[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2898":3,"related-2898":94},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":8,"published_at":11,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":23,"tags":25,"primary_topic":32,"secondary_topics":38,"classification":39,"search_phrases":88,"slug":91,"view_count":92,"doi":8,"paper":8,"created_at":93},2898,"苏垦农发神农慧种农业AI大模型规模化落地:天空地一体化闭环,百万亩自有农田实景数据","https:\u002F\u002Fcaifuhao.eastmoney.com\u002Fnews\u002F20260918101757264727920","苏垦农发9月18日发文,公司依托百万亩自有连片高标准农田,持续产出真实大田数据训练神农慧种农业AI智能体,实现天空地一体化数据闭环:空中多光谱无人机集群常态化农田巡测;地面全域四情监测传感器、北斗智能农机、智能灌溉终端;云端苏垦智云平台+神农慧种AI模型,形成采集数据→AI分析研判→输出水肥植保方案→农机落地执行完整闭环。苏垦智云是全国农林牧渔领域唯一入选工信部信创典型案例的农业数字化平台。",null,"[在东方财富看资讯行情，选东方财富证券一站式开户交易>>](https:\u002F\u002Facttg.eastmoney.com\u002Fpub\u002Fwebtg_hskh_act_zixun_01_01_01_0)\n\n（国内A股找不到第二家，像苏垦农发依托百万亩自有连片高标准农田，持续产出真实大田数据训练神农慧种农业AI智能体；苏垦实现天空地一体化数据闭环，苏垦智云是农林牧渔唯一工信部信创典型案例，智慧农业+低空经济双主线落地。）\n\n- 空中：多光谱无人机集群开展农田巡测；\n\n![Image 1](https:\u002F\u002Fgbres.dfcfw.com\u002FFiles\u002Fpicture\u002F20260918\u002FB9B393E75E9CCC55E11A2686662765C9_w1080h720.jpg)\n\n- 地面：农田四情监测传感器、北斗智能农机；\n\n![Image 2](https:\u002F\u002Fgbres.dfcfw.com\u002FFiles\u002Fpicture\u002F20260918\u002F4D780511671CDAB50B4A1CCFE057703D_w1339h892.jpg)\n\n- 云端：苏垦智云平台与神农慧种AI模型，形成「采集数据→AI分析研判→输出水肥植保方案→农机落地执行」完整闭环。\n\n![Image 3](https:\u002F\u002Fgbres.dfcfw.com\u002FFiles\u002Fpicture\u002F20260918\u002F1059027DFA2A605F6861D7C69D6D4309_w1440h1080.jpg)\n\n百万亩自有连片农田源源不断产出真实田间数据，持续迭代优化AI模型。国内很多农业AI企业仅拥有小片试验田，唯有苏垦农发拥有大规模现代农业实景数据用于农业模型训练。\n\n苏垦智云平台，也是全国农林牧渔领域唯一入选工信部信创典型案例的农业数字化平台。苏垦农发一一智慧农业与低空经济的天空地一体化闭环落地，AI大田规模化实体应用！\n\n苏垦农发打造天空地一体化智慧农业完整体系，AI大田并非实验室试验，而是在百万亩自有连片农田实现规模化落地运行。\n\n1、空中：多光谱无人机集群常态化农田巡测，低空遥感采集作物长势、病虫害、墒情数据；依托农业农村部低空技术创新重点实验室，主攻低空多模态农情感知。\n\n2、地面：全域农田“四情”监测传感器、北斗智能农机、智能灌溉终端，实时采集土壤、苗情、虫情、气象数据。\n\n3、云端：苏垦智云平台 神农慧种农业AI智能体，形成完整闭环：采集田间数据→AI模型分析研判→输出水肥、植保作业处方→下发农机执行落地。\n\n4、核心稀缺壁垒：手握百万亩自有连片高标准农田，源源不断产出真实大田实景数据，持续迭代训练神农慧种AI模型。\n\n国内绝大多数农业AI企业，仅拥有小片试验田做演示；苏垦是少数拥有大规模真实农业场景用于模型训练与生产验证的实体龙头。\n\n5、苏垦智云一体化平台，也是全国农林牧渔领域唯一入选工信部信创典型案例的农业数字化平台，国产化底层架构，是农业数字化可复制的标杆样板。配套全国首个农业农村部农业低空技术创新重点实验室（苏垦为依托单位、河海大学共建），同步布局低空经济与智慧农业新质生产力。\n\n2026-09-18 11:16:07 作者更新了以下内容\n\n全球领先的风险咨询公司Verisk Maplecroft 在周四（9月17日）发布的一份报告中表示，极端天气灾害将加剧亚洲的粮食安全风险，并可能在印度、印尼和菲律宾等脆弱的国家引发动荡。\n\n![Image 4](https:\u002F\u002Fgbres.dfcfw.com\u002FFiles\u002Fpicture\u002F20260918\u002F9ACF9C92E7B35A242970CDC4D55B8DA9_w1080h15645.jpg)\n\n![Image 5](https:\u002F\u002Fgbres.dfcfw.com\u002FFiles\u002Fpicture\u002F20260918\u002FFE1BF075831D4DAEB94ADAE924123EE9_w1080h2400.jpg)\n\n2026-09-18 21:02:06 作者更新了以下内容\n\n苏垦农发一一 AI赋能农业真实落地案例：临海农场——国内首个10万亩级无人值守巡田农场（核心标杆）\n\n地点：江苏盐城临海农场，苏垦智慧农业科技园\n\n1. 空中低空遥感AI巡田\n\n![Image 6](https:\u002F\u002Fgbres.dfcfw.com\u002FFiles\u002Fpicture\u002F20260918\u002FE4E4DDE3ED1EEF061A2D631622A46F73_w1424h800.jpg)\n\n多光谱无人机集群常态化巡航，采集苗情、墒情、病虫害影像数据，AI自动识别长势差异、病斑，生成热力图；替代人工徒步巡田，十几分钟就能完成万亩农田普查。依托农业农村部低空技术创新重点实验室，开展低空多模态农情感知研究。\n\n2. AI智能光伏远程灌溉系统\n\n![Image 7](https:\u002F\u002Fgbres.dfcfw.com\u002FFiles\u002Fpicture\u002F20260918\u002F9FF2DAAEBED53D7BB8C825B388C102BD_w1424h800.jpg)\n\n万亩稻田布设太阳能智能闸门，通过土壤墒情传感器采集数据，AI分析土壤缺水程度，手机APP一键远程开关水渠闸门。\n\n量化效果：过去管500亩农田，人工开关闸门半天；现在2分钟完成全部闸门调控，灌溉效率提升20倍，每亩节约管水人工成本约30元。\n\n3. AR眼镜AI虫害识别\n\n![Image 8](https:\u002F\u002Fgbres.dfcfw.com\u002FFiles\u002Fpicture\u002F20260918\u002F9C7DFBF3F14BDE7E73309CA953C0C8E7_w1424h800.jpg)\n\n农技人员佩戴AR眼镜在田间巡查，拍摄虫体，AI毫秒级识别稻飞虱等害虫种类、统计虫口密度，识别准确率＞95%，自动推送防治方案，新手农技员也能快速判别田间虫害。\n\n4. AI变量施肥无人机作业\n\n![Image 9](https:\u002F\u002Fgbres.dfcfw.com\u002FFiles\u002Fpicture\u002F20260918\u002FC257CCA1B1E8AE1621C403E96DF222EE_w1424h800.jpg)\n\nAI读取水稻营养、长势数据，为每一块条田生成独立追肥处方，无人机分区精准施肥，一地一策，实现肥药双减，农药化肥年均用量下降约3%。\n\n5. 