[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2912":3,"related-2912":83},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":24,"tags":26,"search_phrases":32,"slug":35,"view_count":36,"doi":37,"paper":38,"created_at":82},2912,"Spatial optimization of water-saving irrigation in Chinese rice paddies: Balancing yield, greenhouse gases, and cost using NSGA-II","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.agsy.2026.104976","CONTEXT Optimizing the spatial allocation of water-saving irrigation (WSI) promotion is critical for balancing rice production, greenhouse gas (GHG) mitigation, and economic costs, yet remains challenging at a national scale due to computational intractability. OBJECTIVE This study aims to develop a spatially explicit optimization framework to identify WSI promotion pathways across China's rice paddies that simultaneously maximize yield gain, maximize GHG reduction, and minimize implementation cost. METHODS We coupled K-means clustering with a multi-objective evolutionary algorithm (NSGA-II). Based on machine-learning predicted yield and GHG emissions for 157,417 flooded irrigation grids, we first clustered these grids into 500 environmentally and agronomically homogeneous groups. We then formulated a continuous optimization problem with cluster-level conversion rates as decision variables, simultaneously maximizing national yield gain, maximizing GHG reduction, and minimizing implementation cost. RESULTS AND CONCLUSIONS The Pareto front comprised 100 non-dominated solutions spanning promotion rates from 61.8% to 80.3%. The optimal solution, selected by normalized scoring, achieved 6.47 Mt. yield gain and 63.5 Mt. CO 2 e GHG reduction at a cost of 10.14 billion CNY, corresponding to a national promotion rate of 80.3%. Cluster-scale conversion rates exhibited significant spatial heterogeneity and positive correlation with cluster size ( r = 0.23), revealing economies of scale in WSI promotion. Compared with a random promotion strategy at 90% adoption, our optimized solution delivered 74% higher yield gain with 9.7 percentage points lower promotion effort while achieving comparable GHG reduction. SIGNIFICANCE Our framework provides a spatially explicit decision-support tool for precision agricultural policy, demonstrating that smart spatial allocation can substantially enhance the efficiency of limited resources in scaling climate-smart agricultural practices.","背景 优化节水灌溉（WSI）推广的空间配置对于平衡水稻生产、温室气体（GHG）减排和经济成本至关重要，但由于计算上的不可处理性，在全国尺度上仍具挑战性。目的 本研究旨在开发一个空间显式优化框架，以识别中国稻田的WSI推广路径，同时最大化产量增益、最大化GHG减排并最小化实施成本。方法 我们将K-means聚类与多目标进化算法（NSGA-II）相结合。基于机器学习预测的157,417个淹水灌溉网格的产量和GHG排放，我们首先将这些网格聚类为500个在环境和农艺上同质的组。然后，我们构建了一个以组级转换率为决策变量的连续优化问题，同时最大化全国产量增益、最大化GHG减排并最小化实施成本。结果与结论 Pareto前沿包含100个非支配解，推广率从61.8%到80.3%不等。