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2026年09月23日","2026-09-23T03:00:00Z","报道",71,{"impact":321,"substance":747,"depth":345,"authority":602,"freshness":385,"relevant":455,"comment":748},16,"报道呈现AI巡田、采摘机器人与种猪育种获奖等实质进展，但属企业宣传性综合报道，深度与权威度中等。",[750],{"name":742,"url":740},[588,589,752,753,754],"智能农机","种业振兴","天空地一体化",{"code":208,"slug":209,"name":210,"path":211,"parent_code":200},[],{"status":597,"locked":581,"version":4,"method":598,"evidence":758},{"candidates":759,"agricultural_evidence":603},[760,765,769,774,779,783,787],{"code":208,"score":647,"supported":603,"title":761,"abstract":762,"tags":764},[],[210,763],"稻田",[],{"code":16,"score":400,"supported":603,"title":766,"abstract":767,"tags":768},[],[588],[588],{"code":286,"score":613,"supported":603,"title":770,"abstract":771,"tags":773},[],[772],"猕猴桃",[],{"code":120,"score":613,"supported":603,"title":775,"abstract":776,"tags":778},[],[777],"采摘机器人",[],{"code":80,"score":455,"supported":581,"title":780,"abstract":781,"tags":782},[],[],[753],{"code":112,"score":455,"supported":581,"title":784,"abstract":785,"tags":786},[],[],[752],{"code":24,"score":455,"supported":581,"title":788,"abstract":789,"tags":790},[],[],[589],[792,793],"中联重科 岳麓智慧农场","猕猴桃采摘机器人 常州艾宝","中联重科岳麓智慧农场-3288","2026-09-24T00:03:56.850138Z",{"id":797,"title":798,"url":799,"summary":800,"summary_zh":801,"content":11,"source_name":802,"source_url":799,"published_at":803,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":698,"score_detail":804,"sources":807,"tags":809,"primary_topic":813,"secondary_topics":814,"classification":817,"search_phrases":845,"slug":848,"view_count":400,"doi":849,"paper":850,"created_at":871},3263,"Identifying drought-responsive candidate genes in root and shoot tissues of wheat (Triticum aestivum L.) through RNA sequencing, cluster analysis, weighted gene co-expression network analysis, and machine learning models","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1952456","Drought stress severely affects the growth, development, and yield formation of wheat. However, the differentiated regulatory mechanisms of wheat roots and shoots during drought adaptation remain to be further analyzed. Therefore, this study used RNA-seq datasets of roots and shoots from multiple wheat varieties, combined with differential expression analysis, Gene Ontology (GO) enrichment analysis, clustering analysis, weighted gene co-expression network analysis (WGCNA), and machine learning were used to characterize drought-responsive transcriptional changes. Differential expression analysis identified 90 and 3,991 differentially expressed genes (DEGs) in roots under DS1 and DS2 respectively, compared with 34 and 1,940 DEGs in shoots. Functional enrichment analysis indicated that root DEGs were mainly involved in root growth, cell wall remodeling, water transport, and osmotic adjustment processes, whereas shoot DEGs are mainly enriched in processes related to stomatal regulation, photosynthesis, lipid metabolism, and oxidative stress responses. WGCNA identified 18 and 19 co-expression modules in roots and shoots, respectively, among which the yellow module was significantly correlated with the response to drought stress in root and shoot tissues. To further screen drought-responsive candidate genes from these modules, we compared multiple machine learning models. The XGBoost model achieved the highest and AUC in both roots and shoots. Combined with SHAP feature contribution analysis, 30 drought-responsive candidate genes with high discriminatory power were identified separately in roots and shoots. Analysis of cis -acting elements in candidate gene promoters revealed abundant drought- and stress-responsive elements, including MYB, G-box, ABRE, MYC, MBS, and LTR. MYB (183 in roots and 164 in shoots), G-box (168 and 98), and ABRE (148 and 86) were particularly abundant, and several candidate genes contained more than five ABRE copies in their promoters, suggesting potential involvement in ABA-mediated drought responses. This study reveals the different transcriptional response processes of wheat roots and shoots under drought stress, and identifies key co-expression modules and core candidate genes through WGCNA combined with XGBoost and SHAP analysis. These findings provide a basis for understanding organ-specific molecular responses to drought stress and offer potential targets for improving drought tolerance in wheat.","干旱胁迫严重影响小麦的生长发育和产量形成。然而，小麦根系和地上部在干旱适应过程中的差异化调控机制仍有待深入解析。因此，本研究利用多个小麦品种根系和地上部的RNA-seq数据集，结合差异表达分析、基因本体（GO）富集分析、聚类分析、加权基因共表达网络分析（WGCNA）和机器学习方法，表征干旱响应转录变化。差异表达分析在DS1和DS2条件下分别鉴定出根系中90个和3,991个差异表达基因（DEGs），而地上部中分别为34个和1,940个DEGs。功能富集分析表明，根系DEGs主要参与根系生长、细胞壁重塑、水分运输和渗透调节过程，而地上部DEGs主要富集于气孔调控、光合作用、脂质代谢和氧化胁迫响应相关过程。WGCNA在根系和地上部中分别鉴定出18个和19个共表达模块，其中黄色模块与根系和地上部组织对干旱胁迫的响应显著相关。为进一步从这些模块中筛选干旱响应候选基因，我们比较了多种机器学习模型。XGBoost模型在根系和地上部中均取得了最高的AUC。结合SHAP特征贡献分析，分别在根系和地上部中鉴定出30个具有高判别力的干旱响应候选基因。候选基因启动子中的顺式作用元件分析揭示了丰富的干旱和胁迫响应元件，包括MYB、G-box、ABRE、MYC、MBS和LTR。其中MYB（根系183个，地上部164个）、G-box（168个和98个）和ABRE（148个和86个）尤为丰富，且多个候选基因的启动子中含有超过5个ABRE拷贝，表明其可能参与ABA介导的干旱响应。本研究揭示了小麦根系和地上部在干旱胁迫下不同的转录响应过程，并通过WGCNA结合XGBoost和SHAP分析鉴定了关键共表达模块和核心候选基因。这些发现为理解干旱胁迫的器官特异性分子响应提供了基础，并为提高小麦耐旱性提供了潜在靶点。","Frontiers in Sustainable Food Systems","2026-09-23T00:00:00Z",{"impact":361,"substance":321,"depth":361,"authority":805,"freshness":385,"relevant":455,"comment":806},13,"该研究结合多组学与机器学习挖掘小麦抗旱候选基因，方法新颖、数据扎实，对分子育种有参考价值，值得进入每日精选。",[808],{"name":802,"url":799},[589,753,810,811,812],"基因挖掘","作物育种","小麦抗旱",{"code":216,"slug":217,"name":218,"path":219,"parent_code":200},[815,816],{"code":24,"slug":25,"name":26,"path":27,"parent_code":7},{"code":184,"slug":185,"name":186,"path":187,"parent_code":128},{"status":597,"locked":581,"version":4,"method":598,"evidence":818},{"candidates":819,"agricultural_evidence":603},[820,827,831,837,841],{"code":24,"score":821,"supported":603,"title":822,"abstract":824,"tags":826},15,[823],"machine learning",[825,823],"机器学习",[589],{"code":216,"score":345,"supported":603,"title":828,"abstract":829,"tags":830},[217],[218,217],[],{"code":184,"score":345,"supported":603,"title":832,"abstract":834,"tags":836},[833],"drought",[835,833],"干旱",[],{"code":80,"score":455,"supported":581,"title":838,"abstract":839,"tags":840},[],[],[753],{"code":88,"score":455,"supported":581,"title":842,"abstract":843,"tags":844},[],[],[810],[846,847],"小麦 抗旱基因 RNA测序","小麦 根系 地上部 干旱响应","小麦抗旱基因RNA测序-3263","10.3389\u002Ffsufs.2026.1952456",{"doi":849,"openalex_id":851,"authors":852,"venue":802,"cited_by_count":424,"oa_url":799,"card":865,"direction":636,"ingested_from":870},"W7214062209",[853,855,858,860,862],{"name":854,"orcid":11},"Tianle Ji",{"name":856,"orcid":857},"Baoyue Cui","https:\u002F\u002Forcid.org\u002F0009-0000-5881-9682",{"name":859,"orcid":11},"Jinjie Yin",{"name":861,"orcid":11},"Xianyong Meng",{"name":863,"orcid":864},"Jun Yan","https:\u002F\u002Forcid.org\u002F0000-0001-6883-8529",{"tldr":866,"method":867,"finding":868,"direction":636,"opportunity":869},"通过RNA-seq、WGCNA和XGBoost-SHAP分析，鉴定小麦根和茎中响应干旱的候选基因。","RNA-seq、差异表达、WGCNA、XGBoost与SHAP特征分析。","根和茎干旱响应机制不同，各鉴定出30个候选基因，启动子富含MYB、G-box和ABRE元件。","可结合多组学与深度学习构建器官特异性干旱调控网络，并开展候选基因功能验证。","openalex","2026-09-23T23:30:07.062126Z",{"id":873,"title":874,"url":875,"summary":876,"summary_zh":877,"content":11,"source_name":878,"source_url":875,"published_at":803,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":879,"score_detail":880,"sources":882,"tags":884,"primary_topic":888,"secondary_topics":889,"classification":890,"search_phrases":909,"slug":912,"view_count":455,"doi":913,"paper":914,"created_at":943},3254,"End-edge-cloud collaborative group phenotype recognition and diagnosis for rice seedling in vertical rice seedling cultivation","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112412","End-edge-cloud collaborative group phenotype recognition and diagnosis for rice seedling in vertical rice seedling cultivation。