北斗智能农机 AI收割决策\n\n![Image 10](https:\u002F\u002Fgbres.dfcfw.com\u002FFiles\u002Fpicture\u002F20260918\u002F00A2AE87C3E170BFBFE610FABE2556A5_w1424h800.jpg)\n\n北斗导航插秧机、无人收割机，AI根据成熟度、含水率数据，指导分块错峰收割，减少粮食收割损耗。\n\n[恭喜解锁12个月手机L2专属领取资格，立即领取>>](https:\u002F\u002Facttg.eastmoney.com\u002Fpub\u002Fwebtg_hskh_act_zixun_01_01_01_0)\n\n暗盘资金榜已更新!这些个股\u002F板块可以关注>\n\n追加内容\n\n本文作者可以追加内容哦 !\n\n**郑重声明：**用户在社区发表的所有信息将由本网站记录保存，仅代表作者个人观点，与本网站立场无关，不对您构成任何投资建议，据此操作风险自担。**请勿相信代客理财、免费荐股和炒股培训等宣传内容，远离非法证券活动。请勿添加发言用户的手机号码、公众号、微博、微信及QQ等信息，谨防上当受骗！**\n\n[![Image 11](https:\u002F\u002Favator.eastmoney.com\u002Fqface\u002F9825094237066000\u002F360)](https:\u002F\u002Fi.eastmoney.com\u002F9825094237066000)\n\n总收益 20日收益 日收益\n------\n\n历史收益率走势(%)\n\nChart\n\n代码 名称 最新价 涨跌幅\n[查看更多](http:\u002F\u002Figuba.eastmoney.com\u002F9825094237066000)\n\n浪客视频\n\n![Image 12](https:\u002F\u002Fnp-newspic.dfcfw.com\u002Fdownload\u002FD25261481966621695940_w340h340.jpg)\n\n![Image 13](https:\u002F\u002Fgbapi.eastmoney.com\u002Fshareopt\u002Fweb\u002Fweb_click.gif?id=20260918101757264727920&type=20&version=200&product=EastMoney&plat=Web&deviceid=caifuhao)\n\n郑重声明：东方财富网发布此信息的目的在于传播更多信息，与本站立场无关。东方财富网不保证该信息（包括但不限于文字、视频、音频、数据及图表）全部或者部分内容的准确性、真实性、完整性、有效性、及时性、原创性等。相关信息并未经过本网站证实，不对您构成任何投资建议，据此操作，风险自担。","东方财富财富号\u002F苏垦农发","2026-09-18T00:00:00Z","报道",10,false,69,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":13,"relevant":21,"comment":22},22,18,14,5,1,"苏垦农发百万亩自有农田上实现天空地一体化AI闭环，含临海农场10万亩无人巡田等量化案例，产业参考价值较高，但来源为财富号自媒体、宣传色彩浓，权威性偏弱。",[24],{"name":10,"url":6},[26,27,28,29,30,31],"智慧农业","低空经济","农业人工智能","智能农机","遥感监测","数字农田",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[],{"status":40,"locked":14,"version":41,"method":42,"evidence":43},"classified","2026-10-22.2","rules",{"candidates":44,"agricultural_evidence":47},[45,52,59,65,72,78,83],{"code":33,"score":46,"supported":47,"title":48,"abstract":50,"tags":51},9,true,[49],"大模型",[],[28],{"code":53,"score":54,"supported":47,"title":55,"abstract":56,"tags":58},"F5",4,[],[57],"农机",[29],{"code":60,"score":54,"supported":47,"title":61,"abstract":62,"tags":64},"H1",[],[63],"高标准农田",[31],{"code":66,"score":67,"supported":47,"title":68,"abstract":69,"tags":71},"F4",3,[],[70],"植保",[],{"code":73,"score":67,"supported":47,"title":74,"abstract":75,"tags":77},"H3",[],[76],"灌溉",[],{"code":79,"score":21,"supported":14,"title":80,"abstract":81,"tags":82},"I1",[],[],[26],{"code":84,"score":21,"supported":14,"title":85,"abstract":86,"tags":87},"I3",[],[],[30],[89,90],"苏垦农发 神农慧种 AI大模型","临海农场 无人值守巡田","苏垦农发神农慧种AI大模型-2898",7,"2026-09-19T00:06:07.612319Z",{"total":95,"page":21,"page_size":95,"items":96},6,[97,195,260,339,409,449],{"id":98,"title":99,"url":100,"summary":101,"summary_zh":102,"content":8,"source_name":103,"source_url":100,"published_at":104,"category":105,"cover_url":8,"hotness":13,"is_selected":14,"score":106,"score_detail":107,"sources":112,"tags":114,"primary_topic":117,"secondary_topics":118,"classification":125,"search_phrases":175,"slug":178,"view_count":21,"doi":179,"paper":180,"created_at":194},5086,"Artificial Intelligence for Environmental Sustainability and Climate Change Mitigation: Recent Advances, Challenges, and Future Perspectives","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23399627","Abstract Climate change and environmental degradation have emerged as two of the most pressing global challenges of the twenty-first century, threatening ecosystems, biodiversity, human health, and sustainable development. Artificial Intelligence (AI) has transformed environmental research and management by enabling advanced data analytics, real-time monitoring, predictive modeling, and intelligent decision-support systems. This review critically examines the applications of AI in addressing environmental and climate-related challenges, including climate forecasting, air