通过归一化评分选出的最优解实现了6.47 Mt的产量增益和63.5 Mt CO₂e的GHG减排，成本为101.4亿元人民币，对应全国推广率为80.3%。组尺度转换率表现出显著的空间异质性，并与组规模呈正相关（r = 0.23），揭示了WSI推广中的规模经济。与90%采纳率的随机推广策略相比，我们的优化方案在推广力度低9.7个百分点的情况下实现了高出74%的产量增益，同时实现了相当的GHG减排。意义 我们的框架为精准农业政策提供了一个空间显式的决策支持工具，表明智能空间配置可以显著提高有限资源在推广气候智慧型农业实践中的效率。",null,"Agricultural Systems","2026-09-18T00:00:00Z","论文",10,true,89,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},24,23,19,14,9,1,"基于NSGA-II的全国稻田节水灌溉空间优化框架，数据规模大、结论具体，对气候智慧型农业政策有决策参考价值。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","水稻","空间优化","节水灌溉","农业减排",[33,34],"中国稻田 节水灌溉 空间优化","NSGA-II 水稻 温室气体 减排","中国稻田节水灌溉空间优化-2912",0,"10.1016\u002Fj.agsy.2026.104976",{"doi":37,"openalex_id":39,"authors":40,"venue":10,"cited_by_count":36,"oa_url":6,"card":75,"direction":79,"ingested_from":81},"W7213551595",[41,43,46,49,51,53,55,57,60,62,65,68,71,73],{"name":42,"orcid":9},"Qiang Xu",{"name":44,"orcid":45},"Fan Yao","https:\u002F\u002Forcid.org\u002F0000-0002-4393-7296",{"name":47,"orcid":48},"Dan Wei","https:\u002F\u002Forcid.org\u002F0000-0003-4401-567X",{"name":50,"orcid":9},"Hui Gao",{"name":52,"orcid":9},"Min Jiang",{"name":54,"orcid":9},"Wenya Chen",{"name":56,"orcid":9},"Yourui Cao",{"name":58,"orcid":59},"Peng Zhang","https:\u002F\u002Forcid.org\u002F0000-0002-3036-1507",{"name":61,"orcid":9},"A. I. Abdo",{"name":63,"orcid":64},"Liujun Xiao","https:\u002F\u002Forcid.org\u002F0000-0002-1900-1586",{"name":66,"orcid":67},"Hao Liang","https:\u002F\u002Forcid.org\u002F0000-0002-9955-6492",{"name":69,"orcid":70},"Xiaoqing Cui","https:\u002F\u002Forcid.org\u002F0000-0002-1970-5145",{"name":72,"orcid":9},"Xia Liang",{"name":74,"orcid":9},"Huiqing Bai",{"tldr":76,"method":77,"finding":78,"direction":79,"opportunity":80},"构建空间优化框架，为中国稻田节水灌溉推广寻找兼顾产量、温室气体与成本的路径。","耦合K-means聚类与NSGA-II多目标进化算法，基于15.7万网格的机器学","最优方案增产6.47 Mt、减排63.5 Mt CO2e，成本101.4亿元，推广率80.3%，存在","农业绿色发展与碳","可将该空间优化框架扩展到其他气候智慧型农业技术，并耦合农户采纳行为与政策激励。","openalex","2026-09-19T23:30:05.508592Z",{"total":84,"page":22,"page_size":84,"items":85},6,[86,119,159,193,221,267],{"id":87,"title":88,"url":89,"summary":90,"summary_zh":9,"content":9,"source_name":91,"source_url":9,"published_at":92,"category":12,"cover_url":9,"hotness":13,"is_selected":93,"score":94,"score_detail":95,"sources":101,"tags":103,"search_phrases":106,"slug":109,"view_count":36,"doi":9,"paper":110,"created_at":118},2395,"[预印本]Simulating the Marginal Green Contribution of AI Modules in a Smart-Agriculture Platform: Evidence from Two Monte Carlo Experiments","https:\u002F\u002Farxiv.org\u002Fabs\u002F2609.06740","arXiv 2609.06740（2026-09-10）。基于海南智慧农业平台两项蒙特卡洛实验：实验1显示AI模式下20%农药减量概率从近零升至20.7%（基线）至49%（诊断精度0.95、采纳率0.85），15%化肥减量概率从近零升至52.0%；实验2显示AI灌溉调度增加5.0个百分点节水至16.0%，稻田CH4减排30.5%。