数百份种质资源中筛选出多个耐热性突出的候选材料，创制38份具有耐热抗病宜机收性状的优质种质资源。","![Image 1](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-axegupay5k\u002F8344e34645a34aa38402cef3d76a6a08~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1789949443&x-signature=AUIAppV4LZhLWVBpYxD7YJu75rk%3D)\n“外界温度三十八九度，不仅要降温，还要除湿。湿度降不下来，真菌性病害可能就来了。但除湿和降温同时做，能耗非常高。”说起上海夏天的温室种植难点，上海市农业科学院智慧农业研究中心主任丁小涛研究员给大家算了一笔环境账：对于番茄，自然光温室里理想的控制目标是白天28℃、湿度70%—80%，但到了极端高温天气，设备能力有限，不一定能达到这个理想条件。\n\n这是上海夏季蔬菜生产技术本质：不是单纯的热，而是热与湿的双重绞杀。更麻烦的是，上海的夏天还有第三种考验——梅雨期的弱光。“不仅温度高，弱光还需要补光。”丁小涛说。降温、除湿、补光三件事同时做，能耗会大幅上升。因此，丁小涛团队将研究重点放在了节能技术上。\n\n与此同时，种质资源也是制约夏季蔬菜生产的另一个要素，品种本身耐不耐热，能否满足机械化种植要求，都直接影响着夏季蔬菜的产量与品质。上海市农业科学院绿叶菜技术创新团队首席科学家、研究员朱红芳表示，近年来，团队在耐热品种的筛选与培育上投入了大量精力，已取得阶段性成果。团队从数百份种质资源中筛选出多个耐热性突出的候选材料，并在此基础上开展耐热机理研究，配制大量的杂交组合，并结合田间高温淹水鉴定，目前已形成一批适合夏季高温高湿环境种植的专用品种。\n\n上海市农业科学院两项成果同获2025年上海市科技进步奖二等奖：朱红芳团队的耐热耐湿宜机化青菜种质创新与新品种选育，丁小涛团队的自然光植物工厂主要蔬菜高效生产关键技术与装备研发应用。奖项是节点，问题才是起点——上海的盛夏，到底能不能不再“淡”？\n\n**田头十五年的“耐热答卷”**\n\n青菜偏爱冷凉，这是刻在基因里的天性。每年6月至8月，梅雨加高温轮番上阵，传统品种“水土不服”：叶片焦边、纤维粗硬，霜霉病和根肿病接踵而至。过去优质耐热种源依赖进口，生产企业一到伏季就压力陡增。朱红芳的解题思路是：既然高温躲不掉，就让青菜自己扛住。\n\n![Image 2](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-tjoges91tu\u002F781d7fe85eeab226319684a0650b1482~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1789949443&x-signature=rufzxTnFfs8zdtFhvw7FNHRp3cU%3D)\n“青菜育种，最终是为老百姓的饭碗服务的。好不好种、好不好吃、安不安全、稳不稳定，就是唯一标准。”朱红芳的话朴素，但背后的技术路径并不简单。团队把耐热、耐湿、抗病、株型、口感等性状逐一量化，开发出13个关键性状分子标记、挖掘9个核心调控基因。“相当于给每株青菜配了一张基因身份证，苗期就能锁定优质单株。”配合分子标记辅助选择等技术，创制了38份具有耐热、抗病、宜机收等性状的优质种质资源。15年积累下来，已拥有3000余份种质资源。\n\n成果最终要经得起天气的检验。“夏青5号”和“新夏青7号”在35℃以上持续高温中长势齐整、病害显著减少。当前，团队选育的青菜全系列新品种在长三角夏季市场占有率已达36%。但真正让朱红芳对这批品种有底气的，是一场不期而至的台风。2026年台风“白海豚”过境，庄行试验站的菜地泡在了水里。风雨过后，她下田去看，心里不是没有担心。结果，“新夏青7号”的表现让她松了口气：水淹近48小时，成活率仍在八成以上。朱红芳指着一片依旧挺拔油亮的菜地说：“这是我们夏季生产的‘尖兵’。”在她看来，夏季叶菜生产的敌人从来不止高温一个，台风带来的涝害同样致命，而育种必须同时给出两个答案。9月开学后，沪上部分学校食堂的青菜就来自这些经受过台风考验的品种。\n\n耐热解决了“种得活”，宜机化则解决“种得起”。国内首款适配全程机械化生产的青菜品种“新夏青6号”，株型直立紧凑、胚轴较长适合切割，配合全程机械化生产规程和“四诱一网”绿色防控体系，化学农药用量减少25%，采收效率提升20倍，劳动力成本降低26.2%。松江种植户王义平最有发言权：“以前收菜从早弯腰到晚，现在机器一小时抵十个人工，工作量减半，还想再多种十几亩。”目前这些品种已覆盖上海9个涉农区，年推广种植约5万亩。\n\n![Image 3](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-tjoges91tu\u002F1931708e75cd67cc93bd1960a584633a~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1789949443&x-signature=AENrjQ%2FuvPJ2UOUVO3nMV9mIEq0%3D)\n育种解决了“种什么”，但上海的土地资源终究有限。朱红芳团队的应对策略是跳出上海看供应：秋冬在上海本地生产，夏季把品种和技术整体输出到宁夏、云南等冷凉地区，利用当地气候和土地资源规模化种植，再通过标准化采收和冷链物流返销上海。“好品种要走出去，好青菜才能返回来。”这套“候鸟式”模式的核心在于：种源和标准留在上海，生长环节放到更适宜的地方。换句话说，不是所有问题都必须在本地的土地上解决，但所有品质定义权必须握在自己手里。