[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-595":3,"related-595":73},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":8,"published_at":11,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":24,"tags":26,"primary_topic":31,"secondary_topics":37,"classification":38,"search_phrases":67,"slug":70,"view_count":71,"doi":8,"paper":8,"created_at":72},595,"山西大同定调'十五五'农业方向:打造国家级冷凉蔬菜产业基地","https:\u002F\u002Fwww.agri.cn\u002Fsj\u002Fscdt\u002F202608\u002Ft20260813_8864053.htm","山西大同市委副书记、市长刘俊义在'开局起步十五五'系列主题首场新闻发布会上表示,'十五五'期间大同将建设特优农业和有机旱作基地。设施蔬菜现有 16 万亩基础上再新增 7 万亩,达 23 万亩、年产值突破 40 亿元,打造国家级冷凉蔬菜产业基地和京津冀'菜篮子';肉蛋奶总产量预计达 90 万吨,杂粮优品率超 80%、精深加工超 50%。",null,"[![Image 1](https:\u002F\u002Fwww.agri.cn\u002Fimages\u002Fny_logo.png)](https:\u002F\u002Fwww.agri.cn\u002F)\n\n[无障碍](javascript:void(0);)[中华人民共和国农业农村部](https:\u002F\u002Fwww.moa.gov.cn\u002F)\n\n*   [首页](https:\u002F\u002Fwww.agri.cn\u002F)\n*  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\n\n分享:[](https:\u002F\u002Fsns.qzone.qq.com\u002Fcgi-bin\u002Fqzshare\u002Fcgi_qzshare_onekey?url=https:\u002F\u002Fwww.agri.cn\u002Fsj\u002Fscdt\u002F202608\u002Ft20260813_8864053.htm&title=%E5%B1%B1%E8%A5%BF%E5%A4%A7%E5%90%8C%E5%AE%9A%E8%B0%83%E2%80%9C%E5%8D%81%E4%BA%94%E4%BA%94%E2%80%9D%E5%86%9C%E4%B8%9A%E6%96%B9%E5%90%91%EF%BC%9A%E6%89%93%E9%80%A0%E5%9B%BD%E5%AE%B6%E7%BA%A7%E5%86%B7%E5%87%89%E8%94%AC%E8%8F%9C%E4%BA%A7%E4%B8%9A%E5%9F%BA%E5%9C%B0 \"qq空间\")[](javascript:void(0); \"微信\")[](http:\u002F\u002Fservice.weibo.com\u002Fshare\u002Fshare.php?url=https:\u002F\u002Fwww.agri.cn\u002Fsj\u002Fscdt\u002F202608\u002Ft20260813_8864053.htm&title=%E5%B1%B1%E8%A5%BF%E5%A4%A7%E5%90%8C%E5%AE%9A%E8%B0%83%E2%80%9C%E5%8D%81%E4%BA%94%E4%BA%94%E2%80%9D%E5%86%9C%E4%B8%9A%E6%96%B9%E5%90%91%EF%BC%9A%E6%89%93%E9%80%A0%E5%9B%BD%E5%AE%B6%E7%BA%A7%E5%86%B7%E5%87%89%E8%94%AC%E8%8F%9C%E4%BA%A7%E4%B8%9A%E5%9F%BA%E5%9C%B0 \"新浪微博\")\n\n黄花、小米、苦荞……近年来，一批带有鲜明地域特色的农产品，正成为山西大同推进乡村振兴、带动农民增收的重要抓手。进入“十五五”，大同将进一步深耕农业赛道，把“土特产”做成富民大产业。\n\n11日，中共山西省委宣传部、山西省政府新闻办公室举行“开局起步‘十五五’”系列主题首场新闻发布会，大同市委副书记、市长刘俊义表示，未来五年，大同将建设特优农业和有机旱作基地，推动资源优势转化为发展实效。\n\n黄花是大同特色农业的一张名片。目前，大同市黄花种植面积稳定在26万亩，全产业链总产值超过40亿元，1.7万脱贫人口依靠黄花实现稳定增收。\n\n“十五五”期间，大同将继续实施“特”“优”战略，推动农业由单一种植向全产业链发展延伸。大同市副市长李东升表示，大同将推广先进种植模式，推动特色蔬菜提档升级。在现有16万亩设施蔬菜基础上，未来五年再新增7万亩，总面积达到23万亩，年产值力争突破40亿元，打造国家级冷凉蔬菜产业基地和京津冀地区“菜篮子”。\n\n畜牧业也将成为农业增值的重要支撑。依托北纬40度黄金养殖带和农牧交错带优势，大同将按照“稳猪、强牛、兴羊、促禽”思路，建设晋北优质牛羊核心产区，并补齐屠宰加工短板。到2030年，肉蛋奶总产量预计达到90万吨，畜牧业年产值超过85亿元。\n\n对于小米、苦荞、红芸豆等杂粮产业，当地将重点在种业改良、标准化种植和精深加工上下功夫。