研究指出农户采纳是主要瓶颈。","arXiv | 2026-09-10","2026-09-10T00:00:00Z",false,78,{"impact":96,"substance":97,"depth":96,"authority":98,"freshness":99,"relevant":22,"comment":100},18,22,12,8,"预印本以蒙特卡洛模拟量化AI模块在农药化肥减量与稻田减排上的边际绿色贡献，数据与结论有新意，但尚待同行评议，属细分领域前沿进展。",[102],{"name":91,"url":89},[27,104,105,30,31],"农业人工智能","精准农业",[107,108],"农业人工智能 农业减排 智慧农业 精准农业","农业人工智能 农业减排","农业人工智能农业减排智慧农业精准农业-2395",{"doi":9,"openalex_id":9,"authors":111,"venue":9,"cited_by_count":36,"oa_url":9,"card":112,"direction":79,"ingested_from":117},[],{"tldr":113,"method":114,"finding":115,"direction":79,"opportunity":116},"用蒙特卡洛实验模拟智慧农业平台中AI模块对农药化肥减量与节水减排的边际绿色贡献。","海南智慧农业平台数据，两项蒙特卡洛实验，含诊断精度与采纳率情景。","AI使农药减量20%概率升至20.7%-49%，化肥减量15%概率达52%，稻田CH4减排30.5%","可将农户采纳行为内生化，研究采纳率提升机制与AI模块绿色效益的经济激励设计。","agent","2026-09-14T00:06:32.920282Z",{"id":120,"title":121,"url":122,"summary":123,"summary_zh":124,"content":9,"source_name":125,"source_url":122,"published_at":126,"category":12,"cover_url":9,"hotness":13,"is_selected":93,"score":127,"score_detail":128,"sources":131,"tags":133,"search_phrases":137,"slug":140,"view_count":36,"doi":141,"paper":142,"created_at":158},2044,"Climate-Smart Rice Production through Integrated Water and Carbon Management: Methane Mitigation, Biochar and Yield Resilience","https:\u002F\u002Fdoi.org\u002F10.9734\u002Farja\u002F2026\u002Fv19i4912","Rice production occupies a difficult position in climate-smart agriculture because flooded paddy soils support high and stable yields yet create conditions favourable to methane formation, consume substantial irrigation water, and can alter the availability of potentially toxic elements in grain. This critical narrative review evaluates whether methane mitigation, water management, biochar amendment and yield resilience can be integrated into a coherent management strategy rather than treated as separate objectives. Literature published from 2000 to 3 July 2026 was considered, with earlier foundational evidence retained selectively where necessary. The strongest and most consistent evidence supports non-continuous flooding, particularly well-managed alternate wetting and drying (AWD), as the principal near-term field lever for lowering methane emissions and irrigation demand. Across recent meta-analyses, methane reductions are substantial, but nitrous oxide commonly increases, and yield responses depend strongly on drying severity, timing, soil properties, nitrogen supply and cultivar. Mild AWD is therefore better supported than severe drying as a production-compatible mitigation practice. Biochar can improve rice yield, nitrogen-use efficiency and soil carbon status while moderating greenhouse-gas emissions, but average methane mitigation is less consistent than that achieved through water management and is highly contingent on