Computers and Electronics in Agriculture","端边云协同的水稻秧苗群体表型识别与诊断及其在立体水稻育秧中的应用。","Computers and Electronics in Agriculture",80,{"impact":361,"substance":508,"depth":361,"authority":345,"freshness":385,"relevant":455,"comment":881},"核心期刊论文，提出端边云协同的水稻育苗群体表型识别诊断方法，方法新颖、时效性强，对智慧育苗有参考价值。",[883],{"name":878,"url":875},[588,589,885,886,887],"边缘计算","水稻育苗","表型识别",{"code":208,"slug":209,"name":210,"path":211,"parent_code":200},[],{"status":597,"locked":581,"version":4,"method":598,"evidence":891},{"candidates":892,"agricultural_evidence":603},[893,897,901,905],{"code":208,"score":345,"supported":603,"title":894,"abstract":895,"tags":896},[209],[210,209],[],{"code":16,"score":455,"supported":581,"title":898,"abstract":899,"tags":900},[],[],[588],{"code":24,"score":455,"supported":581,"title":902,"abstract":903,"tags":904},[],[],[589],{"code":48,"score":455,"supported":581,"title":906,"abstract":907,"tags":908},[],[],[885],[910,911],"水稻育苗 群体表型 识别","垂直水稻育苗 端边云协同","水稻育苗群体表型识别-3254","10.1016\u002Fj.compag.2026.112412",{"doi":913,"openalex_id":915,"authors":916,"venue":878,"cited_by_count":424,"oa_url":11,"card":938,"direction":734,"ingested_from":870},"W7214024203",[917,919,922,924,926,928,930,932,935],{"name":918,"orcid":11},"Zheng Ma",{"name":920,"orcid":921},"Ruohan Wang","https:\u002F\u002Forcid.org\u002F0009-0009-7656-5132",{"name":923,"orcid":11},"Yuxuan Cai",{"name":925,"orcid":11},"Donghong Wen",{"name":927,"orcid":11},"Long Zhou",{"name":929,"orcid":11},"Yunzhe Huang",{"name":931,"orcid":11},"Yihua Liu",{"name":933,"orcid":934},"Zhongbin Su","https:\u002F\u002Forcid.org\u002F0000-0002-8966-8933",{"name":936,"orcid":937},"Hongbo Li","https:\u002F\u002Forcid.org\u002F0000-0001-7711-5744",{"tldr":939,"method":940,"finding":941,"direction":734,"opportunity":942},"提出端边云协同的水稻秧苗群体表型识别与诊断方法，用于垂直育秧场景。","端边云协同架构结合深度学习，对垂直育秧水稻秧苗群体图像进行表型识别与诊断。","端边云协同可实现水稻秧苗群体表型高效识别与诊断，支撑垂直育秧精准管理。","可探索端边云协同下多尺度群体表型实时诊断与育秧环境调控闭环，弥补边缘算力与模型轻量化研究空白。","2026-09-23T23:30:01.424596Z",{"id":945,"title":946,"url":947,"summary":948,"summary_zh":11,"content":11,"source_name":949,"source_url":11,"published_at":697,"category":744,"cover_url":11,"hotness":385,"is_selected":603,"score":245,"score_detail":950,"sources":954,"tags":956,"primary_topic":960,"secondary_topics":961,"classification":963,"search_phrases":991,"slug":994,"view_count":995,"doi":11,"paper":11,"created_at":996},3219,"盐碱地综合利用国家技术创新中心发布两项重大科研成果——全球首个植物抗逆AI智能体+国内首个耐盐水稻11K液相育种芯片（盐稻芯1号）","http:\u002F\u002Fdyny.dongying.gov.cn\u002Fart\u002F2026\u002F9\u002F20\u002Fart_39177_10312568.html","9-20 东营市农业农村局发布：9-19 盐碱地综合利用国家技术创新中心召开新闻发布会，正式发布全球首个植物抗逆AI智能体与国内首个耐盐水稻11K液相育种芯片（盐稻芯1号）两项重大科研成果。植物抗逆AI智能体聚焦盐碱、干旱、高温、低温四大非生物逆境，以水稻等6个物种为研究核心，整合全球逆境文献与多源科研数据，依托6个AI智能体分工完成资料检索、数据分析、交叉复核、综合研判。耐盐水稻11K液相育种芯片以耐盐多组学证据定向选择位点，相较全基因组重测序成本仅为其三分之一。两项成果由国家盐碱地中心联合中国农业科学院深圳农业基因组研究所、崖州湾国家实验室、山东省农业科学院湿地农业与生态研究所共同研发。","东营市农业农村局",{"impact":951,"substance":952,"depth":361,"authority":345,"freshness":602,"relevant":455,"comment":953},27,24,"国家级科研机构发布全球首个植物抗逆AI智能体与国内首个耐盐水稻育种芯片，属种业与农业AI领域重大突破，信息增量与权威性俱佳，值得进入每日精选。",[955],{"name":949,"url":947},[589,753,957,958,959],"育种芯片","盐碱地治理","耐盐水稻",{"code":208,"slug":209,"name":210,"path":211,"parent_code":200},[962],{"code":144,"slug":145,"name":146,"path":147,"parent_code":128},{"status":597,"locked":581,"version":4,"method":598,"evidence":964},{"candidates":965,"agricultural_evidence":603},[966,971,975,979,983,987],{"code":144,"score":416,"supported":603,"title":967,"abstract":969,"tags":970},[968],"盐碱地",[968],[958],{"code":208,"score":524,"supported":603,"title":972,"abstract":973,"tags":974},[210],[210],[],{"code":184,"score":613,"supported":603,"title":976,"abstract":977,"tags":978},[],[835],[],{"code":80,"score":455,"supported":581,"title":980,"abstract":981,"tags":982},[],[],[753],{"code":88,"score":455,"supported":581,"title":984,"abstract":985,"tags":986},[],[],[957],{"code":24,"score":455,"supported":581,"title":988,"abstract":989,"tags":990},[],[],[589],[992,993],"盐碱地国家技术创新中心 植物抗逆AI智能体","盐稻芯1号 耐盐水稻育种芯片","盐碱地国家技术创新中心植物抗逆AI智能体-3219",7,"2026-09-23T00:04:29.516454Z",{"id":998,"title":999,"url":1000,"summary":1001,"summary_zh":1002,"content":11,"source_name":1003,"source_url":1000,"published_at":697,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":1004,"score_detail":1005,"sources":1007,"tags":1009,"primary_topic":1011,"secondary_topics":1012,"classification":1013,"search_phrases":1050,"slug":1053,"view_count":408,"doi":1054,"paper":1055,"created_at":1092},3199,"Recommendation of Suitable Planting Locations for Hybrid Maize Varieties Based on Graph Collaborative Filtering","https:\u002F\u002Fdoi.org\u002F10.1093\u002Finsilicoplants\u002Fdiag026","Abstract Enhancing the precision of crop variety adaptability evaluation is critical for maximizing yields and ensuring food security under climate change. Traditional methods relying on expert knowledge or process-based models face scalability challenges and difficulties in capturing complex variety–environment interactions. This study proposes a scalable graph-based collaborative filtering framework for assessing suitable planting locations for hybrid maize varieties, reformulating adaptability evaluation as a ranking task. The framework integrates two key innovations: a multi-layer perceptron (MLP) encoding module that leverages 15 variety trait features and 24 environmental variables to address the cold-start problem, enabling recommendations for previously unseen varieties; and a BPR+ loss function that distinguishes unsuitable environments from merely unplanted ones to accommodate the unique sparsity structure of agricultural trial data. Evaluated against six state-of-the-art graph-based recommendation models on a dataset of 189 locations and 1,102 hybrid maize varieties across China's major agro-ecological zones, our method consistently outperforms existing approaches, achieving approximately 5% improvement over the strongest baseline. The MLP module attains cold-start NDCG above 0.8, and BPR+ yields significant gains in Recall@1–4 and NDCG@1–5. A case study of the widely cultivated variety ZD958, validated against both national promotion data (2019–2024) and multi-year field-trial yields from 40 locations (2017–2024), confirms that the model's rank-ordered recommendations align closely with independent yield evidence: top-ranked locations achieve both the highest productivity and the greatest yield stability. This framework provides an AI-based decision support tool for variety placement optimization and climate-resilient agriculture.","摘要 提高作物品种适应性评价的精度，对于在气候变化背景下最大化产量和保障粮食安全至关重要。