and water quality monitoring, biodiversity conservation, precision agriculture, renewable energy optimization, carbon emission assessment, waste management, and disaster risk reduction. Furthermore, The review also identifies current research gaps and highlights future opportunities for developing transparent, energy-efficient, and explainable AI systems that support evidence-based environmental decisionmaking. It concludes that AI has become a transformative tool for accelerating progress toward climate change mitigation, environmental sustainability, and the achievement of the United Nations Sustainable Development Goals (SDGs). Strengthening interdisciplinary collaboration, responsible AI governance, and policy support will be essential to maximize the environmental benefits of AI while minimizing its ecological footprint. Keywords: Artificial Intelligence; Climate Change; Environmental Sustainability; Machine Learning; Deep Learning; Remote Sensing; Internet of Things; Renewable Energy; Biodiversity Conservation; Sustainable Development Goals.","摘要 气候变化和环境退化已成为二十一世纪最紧迫的全球性挑战之一，威胁着生态系统、生物多样性、人类健康和可持续发展。人工智能（AI）通过实现先进的数据分析、实时监测、预测建模和智能决策支持系统，改变了环境研究与管理的方式。本文综述批判性地考察了AI在应对环境和气候相关挑战中的应用，包括气候预测、空气和水质监测、生物多样性保护、精准农业、可再生能源优化、碳排放评估、废物管理和灾害风险降低。此外，本综述还指出了当前的研究空白，并强调了未来开发透明、节能且可解释的AI系统的机遇，以支持基于证据的环境决策。文章得出结论，AI已成为加速气候变化减缓、环境可持续发展以及实现联合国可持续发展目标（SDGs）的变革性工具。加强跨学科合作、负责任的AI治理和政策支持，对于最大化AI的环境效益并同时最小化其生态足迹至关重要。关键词：人工智能；气候变化；环境可持续性；机器学习；深度学习；遥感；物联网；可再生能源；生物多样性保护；可持续发展目标。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-15T00:00:00Z","论文",70,{"impact":18,"substance":108,"depth":109,"authority":110,"freshness":46,"relevant":21,"comment":111},16,15,12,"综述性论文，覆盖AI在气候预测、精准农业、遥感监测等方向的应用，与农业信息化相关但偏宏观综述，增量有限。",[113],{"name":103,"url":100},[26,28,115,116,30],"可持续发展","气候变化",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[119],{"code":120,"slug":121,"name":122,"path":123,"parent_code":124},"H7","climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate","H",{"status":40,"locked":14,"version":41,"method":42,"evidence":126},{"candidates":127,"agricultural_evidence":47},[128,137,142,148,154,161,168],{"code":33,"score":18,"supported":47,"title":129,"abstract":131,"tags":136},[130],"artificial intelligence",[132,133,134,135,130],"深度学习","机器学习","machine learning","deep learning",[28],{"code":120,"score":109,"supported":47,"title":138,"abstract":140,"tags":141},[139],"climate change",[116,139],[116],{"code":79,"score":92,"supported":47,"title":143,"abstract":144,"tags":147},[],[145,146],"精准农业","precision agriculture",[26],{"code":84,"score":92,"supported":47,"title":149,"abstract":150,"tags":153},[],[151,152],"遥感","remote sensing",[30],{"code":155,"score":95,"supported":47,"title":156,"abstract":157,"tags":160},"H4",[],[158,159],"碳排放","carbon emission",[],{"code":162,"score":95,"supported":47,"title":163,"abstract":164,"tags":167},"H6",[],[165,166],"生物多样性","biodiversity",[],{"code":169,"score":95,"supported":47,"title":170,"abstract":171,"tags":174},"I5",[],[172,173],"物联网","internet of things",[],[176,177],"AI 气候变化","人工智能 环境可持续","AI气候变化-5086","10.5281\u002Fzenodo.23399627",{"doi":179,"openalex_id":181,"authors":182,"venue":103,"cited_by_count":185,"oa_url":100,"card":186,"direction":192,"ingested_from":193},"W7222092855",[183],{"name":184,"orcid":8},"Deepmala Suryavanshi",0,{"tldr":187,"method":188,"finding":189,"direction":190,"opportunity":191},"综述AI在气候变化缓解与环境可持续领域的应用进展、挑战与未来方向。","文献综述，涵盖机器学习、深度学习、遥感、物联网等技术应用。","AI已成为推动气候减缓与可持续发展目标实现的变革性工具。","农业绿色发展与碳","可聚焦可解释、低能耗AI在农业碳减排与精准环境决策中的落地研究。","智慧农业 \u002F 