[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2520":3,"related-2520":122},{"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":29,"primary_topic":35,"secondary_topics":41,"classification":42,"search_phrases":82,"slug":85,"view_count":86,"doi":87,"paper":88,"created_at":121},2520,"Exemples d'apports de l'intelligence artificielle (IA) pour l'envirotypage et Phénotypage numérique des plantes","https:\u002F\u002Fdoi.org\u002F10.17180\u002Fciag-2026-vol114-art03","The characterization of plants and their environment is changing scale with the development of digital plant phenotyping tools and methods. Phenotyping and envirotyping tools are more accessible, enabling multi-scale observation of plants and their environment—even molecules—through multi-plot observation via satellite. At the same time, large amounts of data are more accessible and easier to exploit through indexing in open information systems. Processing is facilitated by an increasing number of sophisticated tools, particularly with the help of artificial intelligence, thus facilitating analysis by the operator. Two applicated examples are detailled here regarding segmentation for combined mixed crops and insects identification and counting. Environmental and phenotypic data can be combined in predictive or decision support models. These data are valuable assets for accelerating the deployment of agroecology and resilient, sustainable agriculture.","随着数字植物表型分析工具和方法的发展，植物及其环境的表征正在发生尺度上的变化。表型分析和环境型分析工具变得更加普及，使得通过卫星进行多地块观测，实现对植物及其环境——甚至分子——的多尺度观察成为可能。与此同时，大量数据更易获取，并且通过开放信息系统中的索引编制更易于利用。越来越多的复杂工具促进了数据处理，尤其是在人工智能的帮助下，从而便于操作人员进行分析。本文详细介绍了两个应用实例，分别涉及混合种植作物的分割以及昆虫的识别与计数。环境和表型数据可以结合到预测模型或决策支持模型中。这些数据是加速部署生态农业以及具有韧性的可持续农业的宝贵资产。",null,"INRAE","2026-09-14T00:00:00Z","论文",25,false,78,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},18,20,17,15,8,1,"INRAE 发布数字表型与环境型技术应用论文，展示 AI 在混合种植分割和昆虫识别计数中的落地案例，方法新颖、数据规模可观，对智慧农业与育种数字化有参考价值。",[25,26],{"name":10,"url":6},{"name":27,"url":28},"HAL (Le Centre pour la Communication Scientifique Directe)","https:\u002F\u002Fhal.inrae.fr\u002Fhal-05748608",[30,31,32,33,34],"智慧农业","农业人工智能","遥感监测","作物育种","数字表型",{"code":36,"slug":37,"name":38,"path":39,"parent_code":40},"H8","sustainable-agriculture","可持续与保护性农业","\u002Ftopic\u002Fenvironment\u002Fsustainable-agriculture","H",[],{"status":43,"locked":14,"version":44,"method":45,"evidence":46},"classified","2026-10-22.2","rules",{"candidates":47,"agricultural_evidence":50},[48,57,65,72,77],{"code":36,"score":49,"supported":50,"title":51,"abstract":52,"tags":56},9,true,[],[53,54,55],"可持续农业","生态农业","sustainable agriculture",[],{"code":58,"score":59,"supported":50,"title":60,"abstract":61,"tags":64},"I2",7,[],[62,63],"artificial intelligence","decision support",[31],{"code":66,"score":67,"supported":50,"title":68,"abstract":69,"tags":71},"H6",3,[],[70],"agroecology",[],{"code":73,"score":22,"supported":14,"title":74,"abstract":75,"tags":76},"I1",[],[],[30],{"code":78,"score":22,"supported":14,"title":79,"abstract":80,"tags":81},"I3",[],[],[32],[83,84],"农业人工智能 作物育种 数字表型 智慧农业","农业人工智能 作物育种","农业人工智能作物育种数字表型智慧农业-2520",2,"10.17180\u002Fciag-2026-vol114-art03",{"doi":87,"openalex_id":89,"authors":90,"venue":10,"cited_by_count":113,"oa_url":6,"card":114,"direction":118,"ingested_from":120},"W7213212183",[91,93,96,99,101,103,105,107,109,111],{"name":92,"orcid":9},"Benoit