依赖专家知识或过程模型的传统方法面临可扩展性挑战，且难以捕捉复杂的品种–环境互作。本研究提出了一种可扩展的基于图协同过滤框架，用于评估杂交玉米品种的适宜种植地点，将适应性评价重新表述为排序任务。该框架融合了两项关键创新：一是多层感知机（MLP）编码模块，利用15个品种性状特征和24个环境变量来缓解冷启动问题，从而能够对未见过的品种进行推荐；二是BPR+损失函数，将不适宜环境与仅未种植环境区分开来，以适应农业试验数据特有的稀疏结构。在中国主要农业生态区189个地点和1，102个杂交玉米品种的数据集上，与六种最先进的基于图的推荐模型进行对比评估，我们的方法始终优于现有方法，相较最强基线提升约5%。MLP模块的冷启动NDCG达到0.8以上，BPR+在Recall@1–4和NDCG@1–5上均带来显著提升。对广泛种植品种ZD958的案例研究，结合国家推广数据（2019–2024年）和40个地点多年田间试验产量数据（2017–2024年）进行验证，证实模型的排序推荐与独立产量证据高度一致：排名靠前的地点既具有最高生产力，也具有最大产量稳定性。该框架为品种布局优化和气候韧性农业提供了基于人工智能的决策支持工具。","in silico Plants",79,{"impact":361,"substance":321,"depth":361,"authority":805,"freshness":173,"relevant":455,"comment":1006},"提出基于图协同过滤的杂交玉米品种适宜种植区推荐框架，覆盖全国189个地点、1102个品种，冷启动与排序指标均有提升，对品种布局优化有实用价值。",[1008],{"name":1003,"url":1000},[588,589,753,226,1010],"品种布局",{"code":224,"slug":225,"name":226,"path":227,"parent_code":200},[],{"status":597,"locked":581,"version":4,"method":598,"evidence":1014},{"candidates":1015,"agricultural_evidence":603},[1016,1020,1026,1032,1037,1042,1046],{"code":224,"score":821,"supported":603,"title":1017,"abstract":1018,"tags":1019},[225],[226,225],[226],{"code":263,"score":647,"supported":603,"title":1021,"abstract":1022,"tags":1025},[],[1023,1024],"粮食安全","food security",[],{"code":184,"score":647,"supported":603,"title":1027,"abstract":1028,"tags":1031},[],[1029,1030],"气候变化","climate change",[],{"code":24,"score":400,"supported":603,"title":1033,"abstract":1034,"tags":1036},[],[1035],"decision support",[589],{"code":176,"score":613,"supported":603,"title":1038,"abstract":1039,"tags":1041},[],[1040],"农业生态",[],{"code":80,"score":455,"supported":581,"title":1043,"abstract":1044,"tags":1045},[],[],[753],{"code":16,"score":455,"supported":581,"title":1047,"abstract":1048,"tags":1049},[],[],[588],[1051,1052],"杂交玉米 品种 适宜种植区","图协同过滤 玉米 推荐","杂交玉米品种适宜种植区-3199","10.1093\u002Finsilicoplants\u002Fdiag026",{"doi":1054,"openalex_id":1056,"authors":1057,"venue":1003,"cited_by_count":424,"oa_url":1000,"card":1087,"direction":636,"ingested_from":870},"W7213886755",[1058,1061,1064,1067,1069,1072,1075,1078,1081,1084],{"name":1059,"orcid":1060},"Yanyun Han","https:\u002F\u002Forcid.org\u002F0000-0003-4333-8565",{"name":1062,"orcid":1063},"Zhongqiang Liu","https:\u002F\u002Forcid.org\u002F0009-0008-4290-8718",{"name":1065,"orcid":1066},"Aiwen Wang","https:\u002F\u002Forcid.org\u002F0000-0003-0031-5857",{"name":1068,"orcid":11},"Yong Zhang",{"name":1070,"orcid":1071},"Shouhui Pan","https:\u002F\u002Forcid.org\u002F0000-0003-4917-9921",{"name":1073,"orcid":1074},"Xiangyu Zhao","https:\u002F\u002Forcid.org\u002F0000-0001-7668-1509",{"name":1076,"orcid":1077},"Qiusi Zhang","https:\u002F\u002Forcid.org\u002F0000-0001-5469-1802",{"name":1079,"orcid":1080},"Qi Zhang","https:\u002F\u002Forcid.org\u002F0009-0004-5061-5422",{"name":1082,"orcid":1083},"Xinglin Piao","https:\u002F\u002Forcid.org\u002F0000-0003-3774-5789",{"name":1085,"orcid":1086},"Kaiyi Wang","https:\u002F\u002Forcid.org\u002F0009-0000-5365-7178",{"tldr":1088,"method":1089,"finding":1090,"direction":636,"opportunity":1091},"提出图协同过滤框架，将玉米品种适种区评估转化为排序任务，推荐适宜种植地点。","图协同过滤+MLP编码15个品种性状与24个环境变量，BPR+损失，189地点1","方法优于六个基线约5%，冷启动NDCG超0.8，推荐排名与独立产量证据高度一致。","可拓展至多作物多品种跨区域迁移推荐，并融合气候情景预测未来适种区变化。","2026-09-22T23:30:49.262877Z",{"id":1094,"title":1095,"url":1096,"summary":1097,"summary_zh":1098,"content":11,"source_name":1099,"source_url":1096,"published_at":1100,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":1101,"score_detail":1102,"sources":1104,"tags":1106,"primary_topic":1109,"secondary_topics":1110,"classification":1112,"search_phrases":1129,"slug":1132,"view_count":408,"doi":1133,"paper":1134,"created_at":1150},3179,"Multi-features and Fuzzy with Residual Lenet for Cotton Classification Using Remote Sensing Image","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs12524-026-02541-8","Multi-features and Fuzzy with Residual Lenet for Cotton Classification Using Remote Sensing Image。Journal of the Indian Society of Remote Sensing","多特征与模糊残差LeNet用于基于遥感图像的棉花分类。《印度遥感学会杂志》","Journal of the Indian Society of Remote Sensing","2026-09-21T00:00:00Z",58,{"impact":173,"substance":345,"depth":821,"authority":416,"freshness":602,"relevant":455,"comment":1103},"方法类论文，提出多特征与模糊结合残差LeNet用于遥感棉花分类，专业价值尚可但影响面窄，适合作为技术参考而非每日精选头条。",[1105],{"name":1099,"url":1096},[589,1107,610,1108],"棉花","作物分类",{"code":248,"slug":249,"name":250,"path":251,"parent_code":200},[1111],{"code":32,"slug":33,"name":34,"path":35,"parent_code":7},{"status":597,"locked":581,"version":4,"method":598,"evidence":1113},{"candidates":1114,"agricultural_evidence":603},[1115,1120,1125],{"code":32,"score":747,"supported":603,"title":1116,"abstract":1118,"tags":1119},[1117],"remote sensing",[610,1117],[1108,610],{"code":248,"score":821,"supported":603,"title":1121,"abstract":1123,"tags":1124},[1122],"cotton",[1107,1122],[1107],{"code":24,"score":455,"supported":581,"title":1126,"abstract":1127,"tags":1128},[],[],[589],[1130,1131],"Residual LeNet 棉花 遥感分类","农业人工智能 作物分类 棉花 遥感","ResidualLeNet棉花遥感分类-3179","10.1007\u002Fs12524-026-02541-8",{"doi":1133,"openalex_id":1135,"authors":1136,"venue":1099,"cited_by_count":424,"oa_url":11,"card":1145,"direction":734,"ingested_from":870},"W7213944040",[1137,1139,1141,1143],{"name":1138,"orcid":11},"G. Suresh",{"name":1140,"orcid":11},"G. Bhuvaneswari",{"name":1142,"orcid":11},"G. Manikandan",{"name":1144,"orcid":11},"M. Robinson Joel",{"tldr":1146,"method":1147,"finding":1148,"direction":734,"opportunity":1149},"提出多特征与模糊结合残差LeNet的遥感影像棉花分类方法。","多特征提取、模糊逻辑与残差LeNet网络，用于遥感图像分类。","该方法能有效提升棉花分类精度，验证了深度残差网络结合模糊特征的可行性。","可探索多特征与模糊深度学习在其他作物遥感分类中的泛化能力及轻量化部署。","2026-09-22T23:30:23.811658Z",{"id":1152,"title":1153,"url":1154,"summary":1155,"summary_zh":1156,"content":11,"source_name":1157,"source_url":1154,"published_at":1100,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":879,"score_detail":1158,"sources":1160,"tags":1162,"primary_topic":1164,"secondary_topics":1165,"classification":1168,"search_phrases":1192,"slug":1195,"view_count":69,"doi":1196,"paper":1197,"created_at":1213},3164,"Spatiotemporal Deep Learning for Rice Plant Height Estimation from Multi-Temporal UAV RGB Imagery","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fagriculture16182034","Accurate plant height estimation is important for monitoring crop growth and supporting precision agricultural management. Manual measurements are labor-intensive, while LiDAR-based methods are expensive and require complex processing. UAV photogrammetry provides a lower-cost alternative but remains challenging in flooded rice paddies because of canopy deformation and difficulties in terrain extraction. This study proposes Rice-STNet, a spatiotemporal deep learning framework for end-to-end rice plant height estimation using multi-temporal UAV RGB imagery. Rice-STNet integrates a convolutional neural network for spatial feature extraction, Time2Vec for temporal encoding, and a gated recurrent unit network for modeling temporal dependencies across observation dates. The framework was evaluated using field data collected from rice paddies over two growing seasons. Rice-STNet achieved an R2 of 0.97, a root mean squared error of 1.97 cm, and a mean absolute error of 1.14 cm. It outperformed random forest, support vector regression, a CNN-only baseline, and a UAV photogrammetry-based point-cloud approach. In addition, the framework generated high-resolution plant height maps for field-scale analysis of spatial growth variability. These results underscore the importance of jointly modeling spatial and temporal characteristics for continuously evolving crop traits. The proposed framework offers an accurate, scalable, and non-destructive solution for large-scale crop phenotyping and precision agriculture.","准确的株高估算对于监测作物生长和支持精准农业管理具有重要意义。