李东升表示，到“十五五”末，大同杂粮优品率将超过80%，精深加工水平提升至50%以上，种植面积稳定在180万亩，推动“大同好粮”走向全国、进入高端市场。\n\n刘俊义表示，大同将大力发展黄花、设施蔬菜等富民产业，让民众“有盼头、能致富”，并把就业、增收与乡村产业发展更紧密结合起来。从一朵黄花到一篮蔬菜，从一粒杂粮到一条产业链，大同正以特色化、规模化、品牌化发展，推动农业增效、农村增活力、农民增收入。\n\n![Image 3](https:\u002F\u002Fwww.agri.cn\u002Fimages\u002Fnxw_back.png)\n返回顶部\n\n*   [外交部](https:\u002F\u002Fwww.fmprc.gov.cn\u002Fweb\u002F)\n*   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大同十五五专题","2026-08-13T00:00:00Z","报道",10,false,72,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},18,20,15,12,7,1,"报道大同市十五五农业规划，涉及冷凉蔬菜基地、畜牧业和杂粮产业，数据具体，来源权威，但为地方级动态，影响范围有限。",[25],{"name":10,"url":6},[27,28,29,30],"十五五规划","山西大同","冷凉蔬菜","有机旱作",{"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":46},[44,50,57,62],{"code":32,"score":45,"supported":46,"title":47,"abstract":48,"tags":49},11,true,[34],[34],[],{"code":51,"score":52,"supported":46,"title":53,"abstract":54,"tags":56},"B7",3,[],[55],"设施蔬菜",[],{"code":58,"score":22,"supported":14,"title":59,"abstract":60,"tags":61},"H3",[],[],[30],{"code":63,"score":22,"supported":14,"title":64,"abstract":65,"tags":66},"J1",[],[],[27],[68,69],"十五五规划 冷凉蔬菜 山西大同 有机旱作","十五五规划 冷凉蔬菜","十五五规划冷凉蔬菜山西大同有机旱作-595",4,"2026-08-16T23:55:38.775649Z",{"total":74,"page":22,"page_size":74,"items":75},6,[76,138,195,252,309,353],{"id":77,"title":78,"url":79,"summary":80,"summary_zh":81,"content":8,"source_name":82,"source_url":79,"published_at":83,"category":84,"cover_url":8,"hotness":13,"is_selected":14,"score":85,"score_detail":86,"sources":90,"tags":92,"primary_topic":98,"secondary_topics":99,"classification":100,"search_phrases":118,"slug":121,"view_count":122,"doi":123,"paper":124,"created_at":137},5565,"A Unified Survival-Based Energy Framework Linking Plant Color, Stress, and Productivity","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23540961","Plant coloration, expressed through leaf greenness, floral pigmentation, and fruit color intensity, reflects the integrated outcome of pigment biosynthesis, optical absorption, metabolic regulation, and energy–mass exchange with the environment. While the biochemical pathways governing the synthesis and degradation of major pigment classes—including chlorophylls, carotenoids, and anthocyanins—are well established, existing models largely interpret color expression as a localized biochemical phenomenon. Such approaches do not explicitly account for whole-plant energy balance, transpiration-driven losses, or thermodynamic constraints, limiting their ability to explain widely observed patterns of color