DE SELON",{"name":94,"orcid":95},"Jean‐Eudes Hollebecq","https:\u002F\u002Forcid.org\u002F0000-0002-5089-9179",{"name":97,"orcid":98},"Jordan Bernigaud-Samatan","https:\u002F\u002Forcid.org\u002F0009-0006-2624-008X",{"name":100,"orcid":9},"Caroline Chaynes",{"name":102,"orcid":9},"Solenne Faul-Godec",{"name":104,"orcid":9},"Mathieu MARGUERIE",{"name":106,"orcid":9},"Afonso Ponce",{"name":108,"orcid":9},"Mario Serouart",{"name":110,"orcid":9},"Samuel Thomas",{"name":112,"orcid":9},"Tania ROUGIER",0,{"tldr":115,"method":116,"finding":117,"direction":118,"opportunity":119},"综述人工智能在植物环境型与数字表型中的应用，并给出混合种植分割和昆虫计数两个实例。","数字表型与环境型工具、卫星多尺度观测、开放信息系统及AI图像分割与识别。","AI可提升植物与环境数据的分割、昆虫识别计数效率，并支撑预测与决策模型。","农业遥感与作物表型","可探索多尺度表型与环境数据融合的轻量AI模型，用于混合种植和病虫害实时决策。","openalex","2026-09-15T23:30:15.799807Z",{"total":123,"page":22,"page_size":123,"items":124},6,[125,226,307,360,446,526],{"id":126,"title":127,"url":128,"summary":129,"summary_zh":130,"content":9,"source_name":131,"source_url":128,"published_at":132,"category":12,"cover_url":9,"hotness":133,"is_selected":14,"score":134,"score_detail":135,"sources":140,"tags":142,"primary_topic":146,"secondary_topics":152,"classification":159,"search_phrases":207,"slug":210,"view_count":22,"doi":211,"paper":212,"created_at":225},4073,"Intelligent soil monitoring system for sustainable agriculture using TinyML and ultra-low power wireless sensor networks","https:\u002F\u002Fdoi.org\u002F10.35784\u002Fiapgos.9467","Precision agriculture demands continuous, energy-autonomous soil monitoring capable of detecting critical conditions such as rapid moisture loss or nutrient depletion approaching crop-threatening thresholds. Conventional wireless sensor networks face a fundamental trade-off between multi-year battery life and sub-second response latency, as duty-cycled polling wastes energy on idle queries while always-on communication remains infeasible for battery-powered field deployments spanning hundreds of acres. This work presents a novel integration of TinyML-based edge inference with a custom-designed 40 nm CMOS ultra-low power wake-up receiver (WuRX), enabling event-driven, asynchronous soil condition monitoring without reliance on periodic polling schedules. The proposed three-tier edge-fog-cloud architecture employs sensor nodes measuring moisture, temperature, pH, nitrogen, phosphorus, and potassium, with quantized neural networks trained and validated on 64,800 labelled samples collected from instrumented corn and soybean field deployments over 180 days, achieving a classification accuracy of 91.3% and F1-score of 93.0% at an inference energy cost of 0.87 mJ per cycle. Circuit-level simulations of the 40 nm CMOS WuRX confirm an input return loss of -15.8 dB, transmission gain of +18.2 dB, and power-added efficiency of 45.3% at -60 dBm sensitivity, with continuous standby consumption of 45–53 µW. System-level power budget analysis validates battery lifetimes of 200–300 days using 240 mAh lithium cells augmented by a 5 cm² monocrystalline solar harvester. Key limitations include the restriction of field validation to two crop types and dependence on adequate solar irradiance for energy-neutral operation. The proposed architecture reduces communication overhead by 80–90% relative to threshold-based systems, providing a practical framework for scalable, resource-constrained precision agriculture deployments.","精准农业需要能够持续、能源自主地监测土壤，以检测诸如水分快速流失或养分耗竭接近威胁作物阈值等关键状况。