人工测量劳动强度大，而基于激光雷达（LiDAR）的方法成本高昂且需要复杂的处理。无人机摄影测量提供了一种成本较低的替代方案，但在淹水稻田中仍面临挑战，原因在于冠层变形和地形提取困难。本研究提出了Rice-STNet，一种时空深度学习框架，用于利用多时相无人机RGB影像进行端到端水稻株高估算。Rice-STNet集成了用于空间特征提取的卷积神经网络、用于时间编码的Time2Vec，以及用于建模观测日期之间时间依赖关系的门控循环单元网络。该框架利用两个生长季从稻田采集的田间数据进行了评估。Rice-STNet取得了R²为0.97、均方根误差为1.97 cm、平均绝对误差为1.14 cm的结果。其性能优于随机森林、支持向量回归、仅使用CNN的基线方法以及基于无人机摄影测量的点云方法。此外，该框架生成了高分辨率株高图，用于田块尺度空间生长变异性分析。这些结果凸显了联合建模空间与时间特征对于持续变化的作物性状的重要性。所提出的框架为大规模作物表型分析和精准农业提供了一种准确、可扩展且非破坏性的解决方案。","Agriculture",{"impact":361,"substance":321,"depth":361,"authority":805,"freshness":602,"relevant":455,"comment":1159},"提出时空深度学习框架Rice-STNet，用多时相无人机RGB影像实现水稻株高高精度估算，方法新颖、数据跨两个生长季，对作物表型与精准农业有实用价值。",[1161],{"name":1157,"url":1154},[588,589,210,610,1163],"作物表型",{"code":208,"slug":209,"name":210,"path":211,"parent_code":200},[1166,1167],{"code":24,"slug":25,"name":26,"path":27,"parent_code":7},{"code":32,"slug":33,"name":34,"path":35,"parent_code":7},{"status":597,"locked":581,"version":4,"method":598,"evidence":1169},{"candidates":1170,"agricultural_evidence":603},[1171,1175,1181,1186],{"code":208,"score":361,"supported":603,"title":1172,"abstract":1173,"tags":1174},[209],[210,763,209],[210],{"code":24,"score":821,"supported":603,"title":1176,"abstract":1178,"tags":1180},[1177],"deep learning",[1179,1177],"深度学习",[589],{"code":32,"score":173,"supported":603,"title":1182,"abstract":1183,"tags":1185},[],[1163,1184],"crop phenotyping",[1163,610],{"code":16,"score":995,"supported":603,"title":1187,"abstract":1188,"tags":1191},[],[1189,1190],"精准农业","precision agriculture",[588],[1193,1194],"无人机 RGB 水稻株高","Rice-STNet 水稻表型","无人机RGB水稻株高-3164","10.3390\u002Fagriculture16182034",{"doi":1196,"openalex_id":1198,"authors":1199,"venue":1157,"cited_by_count":424,"oa_url":1154,"card":1208,"direction":734,"ingested_from":870},"W7213887432",[1200,1203,1205],{"name":1201,"orcid":1202},"Weiguo Wang","https:\u002F\u002Forcid.org\u002F0009-0003-4028-9363",{"name":1204,"orcid":11},"Noboru Noguchi",{"name":1206,"orcid":1207},"Liangliang Yang","https:\u002F\u002Forcid.org\u002F0000-0002-5055-3987",{"tldr":1209,"method":1210,"finding":1211,"direction":734,"opportunity":1212},"提出Rice-STNet时空深度学习框架，用多时相无人机RGB影像估算水稻株高。","CNN提取空间特征，Time2Vec编码时间，GRU建模时序依赖，两季稻田数据验","R²达0.97、RMSE 1.97cm，优于随机森林、SVR、纯CNN及点云方法。","可迁移至其他作物与多源遥感融合，探索轻量化模型及实时田间部署。","2026-09-22T23:30:18.545958Z",{"id":1215,"title":1216,"url":1217,"summary":1218,"summary_zh":1219,"content":11,"source_name":1220,"source_url":1217,"published_at":1221,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":1222,"score_detail":1223,"sources":1225,"tags":1227,"primary_topic":1231,"secondary_topics":1232,"classification":1234,"search_phrases":1262,"slug":1265,"view_count":455,"doi":1266,"paper":1267,"created_at":1294},3153,"Explainable optimized deep learning and generative AI based framework for finger millet disease detection in smart agriculture","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs44163-026-02222-y","Abstract Eleusine coracana, locally known as finger millet (ragi), is a wholesome, climate-resilient crop. It is a staple food in semi-dry and dry areas of Asia and Africa. The productivity of this crop can be severely reduced by diseases such as downy, mottle, seedling, smut, and wilt. Conventional diagnosis methods are often time-consuming and unsuitable for large-scale farming. Therefore,Deep Learning (DL) offers an alternative for smart commercial farming that can streamline disease screening and improve productivity. This work offers an AI-integrated framework for early disease intervention combining DL, Explainable AI (XAI), and an image-based decision-support interface. The finger millet (ragi) dataset from Kaggle was used to train and evaluate custom CNN, VGG16, and ResNet50 models. To reduce feature redundancy and improve generalization, Grey Wolf Optimizer (GWO) based feature selection was applied to the penultimate-layer features of each model. Custom CNN, VGG16, and ResNet50 achieved accuracies of 89.40%, 83.11%, and 91.83%, respectively. Further, the use of GWO improved the accuracies to 97.71%, 90.84%, and 98.36%, respectively. ResNet50 achieved the highest accuracy 98.36% and was selected as the final backbone. The final ResNet50 model is integrated with the Gemini API to provide disease-specific recommendations based on the predicted class and user queries. To increase prediction transparency, Grad-CAM was used to highlight image regions that influenced the model output. The integration of DL, XAI, and generative AI provides a scalable approach for detecting and managing finger millet diseases. The proposed work boosts precision agriculture by providing AI-based and XAI-supported decision-making, while fostering sustainable agricultural practices.","摘要 穇子（Eleusine coracana），当地称为指黍（ragi），是一种有益健康且气候适应性强的作物。它是亚洲和非洲半干旱及干旱地区的主食。霜霉病、斑驳病、苗枯病、黑穗病和枯萎病等病害可严重降低该作物的产量。传统诊断方法往往耗时且不适合大规模种植。因此，深度学习（DL）为智能商业农业提供了一种替代方案，可简化病害筛查并提高生产力。本研究提出了一种人工智能集成框架，用于早期病害干预，结合了深度学习、可解释人工智能（XAI）和基于图像的决策支持界面。使用来自Kaggle的指黍（ragi）数据集训练和评估了自定义CNN、VGG16和ResNet50模型。为减少特征冗余并提高泛化能力，将灰狼优化器（GWO）基于特征选择应用于每个模型的倒数第二层特征。自定义CNN、VGG16和ResNet50分别达到了89.40%、83.11%和91.83%的准确率。此外，使用GWO将准确率分别提高到97.71%、90.84%和98.36%。ResNet50达到了最高准确率98.36%，并被选为最终骨干网络。最终的ResNet50模型与Gemini API集成，根据预测类别和用户查询提供针对特定病害的建议。为提高预测透明度，使用Grad-CAM突出显示影响模型输出的图像区域。深度学习、可解释人工智能和生成式人工智能的集成提供了一种可扩展的方法，用于检测和管理指黍病害。所提出的工作通过提供基于人工智能和可解释人工智能支持的决策，促进了精准农业，同时推动了可持续农业实践。","Discover Artificial Intelligence","2026-09-22T00:00:00Z",75,{"impact":821,"substance":508,"depth":361,"authority":416,"freshness":385,"relevant":455,"comment":1224},"将深度学习、可解释AI与生成式AI结合用于指状粟病害识别，方法新颖、数据充分，对智慧农业有参考价值。",[1226],{"name":1220,"url":1217},[588,589,1228,1229,1230],"可解释AI","病害识别","小米作物",{"code":240,"slug":241,"name":242,"path":243,"parent_code":200},[1233],{"code":24,"slug":25,"name":26,"path":27,"parent_code":7},{"status":597,"locked":581,"version":4,"method":598,"evidence":1235},{"candidates":1236,"agricultural_evidence":603},[1237,1242,1247,1252,1257],{"code":24,"score":1238,"supported":603,"title":1239,"abstract":1240,"tags":1241},17,[1177],[1179,1177],[589,1228,1229],{"code":16,"score":821,"supported":603,"title":1243,"abstract":1245,"tags":1246},[1244],"smart agriculture",[1189,1190],[588],{"code":240,"score":524,"supported":603,"title":1248,"abstract":1250,"tags":1251},[1249],"millet",[1249],[],{"code":184,"score":647,"supported":603,"title":1253,"abstract":1254,"tags":1256},[],[1255,835],"气候适应",[],{"code":192,"score":613,"supported":603,"title":1258,"abstract":1259,"tags":1261},[],[1260],"可持续农业",[],[1263,1264],"ResNet50 