saturation, stress-induced fading, and species-specific stability across environmental gradients. Here we develop a unified framework for plant color generation and stability that treats pigmentation as a survival-limited energetic process embedded within the broader dynamics of absorption, transport, metabolism, and entropy production. Central to the framework is a dimensionless energy survival factor, Ψ=AE\u002FTE+ε where AE represents absorbed and retained energetic and elemental resources relevant to pigment formation, TE denotes transpiration-linked, metabolic, and environmental dissipation losses, and ε captures irreducible entropy-producing degradation inherent to non-equilibrium biological systems. By construction, 0\u003CΨ\u003C1, ensuring full compliance with the second law of thermodynamics. When combined with pigment-specific optical absorption coefficients and physiological limitation functions, the framework yields a physically interpretable description of both pigment concentration and color stability. Empirical ranges for absorbed photosynthetically active radiation (≈45–70%), excitation survival (≈85–95%), and respiratory and transpiration losses (≈30–60% of assimilated resources) naturally constrain Ψ, predicting observed limits on sustained pigment accumulation without invoking biochemical inefficiency or ad hoc correction factors. The analysis demonstrates that increased light availability or enhanced biosynthetic capacity alone does not intensify coloration unless absorbed resources survive transport and loss processes. Stress-induced chlorosis, transient anthocyanin accumulation, and saturation of greenness under high irradiance emerge as direct consequences of reduced energy survival rather than impaired pigment synthesis. By explicitly separating survival from synthesis, the framework reconciles molecular-scale pigment biochemistry with whole-plant and ecosystem-scale color dynamics. This survival-based formulation provides a unified physical basis for interpreting plant color variation across species and environments and offers a scalable foundation for crop stress diagnostics, remote-sensing interpretation, and comparative plant phenotyping. More broadly, it extends survival-limited energy principles to visible plant traits, advancing plant science beyond isolated biochemical descriptions toward a systems-level understanding of how energy governs phenotypic expression.","植物色泽通过叶片绿色度、花色苷着色和果实颜色强度体现，反映了色素生物合成、光学吸收、代谢调控以及与环境的能量-物质交换的综合结果。