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quality and soil biological diversity in long-term organic and IPM apple orchards","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1943487","Introduction While several studies have compared fruit quality in organic and conventional apple production, integrated assessments of fruit quality and soil biological diversity remain limited. Majority of previous studies have been based on relatively short-term experimental comparisons, although responses of soil biota and soil properties often require many years of consistent management to become fully expressed. Methods This study compared yield, fruit quality, and soil biological diversity in long-term organic (ORG) apple orchard and long-term integrated pest management (IPM) orchard under Nordic conditions. The study was conducted in 2022–2024 at the Polli Horticultural Research Centre (Estonia) using the cultivars “Katre”, “Krista,” and “Alesja”. Apple, yield, fruit quality attributes (soluble solids and titratable acids content and their ratio, ascorbic acid content and total polyphenols, total anthocyanins, fruit firmness, fruit colour, fruit mineral elements content), soil chemical parameters, earthworm and springtail communities were analysed. Results and Discussion Higher total and fresh-market yields were observed in the IPM orchard in two of the three cultivars, whereas the proportion of fresh-market fruit did not differ consistently between the two orchards. When averaged across cultivars, apples from ORG orchard exhibited more intense peel coloration. Differences in taste-related traits and antioxidant composition between cultivation systems were cultivar- and year-dependent rather than consistently associated with cultivation system. Total polyphenol content was consistently higher under ORG management only in “Katre,” while “Alesja” exhibited the highest total polyphenol and anthocyanin contents irrespective of cultivation system, highlighting the dominant role of genotype. Soil in the ORG orchard had higher pH and greater phosphorus and calcium contents, supporting a higher proportion of anecic earthworms, particularly Aporrectodea longa . Total earthworm abundance was higher in the IPM orchard, springtail abundance did not differ significantly between systems. These results suggest that the conditions in the long-term ORG were more favourable for ecologically important earthworm groups without consistently affecting internal fruit quality compared to IPM orchard.","引言 尽管已有若干研究比较了有机与常规苹果生产中的果实品质，但结合果实品质与土壤生物多样性的综合评价仍较为有限。以往研究大多基于相对短期的试验比较，而土壤生物区系和土壤性质的响应往往需要多年持续一致的管理才能充分显现。方法 本研究在北欧条件下比较了长期有机（ORG）苹果园与长期综合病虫害管理（IPM）苹果园的产量、果实品质及土壤生物多样性。研究于2022—2024年在爱沙尼亚Polli园艺研究中心开展，供试品种为“Katre”“Krista”和“Alesja”。分析了苹果产量、果实品质指标（可溶性固形物与可滴定酸含量及其比值、抗坏血酸含量、总多酚、总花青素、果实硬度、果实色泽、果实矿质元素含量）、土壤化学参数、蚯蚓和跳虫群落。结果与讨论 在3个品种中的2个品种上，IPM果园的总产量和鲜食市场产量较高，而鲜食市场果实比例在两种果园间并无一致差异。按品种平均，ORG果园苹果果皮色泽更深。栽培系统间口感相关性状和抗氧化成分的差异因品种和年份而异，而非与栽培系统稳定相关。总多酚含量仅在“Katre”中于ORG管理下持续较高，而“Alesja”无论栽培系统如何均表现出最高的总多酚和花青素含量，凸显了基因型的主导作用。ORG果园土壤具有较高的pH以及较高的磷和钙含量，支持了较高比例的内栖型蚯蚓，尤其是长穴居蚯蚓（Aporrectodea longa）。IPM果园蚯蚓总丰度较高，跳虫丰度在系统间无显著差异。这些结果表明，与IPM果园相比，长期ORG的条件更有利于生态上重要的蚯蚓类群，但并未对果实内部品质产生一致影响。","Frontiers in Sustainable Food Systems","2026-10-21T00:00:00Z","论文",false,66,{"impact":173,"substance":508,"depth":584,"authority":585,"freshness":586,"relevant":455,"comment":587},16,13,9,"北欧长期定位试验对比有机与IPM苹果园的果实品质与土壤生物多样性，结论有新意但属区域尺度研究，公共影响有限。",[589],{"name":578,"url":575},[591,592,593,594,595],"长期定位试验","有机果园","土壤生物多样性","苹果品质","IPM",{"code":286,"slug":287,"name":288,"path":289,"parent_code":271},[598],{"code":144,"slug":145,"name":146,"path":147,"parent_code":128},{"status":600,"locked":581,"version":4,"method":601,"evidence":602},"classified","rules",{"candidates":603,"agricultural_evidence":606},[604,613,618],{"code":286,"score":605,"supported":606,"title":607,"abstract":609,"tags":612},17,true,[608],"apple",[610,611,608],"苹果","orchard",[],{"code":144,"score":345,"supported":606,"title":614,"abstract":615,"tags":617},[145],[616,145],"土壤",[],{"code":176,"score":619,"supported":606,"title":620,"abstract":621,"tags":623},3,[],[622],"生物多样性",[],[625,626],"爱沙尼亚 Polli 苹果 有机 IPM","苹果园 蚯蚓 跳虫 土壤","爱沙尼亚Polli苹果有机IPM-5547","10.3389\u002Ffsufs.2026.1943487",{"doi":628,"openalex_id":630,"authors":631,"venue":578,"cited_by_count":424,"oa_url":575,"card":646,"direction":650,"ingested_from":652},"W7223104118",[632,635,638,640,643],{"name":633,"orcid":634},"Ulvi Moor","https:\u002F\u002Forcid.org\u002F0000-0002-8808-8494",{"name":636,"orcid":637},"Annely Kuu","https:\u002F\u002Forcid.org\u002F0000-0002-0335-6243",{"name":639,"orcid":11},"K. Kahu",{"name":641,"orcid":642},"P. Põldma","https:\u002F\u002Forcid.org\u002F0000-0002-7906-823X",{"name":644,"orcid":645},"Reelika Rätsep","https:\u002F\u002Forcid.org\u002F0000-0002-8673-9978",{"tldr":647,"method":648,"finding":649,"direction":650,"opportunity":651},"比较长期有机与IPM苹果园的果实品质和土壤生物多样性。","在爱沙尼亚长期试验园测定三个品种的产量、品质及蚯蚓和跳虫群落。","有机园果皮色泽更深、土壤pH和磷钙更高，但果实内在品质差异多随品种和年份变化。","农业绿色发展与碳","可结合长期定位试验，量化土壤动物群落与果实品质的关联机制，并拓展多区域验证。","openalex","2026-10-22T11:20:15.557635Z",{"id":655,"title":656,"url":657,"summary":658,"summary_zh":11,"content":659,"source_name":660,"source_url":11,"published_at":661,"category":662,"cover_url":11,"hotness":385,"is_selected":581,"score":663,"score_detail":664,"sources":667,"tags":669,"primary_topic":675,"secondary_topics":676,"classification":677,"search_phrases":692,"slug":695,"view_count":424,"doi":11,"paper":11,"created_at":696},5516,"上海农科院葡萄团队赴银川参加第47届世界葡萄与葡萄酒大会","https:\u002F\u002Fwww.saas.sh.cn\u002Fhzjl\u002Fgjhz\u002Fcontent_57128","10月12日至16日第47届世界葡萄与葡萄酒大会在银川举办，智慧农业研究中心葡萄团队负责人奚晓军研究员作专题报告，阐明疏穗与环剥处理对果实糖、花青素及脱落酸积累的源库调控机制。团队还观摩酿酒葡萄AI综合作业机器人成果展示，与研发单位围绕葡萄智慧装备与果园智能化管理深入交流。","2026年10月12-16日，第47届世界葡萄与葡萄酒大会在银川举办，智慧农业研究中心葡萄团队负责人奚晓军研究员一行赴会交流。团队围绕“提升葡萄种植生产韧性与可持续发展能力”主题，向全球同行展示最新科研成果。\n\n会上，奚晓军作“Changes in sugar, anthocyanin and ABA accumulation in response to cluster thinning and girdling in ‘Jumeigui’grape”专题报告。报告围绕葡萄疏穗与环剥处理开展研究，阐明源库比对果实糖、花青素及脱落酸积累的调控机制，为葡萄优质栽培技术提供理论支撑，报告内容引发国内外园艺专家广泛研讨。参会期间，团队前往银川国际会展中心观摩酿酒葡萄 AI 综合作业机器人成果展示，详细了解该装备在葡萄园智能作业方面的技术特点与应用前景，并与研发单位围绕葡萄智慧装备研发、果园智能化管理等方向开展深入交流。\n\n本次参会是葡萄团队持续推进科研国际化、对标国际前沿开展研究的重要实践。","上海市农业科学院 2026-10-19","2026-10-19T00:00:00Z","报道",65,{"impact":584,"substance":605,"depth":585,"authority":585,"freshness":665,"relevant":455,"comment":666},6,"国际学术会议交流并涉及葡萄AI作业机器人观摩，有实质科研内容与行业参考价值，但属团队参会动态，影响面有限。",[668],{"name":660,"url":657},[670,671,672,673,674],"智慧农业","智能农机","葡萄","栽培技术","国际交流",{"code":286,"slug":287,"name":288,"path":289,"parent_code":271},[],{"status":600,"locked":581,"version":4,"method":601,"evidence":678},{"candidates":679,"agricultural_evidence":606},[680,684,688],{"code":286,"score":416,"supported":606,"title":681,"abstract":682,"tags":683},[672],[672],[672],{"code":16,"score":400,"supported":606,"title":685,"abstract":686,"tags":687},[],[670],[670],{"code":112,"score":455,"supported":581,"title":689,"abstract":690,"tags":691},[],[],[671],[693,694],"上海农科院 葡萄 银川","世界葡萄与葡萄酒大会 奚晓军","上海农科院葡萄银川-5516","2026-10-22T00:07:04.061911Z",{"id":698,"title":699,"url":700,"summary":701,"summary_zh":702,"content":11,"source_name":578,"source_url":700,"published_at":703,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":704,"score_detail":705,"sources":708,"tags":710,"primary_topic":716,"secondary_topics":717,"classification":718,"search_phrases":734,"slug":737,"view_count":424,"doi":738,"paper":739,"created_at":764},5468,"Farm-level technical efficiency in temperate horticulture: evidence from apple, pear, and cherry growers","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1928386","Improving resource-use efficiency is fundamental to enhancing agricultural productivity and promoting sustainable horticultural development, particularly in regions where opportunities for expanding cultivated land and increasing input use are limited. This study evaluates the technical efficiency of apple, pear, and cherry growers in the Kashmir Valley and examines the socioeconomic, institutional, and regional factors associated with farm-level technical efficiency. Primary data were collected from 418 horticultural households during 2023 using a multi-stage sampling approach. Technical efficiency was first estimated separately for apple, pear, and cherry growers using an input-oriented Data Envelopment Analysis (DEA) framework under both Constant Returns to Scale (CRS) and Variable Returns to Scale (VRS) assumptions. Scale efficiency was subsequently estimated, while a pooled DEA model was employed to generate farm-level technical efficiency scores for the second-stage analysis. The determinants of technical efficiency were examined using Tobit regression, supported by a fractional logit model as a robustness check, and regional disparities were assessed using Kruskal–Wallis and Mann–Whitney non-parametric tests. The results indicate that apple growers achieved the highest overall technical efficiency, followed by pear and cherry growers. The VRS estimates reveal substantially higher levels of pure technical efficiency, whereas the scale efficiency analysis suggests that a considerable proportion of technical inefficiency arises from farms operating below their optimal production scale. The second-stage analysis identifies family size, education, irrigation facilities, access to credit, and cold storage infrastructure as significant positive factors associated with farm-level technical efficiency, while significant regional disparities are observed across the Kashmir Valley. The robustness analysis confirms the consistency of the estimated relationships. These findings highlight that improving horticultural performance requires not only efficient farm-level resource management but also stronger institutional support, investments in irrigation and post-harvest infrastructure, improved access to rural credit, and region-specific development strategies. The study contributes to the literature by integrating crop-specific and pooled efficiency analyses with socioeconomic and regional assessments, providing policy-relevant evidence for promoting resource-efficient and sustainable horticultural production.","提高资源利用效率是提升农业生产力、促进园艺产业可持续发展的基础，在耕地扩张和投入增加机会有限的地区尤为如此。