GWO 病害识别","农业人工智能 小米作物 智慧农业 病害识别","ResNet50GWO病害识别-3153","10.1007\u002Fs44163-026-02222-y",{"doi":1266,"openalex_id":1268,"authors":1269,"venue":1220,"cited_by_count":424,"oa_url":1217,"card":1288,"direction":1293,"ingested_from":870},"W7213978567",[1270,1273,1275,1277,1279,1282,1285],{"name":1271,"orcid":1272},"Sunil Kumar Mohapatra","https:\u002F\u002Forcid.org\u002F0000-0002-5865-095X",{"name":1274,"orcid":11},"A. Sanjib Kumar Patro",{"name":1276,"orcid":11},"Lulen Kumar Sahu",{"name":1278,"orcid":11},"Chinmaye Dora",{"name":1280,"orcid":1281},"Sujata Chakravarty","https:\u002F\u002Forcid.org\u002F0000-0002-1293-5378",{"name":1283,"orcid":1284},"Kshira Sagar Sahoo","https:\u002F\u002Forcid.org\u002F0000-0002-6435-5738",{"name":1286,"orcid":1287},"Byomakesh Mahapatra","https:\u002F\u002Forcid.org\u002F0000-0002-9126-1729",{"tldr":1289,"method":1290,"finding":1291,"direction":636,"opportunity":1292},"提出融合深度学习、可解释AI与生成式AI的指状粟病害检测框架，实现高精度识别与决策支持。","用Kaggle指状粟图像训练CNN、VGG16、ResNet50，并用灰狼优化做","GWO特征选择显著提升精度，ResNet50达98.36%，结合Grad-CAM与Gemini AP","可探索轻量化模型与多作物泛化，并将XAI与生成式建议在田间移动端实时验证。","智慧农业 \u002F 农业物联网","2026-09-22T23:30:10.255790Z",{"id":1296,"title":1297,"url":1298,"summary":1299,"summary_zh":11,"content":11,"source_name":1300,"source_url":1298,"published_at":1221,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":45,"score_detail":1301,"sources":1303,"tags":1305,"primary_topic":1307,"secondary_topics":1308,"classification":1311,"search_phrases":1335,"slug":1338,"view_count":400,"doi":1339,"paper":1340,"created_at":1348},3152,"An integrated IoT and machine learning framework for maize yield prediction and smart agriculture in Tanzania","https:\u002F\u002Fdoi.org\u002F10.1080\u002F23311932.2026.2725400","An integrated IoT and machine learning framework for maize yield prediction and smart agriculture in Tanzania。Cogent Food & Agriculture","Cogent Food & Agriculture",{"impact":416,"substance":361,"depth":747,"authority":805,"freshness":385,"relevant":455,"comment":1302},"论文提出物联网与机器学习融合的玉米产量预测框架，方法有新意但属区域性应用研究，影响力有限，时效性强。",[1304],{"name":1300,"url":1298},[588,589,590,1306,226],"物联网",{"code":224,"slug":225,"name":226,"path":227,"parent_code":200},[1309,1310],{"code":24,"slug":25,"name":26,"path":27,"parent_code":7},{"code":48,"slug":49,"name":50,"path":51,"parent_code":7},{"status":597,"locked":581,"version":4,"method":598,"evidence":1312},{"candidates":1313,"agricultural_evidence":603},[1314,1318,1322,1326,1331],{"code":224,"score":416,"supported":603,"title":1315,"abstract":1316,"tags":1317},[225],[225],[226],{"code":16,"score":416,"supported":603,"title":1319,"abstract":1320,"tags":1321},[1244],[1244],[588],{"code":24,"score":416,"supported":603,"title":1323,"abstract":1324,"tags":1325},[823],[823],[589],{"code":48,"score":416,"supported":603,"title":1327,"abstract":1329,"tags":1330},[1328],"IoT",[1328],[1306],{"code":32,"score":455,"supported":581,"title":1332,"abstract":1333,"tags":1334},[],[],[590],[1336,1337],"坦桑尼亚 玉米 产量预测","物联网 机器学习 智慧农业","坦桑尼亚玉米产量预测-3152","10.1080\u002F23311932.2026.2725400",{"doi":1339,"openalex_id":1341,"authors":1342,"venue":1300,"cited_by_count":424,"oa_url":1298,"card":11,"direction":1293,"ingested_from":870},"W7213978365",[1343,1346],{"name":1344,"orcid":1345},"Alcardo Alex Barakabitze","https:\u002F\u002Forcid.org\u002F0000-0001-8960-8415",{"name":1347,"orcid":11},"Yasinta Nzogera","2026-09-22T23:30:10.178097Z",{"id":1350,"title":1351,"url":1352,"summary":1353,"summary_zh":11,"content":11,"source_name":1354,"source_url":11,"published_at":1355,"category":580,"cover_url":11,"hotness":385,"is_selected":603,"score":1356,"score_detail":1357,"sources":1361,"tags":1363,"primary_topic":1368,"secondary_topics":1369,"classification":1370,"search_phrases":1400,"slug":1403,"view_count":400,"doi":11,"paper":1404,"created_at":1411},3125,"Full-Season Agentic Farm System FAIRY: Event-Driven Multi-Agent Orchestration for Soybean Production（FAIRY 全季节智能体农场系统：大豆生产的事件驱动多智能体编排）","https:\u002F\u002Faiagentstore.ai\u002Fai-agent-news\u002Ftopic\u002Fagriculture-food\u002F2026-08-11","哈尔滨工业大学研究人员发布并部署全栈、事件驱动的智能体引擎 FAIRY 于中国运行中的大豆研究农场。FAIRY 集成传感器、无人机、卫星植被产品、机械 API、作物过程模型与多智能体编排层，执行起垄→播种→灌溉→病虫害防治→收获→干燥→存储工作流，并在 64 垄研究场上跨 100 个全季节场景评估 9 个智能体控制器。这是智能体系统能够在大农业时间尺度和延迟结果下进行推理的最清晰演示之一，将农业中的智能体工作从实验室演示推进到全过程评估。同期 arXiv 推出 HarvestBench 基准将 LLM 驱动智能体置于农场网格世界（拖拉机面临动物选择绕行或碾压），结果显示模型差异巨大、对道德简报高度敏感、避免意愿具有可衡量的价格弹性。","Harbin Institute of Technology \u002F arXiv","2026-09-18T00:00:00Z",89,{"impact":952,"substance":1358,"depth":1359,"authority":345,"freshness":602,"relevant":455,"comment":1360},23,19,"哈工大在真实大豆农场部署全季节事件驱动多智能体系统并配套 HarvestBench 基准，是农业智能体从演示走向全过程评估的标志性进展，专业深度与信息增量俱佳。",[1362],{"name":1354,"url":1352},[588,1364,589,1365,1366,1367],"无人农场","农业遥感","多智能体","大豆生产",{"code":232,"slug":233,"name":234,"path":235,"parent_code":200},[],{"status":597,"locked":581,"version":4,"method":598,"evidence":1371},{"candidates":1372,"agricultural_evidence":603},[1373,1379,1384,1388,1392,1396],{"code":232,"score":1359,"supported":603,"title":1374,"abstract":1377,"tags":1378},[1375,1376],"大豆","soybean",[1375],[],{"code":152,"score":613,"supported":603,"title":1380,"abstract":1381,"tags":1383},[],[1382],"灌溉",[],{"code":24,"score":69,"supported":581,"title":1385,"abstract":1386,"tags":1387},[],[],[589,1366],{"code":120,"score":455,"supported":581,"title":1389,"abstract":1390,"tags":1391},[],[],[1364],{"code":16,"score":455,"supported":581,"title":1393,"abstract":1394,"tags":1395},[],[],[588],{"code":32,"score":455,"supported":581,"title":1397,"abstract":1398,"tags":1399},[],[],[1365],[1401,1402],"哈工大 FAIRY 大豆","HarvestBench 智能体 农场","哈工大FAIRY大豆-3125",{"doi":11,"openalex_id":11,"authors":1405,"venue":11,"cited_by_count":424,"oa_url":11,"card":1406,"direction":636,"ingested_from":638},[],{"tldr":1407,"method":1408,"finding":1409,"direction":636,"opportunity":1410},"部署全季节事件驱动多智能体系统FAIRY，在大豆农场完成从起垄到存储的全流程评估。","集成传感器、无人机、卫星、作物模型与机械API，用9个智能体控制器在64垄100","智能体系统能在大农业时间尺度下推理，模型差异大且对道德简报敏感。","可研究多智能体在长周期、延迟反馈农业任务中的鲁棒性与伦理约束机制。","2026-09-22T00:05:38.611717Z",{"id":1413,"title":1414,"url":1415,"summary":1416,"summary_zh":11,"content":1417,"source_name":1418,"source_url":11,"published_at":1419,"category":744,"cover_url":11,"hotness":385,"is_selected":581,"score":1420,"score_detail":1421,"sources":1423,"tags":1425,"primary_topic":1427,"secondary_topics":1428,"classification":1429,"search_phrases":1455,"slug":1458,"view_count":455,"doi":11,"paper":11,"created_at":1459},3101,"AI 接管稻田：四川眉山永丰村 300 亩 AI 试点水稻亩产 826.8-863.6 公斤","https:\u002F\u002Fai-damn.com\u002Fai-takes-over-the-rice-fields-863-6-kg-per-mu-in-sichuan-pilot-1789513373662","9-14 四川省眉山市东坡区太和镇永丰村千亩高标准农田 300 亩 AI 试点田通过专家组测产验收：\"华浙优 210\"（高产优质杂交稻）亩产 826.8 公斤、\"胜两优 222\"（超高产籼粳杂交稻）亩产 863.6 公斤、\"全优 169\"（超高产杂交籼稻）亩产 858.8 公斤。AI 系统通过无人机巡检采集数据，对种植、水肥调控和病虫害早期预警提供精准建议。四川农业大学水稻栽培专家马均教授表示，结合良种、良法与 AI 精准管理可有效释放水稻增产潜力，为大规模单产提升提供可复制技术路径；今年永丰村共有 240 余个新品种在产量\u002F株型\u002F米质上表现良好，智能精准播种技术与 AI 应用已初步见效。","## AI Takes Over the Rice Fields: 863.6 kg per Mu in Sichuan Pilot\n\nIn the rolling fields of Yongfeng Village, Tahe Town, Dongpo District, Meishan City, Sichuan Province, something unusual happened this harvest season. On September 14, as combines rolled through the thousand-mu high-standard farmland, 300 mu of it had been managed not by traditional farming wisdom alone, but by an **AI model** specifically designed for rice cultivation.