feedstock, pyrolysis conditions, application rate, soil properties and mineral nitrogen input. Direct factorial evidence combining AWD and biochar is still limited, although several multi-year field studies indicate that biochar can partly buffer nutrient losses, contaminant trade-offs and physiological constraints associated with soil drying. Evidence that AWD itself increases yield resilience to drought or heat is mechanistically plausible but remains cultivar- and experiment-specific; it should not yet be equated with proven long-term yield stability under climate extremes. The synthesis supports a hierarchical strategy in which water regime is the primary control, biochar and nitrogen management are context-dependent modifiers, and cultivar choice provides a resilience layer. Future progress requires multi-site factorial trials, explicit life-cycle accounting, multi-contaminant grain-safety assessment and measurement frameworks that verify water status, greenhouse gases and yield stability together.","水稻生产在气候智慧型农业中处于两难境地：淹水稻田土壤虽能支撑高产稳产，却为甲烷生成创造了有利条件，消耗大量灌溉用水，并可能改变籽粒中潜在有毒元素的生物有效性。本篇批判性叙事综述评估了甲烷减排、水分管理、生物炭施用与产量韧性能否整合为协调一致的管理策略，而非被当作彼此独立的目标。文献检索范围涵盖2000年至2026年7月3日发表的文献，必要时选择性保留了更早的基础性证据。最强且最一致的证据支持非连续淹水，尤其是管理良好的干湿交替（AWD），作为近期降低甲烷排放和灌溉需求的主要田间调控手段。近期多项荟萃分析显示，甲烷减排幅度可观，但氧化亚氮排放通常增加，且产量响应在很大程度上取决于晒田强度、时机、土壤性质、氮素供应和品种。因此，轻度AWD比重度晒田更适合作为与生产兼容的减排措施。生物炭可提高水稻产量、氮素利用效率和土壤碳储量，同时调节温室气体排放，但其平均甲烷减排效果不如水分管理稳定，且高度依赖于原料、热解条件、施用量、土壤性质和矿质氮投入。将AWD与生物炭相结合的直接析因证据仍然有限，尽管若干多年田间研究表明，生物炭可部分缓冲与土壤干燥相关的养分损失、污染物权衡和生理限制。AWD本身能否提高水稻对干旱或高温的产量韧性，其机制虽具合理性，但仍因品种和试验条件而异；目前尚不能将其等同于在气候极端条件下已获验证的长期产量稳定性。本综述支持一种分层策略，即以水分 regime 为首要调控手段，生物炭和氮素管理为情境依赖的调节因子，品种选择则提供韧性层。未来进展需要多地点析因试验、明确的生命周期核算、多污染物籽粒安全评估，以及能够同步验证水分状态、温室气体和产量稳定性的测量框架。","Asian Research Journal of Agriculture","2026-09-09T00:00:00Z",79,{"impact":96,"substance":97,"depth":96,"authority":129,"freshness":99,"relevant":22,"comment":130},13,"系统综述整合水分管理与生物炭的稻田甲烷减排路径，结论可靠但属学术综述，对产业实践有中长期参考价值。",[132],{"name":125,"url":122},[27,28,134,135,30,136],"生物炭","气候变化","甲烷减排",[138,139],"智慧农业 气候变化 甲烷减排 节水灌溉","智慧农业 气候变化","智慧农业气候变化甲烷减排节水灌溉-2044","10.9734\u002Farja\u002F2026\u002Fv19i4912",{"doi":141,"openalex_id":143,"authors":144,"venue":125,"cited_by_count":36,"oa_url":122,"card":152,"direction":157,"ingested_from":81},"W7212070936",[145,148,150],{"name":146,"orcid":147},"Abhishek Sinha","https:\u002F\u002Forcid.org\u002F0000-0001-7220-0691",{"name":149,"orcid":9},"Sanchita Sarkar",{"name":151,"orcid":9},"Srinivasa Rao Meesala",{"tldr":153,"method":154,"finding":155,"direction":79,"opportunity":156},"综述评估水稻甲烷减排、水分管理、生物炭与产量韧性的整合策略，提出以水分管理为主的分层管理框架。","2000-2026年文献综述，整合meta分析与多年田间试验证据。","适度干湿交替是近期最可靠的减排增产手段，生物炭效果依赖条件，二者联合证据有限。","亟需多点多因素试验，将水分、生物炭、氮肥与品种整合，并同步验证温室气体、产量稳定性和籽粒安全。","智慧农业 \u002F 