农业物联网","openalex","2026-10-16T23:30:15.059867Z",{"id":196,"title":197,"url":198,"summary":199,"summary_zh":200,"content":8,"source_name":201,"source_url":198,"published_at":202,"category":105,"cover_url":8,"hotness":13,"is_selected":14,"score":203,"score_detail":204,"sources":207,"tags":209,"primary_topic":212,"secondary_topics":216,"classification":218,"search_phrases":242,"slug":245,"view_count":67,"doi":246,"paper":247,"created_at":259},4809,"Enhanced crop yield estimation in smart agriculture via AI and IoT using the GODL geo-optimized algorithm","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs12083-026-02299-3","Abstract Precision agriculture is one of the major breakthroughs which has been adopted to counteract some of the major challenges facing contemporary agriculture, such as maximizing crop yield, maximizing resource efficiency, and sustainability. The application of Artificial Intelligence (AI) and the Internet of Things (IoT) technologies can go a long way in enhancing crop yield prediction through data obtained from multiple sources, including satellite images, ground sensors, and weather stations. This research paper proposes a next-generation crop yield prediction system called Geo-Optimized Deep Learning (GODL) algorithm. The GODL algorithm integrates geospatial information with deep learning algorithms for crop yield analysis and prediction. This combination of deep learning and geospatial data allows accurate and scalable crop yield predictions using geospatial and sensor information. In order to ensure model optimization in terms of accuracy and efficiency, the GODL Algorithm makes use of advanced hyperparameter optimization methods that include Adam and Stochastic Gradient Descent (SGD). The research paper highlights the efficiency of the GODL Algorithm by conducting extensive experiments that compare the algorithm’s performance with other crop yield prediction models like Recurrent Neural Network (RNN) and other machine learning techniques. The evaluation takes into consideration important metrics such as accuracy, Jaccard Index, Fowlkes-Mallows Index (FMI), and G-mean, as well as IoT data delay and its effects on the prediction accuracy. In future research, the use of blockchain technology to facilitate data sharing and edge computing to process the data will be considered.","摘要 精准农业是为应对当代农业面临的若干重大挑战而采用的主要突破之一，这些挑战包括最大化作物产量、最大化资源效率以及可持续性。人工智能（AI）与物联网（IoT）技术的应用，能够通过从卫星图像、地面传感器和气象站等多源获取的数据，大幅提升作物产量预测能力。本文提出了一种下一代作物产量预测系统，称为地理优化深度学习（GODL）算法。GODL算法将地理空间信息与深度学习算法相结合，用于作物产量分析与预测。深度学习与地理空间数据的这种结合，使得利用地理空间和传感器信息进行准确且可扩展的作物产量预测成为可能。为确保模型在准确性和效率方面实现优化，GODL算法采用了先进的超参数优化方法，包括Adam和随机梯度下降（SGD）。本文通过开展大量实验，将该算法的性能与其他作物产量预测模型（如循环神经网络（RNN）及其他机器学习技术）进行比较，从而突出GODL算法的效率。评估考虑了准确率、Jaccard指数、Fowlkes-Mallows指数（FMI）和G均值等重要指标，以及物联网数据延迟及其对预测精度的影响。在未来的研究中，将考虑使用区块链技术促进数据共享，并利用边缘计算处理数据。","Peer-to-Peer Networking and Applications","2026-10-12T00:00:00Z",68,{"impact":110,"substance":18,"depth":108,"authority":205,"freshness":46,"relevant":21,"comment":206},13,"提出GODL地理优化深度学习算法融合AI与IoT进行作物估产，方法有创新且对比实验充分，但属学术论文，产业落地影响有限。",[208],{"name":201,"url":198},[26,28,210,30,211],"农业物联网","作物估产",{"code":169,"slug":213,"name":214,"path":215,"parent_code":37},"agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",[217],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":219},{"candidates":220,"agricultural_evidence":47},[221,228,233,237],{"code":169,"score":18,"supported":47,"title":222,"abstract":224,"tags":227},[223],"IoT",[172,225,173,223,226],"边缘计算","edge computing",[210],{"code":79,"score":109,"supported":47,"title":229,"abstract":231,"tags":232},[230],"smart agriculture",[145,146],[26],{"code":33,"score":13,"supported":47,"title":234,"abstract":235,"tags":236},[],[132,133,134,135,130],[28],{"code":84,"score":238,"supported":14,"title":239,"abstract":240,"tags":241},2,[],[],[211,30],[243,244],"GODL 作物估产 算法","AI IoT 智慧农业 