传统无线传感器网络面临多年电池寿命与亚秒级响应延迟之间的根本性权衡，因为占空比轮询会在空闲查询上浪费能量，而始终在线的通信对于覆盖数百英亩的电池供电田间部署而言仍不可行。本研究提出了一种新颖的集成方案，将基于TinyML的边缘推理与定制设计的40 nm CMOS超低功耗唤醒接收器（WuRX）相结合，实现事件驱动的异步土壤状况监测，而无需依赖周期性轮询调度。所提出的三层边缘-雾-云架构采用测量水分、温度、pH、氮、磷和钾的传感器节点，其量化神经网络在180天内从仪器化玉米和大豆田间部署中采集的64,800个标记样本上进行训练和验证，在每次推理周期能耗为0.87 mJ的情况下，实现了91.3%的分类准确率和93.0%的F1分数。对40 nm CMOS WuRX的电路级仿真证实，在-60 dBm灵敏度下，输入回波损耗为-15.8 dB，传输增益为+18.2 dB，功率附加效率为45.3%，连续待机功耗为45–53 µW。系统级功率预算分析验证了使用240 mAh锂电池并辅以5 cm²单晶硅太阳能收集器时，电池寿命可达200–300天。主要局限性包括田间验证仅限于两种作物类型，以及能量中性运行依赖充足的太阳辐照度。与基于阈值的系统相比，所提出的架构将通信开销降低了80–90%，为可扩展、资源受限的精准农业部署提供了实用框架。","Informatyka Automatyka Pomiary w Gospodarce i Ochronie Środowiska","2026-09-30T00:00:00Z",10,80,{"impact":17,"substance":136,"depth":137,"authority":138,"freshness":21,"relevant":22,"comment":139},22,19,13,"将TinyML边缘推理与40nm CMOS超低功耗唤醒接收机结合，实现事件驱动型土壤监测，实测数据扎实、能耗指标明确，对精准农业物联网部署有参考价值。",[141],{"name":131,"url":128},[30,31,143,144,145],"精准农业","无线传感器网络","土壤监测",{"code":147,"slug":148,"name":149,"path":150,"parent_code":151},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot","I",[153,158],{"code":154,"slug":155,"name":156,"path":157,"parent_code":40},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil",{"code":36,"slug":37,"name":38,"path":39,"parent_code":40},{"status":43,"locked":14,"version":44,"method":45,"evidence":160},{"candidates":161,"agricultural_evidence":50},[162,168,174,178,183,190,197,203],{"code":147,"score":20,"supported":50,"title":163,"abstract":165,"tags":167},[164],"wireless sensor",[166,164],"无线传感",[144],{"code":154,"score":169,"supported":50,"title":170,"abstract":171,"tags":173},14,[155],[172,155],"土壤",[],{"code":36,"score":21,"supported":50,"title":175,"abstract":176,"tags":177},[55],[],[],{"code":73,"score":21,"supported":50,"title":179,"abstract":180,"tags":182},[],[143,181],"precision agriculture",[30,143],{"code":184,"score":123,"supported":50,"title":185,"abstract":186,"tags":189},"A3",[],[187,188],"玉米","corn",[],{"code":191,"score":123,"supported":50,"title":192,"abstract":193,"tags":196},"A4",[],[194,195],"大豆","soybean",[],{"code":198,"score":67,"supported":50,"title":199,"abstract":200,"tags":202},"F5",[],[201],"harvester",[],{"code":58,"score":22,"supported":14,"title":204,"abstract":205,"tags":206},[],[],[31],[208,209],"TinyML 土壤监测","超低功耗 唤醒接收机 精准农业","TinyML土壤监测-4073","10.35784\u002Fiapgos.9467",{"doi":211,"openalex_id":213,"authors":214,"venue":131,"cited_by_count":113,"oa_url":218,"card":219,"direction":223,"ingested_from":120},"W7214927134",[215],{"name":216,"orcid":217},"Muhammad Ovais Akhter","https:\u002F\u002Forcid.org\u002F0000-0002-8928-4489","https:\u002F\u002Fph.pollub.pl\u002Findex.php\u002Fiapgos\u002Farticle\u002Fdownload\u002F9467\u002F5631",{"tldr":220,"method":221,"finding":222,"direction":223,"opportunity":224},"提出TinyML边缘推理与超低功耗唤醒接收机集成的土壤监测系统，实现事件驱动异步监测。","TinyML量化神经网络、40nm CMOS唤醒接收机、三层边缘-雾-云架构，6","分类准确率91.3%，通信开销降低80-90%，电池寿命200-300天。","智慧农业 \u002F 