尽管主要色素类别——包括叶绿素、类胡萝卜素和花青素——的合成与降解生化途径已较为明确，但现有模型大多将颜色表达解释为局部生化现象。此类方法未明确考虑整株植物能量平衡、蒸腾驱动的损耗或热力学约束，限制了其解释广泛观察到的颜色饱和度、胁迫诱导褪色以及跨环境梯度的物种特异性稳定性等模式的能力。本文构建了一个统一的植物颜色生成与稳定性框架，将色素沉着视为一种受生存限制的能量过程，并将其嵌入吸收、运输、代谢和熵产生的更广泛动力学之中。该框架的核心是一个无量纲能量生存因子Ψ=AE\u002FTE+ε，其中AE代表与色素形成相关的被吸收并保留的能量和元素资源，TE表示与蒸腾相关的、代谢的和环境的耗散损失，ε则捕捉非平衡生物系统固有的不可约熵产生降解。根据构建方式，0\u003CΨ\u003C1，确保完全符合热力学第二定律。当与色素特异性光学吸收系数和生理限制函数相结合时，该框架可对色素浓度和颜色稳定性给出具有物理可解释性的描述。吸收性光合有效辐射（约45–70%）、激发能存活率（约85–95%）以及呼吸和蒸腾损失（约占同化资源的30–60%）的经验范围自然约束了Ψ，从而预测出所观察到的持续色素积累限度，而无需引入生化低效性或特设校正因子。分析表明，仅增加光照可用性或增强生物合成能力并不会加深颜色，除非被吸收的资源能够在运输和损失过程中存活下来。胁迫诱导的黄化、瞬时花青素积累以及高辐照下绿色度饱和，均表现为能量生存率降低而非色素合成受损的直接后果。通过明确分离生存","Zenodo (CERN European Organization for Nuclear 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项科研成果集中亮相，4 家企业现场发布需求，8 个项目签约金额合计 705 万元。签约项目包括水稻品种「申优331」十五年独占许可（全国）、「上研1820」金针菇品种权转让、「申香1726」香菇品种两年普通使用许可（山西）、「珍珠蜜1号」甜瓜品种转让、远缘杂交创制口感型花苔类蔬菜新种源技术等，合作辐射上海、安徽、山西、山东、湖北等多地。","  日前，以“科技赋能 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国内首款华南型黄瓜育种芯片落地青岛","https:\u002F\u002Fwww.farmer.com.cn\u002F2026\u002F10\u002F20\u002F991031341.html","青岛市农科院联合山东极智生物科技发布华南型黄瓜专用5K液相育种芯片V1.0，是国内首款针对华南型黄瓜遗传背景设计的专用育种芯片，筛选5300多个核心位点（含78个功能前景位点），整体检出率97.57%。依托40份核心种质资源全基因组重测序构建，芯片把瓜类回交转育周期从5至6代约三年缩短到2至3代、一年半，育种周期整体有望缩短一半。这是国产分子工具替代外来方案、支撑种业自主可控的关键一步，也说明AI与基因数据结合正在把传统育种推向精准化。","在青岛市农科院的大棚里，黄瓜幼苗绿意正浓。科研人员弯下腰，用镊子轻轻夹起一片嫩叶，剪下零点几克，送进实验室。别小看这片叶子，它很快就能换来一份黄瓜的“基因体检报告”。\n\n日前，在青岛市华南型黄瓜育种技术创新与产业发展研讨会暨专用育种芯片推介会上，由青岛市农科院联合山东极智生物科技有限公司研发的华南型黄瓜专用5K液相育种芯片V1.0正式发布。这是国内首款针对华南型黄瓜遗传背景设计的专用育种芯片，标志着我国华南型黄瓜生物育种在自主可控的国产化工具上迈出关键一步。\n\n“育种芯片，其实就是一把给黄瓜做体检的尺子。”青岛市农科院蔬菜研究所副研究员、黄瓜课题负责人张功臣介绍，这款芯片针对华南型黄瓜量身设计，“5K”代表5000多个位点，实际筛选了5300多个核心位点，液相芯片相当于把多把“分子尺子”集成在一起。只需取零点几克叶片提取DNA，就能判断材料是否抗病、品质如何、有没有刺瘤、带不带苦味——尤其苦味，可以在早期直接筛掉，种到田里的都是目标材料。\n\n省下的时间，账算得清楚。过去传统育种要把大量材料种进大棚，1万份材料需要很多地，从播种到长成耗时漫长；如今在实验室短时间内即可完成成千上万份材料的检测，精筛后再下田。张功臣以瓜类回交转育为例：过去完成一个回交转育过程需要5至6代、约三年，芯片辅助背景选择后只需2至3代，缩短到一年至一年半，育种周期有望整体缩短一半。\n\n为何要专门为华南型黄瓜研发芯片？张功臣解释，青岛拥有特色华南型黄瓜种质资源，本地产业在国内占比高、地位重要，聚集了不少育种企业，青岛特色蔬菜“鲁黄瓜3号”正是其中代表。然而华南型黄瓜育种长期依赖表型经验选择，周期长、效率低；现有高通量分子检测工具多依赖外来技术方案，成本高、部分标记位点与华南型遗传背景适配度不足。加之市场对品质、营养的要求越来越高，传统杂交育种育成一个品种往往需要5至8年，把现代生物育种与传统育种相结合，成为必然选择。\n\n芯片的“硬核”，藏在一组数据里。研发依托40份遗传差异显著的华南型黄瓜核心种质资源，通过全基因组重测序结合生物信息学分析，筛选获得5373个高质量核心标记位点，其中含78个功能前景位点、5295个背景位点。经多批次材料实测，芯片整体检出率达97.57%，分型精准、稳定性优异，突破了传统分子标记通量低、靶向性差的技术瓶颈。\n\n与会专家指出，这款芯片填补了华南型黄瓜高通量分子检测工具的技术短板。依托产学研协同模式，科研成果转化为可落地的商业化技术服务，对保护黄瓜种质资源、提升我国华南型黄瓜育种自主创新能力具有重要现实意义。\n\n在种业企业看来，芯片的价值同样直接。山东极智生物科技有限公司育种事业部总监荆鑫介绍，芯片可通过材料检测判断遗传背景、亲缘关系，帮助育种单位评估产量、品质、抗性等潜力。相比部分高密度芯片，这款5K液相芯片既满足位点需求，检测速度更快、分析周期更短，成本也有望降低，更适合规模化育种应用。