\n\nGone are the days of \"judging fields by experience.\" Now, it's all about **making decisions based on data**.\n\n### From Experience to Data\n\nThe embankments were crowded with agricultural experts and curious farmers, all gathered to witness a field test. An expert group organized by the Sichuan Provincial Science and Technology Department was evaluating a project led by Sichuan Agricultural University: the \"Integrated Demonstration and Application of High-quality, High-yield, and Efficient Production Technologies for Rice-Vegetable (Medicinal) Crops in the Chengdu Plain.\"\n\nSo how does it work? The AI system collects data through **drone inspections**, then provides precise recommendations on planting, water and fertilizer regulation, and pest and disease early warning.\n\nLocal large-scale grain farmer Zhao Youyong put it simply: \"Before, farming relied on experience for field inspections. Now, using drones and the AI system, we get timely information about pests and diseases, so we can handle them directly. Farming has become more convenient.\"\n\n### The Numbers That Matter\n\nThe expert group's standardized yield test delivered solid results. All three core varieties in the 300-mu AI pilot fields performed impressively:\n\n*   **\"Huazheyous 210\"** (high-yield, high-quality hybrid rice): 826.8 kg per mu\n*   **\"Shengliangyou 222\"** (super-high-yield indica-japonica hybrid rice): **863.6 kg per mu**\n*   **\"Quanyou 169\"** (super-high-yield hybrid indica rice): 858.8 kg per mu\n\nMa Jun, a rice cultivation expert at Sichuan Agricultural University, explained that these yields prove that combining quality seeds with appropriate methods and AI precision management can **effectively release the potential for rice yield increase**. It offers a replicable technical path for large-scale yield improvement.\n\nHe also noted that more than 240 new varieties demonstrated good performance in yield, plant shape, and rice quality in Yongfeng Village this year. The application of intelligent precision sowing technology and AI has already shown initial results.\n\n### What This Means for the Future\n\nThis pilot isn't just about one good harvest. It's a glimpse into how **AI can transform traditional agriculture**. By moving from experience-based to data-driven farming, growers can make more informed decisions, reduce risks, and potentially achieve higher yields sustainably.\n\nAs Ma Jun pointed out, the combination of quality seeds, appropriate methods, and AI precision management provides a technical path that can be replicated on a larger scale. For a country that feeds 20% of the world's population with less than 10% of its arable land, such innovations are more than welcome—they're essential.\n\n### Key Points\n\n*   **AI-managed pilot field** in Sichuan achieved rice yields up to **863.6 kg per mu**.\n*   **Drones and data** replaced traditional experience-based farming for planting, fertilization, and pest control.\n*   **Three rice varieties** all exceeded 826 kg per mu, proving the effectiveness of AI precision management.\n*   **Experts say** this approach offers a replicable path for large-scale yield improvement.\n*   **The future of farming** is shifting from \"judging fields by experience\" to \"making decisions based on data.\"","AI DAMN","2026-09-14T10:00:00Z",76,{"impact":321,"substance":321,"depth":1238,"authority":602,"freshness":647,"relevant":455,"comment":1422},"AI精准管理水稻试点实测亩产数据具体、多方信源，具备可复制的智慧农业示范价值，值得入选每日精选。",[1424],{"name":1418,"url":1415},[588,589,210,1189,1426],"无人機巡田",{"code":208,"slug":209,"name":210,"path":211,"parent_code":200},[],{"status":597,"locked":581,"version":4,"method":598,"evidence":1430},{"candidates":1431,"agricultural_evidence":603},[1432,1437,1442,1447,1451],{"code":208,"score":1358,"supported":603,"title":1433,"abstract":1434,"tags":1436},[210,763],[210,1435],"杂交稻",[210],{"code":263,"score":613,"supported":603,"title":1438,"abstract":1439,"tags":1441},[],[1440],"单产提升",[],{"code":136,"score":613,"supported":603,"title":1443,"abstract":1444,"tags":1446},[],[1445],"高标准农田",[],{"code":16,"score":69,"supported":581,"title":1448,"abstract":1449,"tags":1450},[],[],[588,1189],{"code":24,"score":455,"supported":581,"title":1452,"abstract":1453,"tags":1454},[],[],[589],[1456,1457],"四川眉山 永丰村 AI水稻","四川农业大学 水稻 AI试点","四川眉山永丰村AI水稻-3101","2026-09-22T00:05:33.996455Z",{"id":1461,"title":1462,"url":1463,"summary":1464,"summary_zh":11,"content":1465,"source_name":1466,"source_url":11,"published_at":1100,"category":744,"cover_url":11,"hotness":385,"is_selected":581,"score":1467,"score_detail":1468,"sources":1470,"tags":1472,"primary_topic":1477,"secondary_topics":1478,"classification":1481,"search_phrases":1518,"slug":1521,"view_count":173,"doi":11,"paper":11,"created_at":1522},3100,"秋实满田 智赋丰年——中国移动(成都)产业研究院\"谷丰登\"农业大模型赋能山西大同玉米单产提升试点亩产 2481 斤较非试点增产 15.5%","https:\u002F\u002Fiot.youth.cn\u002Fyw\u002F202609\u002Ft20260921_16881000.htm","9-21 中国青年网物联网频道报道中国移动(成都)产业研究院秋收赋能：以\"九天大模型\"为底座自主研发\"谷丰登\"农业大模型、构建覆盖土壤分析\u002F品种优选\u002F精准水肥药方案\u002F产量预估全生命周期服务体系；联合中储粮打造行业首个储粮场景大模型\"稷元\"已落地 3 个中央储备粮库，粮食储存损失降至 0.2% 以下、化学药剂年使用量减少 1-2 次；牵头制定《农业大模型高质量数据集》《农业种植大模型应用测评规范》两项团体标准。山西大同玉米单产提升标杆项目依托空天地一体化监测+北斗高精定位+农业大模型实现玉米耕种管收全流程无人化作业，试点田亩产 2481 斤较非试点增产 15.5%，被农业农村部评为\"全国智慧农业建设典型案例\"。","九月的田野，稻菽飘香，又是一年丰收时。\n\n随着“十五五”规划的全面实施，农业农村现代化建设蹄疾步稳。传统“看天吃饭”的农业生产模式正在被改写，数字技术驱动下，丰收迎来科技赋能、全链提质、数智增效的全新发展阶段。\n\n当前正值金秋丰收的关键时期，中国移动（成都）产业研究院（以下简称“成研院”）充分发挥科技引领作用，贯通智慧种养、智能储粮、数字治理、产销对接全业务链路，推动数字技术与农业生产融合走深走实，为国家粮食安全与乡村全面振兴注入强劲数字动能。\n\n**科技强农：打造从田间到粮仓的“智慧大脑”**\n\n成研院聚焦AI育种创新、种植生产管理与粮食仓储等关键环节，以中国移动“九天大模型”为底座，自主研发谷丰登农业大模型，构建起全周期、智能化的农业生产服务体系，打造扎根田野的“身边的农业知识专家”。\n\n“藏粮于技”的关键，在于让技术真正下沉到田间地头。成研院打造国内首个种植综合农业大模型，覆盖土壤分析、品种优选、精准水肥药方案、产量预估等全生命周期服务，实现“信息随手握、种养全程导、在线有人帮”。在山西大同玉米单产提升标杆项目中，成研院构建“良田、良种、良机、良法”融合体系，依托空天地一体化监测、北斗高精定位与农业大模型三大核心能力，实现玉米耕、种、管、收全流程无人化作业。当年秋收，试点田亩产达2481斤，较非试点区域增产15.5％，被农业农村部评为“全国智慧农业建设典型案例”。\n\n丰产更要保收，储粮减损是守住丰收成果、保障粮食安全的关键一环。成研院联合中储粮打造行业首个储粮场景大模型“稷元”，内置二十余万条储粮知识单元及千余本专业文献，结合智能语音助手“粮小助”，实时采集、动态监测各类粮情核心指标，智能预判粮情变化趋势，提前预警仓储虫害风险，自动生成仓储调控最优方案，同时采用一体化集成设计，适配粮库现有系统，实现核心业务在库内闭环处理，严守粮食仓储信息安全底线。目前，“稷元”大模型已在中储粮三个中央储备粮库落地，粮食储存损失降至0.2%以下，化学药剂年使用量减少1—2次，大幅提升粮食仓储安全水平，守住丰收成果、筑牢储粮安全防线。\n\n此外，成研院牵头制定《农业大模型高质量数据集》《农业种植大模型应用测评规范》两项团体标准，填补行业规范空白，为大模型规范化、规模化应用筑牢基础。\n\n![Image 1](https:\u002F\u002Fiot.youth.cn\u002Fyw\u002F202609\u002FW020260921554681104915.png)\n\n![Image 2](https:\u002F\u002Fiot.youth.cn\u002Fyw\u002F202609\u002FW020260921554681314335.png)\n\n**数字惠农：激活基层治理民生温度**\n\n成研院以数字技术创新破解基层治理痛点、赋能民生服务、激活乡村发展动能，推动数字技术真正扎根田野、服务农民。\n\n在传承着焦裕禄精神的河南兰考葡萄架乡，成研院打造的数字乡村平台全面落地应用。平台贯通乡、村两级业务全链条，整合民政、社保、农业等多部门数据资源，实现“一次采集、多方复用”。平台提供20余项高频服务“掌上办”，让“数据多跑路、群众少跑腿”，并依托AI智能预警，推动基层治理高效化、智能化。目前，平台已覆盖全乡20个行政村，累计收录人口数据超3万条，助力基层干部事务性工作量降低60%以上，实现网格管理一屏总览、AI智能安防实时监测、积分激励激活村民参与，打通基层治理“最后一公里”。\n\n在甘肃积石山县团结村，智慧乡村大脑聚焦民族地区灾后重建与乡村振兴，集成灾后重建、乡村振兴、民族团结、服务治理、综合监测五大模块，一屏统揽耕地分布、帮扶进展、集体经济、群众诉求等村庄“家底”，为村委科学决策提供精准支撑。同时，村里为50户重点关爱家庭免费安装一键呼叫终端，直连紧急救助与亲情通话，让乡村治理更有精度与温度。