农业物联网","可探索多作物多土壤类型泛化、无光照或弱光下能量收集与事件触发协同优化。","2026-10-03T23:30:17.793958Z",{"id":227,"title":228,"url":229,"summary":230,"summary_zh":231,"content":9,"source_name":232,"source_url":229,"published_at":233,"category":12,"cover_url":9,"hotness":133,"is_selected":14,"score":234,"score_detail":235,"sources":237,"tags":239,"primary_topic":243,"secondary_topics":248,"classification":254,"search_phrases":281,"slug":284,"view_count":21,"doi":285,"paper":286,"created_at":306},3355,"AI and Sustainable Agriculture Through Cost–Benefit Analysis of Smart Irrigation Systems","https:\u002F\u002Fdoi.org\u002F10.22004\u002Fag.econ.412813","The advancing role of Artificial Intelligence (AI) and its application in agriculture have disrupted traditional agricultural practices, with smart irrigation systems representing one of the leading technologies enabling sustainable agriculture. Smart irrigation systems utilize real–time data, machine learning algorithms, and predictive analytics to better optimize irrigation water use, limit wasted resources, and improve the yields of crop products. The proposed research will assess the economic and environmental impacts of AI smart irrigation systems with a full costs–benefits analysis. The proposed research considers both the capital cost and operating cost of smart irrigation systems and compares these traditional irrigation practices while also examining the long–term benefits of potential water savings from Smart Irrigation Systems, expanded agricultural production, and reduced human labour. This will give context for measuring the impacts of Smart Irrigation Systems on farm businesses, including both opportunities and barriers to adoption. Additionally, using a formal literature review to lock down existing research and surveys of irrigation farmers to collect a field data set will provide the proposed researchers a collective sample to measure the efficacy of AI smart irrigation systems, identify barriers, compare opportunities, and measure performance under differing climate and soil properties. The research will find high and substantial respective levels of benefits from the implementation of AI–based smart systems, particularly in water–stressed systems with positive impacts on farm profitability, private, and environmental conservation. This research is essential for informing stakeholders of actions and the delivery of AI–enabled solutions in support of more sustainable agricultural practices.","人工智能（Artificial Intelligence, AI）的不断发展及其在农业中的应用已经颠覆了传统的农业实践，其中智能灌溉系统是实现可持续农业的领先技术之一。智能灌溉系统利用实时数据、机器学习算法和预测分析，更好地优化灌溉用水、减少资源浪费并提高作物产量。拟议研究将通过全面的成本效益分析，评估AI智能灌溉系统的经济和环境影响。该研究将综合考虑智能灌溉系统的资本成本和运营成本，并将其与传统灌溉实践进行比较，同时考察智能灌溉系统在潜在节水、扩大农业生产和减少人力劳动方面的长期效益。这将为衡量智能灌溉系统对农场经营的影响提供背景，包括采用的机会和障碍。此外，通过正式文献综述锁定现有研究成果，并对灌溉农户进行调查以收集实地数据集，将为拟议研究者提供一个集体样本，用以衡量AI智能灌溉系统的效能、识别障碍、比较机会，并评估在不同气候和土壤条件下的表现。研究发现，实施基于AI的智能系统可带来显著且可观的效益，尤其是在水资源紧张的地区，对农场盈利能力、私人利益和环境保护均有积极影响。