本研究评估了克什米尔谷地苹果、梨和樱桃种植者的技术效率，并考察了与农场层面技术效率相关的社会经济、制度和区域因素。研究采用多阶段抽样方法，于2023年从418个园艺农户收集了一手数据。首先，在规模报酬不变（CRS）和规模报酬可变（VRS）两种假设下，运用投入导向的数据包络分析（DEA）框架分别估算苹果、梨和樱桃种植者的技术效率。随后估算规模效率，并采用混合DEA模型生成农场层面技术效率得分用于第二阶段分析。技术效率的决定因素通过Tobit回归进行检验，并以分数logit模型作为稳健性检验加以支持，区域差异则采用Kruskal–Wallis和Mann–Whitney非参数检验进行评估。结果表明，苹果种植者总体技术效率最高，梨和樱桃种植者次之。VRS估计显示纯技术效率水平显著更高，而规模效率分析表明，相当比例的技术无效率源于农场经营规模低于最优生产规模。第二阶段分析识别出家庭规模、教育、灌溉设施、信贷获取和冷库基础设施是与农场层面技术效率显著正相关的因素，同时在克什米尔谷地观察到显著的区域差异。稳健性分析证实了所估计关系的一致性。这些发现表明，提升园艺产业绩效不仅需要有效的农场层面资源管理，还需要更强的制度支持、对灌溉和采后基础设施的投资、改善农村信贷获取以及因地制宜的发展策略。本研究通过将作物特定效率分析与混合效率分析同社会经济和区域评估相结合，为促进资源利用效率提升提供了具有政策相关性的证据。","2026-10-20T00:00:00Z",73,{"impact":416,"substance":706,"depth":361,"authority":585,"freshness":586,"relevant":455,"comment":707},21,"基于418户果农一手数据，用DEA与Tobit回归分析技术效率，方法规范、结论有政策参考价值，但属区域性研究，影响范围有限。",[709],{"name":578,"url":700},[711,712,713,714,715],"可持续农业","园艺产业","苹果种植","技术效率","数据包络分析",{"code":286,"slug":287,"name":288,"path":289,"parent_code":271},[],{"status":600,"locked":581,"version":4,"method":601,"evidence":719},{"candidates":720,"agricultural_evidence":606},[721,725,730],{"code":286,"score":345,"supported":606,"title":722,"abstract":723,"tags":724},[608],[610,608],[],{"code":152,"score":665,"supported":606,"title":726,"abstract":727,"tags":729},[],[728,153],"灌溉",[],{"code":192,"score":455,"supported":581,"title":731,"abstract":732,"tags":733},[],[],[711],[735,736],"数据包络分析 可持续农业 园艺产业 技术效率","数据包络分析 可持续农业","数据包络分析可持续农业园艺产业技术效率-5468","10.3389\u002Ffsufs.2026.1928386",{"doi":738,"openalex_id":740,"authors":741,"venue":578,"cited_by_count":424,"oa_url":700,"card":758,"direction":762,"ingested_from":652},"W7223025732",[742,744,747,750,753,756],{"name":743,"orcid":11},"Seerat Sajjad",{"name":745,"orcid":746},"Khursheed Hussain Dar","https:\u002F\u002Forcid.org\u002F0000-0002-3925-0758",{"name":748,"orcid":749},"Anam Ul Haq Ganie","https:\u002F\u002Forcid.org\u002F0000-0002-4404-5011",{"name":751,"orcid":752},"Ghadda Mohamed Awad Yousif","https:\u002F\u002Forcid.org\u002F0000-0003-4183-2957",{"name":754,"orcid":755},"Mohd Salim","https:\u002F\u002Forcid.org\u002F0000-0003-0162-8433",{"name":757,"orcid":11},"Mahdy S. Othman",{"tldr":759,"method":760,"finding":761,"direction":762,"opportunity":763},"评估克什米尔谷地苹果、梨、樱桃种植户的技术效率及其影响因素。","418户调查数据，DEA测效率，Tobit回归与分数logit分析影响因素。","苹果效率最高，规模效率不足是主因；灌溉、信贷、冷库等显著提升效率。","数字乡村与农业信息化","可结合遥感与物联网数据构建果园投入产出实时效率监测，弥补自报数据局限。","2026-10-21T23:30:07.145395Z",{"id":766,"title":767,"url":768,"summary":769,"summary_zh":770,"content":11,"source_name":578,"source_url":768,"published_at":703,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":771,"score_detail":772,"sources":774,"tags":776,"primary_topic":781,"secondary_topics":782,"classification":783,"search_phrases":804,"slug":807,"view_count":424,"doi":808,"paper":809,"created_at":823},5464,"From symbolic to substantive: How subsidy design shapes cooperative roles and high-quality rural industrial development?","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1949418","Introduction Government subsidies do not necessarily produce high-quality rural industrial development (HQRID), as different subsidy designs elicit different behavioral responses from cooperatives and smallholders. This study examines how subsidy design shapes the strategies of local governments, cooperatives, and farmers. Methods Drawing on the Dingzhuang Grape Cooperative in Jurong, China, this study builds a tripartite evolutionary game model to examine how creation support and function support shape the strategies of the local government, cooperative, and farmer. Results The results distinguish two contrasting pathways. Creation support tends to induce symbolic compliance, in which cooperatives adopt formal structures without providing training, quality control, or market access, leading the system toward a low-quality equilibrium. Function support aligns cooperative incentives with verifiable performance and encourages substantive services that connect smallholders to higher-value markets. Three conclusions follow. First, the transition to substantive service requires function support that gives cooperatives sufficiently strong incentives. Second, substantive services lower the quality-premium share that farmers require, strengthening the economic case for high-quality production. Third, function support is bounded by fiscal capacity, and excessive performance-linked payments can reverse the transition. Discussion Policy should therefore tie support to measurable gains in farmer income and product quality. The study contributes to induced institutional change theory by specifying that external inducements produce substantive institutions only when rewards are tied to verifiable performance rather than to registration, a boundary condition that explains why supported organizations may remain hollow.","引言 政府补贴并不必然带来高质量的农村产业发展（HQRID），因为不同的补贴设计会引发合作社与小农户不同的行为反应。本研究考察补贴设计如何塑造地方政府、合作社与农户的策略。方法 本研究以中国句容市丁庄葡萄合作社为案例，构建三方演化博弈模型，考察创建支持与功能支持如何塑造地方政府、合作社与农户的策略。结果 结果区分出两条截然不同的路径。创建支持倾向于诱发象征性遵从，即合作社采取正式结构却不提供培训、质量控制或市场准入，使系统趋向低质量均衡。功能支持使合作社激励与可验证绩效相一致，并鼓励将小农户连接至更高价值市场的实质性服务。由此得出三点结论。第一，向实质性服务的转变需要功能支持，使合作社获得足够强的激励。第二，实质性服务降低了农户所需的质量溢价份额，强化了高质量生产的经济合理性。第三，功能支持受财政能力约束，过度的绩效挂钩支付可能逆转这一转变。讨论 因此，政策应将支持与农户收入和产品质量的可测量改善挂钩。本研究对诱致性制度变迁理论作出贡献，明确指出外部诱因只有在奖励与可验证绩效而非与登记注册挂钩时才会产生实质性制度，这一边界条件解释了受支持组织为何可能保持空壳化。",77,{"impact":584,"substance":321,"depth":361,"authority":585,"freshness":173,"relevant":455,"comment":773},"以句容葡萄合作社为案例的三方演化博弈研究，区分创建型与功能型补贴对合作社行为的影响，结论对补贴政策设计有实质参考价值。",[775],{"name":578,"url":768},[777,778,779,780],"乡村产业","合作社","小农户","补贴设计",{"code":286,"slug":287,"name":288,"path":289,"parent_code":271},[],{"status":600,"locked":581,"version":4,"method":601,"evidence":784},{"candidates":785,"agricultural_evidence":606},[786,791,795,800],{"code":286,"score":665,"supported":606,"title":787,"abstract":788,"tags":790},[],[672,789],"grape",[],{"code":496,"score":408,"supported":606,"title":792,"abstract":793,"tags":794},[],[779],[778,779],{"code":527,"score":619,"supported":606,"title":796,"abstract":797,"tags":799},[],[798],"农户收入",[],{"code":511,"score":455,"supported":581,"title":801,"abstract":802,"tags":803},[],[],[777],[805,806],"句容 丁庄 葡萄合作社","补贴设计 合作社 小农户","句容丁庄葡萄合作社-5464","10.3389\u002Ffsufs.2026.1949418",{"doi":808,"openalex_id":810,"authors":811,"venue":578,"cited_by_count":424,"oa_url":768,"card":818,"direction":762,"ingested_from":652},"W7222997429",[812,814,816],{"name":813,"orcid":11},"Man Huang",{"name":815,"orcid":11},"蒋礼文",{"name":817,"orcid":11},"Jincai Zhuang",{"tldr":819,"method":820,"finding":821,"direction":762,"opportunity":822},"用三方演化博弈分析补贴设计如何影响合作社行为与农村产业高质量发展。","构建政府、合作社、农户三方演化博弈模型，基于句容丁庄葡萄合作社案例。","创建型补贴诱发象征性合规，功能型补贴促成实质性服务并降低农户质量溢价要求。","可将补贴绩效挂钩机制与可验证数据采集结合，研究数字化考核如何抑制合作社空心化。","2026-10-21T23:30:06.732822Z",{"id":825,"title":826,"url":827,"summary":828,"summary_zh":829,"content":11,"source_name":830,"source_url":827,"published_at":831,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":832,"score_detail":833,"sources":835,"tags":837,"primary_topic":842,"secondary_topics":843,"classification":844,"search_phrases":868,"slug":871,"view_count":424,"doi":872,"paper":873,"created_at":895},5401,"A HIERARCHICAL DISTRIBUTED EDGE AI FRAMEWORK FOR REAL-TIME APPLE LEAF DISEASE CLASSIFICATION","https:\u002F\u002Fdoi.org\u002F10.22190\u002Ffuacr260506002g","The application of artificial intelligence (AI) in agriculture has transformed crop management, particularly in the detection of plant diseases. Apple trees, a key agricultural commodity, are vulnerable to various diseases, which can severely impact crop yields. Traditional disease detection methods, often reliant on manual inspections, are time-consuming and prone to error. In response, this study proposes a novel two-stage edge AI framework for apple leaf disease classification that integrates RGB imagery with distributed edge computing. The first stage performs binary classification to distinguish between healthy and diseased leaves, achieving an F1 score of 95.50%. This optimization is achieved by utilizing lightweight models on edge devices, thereby enhancing computational efficiency. The second stage employs a consensus-based multiclass classification approach, achieving a 96.44% F1 score in identifying specific diseases. This dual-stage system, supported by an edge AI architecture, ensures real-time disease detection while minimizing resource consumption and enhancing scalability in large-scale agricultural environments. The full system achieved a 95.50% accuracy and F1 score on the special dataset. The results demonstrate the system’s efficacy in controlled laboratory conditions, offering a promising solution for precise, real-time agricultural monitoring. This study underscores the potential of distributed edge AI systems for scalable and efficient disease detection in precision agriculture.","人工智能（AI）在农业中的应用已深刻改变了作物管理方式，尤其是在植物病害检测方面。苹果树作为重要的农业经济作物，易受多种病害侵袭，严重影响作物产量。传统的病害检测方法通常依赖人工检查，耗时且易出错。为此，本研究提出了一种新颖的两阶段边缘AI框架，用于苹果叶片病害分类，该框架将RGB图像与分布式边缘计算相结合。第一阶段执行二分类以区分健康叶片与患病叶片，F1分数达到95.50%。该优化通过在边缘设备上采用轻量级模型实现，从而提升了计算效率。第二阶段采用基于共识的多类分类方法，在识别具体病害方面F1分数达到96.44%。该双阶段系统在边缘AI架构的支持下，实现了实时病害检测，同时最大限度地降低了资源消耗，并增强了在大规模农业环境中的可扩展性。完整系统在专用数据集上达到了95.50%的准确率和F1分数。结果表明，该系统在受控实验室条件下具有有效性，为精准、实时的农业监测提供了一种有前景的解决方案。