\n\n**一座温室的“能耗突围”**\n\n如果说朱红芳在田头跟天气比拼，丁小涛则在玻璃房内与能耗较劲。\n\n温室用天然气加温，能耗占成本的比例非常高，上海气价高时每立方米要五元多。国家设施农业工程技术研究中心崇明基地现代温室的破局尝试藏在崇明的地下——含水层储能技术配合高效热泵系统，把地下含水层变成一座“储能银行”：冬天把热泵产生的热能用于温室供暖，同时把冷能储入地下；夏天再提取冷能降温，把热量打回地下。\n\n“冬天产生的冷气回灌到地下含水层储存起来，夏天只需要一个泵的动力把它提上来用于温室降温。”丁小涛说，磁悬浮热泵机组的能效比可以达到7以上。今年7月上海高温天，改造后的温室“大部分时间最高温度控制在28—30℃，只有极个别极端天气冲到30℃出头”。\n\n![Image 4](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-tjoges91tu\u002F9bc5020103b1b9dd985d7e0feae0eebc~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1789949443&x-signature=mzK4zufSIYjN4w4hmasqsyg7yB0%3D)\n节能的突破口不止一个。LED补光灯“近年来成本一下子降低非常多”，国产高效灯的光合光子效率（PPE）已达3.6以上，是普通钠灯的两倍。梅雨季没光照的时候补一下，“对提高产量，尤其是阴雨天的产量提升非常明显，同时可以提升果实的品质。”品种筛选也在同步推进：崇明基地番茄区种着约20个品种，一个重要任务是找出能替代进口的国产串收番茄。“进口种子贵的一粒要15到18元，我们不能一直被人卡着脖子。”\n\n关于“植物工厂的菜没菜味”的质疑，丁小涛的回应很具体：温室白天温度控制在28℃左右，夜间降到16.5℃至18℃，拉大昼夜温差可以减少植株夜间呼吸消耗，让光合产物更多积累到果实里。“甜度、脆嫩度、维生素C含量这些指标，是可以通过调控温度、湿度、光照和营养液来改善的。”湿度控制好了，真菌性病害就少；虫害可以投放捕食螨等天敌来防，“可以很少使用农药甚至几乎不使用农药”。这在食品安全高度敏感的上海市场，本身就是竞争力。\n\n**数据、人才与下一个命题**\n\n![Image 5](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-tjoges91tu\u002F0169f0e0bdfba997de4ea4493dded4b4~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1789949443&x-signature=yqhW%2BwsHU6sB0rt%2FmqGnVydwFRI%3D)\n品种和设施解决了当下面对的难题，丁小涛还想到更远的“以后”。他设想温室管理的终极形态是AI化——“不要让每个人凭经验去控制，AI可以综合大多数人的策略”。但前提是数据，“传感器采集环境数据不难，难的是植物的数据。”株高动态、叶面积变化、光合速率等，这些直接反映植株状态的参数大多仍靠人工测量。“采集的数据必须是真实且有用的，植物在什么条件下状态最好，用什么参数来反映，都需要长期积累。”\n\n他同时点出了一个容易被忽视的短板：人才。“现代温室的管理，需要有热情、非常愿意投入和懂设施农业的人。能耗怎么降，栽培怎么调，不是学农的人单独能解决的，需要交叉学科的人一起进来。”技术可以攻关，设备可以投入，但能同时理解作物生理、环境控制与能源管理的人，目前仍然稀缺。\n\n育苗环节则已经看到了商业化的苗头。团队在人工光植物工厂里做番茄育苗，单位面积可育数百棵苗，4到6层立体排布，温光水气全可控。他们还摸索出按需调节的“光周期策略”：“如果苗子价格高、用户急着要，就把光照时间从12小时拉到20小时，可以提前出苗；如果不急，就缩短光周期省能耗。”丁小涛解释，番茄苗对光周期不太敏感，光照时长可以变成一道成本计算题，而非技术约束。\n\n朱红芳的团队还在田头忙碌。今年夏天，又一批新品系进入耐热与耐淹的双重筛选。丁小涛的温室里，串收番茄已经进入新一季采收期。“如果在上海能种好，相信到其他地区更能种好。”这句话背后是对上海难度与上海机会的双重确认。\n\n上海的夏天不会变温柔，高温高湿、台风暴雨、弱光高能耗，这些约束条件一项都不会少。但正是这些约束，逼出了更精细的管理、更硬核的技术和更贴近市场的品种。\n\n文字：施勰贇\n\n图片：王金秀 上海市农业科学院\n\n编辑：韦静\n\n特别声明：本文经上观新闻客户端的“上观号”入驻单位授权发布，仅代表该入驻单位观点，“上观新闻”仅为信息发布平台，如您认为发布内容侵犯您的相关权益，请联系删除!","上观新闻 | 2026-09-11","2026-09-11T00:00:00Z","报道",80,{"impact":18,"substance":117,"depth":17,"authority":13,"freshness":118,"relevant":21,"comment":119},23,7,"上海农科院两项市科技进步二等奖成果的深度报道，耐热宜机化青菜育种与自然光植物工厂节能技术均有具体数据与多方信源，专业含量高，值得进入每日精选。",[121],{"name":112,"url":109},[26,27,123,28,124,125],"种业振兴","耐热蔬菜","节能降耗",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":129},{"candidates":130,"agricultural_evidence":45},[131,136,140,146],{"code":32