农业物联网","2026-09-10T23:30:09.553677Z",{"id":160,"title":161,"url":162,"summary":163,"summary_zh":164,"content":9,"source_name":165,"source_url":162,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":93,"score":94,"score_detail":166,"sources":169,"tags":171,"search_phrases":174,"slug":177,"view_count":36,"doi":178,"paper":179,"created_at":192},2920,"Breeding rice for optimal maturity across diverse sowing windows under future climate change scenarios in Chongqing area","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1899019","Introduction It is of great significance to optimize rice cultivars for different sowing dates under future climate change for rice sustainable production in Chongqing. Methods In this study, using the APSIM-Rice model and Coupled Model Intercomparison Project Phase 6 (CMIP6) Shared Socioeconomic Pathways (SSP) scenarios, we investigated the changes of yield, water consumption and water use efficiency (WUE) across six sowing dates and three cultivars (early, normal and late-maturing cultivars) under baseline period (1981 – 2010) and future climate 2scenarios (2031-2100, SSP2-4.5 and SSP5-8.5). In this study, the climate model BCC-CSM2-MR was selected due to its reliable simulation of China’s climate and compatibility with the APSIM-Rice model. Results Results showed that rice yield with normal cultivar in the baseline period peaked on March 30, with the value of 6,310 kg ha− −1 , and reached its minimum on March 1, with the value of 4,580 kg ha− −1 . Water consumption during the rice growing period increased with the delayed sowing dates (212 mm on March 1 to 276 mm on April 20). The response trend of water use efficiency (WUE) to different sowing dates was identical to that of yield, with the maximum WUE of 23.30 kg ha− −1 mm −1 achieved when sown on March 30. Under future SSP2-4.5 and SSP5-8.5 scenarios, early sowing (March 1–20) consistently enhanced yield and WUE for normal cultivars (maximum increments were 25.40% and 29.00% for yield and WUE), while late sowing (March 30–April 20) caused severe losses (up to 26.90% and 34.60% for yield and WUE for April 10 under SSP5-8.5 in the 2060s), with water consumption rising across all sowing dates. Early-maturing cultivars reduced yield (10.60%–44.60%) and WUE (7.70%–43.90%) across all sowing dates under future scenarios, whereas late-maturing cultivars synergized with early sowing to boost yield (up to 25.62%) and WUE (up to 32.85%) but exacerbated losses for late sowing, with water consumption increasing significantly (up to 56.30% under SSP5-8.5 in the 2060s). Discussion These findings provide critical scientific support for optimizing sowing date and cultivar combinations, thereby enhancing the climate resilience and sustainability of rice production in Chongqing and similar subtropical monsoon regions. However, in the future, more climate models, extreme climate impacts, and agronomic factors should be considered","引言 优化不同播期下的水稻品种对重庆未来气候变化背景下的水稻可持续生产具有重要意义。