本研究凸显了分布式边缘AI系统在精准农业中实现可扩展且高效病害检测的潜力。","Facta Universitatis Series Automatic Control and Robotics","2026-10-18T00:00:00Z",76,{"impact":361,"substance":706,"depth":605,"authority":416,"freshness":173,"relevant":455,"comment":834},"该研究提出两级边缘AI框架，在苹果叶片病害识别上取得95%以上F1值，方法新颖、数据可靠，对精准农业实时监测有参考价值，但尚处实验室验证阶段，产业影响有限。",[836],{"name":830,"url":827},[670,838,839,840,841],"农业人工智能","边缘计算","作物病害识别","苹果病害",{"code":286,"slug":287,"name":288,"path":289,"parent_code":271},[],{"status":600,"locked":581,"version":4,"method":601,"evidence":845},{"candidates":846,"agricultural_evidence":606},[847,851,858,863],{"code":286,"score":361,"supported":606,"title":848,"abstract":849,"tags":850},[608],[288,610,608],[841],{"code":16,"score":852,"supported":606,"title":853,"abstract":854,"tags":857},7,[],[855,856],"精准农业","precision agriculture",[670],{"code":48,"score":852,"supported":606,"title":859,"abstract":860,"tags":862},[],[839,861],"edge computing",[839],{"code":24,"score":408,"supported":606,"title":864,"abstract":865,"tags":867},[],[866],"artificial intelligence",[840,838],[869,870],"苹果叶片病害 边缘AI","分布式边缘计算 病害分类","苹果叶片病害边缘AI-5401","10.22190\u002Ffuacr260506002g",{"doi":872,"openalex_id":874,"authors":875,"venue":830,"cited_by_count":424,"oa_url":827,"card":888,"direction":894,"ingested_from":652},"W7222964752",[876,879,882,885],{"name":877,"orcid":878},"Milan Grujev","https:\u002F\u002Forcid.org\u002F0009-0002-3211-6844",{"name":880,"orcid":881},"Dragan H. Stojanovic","https:\u002F\u002Forcid.org\u002F0000-0003-4893-3814",{"name":883,"orcid":884},"Miloš Ilić","https:\u002F\u002Forcid.org\u002F0000-0002-9696-2874",{"name":886,"orcid":887},"Aleksandar Milosavljević","https:\u002F\u002Forcid.org\u002F0000-0003-3057-7727",{"tldr":889,"method":890,"finding":891,"direction":892,"opportunity":893},"提出两级边缘AI框架，用RGB图像实时分类苹果叶片病害。","分布式边缘计算+轻量模型，先二分类再共识多分类。","两级系统准确率与F1均达95.50%，多分类F1为96.44%。","智慧农业 \u002F 农业物联网","仅在受控实验室验证，可延伸至田间复杂光照与多作物泛化及边缘能耗优化。","农业人工智能与决策模型","2026-10-20T23:30:33.830748Z",{"id":897,"title":898,"url":899,"summary":900,"summary_zh":11,"content":11,"source_name":901,"source_url":11,"published_at":902,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":903,"score_detail":904,"sources":906,"tags":908,"primary_topic":913,"secondary_topics":914,"classification":915,"search_phrases":926,"slug":929,"view_count":424,"doi":11,"paper":930,"created_at":938},5297,"高志永团队《基于特征优选与优化算法的干热河谷区柑橘蒸腾模拟》","https:\u002F\u002Fwww.sohu.com\u002Fa\u002F1087305323_122014422","《智慧农业(中英文)》2026年第8卷第4期刊载云南农业大学吴明清、高志永等团队论文,以10年生柑橘树为研究对象收集2020—2022年树干液流与环境因子数据;针对干热河谷加快建设智慧灌区背景下传统机理模型参数需求高、对部分参数高度敏感且误差来源复杂等难题,提出基于特征优选与优化算法的蒸腾模拟方法,可为优化灌区水分调控与配水策略提供决策支撑。","智慧农业(中英文)(2026,8(4))","2026-10-13T00:00:00Z",72,{"impact":584,"substance":706,"depth":605,"authority":585,"freshness":408,"relevant":455,"comment":905},"核心期刊论文，方法有创新且数据规模扎实，对干热河谷智慧灌区水分调控有决策参考价值，但属细分领域研究，时效性一般。",[907],{"name":901,"url":899},[670,909,910,911,912],"云南","柑橘","智慧灌区","蒸腾模拟",{"code":286,"slug":287,"name":288,"path":289,"parent_code":271},[],{"status":600,"locked":581,"version":4,"method":601,"evidence":916},{"candidates":917,"agricultural_evidence":606},[918,922],{"code":286,"score":416,"supported":606,"title":919,"abstract":920,"tags":921},[910],[910],[910],{"code":16,"score":400,"supported":606,"title":923,"abstract":924,"tags":925},[],[670],[670],[927,928],"云南农业大学 柑橘 蒸腾模拟","干热河谷 智慧灌区","云南农业大学柑橘蒸腾模拟-5297",{"doi":11,"openalex_id":11,"authors":931,"venue":11,"cited_by_count":424,"oa_url":11,"card":932,"direction":892,"ingested_from":937},[],{"tldr":933,"method":934,"finding":935,"direction":892,"opportunity":936},"用特征优选与优化算法模拟干热河谷区柑橘蒸腾。","基于2020—2022年树干液流与环境因子数据构建特征优选与优化算法模型。","该方法可降低机理模型参数依赖与敏感性，提升蒸腾模拟精度。","可探索将蒸腾模拟嵌入智慧灌区实时配水决策，研究多树种迁移与边缘部署。","agent","2026-10-19T00:07:48.135945Z",{"id":940,"title":941,"url":942,"summary":943,"summary_zh":11,"content":944,"source_name":945,"source_url":11,"published_at":946,"category":662,"cover_url":11,"hotness":385,"is_selected":581,"score":947,"score_detail":948,"sources":950,"tags":952,"primary_topic":958,"secondary_topics":959,"classification":960,"search_phrases":968,"slug":971,"view_count":424,"doi":11,"paper":11,"created_at":972},5221,"全国七成太秋甜柿种苗来自安徽合肥 省农科院团队赋能高附加值苗木产业","https:\u002F\u002Fwww.aaas.org.cn\u002F4303153\u002F71456725.html","2026年10月11日,安徽省农业科学院园林植物与景观工程团队研究员任杰介绍全国70%太秋甜柿种苗来自合肥,年培育优质种苗120万株,带动枞阳县麒麟镇新安村苗木产业由传统向高附加值转型,太秋甜柿亩均效益约2万元,永恒之火紫荆彩叶苗木亩均效益可达5万元以上。","10月11日，大皖新闻记者在肥西县官亭镇一个示范基地看到，该团队帮助企业引进培育的永恒之火彩叶紫荆新品种，叶形美观、颜色绚丽夺目。任杰介绍，“它春季叶片出来之前，满枝还开紫红色的小花，观赏期长，观赏价值突出，在园林市场，再过20年也不会过时。亩均效益可达5万元以上。”\n\n据介绍，安徽省农业科学院园林植物与景观工程团队指导合肥市苗木企业培育的这两项“拳头产品”，都有着广阔的市场空间。（大皖新闻记者 赵明玉）","安徽省农业科学院(2026-10-11)","2026-10-11T00:00:00Z",64,{"impact":361,"substance":584,"depth":416,"authority":416,"freshness":665,"relevant":455,"comment":949},"省级农科院团队赋能甜柿种苗产业的实质报道，有全国占比数据与品种细节，但属细分行业进展，时效稍旧。",[951],{"name":945,"url":942},[953,954,955,956,957],"农科院","种苗产业","太秋甜柿","高附加值苗木","彩叶紫荆",{"code":286,"slug":287,"name":288,"path":289,"parent_code":271},[],{"status":600,"locked":581,"version":4,"method":601,"evidence":961},{"candidates":962,"agricultural_evidence":606},[963],{"code":286,"score":524,"supported":606,"title":964,"abstract":966,"tags":967},[965],"甜柿",[965],[],[969,970],"安徽省农科院 太秋甜柿","合肥 甜柿种苗","安徽省农科院太秋甜柿-5221","2026-10-18T00:05:23.873469Z",{"id":974,"title":975,"url":976,"summary":977,"summary_zh":11,"content":11,"source_name":978,"source_url":11,"published_at":979,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":980,"score_detail":981,"sources":983,"tags":985,"primary_topic":988,"secondary_topics":989,"classification":990,"search_phrases":1009,"slug":1012,"view_count":424,"doi":11,"paper":1013,"created_at":1020},5130,"DefoliationCAM:面向葡萄藤食叶严重度实时评估的边缘AI系统 [预印本]","https:\u002F\u002Farxiv.org\u002Fabs\u002F2610.20879","Fuchen Li等提出DefoliationCAM(D-CAM),一种太阳能供电的边缘AI系统,用于估算葡萄藤树冠食叶程度并通过LoRa将结果传输至Web仪表盘。流水线使用YOLOX-Nano在4K图像中定位葡萄藤主干,并使用基于MobileNet的轻量级通道注意力分类器对320×320像素的斑块进行分类。29,674个田间采集斑块被标注为背景或四种损伤等级之一;合并中度和重度类别将类别不平衡比从约40:1降至8.8:1。合成增强使macro-F1从0.768提升至0.838。分类器以更少的参数量(1.08M vs 23.52M)在macro-F1上优于ResNet-50。利用TensorRT FP16在NVIDIA Jetson Orin Nano上每张4K图像处理时间约1.5秒,完整运行周期57-60秒。两台设备为期五周的田间部署验证了自主太阳能运行、LoRa通信和远程食叶监测的可行性。","arXiv 2610.20879 2026 年 10 月 14 日","2026-10-14T00:00:00Z",75,{"impact":584,"substance":321,"depth":361,"authority":524,"freshness":173,"relevant":455,"comment":982},"预印本提出太阳能边缘AI系统，实现葡萄藤食叶严重度实时评估与LoRa远程监测，方法新颖、数据规模大、田间验证充分，具智慧农业应用价值。",[984],{"name":978,"url":976},[670,838,839,986,987],"葡萄藤","病害评估",{"code":286,"slug":287,"name":288,"path":289,"parent_code":271},[],{"status":600,"locked":581,"version":4,"method":601,"evidence":991},{"candidates":992,"agricultural_evidence":606},[993,997,1001,1005],{"code":286,"score":524,"supported":606,"title":994,"abstract":995,"tags":996},[672],[672],[],{"code":16,"score":455,"supported":581,"title":998,"abstract":999,"tags":1000},[],[],[670],{"code":24,"score":455,"supported":581,"title":1002,"abstract":1003,"tags":1004},[],[],[838],{"code":48,"score":455,"supported":581,"title":1006,"abstract":1007,"tags":1008},[],[],[839],[1010,1011],"DefoliationCAM 葡萄藤 食叶","Jetson Orin Nano 边缘AI 葡萄","DefoliationCAM葡萄藤食叶-5130",{"doi":11,"openalex_id":11,"authors":1014,"venue":11,"cited_by_count":424,"oa_url":11,"card":1015,"direction":892,"ingested_from":937},[],{"tldr":1016,"method":1017,"finding":1018,"direction":892,"opportunity":1019},"提出太阳能边缘AI系统DefoliationCAM，实时评估葡萄藤食叶严重度并远程传输。","YOLOX-Nano定位主干，MobileNet通道注意力分类，TensorRT","轻量分类器macro-F1达0.838，优于ResNet-50，田间部署验证可行。","可探索多作物迁移、损伤等级细化及边缘模型自适应更新以提升泛化性。","2026-10-17T00:13:44.932340Z",{"id":1022,"title":1023,"url":1024,"summary":1025,"summary_zh":11,"content":1026,"source_name":1027,"source_url":11,"published_at":1028,"category":662,"cover_url":11,"hotness":385,"is_selected":581,"score":1029,"score_detail":1030,"sources":1034,"tags":1036,"primary_topic":1042,"secondary_topics":1043,"classification":1044,"search_phrases":1064,"slug":1067,"view_count":455,"doi":11,"paper":11,"created_at":1068},5026,"深耕'土特产'挑稳'金扁担'——习近平总书记指引特色产业发展纪实","https:\u002F\u002Fwww.agri.cn\u002Fzx\u002Fnyyw\u002F202610\u002Ft20261015_8876548.htm","10月12日至16日,第47届世界葡萄与葡萄酒大会在宁夏银川举办,创办百余年来首次落地亚洲,宁夏葡萄酒产业综合效益超450亿元。文章系统梳理习近平总书记关于特色产业发展的重要论述:2020年6月宁夏考察为葡萄酒产业指明路径,2022年10月陕西延安考察指出陕北'非常适宜发展苹果种植',2024年6月宁夏考察关切闽宁镇近况、勉励'一定不要有浮躁心理,脚踏实地去积累,酒好不怕巷子深'。'十五五'规划纲要将'发展县域富民产业'单列一节,国务院相关文件将'培育壮大乡村特色产业'置于拓宽农民增收致富渠道首位,推动乡村产业蓬勃发展。","10月12日至16日，第47届世界葡萄与葡萄酒大会在宁夏银川举办。创办百余年来，这一业界顶级盛会首次落地亚洲。\n\n“随着人民生活水平不断提高，葡萄酒产业大有前景。”2020年6月，习近平总书记在宁夏考察时，为当地葡萄酒产业指明“提高技术水平，增加文化内涵，加强宣传推介，打造自己的知名品牌，提高附加值和综合效益”的发展路径。\n\n如今，宁夏葡萄酒产业综合效益超450亿元，成为走向世界的一张名片。\n\n发展特色产业是实现乡村振兴的重要途径，关乎农民增收致富、农业强国建设。\n\n2024年6月，习近平总书记在宁夏考察时关切询问闽宁镇近况。这里是总书记在福建工作时亲自提议福建和宁夏共建的生态移民点。\n\n“听说现在建设得很好，也有了特色产业。过去这么艰苦的一个地方，变成了出葡萄美酒的地方，令人感慨和欣慰啊！”习近平总书记动情地说。\n\n从脱贫攻坚到乡村振兴，习近平总书记一次次深入田间地头，为地方特色产业“把脉开方”。这背后饱含着对农民群众的殷殷牵挂，蕴含着对推进农业农村现代化的深邃思考。","新华社 中国农业农村信息网 2026 年 10 月 15 日","2026-10-15T00:00:00Z",81,{"impact":1031,"substance":584,"depth":1032,"authority":1032,"freshness":586,"relevant":455,"comment":1033},26,15,"央媒综述，紧扣特色产业与乡村振兴主线，宁夏葡萄酒450亿数据与大会首次落地亚洲具新闻价值，但属宣传性纪实报道，信息增量有限。",[1035],{"name":1027,"url":1024},[1037,1038,1039,1040,1041],"乡村振兴","特色产业","土特产","宁夏葡萄酒","县域富民产业",{"code":286,"slug":287,"name":288,"path":289,"parent_code":271},[],{"status":600,"locked":581,"version":4,"method":601,"evidence":1045},{"candidates":1046,"agricultural_evidence":606},[1047,1051,1055,1060],{"code":286,"score":665,"supported":606,"title":1048,"abstract":1049,"tags":1050},[],[610,672],[],{"code":511,"score":408,"supported":606,"title":1052,"abstract":1053,"tags":1054},[],[777],[1039,1038],{"code":527,"score":619,"supported":606,"title":1056,"abstract":1057,"tags":1059},[],[1058],"农民增收",[],{"code":480,"score":455,"supported":581,"title":1061,"abstract":1062,"tags":1063},[],[],[1037],[1065,1066],"宁夏 葡萄酒 世界葡萄与葡萄酒大会","宁夏葡萄酒 综合效益 450亿","宁夏葡萄酒世界葡萄与葡萄酒大会-5026","2026-10-16T00:13:02.635607Z",{"id":1070,"title":1071,"url":1072,"summary":1073,"summary_zh":1074,"content":11,"source_name":1075,"source_url":1072,"published_at":979,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":1029,"score_detail":1076,"sources":1078,"tags":1080,"primary_topic":1084,"secondary_topics":1085,"classification":1088,"search_phrases":1134,"slug":1137,"view_count":455,"doi":1138,"paper":1139,"created_at":1160},4988,"Hierarchical cross-modal spatiotemporal attention network for crop yield prediction in complex Himalayan agroecosystems","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffpls.2026.1933033","Introduction Accurate crop yield prediction is essential for precision agriculture, climate-resilient farm management, and timely decision-making. However, yield prediction remains challenging because of complex, nonlinear, and multi-scale interactions among