,"score":132,"supported":45,"title":133,"abstract":134,"tags":135},11,[34],[34],[],{"code":60,"score":61,"supported":45,"title":137,"abstract":138,"tags":139},[],[28],[28],{"code":141,"score":61,"supported":45,"title":142,"abstract":143,"tags":145},"F1",[],[144],"种质资源",[123],{"code":66,"score":21,"supported":14,"title":147,"abstract":148,"tags":149},[],[],[26],[151,152],"智慧农业 植物工厂 种业振兴 耐热蔬菜","智慧农业 植物工厂","智慧农业植物工厂种业振兴耐热蔬菜-2379","2026-09-14T00:06:29.719976Z",{"id":156,"title":157,"url":158,"summary":159,"summary_zh":9,"content":9,"source_name":160,"source_url":9,"published_at":161,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":162,"score_detail":163,"sources":169,"tags":171,"primary_topic":175,"secondary_topics":179,"classification":181,"search_phrases":198,"slug":201,"view_count":95,"doi":9,"paper":202,"created_at":211},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":164,"substance":165,"depth":166,"authority":167,"freshness":13,"relevant":21,"comment":168},16,21,17,13,"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。",[170],{"name":160,"url":158},[26,27,172,173,174],"环境调控","温室番茄","智能决策",{"code":60,"slug":176,"name":177,"path":178,"parent_code":36},"protected-horticulture","设施园艺与植物工厂","\u002Ftopic\u002Fhorticulture\u002Fprotected-horticulture",[180],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":182},{"candidates":183,"agricultural_evidence":45},[184,189,194],{"code":60,"score":167,"supported":45,"title":185,"abstract":187,"tags":188},[186],"温室",[186],[173,172],{"code":32,"score":132,"supported":45,"title":190,"abstract":192,"tags":193},[191],"番茄",[191],[],{"code":66,"score":21,"supported":14,"title":195,"abstract":196,"tags":197},[],[],[26],[199,200],"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":203,"venue":9,"cited_by_count":95,"oa_url":9,"card":204,"direction":208,"ingested_from":210},[],{"tldr":205,"method":206,"finding":207,"direction":208,"opportunity":209},"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。","智慧农业 \u002F 农业物联网","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":213,"title":214,"url":215,"summary":216,"summary_zh":9,"content":9,"source_name":217,"source_url":9,"published_at":218,"category":114,"cover_url":9,"hotness":13,"is_selected":14,"score":219,"score_detail":220,"sources":223,"tags":225,"primary_topic":228,"secondary_topics":229,"classification":231,"search_phrases":252,"slug":255,"view_count":256,"doi":9,"paper":9,"created_at":257},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