方法 本研究利用APSIM-Rice模型和耦合模式比较计划第六阶段（CMIP6）共享社会经济路径（SSP）情景，研究了基准期（1981—2010年）和未来气候情景（2031—2100年，SSP2-4.5和SSP5-8.5）下6个播期和3个品种（早熟、中熟和晚熟品种）的产量、耗水量和水分利用效率（WUE）变化。本研究选择气候模式BCC-CSM2-MR，因其对中国气候的模拟可靠且与APSIM-Rice模型兼容。结果 结果表明，基准期中熟品种水稻产量在3月30日达到峰值，为6 310 kg ha⁻¹，在3月1日降至最低，为4 580 kg ha⁻¹。水稻生育期耗水量随播期推迟而增加（3月1日的212 mm增至4月20日的276 mm）。水分利用效率（WUE）对不同播期的响应趋势与产量一致，3月30日播种时WUE最高，为23.30 kg ha⁻¹ mm⁻¹。在未来SSP2-4.5和SSP5-8.5情景下，早播（3月1—20日）持续提高中熟品种的产量和WUE（产量和WUE的最大增幅分别为25.40%和29.00%），而晚播（3月30日—4月20日）造成严重损失（在2060年代SSP5-8.5情景下，4月10日播种的产量和WUE损失分别高达26.90%和34.60%），且所有播期的耗水量均增加。在未来情景下，早熟品种在所有播期均降低产量（10.60%—44.60%）和WUE（7.70%—43.90%），而晚熟品种与早播协同提高产量（最高25.62%）和WUE（最高32.85%），但加剧了晚播的损失，耗水量显著增加（在2060年代SSP5-8.5情景下最高达56.30%）。讨论 这些发现为优化播期和品种组合提供了关键科学支撑，从而增强重庆及类似亚热带季风区水稻生产的气候韧性和可持续性。然而，未来应考虑更多气候模式、极端气候影响和农艺因素。","Frontiers in Sustainable Food Systems",{"impact":167,"substance":97,"depth":96,"authority":129,"freshness":21,"relevant":22,"comment":168},16,"基于APSIM-Rice与CMIP6情景模拟重庆水稻播期与品种组合，数据扎实、结论对区域气候适应性育种有参考价值，但属细分领域研究，影响力有限。",[170],{"name":165,"url":162},[27,28,135,172,173],"品种选育","播期优化",[175,176],"APSIM-Rice 水稻 播期","重庆 水稻 品种 气候","APSIM-Rice水稻播期-2920","10.3389\u002Ffsufs.2026.1899019",{"doi":178,"openalex_id":180,"authors":181,"venue":165,"cited_by_count":36,"oa_url":162,"card":186,"direction":190,"ingested_from":81},"W7213555399",[182,184],{"name":183,"orcid":9},"Jianzhao Tang",{"name":185,"orcid":9},"Jianping Zhang",{"tldr":187,"method":188,"finding":189,"direction":190,"opportunity":191},"用APSIM-Rice与CMIP6情景模拟重庆不同播期和品种水稻产量、耗水与水分利用效率。","APSIM-Rice模型结合CMIP6 SSP2-4.5\u002FSSP5-8.5情景及","未来早播配晚熟品种可增产提效，晚播则大幅减产，各播期耗水均上升。","农业人工智能与决策模型","可引入多模型集合、极端气候与氮肥管理等农艺因素，优化播期-品种组合的适应策略。","2026-09-19T23:30:08.567885Z",{"id":194,"title":195,"url":196,"summary":197,"summary_zh":9,"content":9,"source_name":198,"source_url":9,"published_at":199,"category":12,"cover_url":9,"hotness":13,"is_selected":93,"score":200,"score_detail":201,"sources":203,"tags":205,"search_phrases":209,"slug":212,"view_count":36,"doi":9,"paper":213,"created_at":220},2900,"Crop-GPA 2.0:安徽农业大学团队发布跨物种基因-表型预测深度学习工具,登The Crop Journal","https:\u002F\u002Fwww.global-agriculture.com\u002Fag-tech-research-news\u002Fchinese-researchers-launch-ai-tool-that-predicts-crop-gene-trait-links-across-species\u002F","安徽农业大学岳振宇教授团队(第一作者高玉佳)开发的Crop-GPA 2.0跨物种基因-表型预测深度学习工具于2026年9月10日发表在The Crop Journal。