crop physiology, soil conditions, weather variability, and management practices. Conventional statistical, machine learning, and deep learning approaches, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory (LSTM) networks, often have limitations in capturing long-range temporal dependencies, hierarchical spatial relationships, heterogeneous data modalities, and predictive uncertainty. Methods This study proposes a Hierarchical Cross-Modal Spatio-Temporal Attention Network (HCM-STAN) for crop yield prediction using a unified attention-based framework for integrating heterogeneous agro-environmental data. The architecture incorporates four principal components: (i) hierarchical spatio-temporal attention operating across tree, block, orchard, and regional scales; (ii) a cross-modal attention module for adaptive fusion of IoT-based soil sensor observations, weather data, plant physiological measurements, and drone-based multispectral imagery; (iii) a soil-aware attention head that explicitly models soil memory, hysteresis effects, and root-zone dynamics; and (iv) an uncertainty-aware prediction module combining Monte Carlo dropout and quantile regression to generate calibrated prediction intervals. The framework was evaluated using a multi-source dataset collected from Himalayan apple orchards during 2023–2026, comprising 1,247 monitored trees across three commercial orchards. Results The proposed HCM-STAN achieved an R 2 of 0.89 for four-week-ahead crop yield prediction, representing a 15.6% improvement over the CNN-LSTM baseline. Furthermore, zero-shot transfer to mango orchards achieved an R 2 of 0.67, corresponding to approximately 75% of the performance obtained under full training. Discussion The results demonstrate that integrating hierarchical spatio-temporal representation learning, cross-modal attention, soil-aware modelling, and uncertainty estimation can effectively address the heterogeneous and dynamic nature of crop yield prediction in complex mountain agroecosystems. The observed zero-shot transfer performance further indicates the potential of HCM-STAN to generalize across crop systems without crop-specific retraining. Overall, the proposed framework provides a promising foundation for reliable, uncertainty-aware yield forecasting and can support precision agriculture, climate-smart farm management, and data-driven decision-making in heterogeneous agroecosystems.","引言 准确的作物产量预测对于精准农业、气候适应性农场管理以及及时决策至关重要。然而，由于作物生理、土壤条件、天气变异和管理措施之间复杂、非线性且多尺度的相互作用，产量预测仍然具有挑战性。传统的统计方法、机器学习以及深度学习方法，包括卷积神经网络（CNN）、循环神经网络（RNN）和长短期记忆（LSTM）网络，在捕捉长程时间依赖关系、层次化空间关系、异构数据模态以及预测不确定性方面往往存在局限。方法 本研究提出了一种用于作物产量预测的层次化跨模态时空注意力网络（HCM-STAN），采用统一的基于注意力的框架来整合异构农业环境数据。该架构包含四个主要组成部分：（i）在树、地块、果园和区域尺度上运行的层次化时空注意力；（ii）用于自适应融合基于物联网的土壤传感器观测、天气数据、植物生理测量和无人机多光谱影像的跨模态注意力模块；（iii）显式建模土壤记忆、滞后效应和根区动态的土壤感知注意力头；（iv）结合蒙特卡洛 dropout 和分位数回归以生成校准预测区间的不确定性感知预测模块。该框架使用2023—2026年在喜马拉雅苹果园采集的多源数据集进行了评估，该数据集包含三个商业果园中1，247棵监测树木。结果 所提出的 HCM-STAN 在提前四周作物产量预测中达到了0.89的 R²，相较于 CNN-LSTM 基线提升了15.6%。此外，零样本迁移至芒果园达到了0.67的 R²，相当于完整训练下所获性能的约75%。讨论 结果表明，整合层次化时空表征学习、跨模态注意力、土壤感知建模和不确定性估计，能够有效应对复杂山地农业生态系统中作物产量预测的异构性和动态性。观察到的零样本迁移性能进一步表明，HCM-STAN 具有在无需作物特定再训练的情况下跨作物系统泛化的潜力。总体而言，该 p","Frontiers in Plant Science",{"impact":361,"substance":321,"depth":361,"authority":345,"freshness":586,"relevant":455,"comment":1077},"提出分层跨模态时空注意力网络，融合物联网土壤传感、气象、生理与无人机多光谱数据，苹果园四周产量预测R²达0.89并实现芒果园零样本迁移，方法新颖、数据扎实，对智慧农业产量预报有参考价值。",[1079],{"name":1075,"url":1072},[670,838,1081,1082,1083],"产量预测","多模态融合","遥感",{"code":24,"slug":25,"name":26,"path":27,"parent_code":7},[1086,1087],{"code":286,"slug":287,"name":288,"path":289,"parent_code":271},{"code":32,"slug":33,"name":34,"path":35,"parent_code":7},{"status":600,"locked":581,"version":4,"method":601,"evidence":1089},{"candidates":1090,"agricultural_evidence":606},[1091,1099,1104,1110,1114,1118,1124,1129],{"code":24,"score":524,"supported":606,"title":1092,"abstract":1093,"tags":1098},[],[1094,1095,1096,1097],"深度学习","机器学习","machine learning","deep learning",[838,1082],{"code":286,"score":586,"supported":606,"title":1100,"abstract":1101,"tags":1103},[],[610,1102,611,608],"芒果",[],{"code":32,"score":173,"supported":606,"title":1105,"abstract":1106,"tags":1109},[],[1107,1108],"无人机多光谱","multispectral",[1081,1083],{"code":16,"score":852,"supported":606,"title":1111,"abstract":1112,"tags":1113},[],[855,856],[670],{"code":144,"score":665,"supported":606,"title":1115,"abstract":1116,"tags":1117},[],[616,145],[],{"code":48,"score":665,"supported":606,"title":1119,"abstract":1120,"tags":1123},[],[1121,1122],"物联网","IoT",[],{"code":176,"score":619,"supported":606,"title":1125,"abstract":1126,"tags":1128},[],[1127],"农业生态",[],{"code":184,"score":619,"supported":606,"title":1130,"abstract":1131,"tags":1133},[],[1132],"气候适应",[],[1135,1136],"喜马拉雅 苹果园 产量预测","HCM-STAN 作物产量预测","喜马拉雅苹果园产量预测-4988","10.3389\u002Ffpls.2026.1933033",{"doi":1138,"openalex_id":1140,"authors":1141,"venue":1075,"cited_by_count":424,"oa_url":1072,"card":1155,"direction":892,"ingested_from":652},"W7221795850",[1142,1145,1147,1149,1151,1153],{"name":1143,"orcid":1144},"Shabir Ahmad Mir","https:\u002F\u002Forcid.org\u002F0000-0003-2311-218X",{"name":1146,"orcid":11},"Umarfarooque Momin",{"name":1148,"orcid":11},"Sudeep Kumar E",{"name":1150,"orcid":11},"Itfaq Ahmad Mir",{"name":1152,"orcid":11},"Ab Rouf Khan",{"name":1154,"orcid":11},"Ghulam Mohammad Mir",{"tldr":1156,"method":1157,"finding":1158,"direction":894,"opportunity":1159},"提出分层跨模态时空注意力网络HCM-STAN，融合多源农业数据预测喜马拉雅山区苹果产量。","用IoT土壤、气象、生理和无人机多光谱数据，结合分层时空与跨模态注意力及不确定性","四周前产量预测R²达0.89，比CNN-LSTM提升15.6%，零样本迁移芒果R²为0.67。","可探索跨作物零样本迁移的领域自适应机制，并验证模型在更多山地农业生态系统的泛化能力。","2026-10-15T23:30:08.750140Z",{"id":1162,"title":1163,"url":1164,"summary":1165,"summary_zh":11,"content":1166,"source_name":1167,"source_url":11,"published_at":902,"category":662,"cover_url":11,"hotness":385,"is_selected":581,"score":1168,"score_detail":1169,"sources":1171,"tags":1173,"primary_topic":1178,"secondary_topics":1179,"classification":1180,"search_phrases":1191,"slug":1194,"view_count":424,"doi":11,"paper":11,"created_at":1195},4958,"广东省农科院朱立学带队赴河北省农林科学院开展调研交流","https:\u002F\u002Fwww.gdaas.cn\u002Fywzx\u002Fcontent\u002Fpost_1340442.html","近日,广东省农科院党委副书记、院长朱立学带队赴河北省农林科学院开展调研交流,院属相关单位(部门)负责同志及专家参加。在石家庄果树研究所,调研组实地参观所史展厅,考察各类果树试验园区,与副所长杨素苗等围绕果树新品种选育、篱壁式栽培模式、果园宜机化改造、设施装备应用等方面进行深入交流。考察鹿泉大桓渊苹果基地篱壁式栽培模式、有机种植管理、功能酵素应用等创新实践。","[![Image 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18](https:\u002F\u002Fwww.gdaas.cn\u002Fpub\u002Ficon_wzjc.png)](https:\u002F\u002Fwww.gdaas.cn\u002Fsyqt\u002Fcontent\u002Fpost_1029428.html)\n\n[网站纠错](https:\u002F\u002Fwww.gdaas.cn\u002Fsyqt\u002Fcontent\u002Fpost_1029428.html)","广东省农业科学院 2026 年 10 月 13 日",39,{"impact":173,"substance":665,"depth":408,"authority":416,"freshness":173,"relevant":455,"comment":1170},"省级农科院之间的常规调研交流通稿，信息增量有限，仅具机构动态参考价值。",[1172],{"name":1167,"url":1164},[1174,1175,1176,1177],"院地合作","农业科技交流","篱壁式栽培","果树栽培",{"code":286,"slug":287,"name":288,"path":289,"parent_code":271},[],{"status":600,"locked":581,"version":4,"method":601,"evidence":1181},{"candidates":1182,"agricultural_evidence":606},[1183,1187],{"code":286,"score":665,"supported":606,"title":1184,"abstract":1185,"tags":1186},[],[288,610],[],{"code":488,"score":455,"supported":581,"title":1188,"abstract":1189,"tags":1190},[],[],[1174],[1192,1193],"广东省农科院 河北省农林科学院 调研交流","朱立学 篱壁式栽培","广东省农科院河北省农林科学院调研交流-4958","2026-10-15T00:06:03.430881Z",{"id":1197,"title":1198,"url":1199,"summary":1200,"summary_zh":1201,"content":11,"source_name":1202,"source_url":1199,"published_at":902,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":1203,"score_detail":1204,"sources":1206,"tags":1208,"primary_topic":1214,"secondary_topics":1215,"classification":1216,"search_phrases":1227,"slug":1230,"view_count":424,"doi":1231,"paper":1232,"created_at":1264},4894,"Asymptomatic colonization by Botrytis cinerea reveals polarized plant-fungus interfaces before widespread disease","https:\u002F\u002Fdoi.org\u002F10.64898\u002F2026.10.12.758352","Botrytis cinerea is a necrotrophic pathogen whose destructive growth is typically studied under conditions that rapidly generate visible disease. Yet symptomless associations are well documented, the fungal and host processes operating before widespread tissue collapse remain poorly resolved. We developped an intact Arabidopsis thaliana seedling pathosystem that extends the interval between infection establishment and widespread visible disease. Fungal colonization was quantified using qPCR and a constitutively bioluminescent reporter strain, together with longitudinal plant phenotyping, hormone profiling and multiscale fluorescence microscopy. Substantial fungal colonization occurred while seedlings remained largely green and continued to grow, whereas robust whole seedling accumulation of salicylic and jasmonic acids occurred later. During this interval, B. cinerea displayed diverse tissue-associated infection morphologies. At individual contact sites, fungal walls showed heterogeneous exposure of chitin-associated epitopes, while host cells exhibited focal plasma-membrane polarization toward fungal contact sites, together with cortical actin reorganization. These results show that the interval preceding widespread visible disease is an active and spatially heterogeneous interaction state, in which substantial fungal development and highly organized local host responses are already established. Intact seedlings therefore provide a powerful system for resolving plant-pathogen interactions that are largely obscured in rapid disease assays.","灰葡萄孢（Botrytis cinerea）是一种死体营养型病原菌，其破坏性生长通常在迅速产生可见病害的条件下进行研究。然而，无症状关联已有充分记载，在组织大面积崩溃之前真菌与宿主所进行的过程仍鲜有阐明。我们建立了一个完整的拟南芥（Arabidopsis thaliana）幼苗病理系统，延长了侵染建立与大面积可见病害之间的时间间隔。利用qPCR和组成型生物发光报告菌株对真菌定殖进行定量，并结合纵向植物表型分析、激素谱分析和多尺度荧光显微镜。在幼苗大部分仍保持绿色并持续生长期间，真菌已发生大量定殖，而整株幼苗中水杨酸和茉莉酸的大量积累则出现较晚。在此期间，灰葡萄孢表现出多样的组织相关侵染形态。在单个接触位点，真菌细胞壁显示出几丁质相关表位的异质性暴露，而宿主细胞则表现出质膜向真菌接触位点的局灶性极化，并伴随皮层肌动蛋白重组。这些结果表明，大面积可见病害出现之前的间隔期是一个活跃且空间异质的互作状态，其中大量的真菌发育和高度有序的局部宿主反应已经建立。