这项研究对于向利益相关者通报行动方案以及推动AI赋能解决方案以支持更可持续的农业实践至关重要。","AgEcon Search (University of Minnesota, USA)","2026-09-23T00:00:00Z",77,{"impact":17,"substance":18,"depth":17,"authority":138,"freshness":21,"relevant":22,"comment":236},"该研究以成本效益分析评估AI智能灌溉的经济与环境效益，方法系统、结论具参考价值，但属学术论文而非政策或产业事件，适合作为智慧农业主题的深度补充。",[238],{"name":232,"url":229},[30,31,240,241,242],"智能灌溉","节水农业","成本效益分析",{"code":244,"slug":245,"name":246,"path":247,"parent_code":40},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation",[249,250],{"code":36,"slug":37,"name":38,"path":39,"parent_code":40},{"code":58,"slug":251,"name":252,"path":253,"parent_code":151},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"status":43,"locked":14,"version":44,"method":45,"evidence":255},{"candidates":256,"agricultural_evidence":50},[257,263,267,273,277],{"code":244,"score":137,"supported":50,"title":258,"abstract":259,"tags":262},[245],[260,261,245],"灌溉","水资源",[240,241],{"code":36,"score":169,"supported":50,"title":264,"abstract":265,"tags":266},[55],[53,55],[],{"code":58,"score":133,"supported":50,"title":268,"abstract":269,"tags":272},[],[270,271,62],"机器学习","machine learning",[31],{"code":154,"score":123,"supported":50,"title":274,"abstract":275,"tags":276},[],[172,155],[],{"code":73,"score":22,"supported":14,"title":278,"abstract":279,"tags":280},[],[],[30],[282,283],"AI 智能灌溉 成本效益","AgEcon Search 智能灌溉","AI智能灌溉成本效益-3355","10.22004\u002Fag.econ.412813",{"doi":285,"openalex_id":287,"authors":288,"venue":232,"cited_by_count":113,"oa_url":229,"card":301,"direction":223,"ingested_from":120},"W7214115789",[289,291,293,295,297,299],{"name":290,"orcid":9},"Venkata Suman Jami",{"name":292,"orcid":9},"Purushotham Prasad Kalisetti",{"name":294,"orcid":9},"Sampath Dakshina Murthy A.",{"name":296,"orcid":9},"Gurunadha R.",{"name":298,"orcid":9},"Hema Mamidipaka",{"name":300,"orcid":9},"Gurrapu Omprakash",{"tldr":302,"method":303,"finding":304,"direction":223,"opportunity":305},"通过成本效益分析评估AI智能灌溉系统的经济与环境影响。","文献综述结合灌溉农户调查数据，进行成本效益分析。","AI智能灌溉在水资源紧张地区效益显著，提升利润并促进环保。","可针对不同气候土壤条件，量化AI灌溉的长期采纳障碍与推广机制。","2026-09-24T23:30:10.558578Z",{"id":308,"title":309,"url":310,"summary":311,"summary_zh":9,"content":9,"source_name":312,"source_url":9,"published_at":313,"category":314,"cover_url":9,"hotness":133,"is_selected":14,"score":315,"score_detail":316,"sources":319,"tags":321,"primary_topic":325,"secondary_topics":326,"classification":327,"search_phrases":355,"slug":358,"view_count":86,"doi":9,"paper":9,"created_at":359},2697,"杭州临平AI养鱼游出高效生态农业新路子——智骏高技AI驱动型数智无人渔业工厂夺冠","https:\u002F\u002Fwww.zj.news.cn\u002F20260915\u002Fd593fdd46dd840c6879ea6ad39a325db\u002Fc.html","杭州临平区智骏高技科技企业凭借AI驱动型数智无人渔业工厂在2026智慧农业创新大赛(雄安)鱼群智能检测设备赛道夺得全国第一名。该系统融合浙江省农科院、浙江大学技术,节水90%、节能30%—50%、主要技术环节无人化率突破95%、作业效率提升50%、幼鱼成活率提升10倍、综合经济效益高出传统模式35%以上。","新华网浙江 2026年9月15日","2026-09-15T00:00:00Z","报道",72,{"impact":17,"substance":18,"depth":169,"authority":317,"freshness":21,"relevant":22,"comment":318},12,"AI驱动无人渔业工厂以节水90%、无人化率95%等硬数据夺得全国大赛冠军，属细分领域技术突破，具行业示范价值。",[320],{"name":312,"url":310},[322,30,31,323,324],"数字乡村","设施渔业","无人渔业",{"code":36,"slug":37,"name":38,"path":39,"parent_code":40},[],{"status":43,"locked":14,"version":44,"method":45,"evidence":328},{"candidates":329,"agricultural_evidence":50},[330,337,341,346,350],{"code":331,"score":332,"supported":50,"title":333,"abstract":335,"tags":336},"D",11,[334],"渔业",[334],[],{"code":36,"score":21,"supported":50,"title":338,"abstract":339,"tags":340},[54],[],[],{"code":73,"score":342,"supported":50,"title":343,"abstract":344,"tags":345},4,[],[30],[30],{"code":58,"score":22,"supported":14,"title":347,"abstract":348,"tags":349},[],[],[31],{"code":351,"score":22,"supported":14,"title":352,"abstract":353,"tags":354},"I6",[],[],[322],[356,357],"农业人工智能 