该工具通过分层基因组表征、跨物种预训练、性状感知学习三种联动技术,在水稻、玉米、小麦测试中跨性状(产量、抗病性、抗逆性、籽粒品质)均优于现有方法,且能在新物种或训练数据有限时保持较好性能,在线平台为育种者提供免费SNP排名服务,极大降低标记辅助选择门槛。","Global Agriculture \u002F The Crop Journal","2026-09-14T00:00:00Z",83,{"impact":97,"substance":18,"depth":96,"authority":20,"freshness":84,"relevant":22,"comment":202},"跨物种基因-表型预测工具登核心期刊，方法新颖、结论可靠，对智能育种有实质推动，值得进入每日精选。",[204],{"name":198,"url":196},[27,104,28,206,207,208],"智能育种","玉米","基因-表型预测",[210,211],"安徽农业大学 Crop-GPA 2.0","Crop-GPA 2.0 基因-表型预测","安徽农业大学Crop-GPA2.0-2900",{"doi":9,"openalex_id":9,"authors":214,"venue":9,"cited_by_count":36,"oa_url":9,"card":215,"direction":190,"ingested_from":117},[],{"tldr":216,"method":217,"finding":218,"direction":190,"opportunity":219},"安徽农业大学团队发布跨物种基因-表型预测深度学习工具Crop-GPA 2.0，并在水稻、玉米、小麦中","分层基因组表征、跨物种预训练、性状感知学习，基于多物种SNP与表型数据。","跨性状预测均优于现有方法，新物种或小样本下仍表现良好，并提供免费在线SNP排名服务。","可探索将跨物种预训练模型与田间表型组、环境数据融合，提升复杂性状预测与育种决策的可解释性。","2026-09-19T00:06:08.595080Z",{"id":222,"title":223,"url":224,"summary":225,"summary_zh":226,"content":9,"source_name":227,"source_url":224,"published_at":228,"category":12,"cover_url":9,"hotness":13,"is_selected":93,"score":94,"score_detail":229,"sources":231,"tags":233,"search_phrases":237,"slug":240,"view_count":36,"doi":241,"paper":242,"created_at":266},2663,"Research on an automated mapping method for rice aboveground biomass based on low-altitude remote sensing","https:\u002F\u002Fdoi.org\u002F10.4081\u002Fjae.2026.2061","Accurate monitoring of rice aboveground biomass (AGB) is crucial for guiding agricultural production management. This study focuses on high-precision estimation and automated mapping of rice AGB. Field experiments were conducted in Nanxun District, Huzhou City, Zhejiang Province. We collected UAV RGB and multispectral images, rice AGB, and plant height data. By integrating vegetation indices, texture features, and plant height information, the AGB estimation model was established using algorithms such as Stacking. A framework combining \"SAM + MobileNetV3-Small classification\" was proposed to achieve automated paddy field extraction and phenology recognition. The results demonstrate that the rice AGB prediction model based on the Stacking ensemble algorithm performed excellently. The introduction of plant height significantly improved model accuracy. For example, during the heading stage, R2 increased from 0.421 to 0.739, and RPIQ rose from 1.995 to 3.015. The automated paddy field extraction and phenology recognition framework developed in this study achieved a segmentation accuracy of 0.968 and a classification accuracy of 0.993 on the dataset used in this study, without requiring manual annotation. This research provides a technical reference for automated and high-precision mapping of rice AGB.","准确监测水稻地上生物量(AGB)对指导农业生产管理至关重要。本研究聚焦水稻AGB的高精度估算与自动化制图。田间试验在浙江省湖州市南浔区开展，采集了无人机RGB和多光谱影像、水稻AGB及株高数据。通过融合植被指数、纹理特征和株高信息，利用Stacking等算法构建AGB估算模型。提出了一种“SAM+MobileNetV3-Small分类”框架，以实现稻田自动化提取和物候识别。结果表明，基于Stacking集成算法的水稻AGB预测模型表现优异，株高的引入显著提高了模型精度。