因此，完整幼苗为解析在快速病害测定中大多被掩盖的植物-病原菌互作提供了一个强有力的系统。","bioRxiv (Cold Spring Harbor Laboratory)",80,{"impact":361,"substance":321,"depth":361,"authority":585,"freshness":586,"relevant":455,"comment":1205},"该研究揭示灰霉病菌无症状定殖阶段的植物-真菌互作机制，方法新颖、结论可靠，对设施农业病害绿色防控与抗病育种有参考价值，但属基础研究，产业影响有限。",[1207],{"name":1202,"url":1199},[1209,1210,1211,1212,1213],"设施农业","植物免疫","抗病育种","植物病理","灰霉病",{"code":286,"slug":287,"name":288,"path":289,"parent_code":271},[],{"status":600,"locked":581,"version":4,"method":601,"evidence":1217},{"candidates":1218,"agricultural_evidence":606},[1219,1223],{"code":286,"score":619,"supported":606,"title":1220,"abstract":1221,"tags":1222},[],[672],[],{"code":88,"score":455,"supported":581,"title":1224,"abstract":1225,"tags":1226},[],[],[1211],[1228,1229],"Botrytis cinerea Arabidopsis 无症状定殖","灰霉病 无症状 侵染机制","BotrytiscinereaArabidopsis无症状定殖-4894","10.64898\u002F2026.10.12.758352",{"doi":1231,"openalex_id":1233,"authors":1234,"venue":1202,"cited_by_count":424,"oa_url":1257,"card":1258,"direction":1263,"ingested_from":652},"W7221591933",[1235,1237,1239,1241,1243,1245,1247,1250,1252,1254],{"name":1236,"orcid":11},"Tguafaiti Ines",{"name":1238,"orcid":11},"Ferdinand Perrine",{"name":1240,"orcid":11},"Adrien Pons",{"name":1242,"orcid":11},"Malbert Bastien",{"name":1244,"orcid":11},"de Vallée Amélie",{"name":1246,"orcid":11},"Christophe Bruel",{"name":1248,"orcid":1249},"Mathias Choquer","https:\u002F\u002Forcid.org\u002F0000-0002-3768-160X",{"name":1251,"orcid":11},"Poussereau Nathalie",{"name":1253,"orcid":11},"Bournaud Caroline",{"name":1255,"orcid":1256},"François Gillet","https:\u002F\u002Forcid.org\u002F0000-0002-3334-1069","https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002Fbiorxiv\u002Fearly\u002F2026\u002F10\u002F13\u002F2026.10.12.758352.full.pdf",{"tldr":1259,"method":1260,"finding":1261,"direction":894,"opportunity":1262},"构建拟南芥幼苗病理系统，揭示灰霉病菌无症状定殖期的植物-真菌互作。","拟南芥幼苗活体接种、qPCR、生物发光报告菌株、激素谱与多尺度荧光显微。","广泛发病前已存在大量真菌定殖和局部宿主极化响应，激素积累滞后。","可开发早期无症状侵染的活体成像与分子标记，用于病害预警和抗性筛选。","农业遥感与作物表型","2026-10-14T23:30:22.477415Z",{"id":1266,"title":1267,"url":1268,"summary":1269,"summary_zh":1270,"content":11,"source_name":1271,"source_url":1268,"published_at":1272,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":1273,"score_detail":1274,"sources":1276,"tags":1278,"primary_topic":1283,"secondary_topics":1284,"classification":1285,"search_phrases":1304,"slug":1307,"view_count":455,"doi":1308,"paper":1309,"created_at":1340},4889,"A Distributed Multirobot Architecture for Decoupled Inspection and Localized Treatment in Precision Agriculture","https:\u002F\u002Fdoi.org\u002F10.1002\u002Faisy.70593","This work presents a distributed multirobot architecture for autonomous inspection and precision treatment in agriculture, validated under real vineyard conditions. The proposed system integrates a heterogeneous fleet of four specialized ground robots (two inspection and two treatment platforms), coordinated through a cloud‐based mission control center (MCC). The MCC acts as the cognitive layer of the system, integrating CNN‐based disease detection, mission planning, and fleet management, while the robotic platforms operate as autonomous agents responsible for sensing and actuation. Inspection and treatment processes are separated through an event‐driven, grid‐based coordination mechanism maintained by the MCC, enabling asynchronous and independent operation of both robot types without direct inter‐robot communication. Georeferenced images acquired by the inspection robots are transmitted in real time to the MCC, where the disease detection model identifies affected areas and updates the treatment grid accordingly. Experimental validation in a commercial vineyard demonstrated the feasibility of the approach: the model achieved a recall of 95.2% against expert annotations at a 16% infection prevalence, no data losses were recorded across communication streams, and 21.1% of acquired images generated treatment events. The proposed architecture is not tied to a specific task and can be extended to other precision agriculture scenarios.","本文提出了一种用于农业自主巡检与精准处理的分布式多机器人架构，并在真实葡萄园条件下进行了验证。所提出的系统集成了由四台专用地面机器人（两台巡检平台和两台处理平台）组成的异构机器人集群，并通过基于云的任务控制中心（MCC）进行协调。MCC作为系统的认知层，集成了基于CNN的病害检测、任务规划和集群管理，而机器人平台则作为负责感知与执行的自主智能体。巡检与处理过程通过MCC维护的事件驱动、基于网格的协调机制实现分离，使两类机器人能够异步独立运行，无需机器人之间的直接通信。巡检机器人采集的地理参考图像实时传输至MCC，由病害检测模型识别受影响区域并相应更新处理网格。在商业葡萄园中的实验验证表明了该方法的可行性：在16%感染率下，模型相对于专家标注达到了95.2%的召回率，各通信流中未记录到数据丢失，21.1%的采集图像触发了处理事件。所提出的架构不局限于特定任务，可扩展至其他精准农业场景。","Advanced Intelligent Systems","2026-10-12T00:00:00Z",79,{"impact":361,"substance":321,"depth":361,"authority":585,"freshness":173,"relevant":455,"comment":1275},"多机器人分布式架构在真实葡萄园验证，检出召回率95.2%，对精准农业机器人协同有参考价值。",[1277],{"name":1271,"url":1268},[670,1279,1280,1281,1282],"农业机器人","精准施药","病害检测","多机器人协同",{"code":286,"slug":287,"name":288,"path":289,"parent_code":271},[],{"status":600,"locked":581,"version":4,"method":601,"evidence":1286},{"candidates":1287,"agricultural_evidence":606},[1288,1292,1296,1300],{"code":16,"score":1032,"supported":606,"title":1289,"abstract":1290,"tags":1291},[856],[855,856],[670],{"code":286,"score":619,"supported":606,"title":1293,"abstract":1294,"tags":1295},[],[672],[],{"code":104,"score":455,"supported":581,"title":1297,"abstract":1298,"tags":1299},[],[],[1280],{"code":120,"score":455,"supported":581,"title":1301,"abstract":1302,"tags":1303},[],[],[1279],[1305,1306],"多机器人 葡萄园 病害检测","精准农业 机器人 架构","多机器人葡萄园病害检测-4889","10.1002\u002Faisy.70593",{"doi":1308,"openalex_id":1310,"authors":1311,"venue":1271,"cited_by_count":424,"oa_url":1334,"card":1335,"direction":892,"ingested_from":652},"W7221635124",[1312,1315,1318,1321,1324,1326,1328,1331],{"name":1313,"orcid":1314},"Jose Maria Bengochea-Guevara","https:\u002F\u002Forcid.org\u002F0000-0003-4081-7325",{"name":1316,"orcid":1317},"Héctor Montes","https:\u002F\u002Forcid.org\u002F0000-0001-8638-9966",{"name":1319,"orcid":1320},"María Nuria Conejero","https:\u002F\u002Forcid.org\u002F0000-0002-9372-3739",{"name":1322,"orcid":1323},"Laura Garrido-Rey","https:\u002F\u002Forcid.org\u002F0009-0002-1782-5175",{"name":1325,"orcid":11},"Carlos Ranz",{"name":1327,"orcid":11},"Angelo Vincenzo Barra",{"name":1329,"orcid":1330},"Dionisio Andújar","https:\u002F\u002Forcid.org\u002F0000-0002-5801-0944",{"name":1332,"orcid":1333},"Ángela Ribeiro","https:\u002F\u002Forcid.org\u002F0000-0001-5807-8132","https:\u002F\u002Fonlinelibrary.wiley.com\u002Fdoi\u002Fpdfdirect\u002F10.1002\u002Faisy.70593",{"tldr":1336,"method":1337,"finding":1338,"direction":892,"opportunity":1339},"提出分布式多机器人架构，在葡萄园实现异步巡检与精准治疗。","四台异构地面机器人、云任务控制中心、CNN病害检测与网格协调。","病害检测召回率95.2%，通信无数据丢失，21.1%图像触发治疗事件。","可探索去中心化协同、多作物迁移及边缘计算下的实时病害决策优化。","2026-10-14T23:30:13.144306Z",{"id":1342,"title":1343,"url":1344,"summary":1345,"summary_zh":1346,"content":11,"source_name":578,"source_url":1344,"published_at":902,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":903,"score_detail":1347,"sources":1349,"tags":1351,"primary_topic":1357,"secondary_topics":1358,"classification":1359,"search_phrases":1390,"slug":1393,"view_count":424,"doi":1394,"paper":1395,"created_at":1431},4873,"Cover plants fixed large amounts of atmospheric N, contributing to the N balance in irrigated fruit crops in the Brazilian semiarid region","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1923393","Introduction Cover crops, or cover plants, especially legumes, can counterbalance the depletion in soil organic matter, the low supply of nitrogen from the organic reserves and the losses of the inorganic fertilizers in irrigated fruit crops. Methods We tested two cover crop mixtures, one with more and the other with less legume proportions, and one treatment where the spontaneous weeds were left to grow, before planting melon or intercropping in a mango orchard. Biomass of the cover crops and the weeds were cut and incorporated or left on the soil surface. Results In general, incorporation or surface placement did not significantly differ. The cover crop aboveground biomass production in the mixtures varied from 5.82 to 11.95 Mg ha −1 y −1 while the spontaneous weeds biomass varied from 2.18 to 6.56 Mg ha −1 y −1 . The N contents of the cover crop mixtures varied from 125 to 333 kg ha −1 y −1 , while those of the spontaneous weeds from 25 to 160 kg ha −1 y −1 . The N fixation varied from 56 to 106 kg ha −1 y −1 in the cover crop mixtures and from 17 to 102 kg ha −1 y −1 in the spontaneous weeds. Discussion These amounts are of the same order as those exported with the fruits. Thus, cover crops, especially those with high legume proportions, are promising practices in tropical irrigated fruit production system, deserving further agronomic and economic studies to validate their recommendation to producers.","引言 覆盖作物或覆盖植物，尤其是豆科植物，可以抵消灌溉果树作物中土壤有机质的耗竭、有机储备氮供应不足以及无机肥料损失的问题。方法 我们在种植甜瓜或在芒果园中间作之前，测试了两种覆盖作物混播组合，一种豆科比例较高，另一种豆科比例较低，并设置了一个让自然杂草生长的处理。覆盖作物和杂草的生物量被刈割后翻压或留在土壤表面。结果 总体而言，翻压或表面覆盖之间没有显著差异。混播组合中覆盖作物的地上部生物量产量在5.82至11.95 Mg ha⁻¹ y⁻¹之间变化，而自然杂草生物量在2.18至6.56 Mg ha⁻¹ y⁻¹之间变化。覆盖作物混播组合的氮含量在125至333 kg ha⁻¹ y⁻¹之间变化，而自然杂草的氮含量在25至160 kg ha⁻¹ y⁻¹之间变化。覆盖作物混播组合的固氮量在56至106 kg ha⁻¹ y⁻¹之间变化，自然杂草的固氮量在17至102 kg ha⁻¹ y⁻¹之间变化。讨论 这些数量与果实带走的养分量处于同一数量级。