数字乡村 无人渔业 智慧农业","农业人工智能 数字乡村","农业人工智能数字乡村无人渔业智慧农业-2697","2026-09-17T00:04:36.977109Z",{"id":361,"title":362,"url":363,"summary":364,"summary_zh":365,"content":9,"source_name":366,"source_url":363,"published_at":367,"category":12,"cover_url":9,"hotness":133,"is_selected":14,"score":234,"score_detail":368,"sources":370,"tags":372,"primary_topic":374,"secondary_topics":375,"classification":381,"search_phrases":420,"slug":423,"view_count":342,"doi":424,"paper":425,"created_at":445},2659,"Application of Remote Sensing and Machine Learning in Sustainable Agriculture","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fsu18189472","Global agriculture is undergoing a period of profound transformation, driven by the need to increase food production in a context characterized by climate change, the degradation of natural resources and increasing pressure on agricultural ecosystems [...]","全球农业正经历一场深刻变革，其驱动力是在气候变化、自然资源退化以及农业生态系统压力日益增大的背景下提高粮食产量的需求。[...]","Sustainability","2026-09-16T00:00:00Z",{"impact":17,"substance":18,"depth":19,"authority":138,"freshness":49,"relevant":22,"comment":369},"发表于核心期刊的遥感与机器学习综述，方法视角新颖、时效性强，对智慧农业技术路线有参考价值，值得进入每日精选。",[371],{"name":366,"url":363},[30,31,270,53,373],"遥感",{"code":58,"slug":251,"name":252,"path":253,"parent_code":151},[376,377],{"code":36,"slug":37,"name":38,"path":39,"parent_code":40},{"code":78,"slug":378,"name":379,"path":380,"parent_code":151},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"status":43,"locked":14,"version":44,"method":45,"evidence":382},{"candidates":383,"agricultural_evidence":50},[384,388,392,397,404,411,416],{"code":58,"score":133,"supported":50,"title":385,"abstract":386,"tags":387},[271],[],[31,270],{"code":36,"score":49,"supported":50,"title":389,"abstract":390,"tags":391},[55],[],[53],{"code":78,"score":49,"supported":50,"title":393,"abstract":395,"tags":396},[394],"remote sensing",[],[373],{"code":398,"score":123,"supported":50,"title":399,"abstract":400,"tags":403},"H7",[],[401,402],"气候变化","climate change",[],{"code":405,"score":123,"supported":50,"title":406,"abstract":407,"tags":410},"J3",[],[408,409],"全球农业","global agriculture",[],{"code":66,"score":67,"supported":50,"title":412,"abstract":413,"tags":415},[],[414],"农业生态",[],{"code":73,"score":22,"supported":14,"title":417,"abstract":418,"tags":419},[],[],[30],[421,422],"农业人工智能 可持续农业 智慧农业 机器学习","农业人工智能 可持续农业","农业人工智能可持续农业智慧农业机器学习-2659","10.3390\u002Fsu18189472",{"doi":424,"openalex_id":426,"authors":427,"venue":366,"cited_by_count":113,"oa_url":363,"card":440,"direction":118,"ingested_from":120},"W7213301703",[428,431,434,437],{"name":429,"orcid":430},"Mihai Valentin Herbei","https:\u002F\u002Forcid.org\u002F0000-0002-3884-3658",{"name":432,"orcid":433},"Ana-Cornelia Badea","https:\u002F\u002Forcid.org\u002F0000-0003-4521-5403",{"name":435,"orcid":436},"Aleksandar Ristić","https:\u002F\u002Forcid.org\u002F0000-0003-0979-3345",{"name":438,"orcid":439},"Paul