产量预测","GODL作物估产算法-4809","10.1007\u002Fs12083-026-02299-3",{"doi":246,"openalex_id":248,"authors":249,"venue":201,"cited_by_count":185,"oa_url":198,"card":254,"direction":192,"ingested_from":193},"W7221560555",[250,252],{"name":251,"orcid":8},"Keerthana T",{"name":253,"orcid":8},"Janarthanan R",{"tldr":255,"method":256,"finding":257,"direction":192,"opportunity":258},"提出GODL算法，融合地理空间信息与深度学习，结合AI和IoT提升作物产量预测精度。","GODL算法，融合地理空间数据与深度学习，采用Adam和SGD超参数优化。","GODL在准确率、Jaccard指数、FMI和G-mean上优于RNN等模型，且IoT数据延迟影响预","可探索区块链保障数据共享安全、边缘计算降低IoT延迟，以及多源异构数据融合的轻量化模型。","2026-10-13T23:30:10.304344Z",{"id":261,"title":262,"url":263,"summary":264,"summary_zh":265,"content":8,"source_name":266,"source_url":263,"published_at":202,"category":105,"cover_url":8,"hotness":13,"is_selected":14,"score":267,"score_detail":268,"sources":271,"tags":273,"primary_topic":276,"secondary_topics":282,"classification":288,"search_phrases":312,"slug":315,"view_count":21,"doi":316,"paper":317,"created_at":338},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",77,{"impact":108,"substance":269,"depth":18,"authority":205,"freshness":46,"relevant":21,"comment":270},21,"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。",[272],{"name":266,"url":263},[26,274,28,30,275],"无人机","病害预警",{"code":277,"slug":278,"name":279,"path":280,"parent_code":281},"B2","fruit","果树","\u002Ftopic\u002Fhorticulture\u002Ffruit","B",[283,284],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"code":84,"slug":285,"name":286,"path":287,"parent_code":37},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"status":40,"locked":14,"version":41,"method":42,"evidence":289},{"candidates":290,"agricultural_evidence":47},[291,299,303,308],{"code":277,"score":292,"supported":47,"title":293,"abstract":295,"tags":298},17,[294],"apple",[296,297,294],"苹果","葡萄",[],{"code":33,"score":109,"supported":47,"title":300,"abstract":301,"tags":302},[135],[132,135],[28],{"code":84,"score":110,"supported":47,"title":304,"abstract":306,"tags":307},[305],"multispectral",[305],[30],{"code":79,"score":92,"supported":47,"title":309,"abstract":310,"tags":311},[],[145,146],[26],[313,314],"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":316,"openalex_id":318,"authors":319,"venue":266,"cited_by_count":185,"oa_url":331,"card":332,"direction":192,"ingested_from":193},"W7221558726",[320,323,326,328],{"name":321,"orcid":322},"Batuhan Aşıroğlu","https:\u002F\u002Forcid.org\u002F0000-0003-0767-6348",{"name":324,"orcid":325},"Aydın Kağan Turkoglu","https:\u002F\u002Forcid.org\u002F0009-0001-8383-8807",{"name":327,"orcid":8},"Fatih Sinan Bir",{"name":329,"orcid":330},"Sibel Senan","https:\u002F\u002Forcid.org\u002F0000-0001-6773-0428","https:\u002F\u002Fdergipark.org.tr\u002Fen\u002Fdownload\u002Farticle-file\u002F5761152",{"tldr":333,"method":334,"finding":335,"direction":336,"opportunity":337},"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。","农业遥感与作物表型","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":340,"title":341,"url":342,"summary":343,"summary_zh":344,"content":8,"source_name":345,"source_url":342,"published_at":346,"category":105,"cover_url":8,"hotness":13,"is_selected":14,"score":347,"score_detail":348,"sources":350,"tags":352,"primary_topic":354,"secondary_topics":360,"classification":363,"search_phrases":388,"slug":391,"view_count":185,"doi":8,"paper":392,"created_at":408},4670,"SMART FERTILIZER DISTRIBUTION SYSTEM: A COMPREHENSIVE REVIEW","https:\u002F\u002Fijmert.com\u002Findex.php\u002Fijmert\u002Farticle\u002Fview\u002F334","Agriculture is an important sector for food production and economic development, and the efficient use of fertilizers is essential for achieving better crop growth and productivity. Conventional fertilizer distribution methods largely depend on manual application, which can lead to uneven distribution, excessive