\n\n“芯片一定要被使用，才能发挥更大价值。”张功臣说。从实验室到田间，这把国产“分子尺子”正在加速落地，为“以国产芯育中国种”注入新动能，也让黄瓜品种更快更新迭代，端上百姓餐桌。","农民日报 2026-10-20",84,{"impact":87,"substance":318,"depth":17,"authority":20,"freshness":88,"relevant":22,"comment":319},23,"国内首款华南型黄瓜专用育种芯片发布，含位点规模、检出率等实质数据与多方信源，产业价值明确，值得进入每日精选。",[321],{"name":315,"url":312},[268,323,324,325,326],"生物育种","黄瓜","育种芯片","分子标记",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":330},{"candidates":331,"agricultural_evidence":46},[332,336,342],{"code":32,"score":20,"supported":46,"title":333,"abstract":334,"tags":335},[324],[324],[324],{"code":295,"score":21,"supported":46,"title":337,"abstract":338,"tags":341},[],[339,340],"种业","种质资源",[268],{"code":343,"score":344,"supported":14,"title":345,"abstract":346,"tags":347},"F2",2,[],[],[323,325],[349,350],"青岛农科院 华南型黄瓜 育种芯片","鲁黄瓜3号 育种芯片","青岛农科院华南型黄瓜育种芯片-5411","2026-10-21T00:09:54.078438Z",{"id":354,"title":355,"url":356,"summary":357,"summary_zh":358,"content":8,"source_name":359,"source_url":356,"published_at":360,"category":84,"cover_url":8,"hotness":13,"is_selected":14,"score":361,"score_detail":362,"sources":364,"tags":366,"primary_topic":370,"secondary_topics":371,"classification":377,"search_phrases":395,"slug":398,"view_count":122,"doi":399,"paper":400,"created_at":414},5387,"LIGHTWEIGHT FEATURE FUSED ATTENTION DRIVEN MULTI LEVEL HYBRID MOBILENET V2 FRAMEWORK FOR EARLY TOMATO LEAF DISEASE DETECTION","https:\u002F\u002Fdoi.org\u002F10.62441\u002Factainnovations.vi.1918","It is crucial to find and treat tomato crop diseases early and accurately to improve crop efficiency and make sure there is no scarcity of crop. However, computerized analysis is provocative due to lighting differences, compound backgrounds, and graphic likenesses among infection regions. Research work proposed a lightweight Attention Driven Multi Level Hybrid MobileNet-V2 algorithm that fuses the handcrafted texture structures with Deep Learning (DL) features, then image preprocessing is incorporated using Gaussian, Bilateral, and Guided filters to minimize noise by preserving the disease lesions. LAB color model along with Contrast Limited Adaptive Histogram Equalization (CLAHE) are used to improve the contrast. Otsu thresholding is involved to effectively separate the leaf segment from diseased parts. Local Binary Pattern (LBP) and Gray Level Co-occurrence Matrix (GLCM) descriptors are used to obtain fine-grained features, while high level features are gained by pretrained MobileNet-V2 