因此，覆盖作物，尤其是豆科比例较高的覆盖作物，是热带灌溉水果生产体系中具有前景的实践措施，值得进一步开展农艺学和经济学研究，以验证其向生产者推广的可行性。",{"impact":416,"substance":706,"depth":605,"authority":585,"freshness":586,"relevant":455,"comment":1348},"巴西半干旱区灌溉果园覆盖作物固氮的实测数据扎实，对绿肥与果园土壤管理有参考价值，但属区域性研究，未达每日精选门槛。",[1350],{"name":578,"url":1344},[1352,1353,1354,1355,1356],"生物固氮","绿肥覆盖作物","果园生草","热带灌溉农业","土壤培肥",{"code":286,"slug":287,"name":288,"path":289,"parent_code":271},[],{"status":600,"locked":581,"version":4,"method":601,"evidence":1360},{"candidates":1361,"agricultural_evidence":606},[1362,1366,1372,1376,1381,1386],{"code":286,"score":586,"supported":606,"title":1363,"abstract":1364,"tags":1365},[],[288,1102,611],[],{"code":256,"score":665,"supported":606,"title":1367,"abstract":1368,"tags":1371},[],[1369,1370],"间作","intercropping",[],{"code":144,"score":665,"supported":606,"title":1373,"abstract":1374,"tags":1375},[],[616,145],[],{"code":96,"score":400,"supported":606,"title":1377,"abstract":1378,"tags":1380},[],[1379],"肥料",[1352],{"code":279,"score":619,"supported":606,"title":1382,"abstract":1383,"tags":1385},[],[1384],"甜瓜",[],{"code":152,"score":619,"supported":606,"title":1387,"abstract":1388,"tags":1389},[],[728],[],[1391,1392],"巴西半干旱区 覆盖作物 固氮","灌溉果园 豆科覆盖作物 氮平衡","巴西半干旱区覆盖作物固氮-4873","10.3389\u002Ffsufs.2026.1923393",{"doi":1394,"openalex_id":1396,"authors":1397,"venue":578,"cited_by_count":424,"oa_url":1344,"card":1426,"direction":650,"ingested_from":652},"W7221637808",[1398,1401,1403,1405,1408,1410,1413,1416,1418,1421,1423],{"name":1399,"orcid":1400},"Renata Janaína Carvalho de Souza","https:\u002F\u002Forcid.org\u002F0000-0002-0901-456X",{"name":1402,"orcid":11},"Vanderlise Giongo",{"name":1404,"orcid":11},"Plínio Barbosa de Camargo",{"name":1406,"orcid":1407},"Jéssica Rafaella de Sousa Oliveira","https:\u002F\u002Forcid.org\u002F0000-0002-1882-2630",{"name":1409,"orcid":11},"Edilândia Farias DANTAS",{"name":1411,"orcid":1412},"Juscélia da Silva Ferreira","https:\u002F\u002Forcid.org\u002F0000-0003-3932-3264",{"name":1414,"orcid":1415},"Cybelle Souza de Oliveira","https:\u002F\u002Forcid.org\u002F0000-0002-1060-3740",{"name":1417,"orcid":11},"Jessyca Adriana Gomes Florêncio da Silva",{"name":1419,"orcid":1420},"Paulo Ivan Fernandes‐Júnior","https:\u002F\u002Forcid.org\u002F0000-0002-6390-3720",{"name":1422,"orcid":11},"Everardo Valadares de Sá Barretto Sampaio",{"name":1424,"orcid":1425},"Ana Dolores Santiago de Freitas","https:\u002F\u002Forcid.org\u002F0000-0001-5808-097X",{"tldr":1427,"method":1428,"finding":1429,"direction":650,"opportunity":1430},"在巴西半干旱灌溉果园测试覆盖作物，发现其固氮量可抵消果实氮输出。","两种豆科比例不同的覆盖作物混播与自然杂草对比，测定生物量和固氮量。","覆盖作物固氮56-106 kg\u002Fha\u002F年，与果实氮输出相当，高豆科比例效果更佳。","可研究覆盖作物固氮与温室气体减排的协同效应，并开展长期经济可行性评估。","2026-10-14T23:30:07.161110Z",{"id":1433,"title":1434,"url":1435,"summary":1436,"summary_zh":11,"content":1437,"source_name":1438,"source_url":11,"published_at":902,"category":1439,"cover_url":11,"hotness":385,"is_selected":581,"score":1440,"score_detail":1441,"sources":1443,"tags":1445,"primary_topic":1451,"secondary_topics":1452,"classification":1453,"search_phrases":1472,"slug":1475,"view_count":424,"doi":11,"paper":11,"created_at":1476},4844,"提产能 补短板 强帮扶 增动能——山西划出'十五五'时期'三农'工作主线","https:\u002F\u002Fwww.163.com\u002Fnews\u002Farticle\u002FL94Q0MJN0001A1UG.html","山西'十五五'时期'三农'工作部署四项重点:一是产业集聚,立足资源禀赋再培育大同黄花、临猗苹果等'土特产'优势产业集群;二是标准化建设,构建从种养到销售完整标准体系,确保进入市场的每一个农产品在大小、色泽、糖度、水分上保持基本一致;三是精深加工,引进国内头部加工企业;四是品牌营销,从'卖原粮'向'卖产品'跃升,推动特优农产品走向国际市场。","相关推荐\n\n热点推荐\n\n*   [![Image 1: 为什么在香港买iPhone18回内地会被拦下？还要罚款？](https:\u002F\u002Fnimg.ws.126.net\u002F?url=http:\u002F\u002Fvideoimg.ws.126.net\u002Fcover\u002F20261013\u002FeZxzVgXvU_cover.jpg&thumbnail=140y88&quality=80&type=jpg)](https:\u002F\u002Fwww.163.com\u002Fv\u002Fvideo\u002FVN864UCSN.html?f=post2020_dy_recommends)### [为什么在香港买iPhone18回内地会被拦下？还要罚款？](https:\u002F\u002Fwww.163.com\u002Fv\u002Fvideo\u002FVN864UCSN.html?f=post2020_dy_recommends)\n\n草莓唠生活 2026-10-13 08:22:37\n\n[2 _跟贴_ 2](https:\u002F\u002Fwww.163.com\u002Fv\u002Fvideo\u002FVN864UCSN.html?f=post2020_dy_recommends) \n*   [![Image 2: 安卓阔折叠用起来怎么样？7 大 App 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外交部：愿更好助力印尼将资源优势转化为发展动能](https:\u002F\u002Fnimg.ws.126.net\u002F?url=http:\u002F\u002Fbjnewsrec-cv.ws.126.net\u002Fdoccover_gen\u002Fc38b8422_4_cover.png&thumbnail=140y88&quality=80&type=jpg)](https:\u002F\u002Fwww.163.com\u002Fdy\u002Farticle\u002FL94TLI7L05346RC6.html?f=post2020_dy_recommends)### [外交部：愿更好助力印尼将资源优势转化为发展动能](https:\u002F\u002Fwww.163.com\u002Fdy\u002Farticle\u002FL94TLI7L05346RC6.html?f=post2020_dy_recommends)\n\n新华社 2026-10-13 17:25:15\n\n[0 _跟贴_ 0](https:\u002F\u002Fwww.163.com\u002Fdy\u002Farticle\u002FL94TLI7L05346RC6.html?f=post2020_dy_recommends) \n*   [![Image 18: 76分像素神作要上手机了，12月8日安卓见](https:\u002F\u002Fnimg.ws.126.net\u002F?url=http:\u002F\u002Fbjnewsrec-cv.ws.126.net\u002Flittle3900f384b9ej00tmutax002od000sg00g0p.jpg&thumbnail=140y88&quality=80&type=jpg)](https:\u002F\u002Fwww.163.com\u002Fdy\u002Farticle\u002FL95LAK6C05561FY6.html?f=post2020_dy_recommends)### 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试玩版只给5%主线，这款近战魔法新游在藏什么？](https:\u002F\u002Fnimg.ws.126.net\u002F?url=http:\u002F\u002Fbjnewsrec-cv.ws.126.net\u002Flittle2708b81d780j00tmu3v7001vd000u000gvp.jpg&thumbnail=140y88&quality=80&type=jpg)](https:\u002F\u002Fwww.163.com\u002Fdy\u002Farticle\u002FL94LSIPM05561FY8.html?f=post2020_dy_recommends)### [试玩版只给5%主线，这款近战魔法新游在藏什么？](https:\u002F\u002Fwww.163.com\u002Fdy\u002Farticle\u002FL94LSIPM05561FY8.html?f=post2020_dy_recommends)\n\n晚星归航2 2026-10-13 15:09:18\n\n[0 _跟贴_ 0](https:\u002F\u002Fwww.163.com\u002Fdy\u002Farticle\u002FL94LSIPM05561FY8.html?f=post2020_dy_recommends) \n*   [![Image 21: 三角洲行动手游台服国际服ios安卓获取教程,一键解决游戏卡顿延迟](https:\u002F\u002Fnimg.ws.126.net\u002F?url=http:\u002F\u002Fbjnewsrec-cv.ws.126.net\u002Flittle83851926113j00tmu9py00ajd000hs0087m.jpg&thumbnail=140y88&quality=80&type=jpg)](https:\u002F\u002Fwww.163.com\u002Fdy\u002Farticle\u002FL94T3K4I0517RQV8.html?f=post2020_dy_recommends)### 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AI联动，苹果押注智能家居新入口！首款显示屏“大脑”HomeView登场，家居配件出“全家桶”](https:\u002F\u002Fnimg.ws.126.net\u002F?url=http:\u002F\u002Fbjnewsrec-cv.ws.126.net\u002Fthree832fd696e32j00tmv4rd0037d000xc00irc.jpg&thumbnail=140y88&quality=80&type=jpg)](https:\u002F\u002Fwww.163.com\u002Fdy\u002Farticle\u002FL963FELD05198NMR.html?f=post2020_dy_recommends)### [Siri AI联动，苹果押注智能家居新入口！首款显示屏“大脑”HomeView登场，家居配件出“全家桶”](https:\u002F\u002Fwww.163.com\u002Fdy\u002Farticle\u002FL963FELD05198NMR.html?f=post2020_dy_recommends)\n\n华尔街见闻官方 2026-10-14 04:26:02\n\n[0 _跟贴_ 0](https:\u002F\u002Fwww.163.com\u002Fdy\u002Farticle\u002FL963FELD05198NMR.html?f=post2020_dy_recommends) \n*   [![Image 24: 黑客把超市冰柜加热到50℃！大批食品报废](https:\u002F\u002Fnimg.ws.126.net\u002F?url=http:\u002F\u002Fbjnewsrec-cv.ws.126.net\u002Fdoccover_gen\u002Fda6760cf_9_cover.png&thumbnail=140y88&quality=80&type=jpg)](https:\u002F\u002Fwww.163.com\u002Fdy\u002Farticle\u002FL959VMB00514EGPO.html?f=post2020_dy_recommends)### 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Pro咏春短片拍摄](https:\u002F\u002Fnimg.ws.126.net\u002F?url=http:\u002F\u002Fvideoimg.ws.126.net\u002Fcover\u002F20261012\u002FgJd785WtZ_cover.jpg&thumbnail=140y88&quality=80&type=jpg)](https:\u002F\u002Fwww.163.com\u002Fv\u002Fvideo\u002FVL83AC5SF.html?f=post2020_dy_recommends)### [库克卸任苹果CEO后首次访华！现身广州 探班iPhone 18 Pro咏春短片拍摄](https:\u002F\u002Fwww.163.com\u002Fv\u002Fvideo\u002FVL83AC5SF.html?f=post2020_dy_recommends)\n\n快科技 2026-10-12 05:59:48\n\n[0 _跟贴_ 0](https:\u002F\u002Fwww.163.com\u002Fv\u002Fvideo\u002FVL83AC5SF.html?f=post2020_dy_recommends) \n*   [![Image 27: iPhone18 Pro又掉漆！勃艮第红褪成大红色，客服：不是材质问题](https:\u002F\u002Fnimg.ws.126.net\u002F?url=http:\u002F\u002Fvideoimg.ws.126.net\u002Fcover\u002F20261012\u002F9mP4OD5nW_cover.jpg&thumbnail=140y88&quality=80&type=jpg)](https:\u002F\u002Fwww.163.com\u002Fv\u002Fvideo\u002FVP84VUK7U.html?f=post2020_dy_recommends)### [iPhone18 Pro又掉漆！勃艮第红褪成大红色，客服：不是材质问题](https:\u002F\u002Fwww.163.com\u002Fv\u002Fvideo\u002FVP84VUK7U.html?f=post2020_dy_recommends)\n\n极果酷玩 2026-10-12 21:36:07\n\n[0 _跟贴_ 0](https:\u002F\u002Fwww.163.com\u002Fv\u002Fvideo\u002FVP84VUK7U.html?f=post2020_dy_recommends) \n*   [![Image 28: 前国务卿蓬佩奥：向俄罗斯买油，就是个错误！](https:\u002F\u002Fnimg.ws.126.net\u002F?url=http:\u002F\u002Fbjnewsrec-cv.ws.126.net\u002Fdoccover_gen\u002Fc38b8422_7_cover.png&thumbnail=140y88&quality=80&type=jpg)](https:\u002F\u002Fwww.163.com\u002Fdy\u002Farticle\u002FL945QALQ0514EGPO.html?f=post2020_dy_recommends)### [前国务卿蓬佩奥：向俄罗斯买油，就是个错误！](https:\u002F\u002Fwww.163.com\u002Fdy\u002Farticle\u002FL945QALQ0514EGPO.html?f=post2020_dy_recommends)\n\n看看新闻Knews 2026-10-13 10:28:25\n\n[1835 _跟贴_ 1835](https:\u002F\u002Fwww.163.com\u002Fdy\u002Farticle\u002FL945QALQ0514EGPO.html?f=post2020_dy_recommends) \n*   [![Image 29: 一加 16 正式发布：首销安卓同期销量 TOP 1，巅峰性能旗舰来了！](https:\u002F\u002Fnimg.ws.126.net\u002F?url=http:\u002F\u002Fvideoimg.ws.126.net\u002Fcover\u002F20261013\u002F2ukgOr4G5_cover.jpg&thumbnail=140y88&quality=80&type=jpg)](https:\u002F\u002Fwww.163.com\u002Fv\u002Fvideo\u002FVQ878DF19.html?f=post2020_dy_recommends)### [一加 16 正式发布：首销安卓同期销量 TOP 1，巅峰性能旗舰来了！](https:\u002F\u002Fwww.163.com\u002Fv\u002Fvideo\u002FVQ878DF19.html?f=post2020_dy_recommends)\n\nPConline太平洋科技 2026-10-13 18:42:31\n\n[0 _跟贴_ 0](https:\u002F\u002Fwww.163.com\u002Fv\u002Fvideo\u002FVQ878DF19.html?f=post2020_dy_recommends) \n*   [![Image 30: 让AI图秒变iPhone直出 AI合影竟然像iPhone随手拍？](https:\u002F\u002Fnimg.ws.126.net\u002F?url=http:\u002F\u002Fvideoimg.ws.126.net\u002Fcover\u002F20260911\u002F0x4JBs3IC_cover.jpg&thumbnail=140y88&quality=80&type=jpg)](https:\u002F\u002Fwww.163.com\u002Fv\u002Fvideo\u002FVV5JP8LKI.html?f=post2020_dy_recommends)### [让AI图秒变iPhone直出 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SP，为什么让掌机圈紧张？](https:\u002F\u002Fnimg.ws.126.net\u002F?url=http:\u002F\u002Fbjnewsrec-cv.ws.126.net\u002Flittle861da25ee24j00tmudty007nd001hc00u0p.jpg&thumbnail=140y88&quality=80&type=jpg)](https:\u002F\u002Fwww.163.com\u002Fdy\u002Farticle\u002FL9526T7K05561FY2.html?f=post2020_dy_recommends)### [不到100美元的FPGA版GBA SP，为什么让掌机圈紧张？](https:\u002F\u002Fwww.163.com\u002Fdy\u002Farticle\u002FL9526T7K05561FY2.html?f=post2020_dy_recommends)\n\n队友祭天法力无边 2026-10-13 18:44:39\n\n[0 _跟贴_ 0](https:\u002F\u002Fwww.163.com\u002Fdy\u002Farticle\u002FL9526T7K05561FY2.html?f=post2020_dy_recommends) \n*   [![Image 33: 一份恶魔城新作攻略，把BOSS战拆成了27分钟时间轴](https:\u002F\u002Fnimg.ws.126.net\u002F?url=http:\u002F\u002Fbjnewsrec-cv.ws.126.net\u002Flittle382c02065edj00tmuipg004td001hc00u0p.jpg&thumbnail=140y88&quality=80&type=jpg)](https:\u002F\u002Fwww.163.com\u002Fdy\u002Farticle\u002FL9587IR105561FY1.html?f=post2020_dy_recommends)### [一份恶魔城新作攻略，把BOSS战拆成了27分钟时间轴](https:\u002F\u002Fwww.163.com\u002Fdy\u002Farticle\u002FL9587IR105561FY1.html?f=post2020_dy_recommends)\n\n奶凶的小霸王 2026-10-13 20:29:52\n\n[0 _跟贴_ 0](https:\u002F\u002Fwww.163.com\u002Fdy\u002Farticle\u002FL9587IR105561FY1.html?f=post2020_dy_recommends) \n*   [![Image 34: “月薪保底5位数”！