",82,{"impact":17,"substance":18,"depth":17,"authority":221,"freshness":20,"relevant":21,"comment":222},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。",[224],{"name":217,"url":215},[26,226,27,173,227],"农业人工智能","生长预测",{"code":60,"slug":176,"name":177,"path":178,"parent_code":36},[230],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":232},{"candidates":233,"agricultural_evidence":45},[234,239,243,247],{"code":60,"score":235,"supported":45,"title":236,"abstract":237,"tags":238},12,[186],[186],[173],{"code":32,"score":132,"supported":45,"title":240,"abstract":241,"tags":242},[191],[191],[],{"code":66,"score":21,"supported":14,"title":244,"abstract":245,"tags":246},[],[],[26],{"code":248,"score":21,"supported":14,"title":249,"abstract":250,"tags":251},"I2",[],[],[226],[253,254],"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 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":259,"title":260,"url":261,"summary":262,"summary_zh":9,"content":263,"source_name":264,"source_url":9,"published_at":265,"category":114,"cover_url":9,"hotness":13,"is_selected":14,"score":266,"score_detail":267,"sources":269,"tags":271,"primary_topic":274,"secondary_topics":275,"classification":276,"search_phrases":295,"slug":298,"view_count":61,"doi":9,"paper":9,"created_at":299},3751,"设施技术国产化让每公斤番茄综合生产成本降至5元","https:\u002F\u002Fnync.yn.gov.cn\u002Fhtml\u002F2026\u002Fyunnongkuanxun-new_0923\u002F1428950.html","云南首个AI+农业全链路示范基地位于昆明市宜良县匡远街道温泉社区。嘉措（云南）农业科技有限公司联合云南指月科技有限公司打造国产化AI种植模式，通过核心算法本地化适配与全链条国产化硬件深度协同，基地里每公斤番茄综合生产成本降至5元，仅为海外高端温室模式的三分之一。","位于昆明市宜良县匡远街道温泉社区的番茄示范基地即将在国庆假期进入采收期。记者近日来到该基地，探访这座云南首个“AI+农业全链路”示范基地。\n\n清晨，基地里一串串番茄在简易薄膜温室内泛着诱人的青红光泽。没有泥泞的土地，没有汗流浃背的弯腰劳作，眼前这座“番茄工厂”安静得只听见蜜蜂授粉时的嗡鸣声和水肥滴灌的微响。\n\n上午8时，有工人陆续进场，偌大的基地只需三五位工人就能打理完毕。物联网感知终端静静矗立，实时采集光照强度、空气温湿度、水肥浓度和作物长势。种苗定植、枝蔓生长、开花坐果到成熟采收，每一个环节都有“数字管家”全程值守。嘉措（云南）农业科技有限公司农场场长李良坐在棚外，掏出手机轻点屏幕，棚内温湿度曲线、土壤含水量、水肥配比参数一目了然。指尖滑动间，灌溉阀自动开启，水肥顺着滴灌带精准送达番茄根部。\n\n“过去种番茄靠经验，现在靠算法。”云南指月科技有限公司项目经理尹文鑫指着棚内的国产化智慧灌溉设备说。这套系统由嘉措（云南）农业科技有限公司联合云南指月科技有限公司打造，创新构建“全域监测+AI算法+智慧灌溉”三位一体的数字化种植模式。\n\n“基地里的全套设备都是国产的。”尹文鑫提高音量信心十足地介绍。长期以来，国内高端设施农业赛道被海外技术路线主导。参照国外植物工厂及高端玻璃温室模式，依托全套进口水肥设备与闭环控制系统虽能实现稳定高产，但每公斤番茄综合生产成本高达13元至15元，加之进口设备造价高、配件周期长、操作门槛高等痛点，技术落地难度极大。而该基地探索的国产化AI种植模式，正在彻底改写这一成本结构。\n\n国产化的意义，最终落在基地每一颗番茄的身价上。通过核心算法本地化适配与全链条国产化硬件深度协同，基地里每公斤番茄综合生产成本降至5元，仅为海外高端温室模式的三分之一。同时，国产化硬件的使用，进一步压缩了采购、部署与运维成本，凸显整套方案规模化落地的性价比。\n\n“云”上管棚，“数”里种田。从传感器、灌溉阀到核心控制系统，基地核心硬件已实现全部国产配套；从数据采集、模型训练到智能决策输出，AI技术真正扎根田间。