\n\n**产业富农：搭建产销双向联通桥梁**\n\n从培育乡村产业带头人到打通农产品出村进城通道，成研院以数字技术为纽带，一头连着田间地头的生产者，一头连着广阔市场的消费者，让好产品卖出好价钱，让乡村产业真正活起来、强起来。\n\n作为农业农村部“头雁”项目的技术支撑方，成研院为“头雁”人才培育系统提供的全方位信息化支撑。目前，“头雁”平台已覆盖全国31省份，累计服务7万余名乡村产业带头人，为乡村培育“造血种子”。\n\n如果说头雁工程是为乡村培育“造血种子”，那么“陆海优品公共服务平台”就是为乡村产业打通“出村进城、走向世界”的黄金通道。平台开设免费电商、直播、品控等培训，助力800多万农民转型“新农人”，推动中国移动对口帮扶的五省（区）八县、成渝地区双城经济圈特色农产品实现精准产销对接。目前，平台已带动超2000万元消费帮扶、实现3亿元农产品出口额，服务全国超600万用户，以数智力量畅通国内国际双循环。\n\n丰收，不再只是田野里的金黄稻浪，更是数据流中奔涌的智慧动能。当技术真正扎根泥土，当数字红利真正惠及农民，丰收便有了更坚实的支撑，乡村便有了更持久的活力。面向未来，成研院将继续以数智之力深耕沃土，让“藏粮于技”落地生根，让“数字乡村”可感可及，在广袤田野上书写属于新时代的丰收故事。","中国青年网",84,{"impact":952,"substance":321,"depth":747,"authority":805,"freshness":602,"relevant":455,"comment":1469},"央媒报道的农业大模型落地标杆案例，含亩产2481斤、增产15.5%、储粮损失降至0.2%等硬数据，产业示范价值突出，值得进入每日精选。",[1471],{"name":1466,"url":1463},[1473,588,589,1474,1475,1476],"数字乡村","农业大模型","玉米单产提升","粮食仓储",{"code":263,"slug":264,"name":265,"path":266,"parent_code":200},[1479,1480],{"code":24,"slug":25,"name":26,"path":27,"parent_code":7},{"code":224,"slug":225,"name":226,"path":227,"parent_code":200},{"status":597,"locked":581,"version":4,"method":598,"evidence":1482},{"candidates":1483,"agricultural_evidence":603},[1484,1488,1493,1497,1501,1505,1509,1514],{"code":263,"score":805,"supported":603,"title":1485,"abstract":1486,"tags":1487},[1440],[1440],[1475,1476],{"code":24,"score":805,"supported":603,"title":1489,"abstract":1491,"tags":1492},[1490],"大模型",[1490],[589,1474],{"code":224,"score":416,"supported":603,"title":1494,"abstract":1495,"tags":1496},[226],[226],[1475],{"code":16,"score":400,"supported":603,"title":1498,"abstract":1499,"tags":1500},[],[588],[588],{"code":144,"score":613,"supported":603,"title":1502,"abstract":1503,"tags":1504},[],[616],[],{"code":48,"score":613,"supported":603,"title":1506,"abstract":1507,"tags":1508},[],[1306],[],{"code":563,"score":613,"supported":603,"title":1510,"abstract":1511,"tags":1513},[],[1512],"秋收",[],{"code":56,"score":455,"supported":581,"title":1515,"abstract":1516,"tags":1517},[],[],[1473],[1519,1520],"中国移动 谷丰登 农业大模型","山西大同 玉米 亩产2481斤","中国移动谷丰登农业大模型-3100","2026-09-22T00:05:33.912382Z",{"id":1524,"title":1525,"url":1526,"summary":1527,"summary_zh":1528,"content":11,"source_name":1529,"source_url":1526,"published_at":1530,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":1531,"score_detail":1532,"sources":1535,"tags":1537,"primary_topic":1539,"secondary_topics":1540,"classification":1543,"search_phrases":1572,"slug":1575,"view_count":400,"doi":1576,"paper":1577,"created_at":1591},3009,"AgriMAC: An Attention Based Multimodal Deep Clustering Framework for Rice Health Assessment","https:\u002F\u002Fdoi.org\u002F10.22266\u002Fijies2026.1031.06","Rice is Indonesia's staple crop, yet its productivity has declined in recent years because pest and disease outbreaks remain difficult to detect at an early stage.Existing precision agriculture approaches commonly process Internet of Things (IoT) sensor data and remote sensing imagery independently and often rely on supervised learning, requiring large amounts of labeled data.Meanwhile, multispectral drone imagery producing the Normalized Difference Vegetation Index (NDVI) provides richer information on crop physiological conditions than RGB-based vegetation indices.This study proposes Agricultural Multimodal Attention Clustering (AgriMAC), an unsupervised framework that integrates UAV derived NDVI imagery, 7-in-1 IoT soil sensor measurements, and historical weather data from the Open-Meteo API for rice field condition monitoring.Each modality is encoded using a dedicated autoencoder and fused through an entropy-regularized attention mechanism before Deep Embedded Clustering is performed.To reduce the influence of crop growth stage, the IoT representation is residualized using growth-phase statistics estimated exclusively from the training fold, enabling the discovered clusters to represent within-phase agronomic conditions rather than crop age.Experiments conducted under a grouped leave-one-field-out protocol produced a Silhouette Score of 0.465 ± 0.048, a Davies Bouldin Index of 0.807 ± 0.036, and a Calinski Harabasz Index of 195 ± 27.The learned groups also showed low normalized mutual information with growth phase (0.079 ± 0.043) and near chance phase decodability (balanced accuracy = 0.554 ± 0.042), indicating that they are only weakly associated with crop growth stage.The learned attention weights identified IoT soil measurements (0.570 ± 0.024) as the dominant modality, while NDVI imagery (0.210 ± 0.014) and weather information (0.220 ± 0.014) provided complementary spatial and temporal context.Overall, AgriMAC provides an interpretable and leakage-aware framework for multimodal clustering of rice field conditions.Although its clustering performance is comparable to that of a capacity-matched IoT-only model, it additionally quantifies the contribution of each sensing modality through attention weights and explicitly mitigates the growth-phase confound, making it suitable for field level agronomic condition monitoring and spatial decision support in precision agriculture.","水稻是印度尼西亚的主要作物，但近年来其生产力有所下降，因为病虫害暴发在早期阶段仍难以检测。现有的精准农业方法通常独立处理物联网（IoT）传感器数据和遥感影像，且往往依赖监督学习，需要大量标注数据。与此同时，生成归一化植被指数（NDVI）的多光谱无人机影像比基于RGB的植被指数能提供更丰富的作物生理状况信息。本研究提出农业多模态注意力聚类（AgriMAC），这是一个无监督框架，整合了无人机获取的NDVI影像、七合一IoT土壤传感器测量数据以及来自Open-Meteo API的历史天气数据，用于稻田状况监测。每种模态均使用专用自编码器进行编码，并通过熵正则化注意力机制进行融合，随后执行深度嵌入聚类。为减少作物生长阶段的影响，IoT表征利用仅从训练折估计的生长阶段统计量进行残差化处理，使发现的聚类能够表征阶段内的农艺状况而非作物年龄。在分组留一田块协议下进行的实验产生了0.465 ± 0.048的轮廓系数、0.807 ± 0.036的Davies-Bouldin指数和195 ± 27的Calinski-Harabasz指数。学习到的分组还显示出与生长阶段的低归一化互信息（0.079 ± 0.043）以及接近随机的阶段可解码性（平衡准确率 = 0.554 ± 0.042），表明它们与作物生长阶段仅存在弱关联。学习到的注意力权重将IoT土壤测量（0.570 ± 0.024）识别为主导模态，而NDVI影像（0.210 ± 0.014）和天气信息（0.220 ± 0.014）则提供了互补的空间和时间背景。总体而言，AgriMAC为稻田状况的多模态聚类提供了一个可解释且感知数据泄漏的框架。尽管其聚类性能与容量匹配的仅IoT模型相当，但它还通过注意力权重量化了每种传感模态的贡献，并明确减轻了生长阶段混杂因素，使其适用于田块级农艺状况监测和精准农业中的空间决策支持。","International journal of intelligent engineering and systems","2026-09-19T00:00:00Z",72,{"impact":416,"substance":1533,"depth":1238,"authority":805,"freshness":602,"relevant":455,"comment":1534},21,"提出无监督多模态注意力聚类框架，融合无人机NDVI、IoT土壤与气象数据评估水稻健康，方法新颖且实验严谨，对精准农业田间监测有参考价值。",[1536],{"name":1529,"url":1526},[588,589,210,1538,610],"多模态融合",{"code":208,"slug":209,"name":210,"path":211,"parent_code":200},[1541,1542],{"code":32,"slug":33,"name":34,"path":35,"parent_code":7},{"code":48,"slug":49,"name":50,"path":51,"parent_code":7},{"status":597,"locked":581,"version":4,"method":598,"evidence":1544},{"candidates":1545,"agricultural_evidence":603},[1546,1550,1555,1560,1564,1568],{"code":208,"score":361,"supported":603,"title":1547,"abstract":1548,"tags":1549},[209],[210,763,209],[210],{"code":32,"score":385,"supported":603,"title":1551,"abstract":1552,"tags":1554},[],[610,1117,1553],"multispectral",[610],{"code":48,"score":602,"supported":603,"title":1556,"abstract":1557,"tags":1559},[],[1306,1558,1328],"internet of things",[],{"code":16,"score":995,"supported":603,"title":1561,"abstract":1562,"tags":1563},[],[1189,1190],[588],{"code":144,"score":647,"supported":603,"title":1565,"abstract":1566,"tags":1567},[],[616,145],[],{"code":24,"score":408,"supported":603,"title":1569,"abstract":1570,"tags":1571},[],[1035],[589,1538],[1573,1574],"AgriMAC 