model. Features fusion is fed to the fully connected layers of deep learning model for training and validation purpose. Results have revealed that the suggested model attains better classification performance as compared to existing models, making it an effective explanation for real time tomato leaf disease detection for smart agriculture applications.","早期准确发现并治疗番茄作物病害，对于提高作物效率、确保作物不短缺至关重要。然而，由于光照差异、复杂背景以及感染区域之间的图形相似性，计算机化分析具有挑战性。研究提出了一种轻量级注意力驱动多层混合MobileNet-V2算法，该算法将手工纹理结构与深度学习（DL）特征相融合，然后结合高斯、双边和引导滤波器进行图像预处理，以在保留病害病变的同时最小化噪声。采用LAB颜色模型和对比度受限自适应直方图均衡化（CLAHE）来提高对比度。引入Otsu阈值分割以有效分离叶片片段与病害部分。使用局部二值模式（LBP）和灰度共生矩阵（GLCM）描述符获取细粒度特征，同时通过预训练的MobileNet-V2模型获取高层特征。特征融合后输入深度学习模型的全连接层进行训练和验证。结果表明，与现有模型相比，所提出的模型取得了更好的分类性能，使其成为智慧农业应用中实时番茄叶片病害检测的有效解决方案。","Acta Innovations","2026-10-18T00:00:00Z",64,{"impact":20,"substance":17,"depth":146,"authority":13,"freshness":219,"relevant":22,"comment":363},"提出融合手工纹理特征与MobileNet-V2的轻量番茄叶病检测方法，有一定方法新颖性，但属单篇论文、应用范围有限，未达每日精选门槛。",[365],{"name":359,"url":356},[93,208,367,368,369],"深度学习","番茄病害","图像识别",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[372],{"code":230,"slug":373,"name":374,"path":375,"parent_code":376},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"status":39,"locked":14,"version":40,"method":41,"evidence":378},{"candidates":379,"agricultural_evidence":46},[380,385,390],{"code":32,"score":19,"supported":46,"title":381,"abstract":383,"tags":384},[382],"tomato",[174,382],[368],{"code":230,"score":88,"supported":46,"title":386,"abstract":387,"tags":389},[],[367,388],"deep learning",[208,369,367],{"code":109,"score":21,"supported":46,"title":391,"abstract":392,"tags":394},[],[93,393],"smart agriculture",[93],[396,397],"MobileNet-V2 番茄叶片病害","番茄叶病 深度学习 检测","MobileNet-V2番茄叶片病害-5387","10.62441\u002Factainnovations.vi.1918",{"doi":399,"openalex_id":401,"authors":402,"venue":359,"cited_by_count":122,"oa_url":408,"card":409,"direction":191,"ingested_from":136},"W7222977034",[403,405],{"name":404,"orcid":8},"Mrs. Vijetalaxmi A Hegde",{"name":406,"orcid":407},"Abhay Ramakant Kasetwar","https:\u002F\u002Forcid.org\u002F0000-0002-5800-8764","https:\u002F\u002Factainnovations.com\u002Findex.php\u002Fpub\u002Farticle\u002Fdownload\u002F1918\u002F981",{"tldr":410,"method":411,"finding":412,"direction":249,"opportunity":413},"提出轻量注意力多级混合MobileNet-V2框架，融合手工纹理与深度学习特征实现番茄叶片病害早期检","高斯\u002F双边\u002F引导滤波去噪，LAB+CLAHE增强，Otsu分割，LBP与GLCM","所提模型分类性能优于现有模型，可有效用于智慧农业实时番茄叶片病害检测。","可探索多作物跨域泛化、田间复杂光照下轻量模型部署与可解释性，及病害早期分级检测。","2026-10-20T23:30:10.424511Z"]