对种植户而言，AI不再是遥远的科技概念，而是手机里能调水肥、棚里能提产量、年底能多增收的实用“新农具”。（记者：王淑娟）","云南省农业农村厅","2026-09-23T12:00:00Z",78,{"impact":18,"substance":18,"depth":166,"authority":132,"freshness":53,"relevant":21,"comment":268},"云南首个AI+农业全链路示范基地，以国产化硬件与算法将番茄综合成本从13-15元\u002F公斤降至5元，数据具体、多方信源，具备行业示范价值。",[270],{"name":264,"url":261},[26,226,27,272,191,273],"云南","国产化替代",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":277},{"candidates":278,"agricultural_evidence":45},[279,283,287,291],{"code":32,"score":235,"supported":45,"title":280,"abstract":281,"tags":282},[191],[191],[191],{"code":60,"score":256,"supported":45,"title":284,"abstract":285,"tags":286},[],[186],[],{"code":66,"score":21,"supported":14,"title":288,"abstract":289,"tags":290},[],[],[26],{"code":248,"score":21,"supported":14,"title":292,"abstract":293,"tags":294},[],[],[226],[296,297],"云南 宜良 AI番茄 示范基地","嘉措农业 指月科技 智慧灌溉","云南宜良AI番茄示范基地-3751","2026-09-29T00:08:25.735852Z",{"id":301,"title":302,"url":303,"summary":304,"summary_zh":305,"content":9,"source_name":306,"source_url":303,"published_at":307,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":308,"sources":310,"tags":312,"primary_topic":315,"secondary_topics":316,"classification":317,"search_phrases":334,"slug":337,"view_count":53,"doi":338,"paper":339,"created_at":361},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。","Biosystems Engineering","2026-09-26T00:00:00Z",{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":309},"该研究提出数据驱动的设施黄瓜育苗光调控决策方法，节能40-46%且提升光合效率，方法新颖、数据可靠，对智慧农业光环境管理有较高参考价值。",[311],{"name":306,"url":303},[26,226,27,313,314],"黄瓜育苗","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":318},{"candidates":319,"agricultural_evidence":45},[320,326,330],{"code":32,"score":19,"supported":45,"title":321,"abstract":323,"tags":325},[322],"cucumber",[324,322],"黄瓜",[],{"code":66,"score":21,"supported":14,"title":327,"abstract":328,"tags":329},[],[],[26],{"code":248,"score":21,"supported":14,"title":331,"abstract":332,"tags":333},[],[],[226],[335,336],"设施农业 光环境 黄瓜育苗","光合速率 光强 光谱 优化","设施农业光环境黄瓜育苗-3606","10.1016\u002Fj.biosystemseng.2026.104601",{"doi":338,"openalex_id":340,"authors":341,"venue":306,"cited_by_count":95,"oa_url":303,"card":356,"direction":100,"ingested_from":102},"W7214421951",[342,345,348,350,353],{"name":343,"orcid":344},"Pan Gao","https:\u002F\u002Forcid.org\u002F0000-0002-5184-5674",{"name":346,"orcid":347},"Huimin Li","https:\u002F\u002Forcid.org\u002F0009-0001-9124-7628",{"name":349,"orcid":9},"Jinghua Xu",{"name":351,"orcid":352},"Miao