fertilizer usage, increased labor requirements, and wastage of agricultural resources. The development of automation and Internet of Things (IoT) technologies has provided new opportunities for improving fertilizer distribution and management in agricultural fields. This review paper presents a comprehensive study of Smart Fertilizer Distribution Systems, focusing on the technologies, control methods, communication techniques, and approaches reported in existing research. Various systems using microcontrollers, IoT devices, wireless communication, web-based platforms, sensors, motor or pump control, driver circuits, and solenoid valves are reviewed. The working principles of automated fertilizer distribution systems and their role in controlling fertilizer flow and reducing manual intervention are discussed. Different communication technologies, including Wi-Fi and other wireless methods, are compared based on their applicability, cost, operating range, reliability, and control capability. The review also examines the major advantages and limitations of existing systems, such as automation, remote operation, improved fertilizer management, reduced labor, network dependency, power requirements, sensor accuracy, and system complexity. A comparative analysis of selected research works is presented to identify the techniques used, their contributions, and the limitations that remain to be addressed. The review further identifies research gaps and discusses future developments such as automatic fertilizer scheduling, multi-zone fertilizer distribution, real-time monitoring, cloud-based data storage, artificial intelligence, machine learning, and integration with renewable energy sources. The study concludes that smart fertilizer distribution can improve the efficiency, accuracy, and sustainability of fertilizer application and has significant potential for future precision agriculture applications.","农业是粮食生产和经济发展的重要部门，而肥料的高效利用对于实现作物更好生长和生产力提升至关重要。传统的肥料分配方法在很大程度上依赖人工施用，这可能导致分配不均、肥料过量使用、劳动力需求增加以及农业资源浪费。自动化和物联网（IoT）技术的发展为改善农田肥料分配与管理提供了新的机遇。本文综述对智能肥料分配系统进行了全面研究，重点关注现有研究中报道的技术、控制方法、通信技术和方法。本文回顾了使用微控制器、物联网设备、无线通信、基于网络的平台、传感器、电机或泵控制、驱动电路和电磁阀的各种系统。讨论了自动化肥料分配系统的工作原理及其在控制肥料流量和减少人工干预方面的作用。基于适用性、成本、工作范围、可靠性和控制能力，比较了包括Wi-Fi和其他无线方法在内的不同通信技术。本综述还考察了现有系统的主要优势和局限性，如自动化、远程操作、改善肥料管理、减少劳动力、网络依赖、功率需求、传感器精度和系统复杂性。本文对选定的研究工作进行了比较分析，以识别所采用的技术、其贡献以及仍有待解决的局限性。本综述进一步指出了研究空白，并讨论了未来发展方向，如自动肥料调度、多区域肥料分配、实时监测、基于云的数据存储、人工智能、机器学习以及与可再生能源的集成。该研究得出结论，智能肥料分配可以提高肥料施用的效率、准确性和可持续性，并在未来精准农业应用中具有巨大潜力。","International Journal of Mechanical Engineering Research and Technology","2026-10-08T00:00:00Z",61,{"impact":110,"substance":108,"depth":109,"authority":110,"freshness":95,"relevant":21,"comment":349},"综述性论文系统梳理智能施肥系统的技术路线与研究空白，对智慧农业领域有参考价值，但属文献综述而非原创突破，时效性一般。",[351],{"name":345,"url":342},[26,28,353,210,29],"精准施肥",{"code":355,"slug":356,"name":357,"path":358,"parent_code":359},"F3","fertilizers","肥料与水肥一体化","\u002Ftopic\u002Finputs-equipment\u002Ffertilizers","F",[361,362],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"code":169,"slug":213,"name":214,"path":215,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":364},{"candidates":365,"agricultural_evidence":47},[366,372,376,380,384],{"code":355,"score":109,"supported":47,"title":367,"abstract":369,"tags":371},[368],"fertilizer",[370,368],"肥料",[353],{"code":33,"score":13,"supported":47,"title":373,"abstract":374,"tags":375},[],[133,134,130],[28],{"code":169,"score":13,"supported":47,"title":377,"abstract":378,"tags":379},[],[172,173,223],[210],{"code":79,"score":92,"supported":47,"title":381,"abstract":382,"tags":383},[],[145,146],[26],{"code":53,"score":21,"supported":14,"title":385,"abstract":386,"tags":387},[],[],[29],[389,390],"智能施肥系统 物联网","精准施肥 