例如，在抽穗期，R2从0.421提升至0.739，RPIQ从1.995提升至3.015。本研究开发的稻田自动化提取与物候识别框架在本研究数据集上实现了0.968的分割精度和0.993的分类精度，且无需人工标注。本研究为水稻AGB的自动化高精度制图提供了技术参考。","Journal of Agricultural Engineering","2026-09-15T00:00:00Z",{"impact":167,"substance":97,"depth":96,"authority":20,"freshness":99,"relevant":22,"comment":230},"基于无人机遥感与Stacking集成模型实现水稻地上生物量自动化制图，方法新颖、数据扎实，对精准农业管理有参考价值。",[232],{"name":227,"url":224},[27,234,235,28,236],"无人机","农业遥感","作物表型",[238,239],"作物表型 农业遥感 智慧农业 无人机","作物表型 农业遥感","作物表型农业遥感智慧农业无人机-2663","10.4081\u002Fjae.2026.2061",{"doi":241,"openalex_id":243,"authors":244,"venue":227,"cited_by_count":36,"oa_url":224,"card":260,"direction":264,"ingested_from":81},"W7213229220",[245,247,249,251,253,256,258],{"name":246,"orcid":9},"Honggang Xu",{"name":248,"orcid":9},"Xuehan Li",{"name":250,"orcid":9},"Jia Shen",{"name":252,"orcid":9},"Ziyi Li",{"name":254,"orcid":255},"Zhe Li","https:\u002F\u002Forcid.org\u002F0009-0008-6496-3697",{"name":257,"orcid":9},"Yiming Li",{"name":259,"orcid":9},"Pengcheng Nie",{"tldr":261,"method":262,"finding":263,"direction":264,"opportunity":265},"基于无人机RGB与多光谱影像，结合株高与Stacking集成算法，实现水稻地上生物量高精度自动制图。","无人机RGB\u002F多光谱影像、植被指数、纹理与株高，Stacking集成及SAM+M","引入株高显著提升精度，抽穗期R²从0.421升至0.739；自动稻田提取与物候识别精度达0.968和","农业遥感与作物表型","可探索多生育期、多品种下株高与纹理特征的迁移性，并耦合深度学习实现全自动生物量时空制图。","2026-09-16T23:30:28.929376Z",{"id":268,"title":269,"url":270,"summary":271,"summary_zh":9,"content":9,"source_name":272,"source_url":9,"published_at":273,"category":12,"cover_url":9,"hotness":13,"is_selected":93,"score":274,"score_detail":275,"sources":277,"tags":279,"search_phrases":282,"slug":285,"view_count":36,"doi":9,"paper":286,"created_at":293},2611,"无人机遥感在水稻高通量表型分析中的研究进展 系统综述","https:\u002F\u002Fwww.ebiotrade.com\u002Fnewsf\u002F2026-9\u002F20260911171723051.htm","发表于Smart Agricultural Technology。依据PRISMA 2020规范系统检索文献最终纳入199项研究(2014–2026年)。研究发现：先进传感、特征集成和建模技术日益支持氮素和叶绿素估算及产量预测；轻量级模型和边缘计算系统在倒伏和病害监测任务中显示出实时部署的可行性；跨区域泛化受环境背景干扰以及地点品种偏倚制约；199项研究中有6项(3.0%)将UAV衍生性状与遗传关联分析联系起来。","Smart Agricultural Technology","2026-09-11T01:00:00Z",77,{"impact":96,"substance":97,"depth":96,"authority":129,"freshness":84,"relevant":22,"comment":276},"基于PRISMA规范纳入199项研究的系统综述，方法严谨、数据规模大，对水稻表型与智慧育种有实质参考价值，但属细分领域学术进展，公共影响有限。",[278],{"name":272,"url":270},[27,104,28,280,281],"无人机遥感","高通量表型",[283,284],"农业人工智能 无人机遥感 高通量表型 智慧农业","农业人工智能 无人机遥感","农业人工智能无人机遥感高通量表型智慧农业-2611",{"doi":9,"openalex_id":9,"authors":287,"venue":9,"cited_by_count":36,"oa_url":9,"card":288,"direction":264,"ingested_from":117},[],{"tldr":289,"method":290,"finding":291,"direction":264,"opportunity":292},"系统综述199项研究，梳理无人机遥感在水稻高通量表型分析中的应用进展与瓶颈。","依据PRISMA 2020系统检索2014–2026年199项研究并归纳分析。","传感与建模支撑氮素、产量预测，跨区域泛化受环境与品种偏倚制约，基因关联研究仅占3%。","UAV表型与遗传关联分析严重不足，可探索跨区域泛化建模及表型-基因型融合方向。","2026-09-16T00:03:52.470505Z"]