水稻 多模态聚类","无人机 NDVI 水稻 病害监测","AgriMAC水稻多模态聚类-3009","10.22266\u002Fijies2026.1031.06",{"doi":1576,"openalex_id":1578,"authors":1579,"venue":1529,"cited_by_count":424,"oa_url":1526,"card":1586,"direction":1293,"ingested_from":870},"W7213619014",[1580,1582,1584],{"name":1581,"orcid":11},"Nurfadhilah Mardianti Andini",{"name":1583,"orcid":11},"Mike Yuliana",{"name":1585,"orcid":11},"Moch. Zen Samsono Hadi",{"tldr":1587,"method":1588,"finding":1589,"direction":1293,"opportunity":1590},"提出无监督多模态聚类框架AgriMAC，融合无人机NDVI、IoT土壤与气象数据评估水稻健康。","各模态自编码器编码，熵正则注意力融合，深度嵌入聚类，按生长阶段残差化。","聚类性能与仅IoT模型相当，但注意力权重可解释模态贡献并弱化生长阶段混淆。","可探索注意力融合机制在更多作物与传感器组合下的泛化性，并引入时序动态聚类。","2026-09-20T23:30:08.419613Z",{"id":1593,"title":1594,"url":1595,"summary":1596,"summary_zh":1597,"content":11,"source_name":878,"source_url":1595,"published_at":1530,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":698,"score_detail":1598,"sources":1600,"tags":1602,"primary_topic":1604,"secondary_topics":1605,"classification":1606,"search_phrases":1626,"slug":1629,"view_count":400,"doi":1630,"paper":1631,"created_at":1664},3006,"Explainable spectral–image fusion multi-task learning for maize canopy biochemical and structural trait retrieval","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112450","Accurate and interpretable estimation of multiple maize canopy traits is important for proximal crop monitoring and high-throughput phenotyping. This study developed a spectral–image fusion multi-task learning framework (MM-MTL) for the simultaneous retrieval of chlorophyll index (Chl index), leaf area index (LAI), nitrogen balance index (NBI), and anthocyanin index (Anth index). Proximal hyperspectral observations were collected using a Specim IQ camera covering 400–1000 nm with 204 spectral bands. For each observation, an ROI-mean spectral vector was extracted to retain fine-grained canopy reflectance information, while a co-registered pseudo-RGB image derived from visible bands of the same hyperspectral cube preserved two-dimensional canopy structure and visible appearance. MM-MTL integrates spectral and image feature extraction, task-specific fusion, and uncertainty-weighted multi-task learning to jointly estimate the four traits. A total of 1,023 valid samples collected from nine field campaigns across the 2024 and 2025 growing seasons were used for model development and evaluation. Under plot-grouped five-fold cross-validation, MM-MTL achieved R 2 values of 0.872, 0.885, 0.722, and 0.807 for Chl index, LAI, NBI, and Anth index, respectively, and consistently outperformed the single-task, single-representation, and conventional regression baselines. Performance decreased under more challenging generalization settings, with R 2 values ranging from 0.543 to 0.680 under leave-one-campaign-out validation and from 0.425 to 0.640 under bidirectional cross-year validation. Ablation and post-hoc analyses further showed that preserving image spatial organization improved prediction, while task-wise routing, input-representation masking, wavelength perturbation, and cross-fold stability analyses revealed trait-dependent use of spectral and spatial information. These results demonstrate that complementary spectral and spatial representations derived from the same hyperspectral observation can improve multi-trait maize canopy retrieval, while transfer across acquisition campaigns, years, and field environments remains an important direction for further improvement.","准确且可解释地估算多个玉米冠层性状，对于近地作物监测和高通量表型分析具有重要意义。本研究开发了一种光谱—图像融合多任务学习框架（MM-MTL），用于同时反演叶绿素指数（Chl index）、叶面积指数（LAI）、氮平衡指数（NBI）和花青素指数（Anth index）。近地高光谱观测使用Specim IQ相机采集，覆盖400–1000 nm，共204个光谱波段。对于每次观测，提取ROI均值光谱向量以保留细粒度冠层反射率信息，同时从同一高光谱立方体的可见光波段生成配准的伪RGB图像，以保留二维冠层结构和可见外观。MM-MTL集成了光谱与图像特征提取、任务特定融合以及不确定性加权多任务学习，以联合估算这四种性状。研究使用2024年和2025年生长季9次田间试验采集的共计1,023个有效样本进行模型开发与评估。在按小区分组的五折交叉验证下，MM-MTL对Chl index、LAI、NBI和Anth index的R²分别为0.872、0.885、0.722和0.807，且持续优于单任务、单表征和传统回归基线。在更具挑战性的泛化设置下，模型性能有所下降，留一试验验证的R²范围为0.543–0.680，双向跨年验证的R²范围为0.425–0.640。消融分析和事后分析进一步表明，保留图像空间组织可提升预测性能，而任务路由、输入表征掩蔽、波长扰动和跨折稳定性分析揭示了光谱与空间信息的性状依赖性利用方式。这些结果表明，从同一高光谱观测中提取的互补光谱与空间表征可改善多性状玉米冠层反演，而跨采集试验、年份和田间环境的迁移仍是未来改进的重要方向。",{"impact":361,"substance":321,"depth":361,"authority":345,"freshness":602,"relevant":455,"comment":1599},"提出光谱-图像融合多任务学习框架，1023份样本跨两年验证，方法新颖且结论可靠，对作物高通量表型研究有实质参考价值。",[1601],{"name":878,"url":1595},[588,589,226,610,1603],"高通量表型",{"code":224,"slug":225,"name":226,"path":227,"parent_code":200},[],{"status":597,"locked":581,"version":4,"method":598,"evidence":1607},{"candidates":1608,"agricultural_evidence":603},[1609,1613,1618,1622],{"code":224,"score":821,"supported":603,"title":1610,"abstract":1611,"tags":1612},[225],[226,225],[226],{"code":32,"score":400,"supported":603,"title":1614,"abstract":1615,"tags":1617},[],[1616],"hyperspectral",[610],{"code":16,"score":455,"supported":581,"title":1619,"abstract":1620,"tags":1621},[],[],[588],{"code":24,"score":455,"supported":581,"title":1623,"abstract":1624,"tags":1625},[],[],[589],[1627,1628],"玉米冠层 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Wu","https:\u002F\u002Forcid.org\u002F0000-0002-8386-1038",{"tldr":1660,"method":1661,"finding":1662,"direction":734,"opportunity":1663},"提出光谱-图像融合多任务学习框架，同时反演玉米冠层四个生化与结构性状。","Specim IQ高光谱与伪RGB融合，不确定性加权多任务学习，1023样本交叉","融合模型精度优于单任务基线，但跨年份与跨环境泛化性能明显下降。","可研究跨年份\u002F跨环境域适应与迁移学习，提升多性状反演泛化能力。","2026-09-20T23:30:01.739142Z",{"id":1666,"title":1667,"url":1668,"summary":1669,"summary_zh":11,"content":11,"source_name":1670,"source_url":11,"published_at":1671,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":582,"score_detail":1672,"sources":1674,"tags":1676,"primary_topic":1679,"secondary_topics":1680,"classification":1681,"search_phrases":1705,"slug":1708,"view_count":69,"doi":11,"paper":1709,"created_at":1716},3000,"Plant-GeoAT：几何感知3D植物点云器官身份解析用于器官级表型分析","https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F18\u002F1809","四川农业大学吴俊杰等提出Plant-GeoAT几何感知解析网络，在RGB融合前编码局部相对-XYZ邻域并将空间邻域与特征空间关系耦合用于密集点预测。在自建油菜数据集、基于图像的大豆数据集、激光扫描Pheno4D玉米和番茄数据集上评估，5个随机种子下mIoU分别达92.26±0.28%、82.50±0.24%、99.74±0.05%、94.75±0.15%，玉米Stem IoU达99.57±0.09%。","MDPI Agronomy 16(18):1809","2026-09-15T00:00:00Z",{"impact":361,"substance":1358,"depth":361,"authority":805,"freshness":647,"relevant":455,"comment":1673},"方法新颖、多作物多数据集验证且精度数据扎实，属器官级表型分析细分领域的重要技术进展，值得进入每日精选。",[1675],{"name":1670,"url":1668},[588,589,1677,1603,1678],"油菜","三维点云",{"code":232,"slug":233,"name":234,"path":235,"parent_code":200},[],{"status":597,"locked":581,"version":4,"method":598,"evidence":1682},{"candidates":1683,"agricultural_evidence":603},[1684,1688,1692,1697,1701],{"code":232,"score":995,"supported":603,"title":1685,"abstract":1686,"tags":1687},[],[1375,1677],[1677],{"code":224,"score":613,"supported":603,"title":1689,"abstract":1690,"tags":1691},[],[226],[],{"code":279,"score":613,"supported":603,"title":1693,"abstract":1694,"tags":1696},[],[1695],"番茄",[],{"code":24,"score":69,"supported":581,"title":1698,"abstract":1699,"tags":1700},[],[],[1678,589],{"code":16,"score":455,"supported":581,"title":1702,"abstract":1703,"tags":1704},[],[],[588],[1706,1707],"四川农业大学 植物点云 器官识别","Plant-GeoAT 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