Lu","https:\u002F\u002Forcid.org\u002F0000-0001-6539-2170",{"name":354,"orcid":355},"Jin Ping Hu","https:\u002F\u002Forcid.org\u002F0000-0001-5532-6890",{"tldr":357,"method":358,"finding":359,"direction":100,"opportunity":360},"构建数据驱动光调控决策框架，优化黄瓜幼苗光强与光谱以提升光合效率并降低能耗。","人工神经网络预测净光合速率，U弦法确定光强，三次样条-全局牛顿法优化光谱，支持向","模型预测精度高，较传统策略降低能耗40-46%，动态光谱调整提升光合效率5%。","可探索多环境因子耦合的实时闭环光调控，并迁移至其他设施作物验证泛化性。","2026-09-27T23:30:09.843641Z",{"id":363,"title":364,"url":365,"summary":366,"summary_zh":9,"content":9,"source_name":367,"source_url":9,"published_at":368,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":266,"score_detail":369,"sources":371,"tags":373,"primary_topic":375,"secondary_topics":376,"classification":377,"search_phrases":396,"slug":399,"view_count":74,"doi":9,"paper":400,"created_at":407},2904,"Decoupled Foundation Models:基于YOLO26m+SAM2+DINOv2的湿度诱导番茄叶坏死实例分割与检测,登MDPI Agriculture 16(18)1997","https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F18\u002F1997","本研究针对温室番茄相对湿度过高引发的非生物胁迫(生理性叶坏死,与生物感染症状相似),提出多步AI管道自动化分割与分类坏死叶斑。采集218张RGB图像、3218个标注(棕色坏死斑\u002F黄色坏死斑\u002F无坏死),系统评估6种端到端实例分割管道(YOLO26m检测+SAM2零样本分割+微调DINOv2或EfficientNet-B3分类);微调DINOv2宏F1达0.926,优于EfficientNet-B3、ResNet-50、Swin-Small基线(0.886-0.901);最佳配置mAP@50=0.828,较YOLO26m单模型提升约8%。","MDPI Agriculture","2026-09-17T00:00:00Z",{"impact":164,"substance":18,"depth":17,"authority":167,"freshness":20,"relevant":21,"comment":370},"方法组合新颖、数据规模与对比基线扎实，对温室番茄生理性叶坏死自动识别有实用价值，值得进入每日精选。",[372],{"name":367,"url":365},[26,226,27,191,374],"病害识别",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":378},{"candidates":379,"agricultural_evidence":45},[380,384,388,392],{"code":32,"score":235,"supported":45,"title":381,"abstract":382,"tags":383},[191],[191],[191],{"code":60,"score":256,"supported":45,"title":385,"abstract":386,"tags":387},[],[186],[],{"code":248,"score":74,"supported":14,"title":389,"abstract":390,"tags":391},[],[],[226,374],{"code":66,"score":21,"supported":14,"title":393,"abstract":394,"tags":395},[],[],[26],[397,398],"番茄叶坏死 实例分割","农业人工智能 智慧农业 病害识别 设施农业","番茄叶坏死实例分割-2904",{"doi":9,"openalex_id":9,"authors":401,"venue":9,"cited_by_count":95,"oa_url":9,"card":402,"direction":100,"ingested_from":210},[],{"tldr":403,"method":404,"finding":405,"direction":100,"opportunity":406},"用YOLO26m+SAM2+DINOv2多步管道分割并分类高湿诱导的番茄叶坏死斑。","218张RGB图像、3218个标注，评估6种实例分割管道并微调DINOv2分类。","微调DINOv2宏F1达0.926，最佳配置mAP@50=0.828，较单模型提升约8%。","可探索零样本基础模型在多种非生物胁迫症状上的泛化与轻量化温室部署。","2026-09-19T00:06:09.021594Z"]