自动化","智能施肥系统物联网-4670",{"doi":8,"openalex_id":393,"authors":394,"venue":345,"cited_by_count":185,"oa_url":342,"card":403,"direction":192,"ingested_from":193},"W7221394563",[395,397,399,401],{"name":396,"orcid":8},"Anjali Manoj Kumbhar",{"name":398,"orcid":8},"Shruti Prashant Kulkarni",{"name":400,"orcid":8},"Snehal Santosh Khune",{"name":402,"orcid":8},"B. B. Godbole",{"tldr":404,"method":405,"finding":406,"direction":192,"opportunity":407},"综述智能施肥系统的技术、控制与通信方法，并指出研究空白与未来方向。","综述基于微控制器、物联网、无线通信、传感器与电磁阀的自动施肥系统研究。","智能施肥可提升施肥效率、精度与可持续性，但存在网络依赖、功耗与传感器精度等局限。","可探索AI驱动的自动施肥调度、多区域分配与云边协同的实时精准施肥系统。","2026-10-11T23:30:16.128775Z",{"id":410,"title":411,"url":412,"summary":413,"summary_zh":8,"content":414,"source_name":415,"source_url":8,"published_at":416,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":417,"score_detail":418,"sources":422,"tags":424,"primary_topic":427,"secondary_topics":428,"classification":429,"search_phrases":444,"slug":447,"view_count":238,"doi":8,"paper":8,"created_at":448},4615,"杭州诚邀全市创新力量共筑智慧农业——启动人工智能+农业应用场景征集","https:\u002F\u002Fwww.hangzhou.gov.cn\u002Fcol\u002Fcol812262\u002Fart\u002F2026\u002Fart_61fd9b1bd08e4756a87e25e3f296e645.html","根据《杭州市大力发展智慧农业打造人工智能+农业创新实践高地实施方案(2026—2030年)》部署,杭州启动人工智能+农业应用场景征集,聚焦农业大模型和智能体、智能农业设施装备、智慧管理服务平台三大方向。场景要求具备合规性、创新性、实效性、示范性、开放性,优先支持向中小企业、民营企业开放测试应用合作;入选场景将有机会在第二十三届中国国际农产品交易会智慧农业展区专题展示。","杭州智慧农业正呼唤更多“最强大脑”——根据《杭州市大力发展智慧农业 打造“人工智能+农业”创新实践高地实施方案（2026—2030年）》部署，杭州日前已启动“人工智能+农业”应用场景征集，面向全市发出“英雄帖”。\n\n本次征集聚焦三大方向：农业大模型和智能体，包括生物育种、生产管理、病虫害监测、疫病防治、产量预测，以及病虫害智能诊断、农事决策支持、智能植保作业规划、农情问答、农业知识智能检索等；智能农业设施装备，包括智能农机、无人驾驶作业、智能采摘、精准施肥施药、水肥一体化智能控制、物联网感知、卫星遥感与无人机监测等；智慧管理服务平台，覆盖智慧种植、智慧畜牧、智慧渔业和综合管理服务，如植物工厂、智慧大田、智慧茶园果园、畜禽体征监测、精准饲喂、疫病AI辅助诊断、陆基工厂化养殖、质量安全溯源、乡村数字治理等。\n\n场景要“上榜”，需满足合规性、创新性、实效性、示范性和开放性。比如，场景建设要符合国家、省相关产业政策、安全规范和行业标准；依托人工智能核心技术，具备技术创新、模式创新或应用创新；已落地或具备明确落地计划，能解决行业痛点、治理难点、民生堵点，降本增效成效可量化、可验证；具备较强的复制性、推广性和通用性；优先支持向中小企业、民营企业开放测试、应用、合作的场景项目。\n\n征集主体须为在杭州市注册或主要应用场景在杭州的企事业单位、科研院所、高等院校、新型农业经营主体、农业社会化服务组织等。可单一报送，也可联合报送，联合报送须明确1家牵头单位，参与单位原则上不超过5家。有意向的主体可先向所在区、县（市）农业农村部门提交材料，经区、县（市）初审推荐，由市级组织专家开展路演，确定首批应用场景名单。","杭州市政府门户网站 2026-10-09","2026-10-09T00:00:00Z",72,{"impact":108,"substance":419,"depth":109,"authority":205,"freshness":420,"relevant":21,"comment":421},20,8,"地市级政策落地动作，条款与征集方向具体，具备行业示范与可复制价值，值得进入每日精选。",[423],{"name":415,"url":412},[26,28,425,29,426],"农业大模型","应用场景征集",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":430},{"candidates":431,"agricultural_evidence":47},[432,436,440],{"code":79,"score":110,"supported":47,"title":433,"abstract":434,"tags":435},[26],[26],[26],{"code":33,"score":20,"supported":47,"title":437,"abstract":438,"tags":439},[],[49],[28,425],{"code":53,"score":21,"supported":14,"title":441,"abstract":442,"tags":443},[],[],[29],[445,446],"杭州 人工智能 应用场景征集","杭州 智慧农业 实施方案","杭州人工智能应用场景征集-4615","2026-10-11T00:09:35.817480Z",{"id":450,"title":451,"url":452,"summary":453,"summary_zh":454,"content":8,"source_name":455,"source_url":452,"published_at":456,"category":105,"cover_url":8,"hotness":13,"is_selected":14,"score":457,"score_detail":458,"sources":460,"tags":462,"primary_topic":465,"secondary_topics":470,"classification":472,"search_phrases":492,"slug":495,"view_count":238,"doi":496,"paper":497,"created_at":510},4431,"A multimodal deep learning for rice crop health monitoring using vegetation index feature selection","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-74895-5","Crop health monitoring is essential for sustaining agricultural productivity and ensuring stable production, particularly in major crop-producing regions that face challenges due to climate variability, resource constraints, and spatial heterogeneity. Traditional methods struggle to effectively integrate spatial heterogeneity and environmental variability from