男生高考522分被调剂马术专业学钉蹄：我是国内持证钉蹄师里最年轻的](https:\u002F\u002Fnimg.ws.126.net\u002F?url=http:\u002F\u002Fbjnewsrec-cv.ws.126.net\u002Fdoccover_gen\u002F58ecf9ef_1_cover.png&thumbnail=140y88&quality=80&type=jpg)](https:\u002F\u002Fwww.163.com\u002Fdy\u002Farticle\u002FL95G93C00514D3UH.html?f=post2020_dy_recommends)### [“月薪保底5位数”！男生高考522分被调剂马术专业学钉蹄：我是国内持证钉蹄师里最年轻的](https:\u002F\u002Fwww.163.com\u002Fdy\u002Farticle\u002FL95G93C00514D3UH.html?f=post2020_dy_recommends)\n\n封面新闻 2026-10-13 22:50:29\n\n[446 _跟贴_ 446](https:\u002F\u002Fwww.163.com\u002Fdy\u002Farticle\u002FL95G93C00514D3UH.html?f=post2020_dy_recommends) \n*   [![Image 35: 台州一摩天轮未关门载人升空，系操作员马虎，已开除](https:\u002F\u002Fnimg.ws.126.net\u002F?url=http:\u002F\u002Fbjnewsrec-cv.ws.126.net\u002Flittle121ac690bfdj00tmshxc0009d2008i008fg004t004r.jpg&thumbnail=140y88&quality=80&type=jpg)](https:\u002F\u002Fwww.163.com\u002Fdy\u002Farticle\u002FL92E8OFQ051492LM.html?f=post2020_dy_recommends)### [台州一摩天轮未关门载人升空，系操作员马虎，已开除](https:\u002F\u002Fwww.163.com\u002Fdy\u002Farticle\u002FL92E8OFQ051492LM.html?f=post2020_dy_recommends)\n\n都市快报橙柿互动 2026-10-12 18:17:38\n\n[627 _跟贴_ 627](https:\u002F\u002Fwww.163.com\u002Fdy\u002Farticle\u002FL92E8OFQ051492LM.html?f=post2020_dy_recommends) \n*   [![Image 36: 男子花300万认养600头澳洲肉牛 结果牧场和牛都是假的](https:\u002F\u002Fnimg.ws.126.net\u002F?url=http:\u002F\u002Fcms-bucket.ws.126.net\u002F2026\u002F1013\u002F5645325bp00tmunvj0032c0009c0070c.png&thumbnail=140y88&quality=80&type=jpg)](https:\u002F\u002Fwww.163.com\u002Fdy\u002Farticle\u002FL95CGVV8055040N3.html?f=post2020_dy_recommends)### [男子花300万认养600头澳洲肉牛 结果牧场和牛都是假的](https:\u002F\u002Fwww.163.com\u002Fdy\u002Farticle\u002FL95CGVV8055040N3.html?f=post2020_dy_recommends)\n\n上观新闻 2026-10-13 21:44:54\n\n[142 _跟贴_ 142](https:\u002F\u002Fwww.163.com\u002Fdy\u002Farticle\u002FL95CGVV8055040N3.html?f=post2020_dy_recommends) \n*   [![Image 37: 定了！CBA恢复升降级！上海篮球德比要来了？](https:\u002F\u002Fnimg.ws.126.net\u002F?url=http:\u002F\u002Fbjnewsrec-cv.ws.126.net\u002Fthree9459aa9d2d9j00tmtn99009td000go00b4m.jpg&thumbnail=140y88&quality=80&type=jpg)](https:\u002F\u002Fwww.163.com\u002Fdy\u002Farticle\u002FL9419BT40512DU6N.html?f=post2020_dy_recommends)### [定了！CBA恢复升降级！上海篮球德比要来了？](https:\u002F\u002Fwww.163.com\u002Fdy\u002Farticle\u002FL9419BT40512DU6N.html?f=post2020_dy_recommends)\n\n新民晚报 2026-10-13 09:10:37\n\n[73 _跟贴_ 73](https:\u002F\u002Fwww.163.com\u002Fdy\u002Farticle\u002FL9419BT40512DU6N.html?f=post2020_dy_recommends) \n*   [![Image 38: 俄军对乌克兰实施集群打击](https:\u002F\u002Fnimg.ws.126.net\u002F?url=http:\u002F\u002Fbjnewsrec-cv.ws.126.net\u002Fdoccover_gen\u002F487b3f90_3_cover.png&thumbnail=140y88&quality=80&type=jpg)](https:\u002F\u002Fwww.163.com\u002Fdy\u002Farticle\u002FL94TUU9A0512D3VJ.html?f=post2020_dy_recommends)### [俄军对乌克兰实施集群打击](https:\u002F\u002Fwww.163.com\u002Fdy\u002Farticle\u002FL94TUU9A0512D3VJ.html?f=post2020_dy_recommends)\n\n新京报 2026-10-13 17:30:22\n\n[381 _跟贴_ 381](https:\u002F\u002Fwww.163.com\u002Fdy\u002Farticle\u002FL94TUU9A0512D3VJ.html?f=post2020_dy_recommends) \n*   [![Image 39: 首店只是引信！解码广州白鹅潭万象城七天百万客流刷屏背后](https:\u002F\u002Fnimg.ws.126.net\u002F?url=http:\u002F\u002Fbjnewsrec-cv.ws.126.net\u002Flittle3960f192a9bj00tmuo9b001td000m800gpg.jpg&thumbnail=140y88&quality=80&type=jpg)](https:\u002F\u002Fwww.163.com\u002Fdy\u002Farticle\u002FL95FBD0405129QAF.html?f=post2020_dy_recommends)### [首店只是引信！解码广州白鹅潭万象城七天百万客流刷屏背后](https:\u002F\u002Fwww.163.com\u002Fdy\u002Farticle\u002FL95FBD0405129QAF.html?f=post2020_dy_recommends)\n\n南方都市报 2026-10-13 22:34:16\n\n[15 _跟贴_ 15](https:\u002F\u002Fwww.163.com\u002Fdy\u002Farticle\u002FL95FBD0405129QAF.html?f=post2020_dy_recommends) \n*   [![Image 40: “大闸蟹价格腰斩”冲上热搜，记者在南京实探：部分降幅超40%，供给放量是最直接原因](https:\u002F\u002Fnimg.ws.126.net\u002F?url=http:\u002F\u002Fbjnewsrec-cv.ws.126.net\u002Flittle398a67d0383j00tmts8o00ijd000fa00bhg.jpg&thumbnail=140y88&quality=80&type=jpg)](https:\u002F\u002Fwww.163.com\u002Fdy\u002Farticle\u002FL948C6PM053469M5.html?f=post2020_dy_recommends)### [“大闸蟹价格腰斩”冲上热搜，记者在南京实探：部分降幅超40%，供给放量是最直接原因](https:\u002F\u002Fwww.163.com\u002Fdy\u002Farticle\u002FL948C6PM053469M5.html?f=post2020_dy_recommends)\n\n上游新闻 2026-10-13 11:13:08\n\n[175 _跟贴_ 175](https:\u002F\u002Fwww.163.com\u002Fdy\u002Farticle\u002FL948C6PM053469M5.html?f=post2020_dy_recommends) \n\n[![Image 41: 10月13日晚间上市公司重大事项公告](https:\u002F\u002Fnimg.ws.126.net\u002F?url=http:\u002F\u002Fdingyue.ws.126.net\u002F2026\u002F1013\u002F50e357fbj00tmukju0099d000pe00j1d.jpg&thumbnail=140y88&quality=90&type=jpg)](https:\u002F\u002Fwww.163.com\u002Fdy\u002Farticle\u002FL95AFEM40553Y7J3.html?f=post1603_tab_news \"10月13日晚间上市公司重大事项公告\")\n\n### [10月13日晚间上市公司重大事项公告](https:\u002F\u002Fwww.163.com\u002Fdy\u002Farticle\u002FL95AFEM40553Y7J3.html?f=post1603_tab_news \"10月13日晚间上市公司重大事项公告\")\n\n风风顺\n\n2026-10-14 01:25:04\n\n[![Image 42: 随着北京国安1-0，鹿岛鹿角1-0，亚冠精英赛","农民日报 网易转载 2026 年 10 月 13 日","政策",54,{"impact":321,"substance":173,"depth":665,"authority":586,"freshness":586,"relevant":455,"comment":1442},"省级三农工作主线报道，主题相关但正文缺失、内容空洞，仅具政策参考价值。",[1444],{"name":1438,"url":1435},[1446,1447,1448,1449,1450],"十五五","三农工作","山西","产能提升","特优农业",{"code":286,"slug":287,"name":288,"path":289,"parent_code":271},[],{"status":600,"locked":581,"version":4,"method":601,"evidence":1454},{"candidates":1455,"agricultural_evidence":606},[1456,1460,1464,1468],{"code":286,"score":619,"supported":606,"title":1457,"abstract":1458,"tags":1459},[],[610],[],{"code":534,"score":455,"supported":581,"title":1461,"abstract":1462,"tags":1463},[],[],[1447],{"code":542,"score":455,"supported":581,"title":1465,"abstract":1466,"tags":1467},[],[],[1446],{"code":549,"score":455,"supported":581,"title":1469,"abstract":1470,"tags":1471},[],[],[1447],[1473,1474],"山西 十五五","山西 特优农业 产业集聚","山西十五五-4844","2026-10-14T00:04:02.581748Z",{"id":1478,"title":1479,"url":1480,"summary":1481,"summary_zh":1482,"content":11,"source_name":1483,"source_url":1480,"published_at":1272,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":771,"score_detail":1484,"sources":1486,"tags":1488,"primary_topic":1492,"secondary_topics":1493,"classification":1496,"search_phrases":1515,"slug":1518,"view_count":455,"doi":1519,"paper":1520,"created_at":1540},4808,"Early Detection of Apple Scab in Orchards and Mildew in Vineyards Using A Novel Deep Learning Model and Multispectral Imagery","https:\u002F\u002Fdoi.org\u002F10.15832\u002Fankutbd.1898342","Agricultural diseases are a significant problem for growers as they cause substantial damage to fruits and leaves, leading to yield losses. Therefore, developing systems for early detection of plant diseases is a crucial issue. In this study, a new deep learning-based hybrid model is presented for the detection and segmentation of plant diseases, specifically apple scab disease in apple orchards and mildew disease in vineyards. Multispectral imagery data collected in agricultural areas of Tekirdag province in Turkey via drones between May 2023 and December 2024 were used in developing the early disease detection system. The datasets contain 3625 images of apple orchards and 5625 images of vineyards. DJI Phantom 4 Multispectral drone, equipped with five spectral bands (Blue, Green, Red, Red Edge, and Near Infrared), was utilized to capture both spectral richness and temporal progression. This study introduces a novel deep learning architecture developed specifically for time series-based disease detection and segmentation using multispectral drone imagery. Changes in plants over time provide important information about the occurrence of plant diseases. For this reason, time series were used in this study to extract features from the changes in plants over time. The proposed model architecture was specifically designed to leverage time series information, enabling robust tracking of crop health changes over time. The model trained with the dataset collected for apple diseases achieved an average Intersection Over Unit (IoU) value of 82.05%, while the model trained with the dataset collected for vineyard diseases reached an IoU value of 79.67%. The findings highlight the effectiveness of integrating drone-acquired multispectral time-series data with advanced deep learning models for scalable, accurate, and timely plant disease detection in precision agriculture.","农业病害是种植者面临的重大问题，会对果实和叶片造成严重损害，导致产量损失。因此，开发植物病害早期检测系统是一个至关重要的问题。本研究提出了一种基于深度学习的新型混合模型，用于植物病害的检测与分割，具体针对苹果园中的苹果黑星病和葡萄园中的霜霉病。该早期病害检测系统的开发使用了2023年5月至2024年12月期间通过无人机在土耳其泰基尔达省农业区域采集的多光谱影像数据。数据集包含3625幅苹果园图像和5625幅葡萄园图像。所用DJI Phantom 4多光谱无人机配备五个光谱波段（蓝、绿、红、红边和近红外），用于捕捉光谱丰富度和时间变化过程。本研究提出了一种专门开发的深度学习架构，用于基于时间序列的多光谱无人机影像病害检测与分割。植物随时间的变化提供了关于植物病害发生的重要信息。因此，本研究使用时间序列从植物随时间的变化中提取特征。所提出的模型架构专门设计以利用时间序列信息，从而能够稳健地跟踪作物健康状况随时间的变化。使用苹果病害数据集训练的模型达到了82.05%的平均交并比（IoU）值，而使用葡萄园病害数据集训练的模型达到了79.67%的IoU值。