multi-source remote sensing (RS) data. To address this problem, the proposed work explores the role of satellite imagery in monitoring rice crop health with the help of Vegetation Indices (VIs) under the impact of changing weather conditions. Multi-temporal Sentinel-1 and Sentinel-2 satellite data were acquired from a rice cultivation field in Gorakhpur, Uttar Pradesh, India, during three Kharif growing seasons (2023-2025). In this work, we propose a supervised MI-based feature selection framework employing Otsu’s adaptive thresholding for selection of VIs that strongly determine the health of rice crop. To ensure data integrity, cloud-contaminated observations were systematically excluded through a two-stage cleaning process. Further, we propose a multimodal deep learning (DL) framework that fuses a Swin Transformer (Swin-T) backbone for spatial feature extraction from vegetation index imagery with a Multi-Layer Perceptron (MLP) for structured numerical modeling of meteorological variables. The proposed framework achieved 94.05% accuracy on an independent held-out test set and a mean accuracy of \\(92.12 \\pm 5.28\\) % under 10-fold cross-validation, consistently outperforming competing hybrid deep learning baselines. These results highlight that integrating transformer-based spatial representations with environmental information provides an efficient approach for rice crop health classification. The proposed framework can be extended to multi-region crop monitoring by incorporating additional vegetation index modalities and diverse climatic conditions.","作物健康监测对于维持农业生产力、保障稳定生产至关重要，尤其是在面临气候变异性、资源约束和空间异质性挑战的主要作物产区。传统方法难以有效整合多源遥感（RS）数据中的空间异质性和环境变异性。为解决这一问题，本研究探索了卫星影像在天气条件变化影响下借助植被指数（VIs）监测水稻作物健康的作用。研究在印度北方邦戈勒克布尔的稻田中，于三个水稻生长季（2023—2025年）获取了多时相Sentinel-1和Sentinel-2卫星数据。在本研究中，我们提出了一种基于互信息（MI）的监督式特征选择框架，采用Otsu自适应阈值法筛选对水稻作物健康具有强判别能力的植被指数。为确保数据完整性，通过两阶段清洗流程系统性地剔除了受云污染的观测数据。此外，我们提出了一种多模态深度学习（DL）框架，该框架融合了Swin Transformer（Swin-T）主干网络用于从植被指数影像中提取空间特征，以及多层感知机（MLP）用于气象变量的结构化数值建模。所提框架在独立留出测试集上达到了94.05%的准确率，在10折交叉验证下平均准确率为\\(92.12 \\pm 5.28\\)%，持续优于竞争的混合深度学习基线模型。这些结果表明，将基于Transformer的空间表征与环境信息相结合，为水稻作物健康分类提供了一种高效方法。所提框架可通过纳入更多植被指数模态和多样化气候条件，扩展至多区域作物监测。","Scientific Reports","2026-10-07T00:00:00Z",81,{"impact":18,"substance":17,"depth":18,"authority":19,"freshness":46,"relevant":21,"comment":459},"方法新颖、数据跨三个生长季且精度可靠，属农业遥感与AI交叉的细分领域进展，值得进入每日精选。",[461],{"name":455,"url":452},[26,28,463,30,464],"水稻","作物长势",{"code":466,"slug":467,"name":463,"path":468,"parent_code":469},"A1","rice","\u002Ftopic\u002Ffield-crops\u002Frice","A",[471],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":473},{"candidates":474,"agricultural_evidence":47},[475,480,484,488],{"code":466,"score":18,"supported":47,"title":476,"abstract":477,"tags":479},[467],[463,478,467],"稻田",[463],{"code":33,"score":109,"supported":47,"title":481,"abstract":482,"tags":483},[135],[132,135],[28],{"code":84,"score":92,"supported":47,"title":485,"abstract":486,"tags":487},[],[151,152],[30],{"code":79,"score":21,"supported":14,"title":489,"abstract":490,"tags":491},[],[],[26],[493,494],"Sentinel-2 水稻 遥感监测","Swin Transformer 作物健康","Sentinel-2水稻遥感监测-4431","10.1038\u002Fs41598-026-74895-5",{"doi":496,"openalex_id":498,"authors":499,"venue":455,"cited_by_count":185,"oa_url":452,"card":505,"direction":336,"ingested_from":193},"W7220838898",[500,503],{"name":501,"orcid":502},"Shashank Gupta","https:\u002F\u002Forcid.org\u002F0000-0003-1291-7951",{"name":504,"orcid":8},"Shefali Arora Chouhan",{"tldr":506,"method":507,"finding":508,"direction":336,"opportunity":509},"融合Sentinel-1\u002F2植被指数与气象数据，用多模态深度学习监测水稻健康。","Sentinel-1\u002F2多时相数据、MI特征选择、Otsu阈值、Swin-T+M","独立测试准确率94.05%，10折交叉验证92.12%，优于混合基线。","可扩展至多区域多作物，探索更多植被指数模态与极端气候下的泛化能力。","2026-10-08T23:30:29.254071Z"]