研究结果凸显了将无人机获取的多光谱时间序列数据与先进深度学习模型相结合，在精准农业中实现可扩展、准确且及时的植物病害检测方面的有效性。","Tarım Bilimleri Dergisi",{"impact":584,"substance":706,"depth":361,"authority":585,"freshness":586,"relevant":455,"comment":1485},"该研究提出基于多光谱时序影像的新型深度学习模型，实现果园与葡萄园病害早期检测，数据规模大、方法新颖，对精准农业病害预警具有参考价值。",[1487],{"name":1483,"url":1480},[670,1489,838,1490,1491],"无人机","遥感监测","病害预警",{"code":286,"slug":287,"name":288,"path":289,"parent_code":271},[1494,1495],{"code":24,"slug":25,"name":26,"path":27,"parent_code":7},{"code":32,"slug":33,"name":34,"path":35,"parent_code":7},{"status":600,"locked":581,"version":4,"method":601,"evidence":1497},{"candidates":1498,"agricultural_evidence":606},[1499,1503,1507,1511],{"code":286,"score":605,"supported":606,"title":1500,"abstract":1501,"tags":1502},[608],[610,672,608],[],{"code":24,"score":1032,"supported":606,"title":1504,"abstract":1505,"tags":1506},[1097],[1094,1097],[838],{"code":32,"score":416,"supported":606,"title":1508,"abstract":1509,"tags":1510},[1108],[1108],[1490],{"code":16,"score":852,"supported":606,"title":1512,"abstract":1513,"tags":1514},[],[855,856],[670],[1516,1517],"苹果黑星病 无人机 多光谱","葡萄园 霜霉病 深度学习","苹果黑星病无人机多光谱-4808","10.15832\u002Fankutbd.1898342",{"doi":1519,"openalex_id":1521,"authors":1522,"venue":1483,"cited_by_count":424,"oa_url":1534,"card":1535,"direction":892,"ingested_from":652},"W7221558726",[1523,1526,1529,1531],{"name":1524,"orcid":1525},"Batuhan Aşıroğlu","https:\u002F\u002Forcid.org\u002F0000-0003-0767-6348",{"name":1527,"orcid":1528},"Aydın Kağan Turkoglu","https:\u002F\u002Forcid.org\u002F0009-0001-8383-8807",{"name":1530,"orcid":11},"Fatih Sinan Bir",{"name":1532,"orcid":1533},"Sibel Senan","https:\u002F\u002Forcid.org\u002F0000-0001-6773-0428","https:\u002F\u002Fdergipark.org.tr\u002Fen\u002Fdownload\u002Farticle-file\u002F5761152",{"tldr":1536,"method":1537,"finding":1538,"direction":1263,"opportunity":1539},"提出一种基于无人机多光谱时序影像的深度学习混合模型，实现苹果黑星病和葡萄霉病的早期检测与分割。","DJI Phantom 4多光谱无人机采集时序影像，构建时序深度学习分割模型。","苹果病IoU达82.05%，葡萄病达79.67%，验证多光谱时序数据有效性。","可探索多作物、多病害的时序多光谱通用模型，并融合气象与地面数据提升早期预警能力。","2026-10-13T23:30:10.242467Z",{"id":1542,"title":1543,"url":1544,"summary":1545,"summary_zh":11,"content":1546,"source_name":1547,"source_url":11,"published_at":946,"category":662,"cover_url":11,"hotness":385,"is_selected":581,"score":45,"score_detail":1548,"sources":1550,"tags":1552,"primary_topic":1557,"secondary_topics":1558,"classification":1559,"search_phrases":1574,"slug":1577,"view_count":424,"doi":11,"paper":11,"created_at":1578},4778,"全国七成太秋甜柿种苗来自安徽合肥 亩均效益可达2万元","https:\u002F\u002Fwww.toutiao.com\u002Farticle\u002F7695409059838837283\u002F","10月11日,枞阳县麒麟镇新安村20名苗木种植户专程赴合肥特色苗木新品种示范基地参观。安徽省农业科学院研究员任杰介绍,全国70%的太秋甜柿种苗来自安徽合肥,合肥年培育优质种苗120万株,是全国最大太秋甜柿种苗供应基地,亩均效益可达2万元左右。同期亮相的永恒之火彩叶紫荆新品种亩均效益可达5万元以上。","2026-10-11 21:57·[大皖新闻](https:\u002F\u002Fwww.toutiao.com\u002Fc\u002Fuser\u002Ftoken\u002FMS4wLjABAAAA4tp_l4jiHNjLzuHp8C82lZnrTkpKO5JnBaflXuhQz0g\u002F?source=tuwen_detail)\n\n大皖新闻讯 10月11日，合肥市蜀山区枫林路一家太秋甜柿示范基地里，一只只柿子像一盏盏黄橙色灯笼隐藏枝叶间。“这种柿子名叫太秋甜柿，最早于上世纪90年代末引入我国，口感优势突出，市场前景非常不错。但苦于砧木匮乏，一直难以推广应用。”安徽省农业科学院研究员任杰向前来取经的枞阳县麒麟镇新安村种植户一行介绍道。任杰告诉大皖新闻记者，全国70%的太秋甜柿种苗来自安徽合肥。\n\n10月11日，枞阳县麒麟镇新安村的20名种植户驱车近两小时，来到合肥特色苗木新品种示范基地参观取经。\n\n据悉，新安村有苗木2500亩，苗木是该村的主导产业。然而，面对全国苗木产业持续下滑、常规苗木趋于饱和的行业形势，新安村亟需推动传统苗木产业向高附加值方向转型。2025年，安徽省农业科学院园林植物与景观工程团队通过省成果转化项目对接了该村。\n\n安徽省农业科学院园林植物与景观工程团队一直致力于彩叶苗木新品种新技术的研究与应用。近年来，该团队积极推动高附加值特色水果苗木——太秋甜柿、系列新优彩叶苗木的成果应用，以期增加种植户效益，服务苗木产业转型发展。\n\n![Image 1](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-axegupay5k\u002Ffd19718d6b2a41509d1d477c08891fa2~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1792454950&x-signature=sTgpgs9aRQltnCrFio9gfxEqSP4%3D)\n\n**黄澄澄的太秋甜柿引来参观者**\n\n说起太秋甜柿种苗的培育，任杰研究员表示，安徽省在全国走在了前列。大别山区有全国唯一的野生甜柿种质资源——小果甜柿，合肥市依托资源优势，以小果甜柿为砧木，目前，年培育优质种苗120万株，是全国最大的太秋甜柿种苗供应基地。“目前，全国70%的太秋甜柿种苗来自安徽合肥。”\n\n任杰同时表示，太秋甜柿在安徽的推广普及仍有很大潜力。“目前全国种植面积约50000亩，安徽有约5000亩。种苗输出地合肥，种植面积也仅有1500亩，进入丰产期的约有800亩。”\n\n任杰告诉记者，相较于传统的柿子，太秋甜柿口感脆甜、汁水丰盈，兼具苹果的脆、梨的水分和蜜香味，无涩味，目前市场反响较好。从经济效益上来说，**目前亩均效益可以2万元左右**。\n\n![Image 2](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-tjoges91tu\u002Faaa0f6016b2ef58a0e62816d27ede315~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1792454950&x-signature=ii091VnMOGbZAoTdZhmC9TZwlac%3D)\n\n**任杰研究员向来访者介绍永恒之火紫荆新品种。郭留明 摄**\n\n10月11日，大皖新闻记者在肥西县官亭镇一个示范基地看到，该团队帮助企业引进培育的永恒之火彩叶紫荆新品种，叶形美观、颜色绚丽夺目。任杰介绍，“它春季叶片出来之前，满枝还开紫红色的小花，观赏期长，观赏价值突出，在园林市场，再过20年也不会过时。**亩均效益可达5万元以上**。”\n\n据介绍，安徽省农业科学院园林植物与景观工程团队指导合肥市苗木企业培育的这两项“拳头产品”，都有着广阔的市场空间。\n\n大皖新闻记者 赵明玉 摄影报道\n\n编辑 崔恒","大皖新闻 2026-10-11",{"impact":361,"substance":508,"depth":1032,"authority":173,"freshness":173,"relevant":455,"comment":1549},"以全国七成种苗供给和亩均2万元效益等硬数据，呈现科研机构推动传统苗木向高附加值特色果苗转型的产业进展，具备细分行业参考价值。",[1551],{"name":1547,"url":1544},[1553,1554,1555,955,1556],"种业振兴","成果转化","特色水果","苗木产业",{"code":286,"slug":287,"name":288,"path":289,"parent_code":271},[],{"status":600,"locked":581,"version":4,"method":601,"evidence":1560},{"candidates":1561,"agricultural_evidence":606},[1562,1566,1570],{"code":286,"score":524,"supported":606,"title":1563,"abstract":1564,"tags":1565},[965],[965],[],{"code":80,"score":455,"supported":581,"title":1567,"abstract":1568,"tags":1569},[],[],[1553],{"code":488,"score":455,"supported":581,"title":1571,"abstract":1572,"tags":1573},[],[],[1554],[1575,1576],"安徽省农科院 太秋甜柿 种苗","合肥 太秋甜柿 亩均效益","安徽省农科院太秋甜柿种苗-4778","2026-10-13T00:08:36.319336Z",{"id":1580,"title":1581,"url":1582,"summary":1583,"summary_zh":1584,"content":11,"source_name":1585,"source_url":1582,"published_at":1586,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":45,"score_detail":1587,"sources":1589,"tags":1591,"primary_topic":1594,"secondary_topics":1595,"classification":1597,"search_phrases":1613,"slug":1616,"view_count":424,"doi":1617,"paper":1618,"created_at":1642},4744,"An empirical performance comparison of deep learning models for apple leaf disease recognition","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs10791-026-10573-4","Currently, Biotic and abiotic factors, in agriculture, are estimated to be responsible for around 22% of the total losses in crop production. It is therefore very important, but at the same time very difficult for farmers to identify these constraints at the earliest possible stage. The most recent developments in computer vision have made it possible to identify as well as classify plant lesions at a primary level. Various deep learning techniques have made it possible to detect biotic stress in apple plant leaves with a very high degree of accuracy. Hence, automated segmentation of images of diseased leaves has become a leading research theme all over the world. In this paper, a dataset from FGVC7, which is an open, source dataset and contains four different classes of leaf diseases of the apple crop is used to measure the performance of the proposed model. A three, step image preprocessing procedure is conducted, which involves image enhancement, removal of background noise, and object identification through a variety of approaches. Using UNet, PSPNet, and UNet++, disease areas are identified. UNet + + delivering the most precise object segmentation. The segmented patch data are then fed into a fusion, based classification model as input. Patch segmentation makes it possible to separate the various disease areas, thus increasing the classification accuracy. The proposed framework combines several CNN architectures, namely, VGG16, InceptionV3, AlexNet, and GoogleNet, in order to take advantage of their complementary strengths, then a Vision Transformer (ViT) is used to identify distinct local features via early feature fusion. After this, the efficiency of the proposed model is check in comparison with the pre-trained CNN models individually. Comparing with all other models the proposed model has shown the accuracy of highest order i.e. 98.85%, clearly out performing individual CNN models.","目前，农业中的生物和非生物因素估计约占作物生产总损失的22%。因此，农民尽早识别这些限制因素非常重要，但同时也非常困难。计算机视觉的最新发展使得在初级水平上识别和分类植物病变成为可能。各种深度学习技术使得以极高准确度检测苹果植株叶片的生物胁迫成为可能。因此，患病叶片图像的自动分割已成为世界范围内的前沿研究主题。本文使用来自FGVC7的数据集来衡量所提出模型的性能，该数据集是一个开放源数据集，包含苹果作物的四类不同叶片病害。进行了三步图像预处理，包括图像增强、背景噪声去除以及通过多种方法进行目标识别。使用UNet、PSPNet和UNet++识别病害区域，其中UNet++提供了最精确的目标分割。然后将分割后的图像块数据作为输入馈入基于融合的分类模型。图像块分割使得能够分离不同的病害区域，从而提高分类准确率。所提出的框架结合了多种CNN架构，即VGG16、InceptionV3、AlexNet和GoogleNet，以利用它们互补的优势，然后使用视觉Transformer（ViT）通过早期特征融合来识别不同的局部特征。此后，将所提出模型的效率与各个预训练CNN模型进行比较。与所有其他模型相比，所提出的模型显示出最高阶的准确率，即98.85%，明显优于各个单独的CNN模型。","Discover Computing","2026-10-10T00:00:00Z",{"impact":416,"substance":508,"depth":584,"authority":585,"freshness":173,"relevant":455,"comment":1588},"方法组合（UNet++分割+CNN与ViT融合）与98.85%准确率有参考价值，但属实验室数据集验证，产业落地影响有限，可作为技术前沿资讯收录。",[1590],{"name":1585,"url":1582},[670,838,1592,1094,1593],"计算机视觉","苹果病害识别",{"code":286,"slug":287,"name":288,"path":289,"parent_code":271},[1596],{"code":24,"slug":25,"name":26,"path":27,"parent_code":7},{"status":600,"locked":581,"version":4,"method":601,"evidence":1598},{"candidates":1599,"agricultural_evidence":606},[1600,1605,1609],{"code":24,"score":508,"supported":606,"title":1601,"abstract":1602,"tags":1604},[1097],[1094,1592,1097,1603],"computer vision",[838,1094,1592],{"code":286,"score":345,"supported":606,"title":1606,"abstract":1607,"tags":1608},[608],[610,608],[],{"code":16,"score":455,"supported":581,"title":1610,"abstract":1611,"tags":1612},[],[],[670],[1614,1615],"苹果叶片病害 深度学习","FGVC7 苹果病害 数据集","苹果叶片病害深度学习-4744","10.1007\u002Fs10791-026-10573-4",{"doi":1617,"openalex_id":1619,"authors":1620,"venue":1585,"cited_by_count":424,"oa_url":1636,"card":1637,"direction":892,"ingested_from":652},"W7221353656",[1621,1623,1626,1628,1630,1633],{"name":1622,"orcid":11},"Apoorva Arora",{"name":1624,"orcid":1625},"Vinay Gautam","https:\u002F\u002Forcid.org\u002F0000-0002-0258-5132",{"name":1627,"orcid":11},"Anand Muni Mishra",{"name":1629,"orcid":11},"Santar Pal Singh",{"name":1631,"orcid":1632},"Yadvendra Pratap Singh","https:\u002F\u002Forcid.org\u002F0000-0001-6396-3355",{"name":1634,"orcid":1635},"Mukund Pratap Singh","https:\u002F\u002Forcid.org\u002F0000-0001-5877-9510","https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002Fs10791-026-10573-4.pdf",{"tldr":1638,"method":1639,"finding":1640,"direction":894,"opportunity":1641},"用UNet++分割苹果病叶并融合多种CNN与ViT分类，实现98.85%识别准确率。","FGVC7数据集，UNet\u002FPSPNet\u002FUNet++分割，VGG16等CNN与","UNet++分割最准，融合模型准确率达98.85%，优于单个CNN模型。","可探索轻量化分割-分类联合模型在田间移动端的实时部署与多作物泛化。","2026-10-12T23:30:09.091475Z",{"id":1644,"title":1645,"url":1646,"summary":1647,"summary_zh":11,"content":1648,"source_name":1649,"source_url":11,"published_at":1650,"category":662,"cover_url":11,"hotness":385,"is_selected":581,"score":980,"score_detail":1651,"sources":1653,"tags":1655,"primary_topic":1659,"secondary_topics":1660,"classification":1661,"search_phrases":1681,"slug":1684,"view_count":69,"doi":11,"paper":11,"created_at":1685},4701,"张柱会见阿塞拜疆农业部部长马马多夫","https:\u002F\u002Fwww.agri.cn\u002Fzx\u002Fnyyw\u002F202610\u002Ft20261010_8875675.htm","农业农村部部长张柱在京会见阿塞拜疆农业部部长马马多夫。双方就加强两国农业政策对话、拓展农业经贸合作、促进农业科技协作深入交流,会后签署谅解备忘录,深化智慧农业、葡萄酒、乡村振兴等领域合作","本网讯　10月10日下午，农业农村部部长张柱在京会见阿塞拜疆农业部部长马马多夫，就加强两国农业农村领域合作进行深入交流。\n\n张柱表示，在两国元首战略引领下，中阿各领域合作发展迅速，农业务实合作取得明显进展。中阿农业互补性强，中方愿与阿方共同努力，贯彻落实好两国元首合作共识，用好农业工作组机制，加强农业政策对话、拓展农业经贸合作、促进农业科技协作，推动两国农业农村领域合作迈上新台阶，为中阿全面战略伙伴关系发展贡献力量。\n\n马马多夫表示，阿方高度重视对华农业合作，希望学习借鉴中国农业农村发展经验，吸引中国农业企业赴阿投资，扩大对华农产品贸易，在农业政策、智慧农业、葡萄酒、乡村振兴等领域深化交流。\n\n会后，双方共同签署了《中华人民共和国农业农村部与阿塞拜疆共和国农业部谅解备忘录》。","中国农业农村信息网 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