Sestraș","https:\u002F\u002Forcid.org\u002F0000-0002-8554-0924",{"tldr":441,"method":442,"finding":443,"direction":118,"opportunity":444},"综述遥感与机器学习在可持续农业中的应用现状与前景。","综述遥感数据与机器学习方法在农业中的应用。","遥感结合机器学习可提升农业监测与可持续管理能力。","可探索多源遥感与可解释机器学习融合，用于小农户精准决策与碳核算。","2026-09-16T23:30:28.640661Z",{"id":447,"title":448,"url":449,"summary":450,"summary_zh":451,"content":9,"source_name":452,"source_url":449,"published_at":11,"category":12,"cover_url":9,"hotness":133,"is_selected":14,"score":453,"score_detail":454,"sources":457,"tags":459,"primary_topic":463,"secondary_topics":464,"classification":466,"search_phrases":499,"slug":502,"view_count":86,"doi":503,"paper":504,"created_at":525},2537,"A Counterfactual-Enabled Agricultural Decision Support Framework for Sustainability-Aware Groundnut Yield Prediction Using Bayesian-Optimized XGBoost","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fai7090364","Sustainable agricultural planning requires predictive frameworks that can capture spatiotemporal variability, sustainability dynamics, and the potential outcomes of alternative management scenarios. The research proposes TSAFI-DT, a retrospectively validated, data-driven Digital Twin prototype integrating spatiotemporal data reconstruction, sustainability-state representation, hierarchical yield forecasting, counterfactual analysis, and scenario simulation. The framework operates on historical district-level APY observations and therefore represents a retrospective approximation of Digital Twin operation rather than a continuously synchronized cyber-physical agricultural Digital Twin. The Extended Regenerative Agriculture Index (eRAI) combines crop diversity, productivity–stability, land-use efficiency, and yield-trend information to characterize district-level sustainability states. A Bayesian-optimized XGBoost model is employed for one-step-ahead yield forecasting under temporal validation, while fixed-effects and synthetic-control analyses provide complementary associational and intervention-associated evidence. Evaluation using district-level groundnut data from India during 1997–2023 demonstrates that the proposed predictor achieves an RMSE of 0.171 t\u002Fha and R2=0.92, outperforming the evaluated baselines with statistically significant differences (p\u003C0.05). The fully adjusted fixed-effects model identifies a positive association between higher sustainability states and yield, while retrospective Digital Twin replay demonstrates close temporal agreement between predicted and observed outcomes. Model-based scenario simulations indicate predicted yield increases of up to 12.4% under the evaluated sustainability-state perturbations; these estimates represent counterfactual sensitivity rather than guaranteed causal effects. TSAFI-DT provides a reproducible framework for sustainability-aware agricultural forecasting, comparative scenario exploration, and data-driven decision support.","可持续农业规划需要能够捕捉时空变异性、可持续性动态以及替代管理情景潜在结果的预测框架。本研究提出TSAFI-DT，一个经回溯验证的数据驱动数字孪生（Digital Twin）原型，集成了时空数据重建、可持续性状态表征、分层产量预测、反事实分析和情景模拟。该框架基于历史地区级APY观测数据运行，因此代表的是数字孪生运行的回溯近似，而非持续同步的网络-物理农业数字孪生。扩展再生农业指数（Extended Regenerative Agriculture Index, eRAI）综合了作物多样性、生产力-稳定性、土地利用效率和产量趋势信息，以刻画地区级可持续性状态。采用贝叶斯优化的XGBoost模型在时间验证下进行一步超前产量预测，同时固定效应和合成控制分析提供互补的关联性证据和干预关联性证据。利用印度1997—2023年地区级花生数据进行评估，结果表明所提出的预测器实现了0.171 t\u002Fha的RMSE和R²=0.92，优于所评估的基线模型且差异具有统计学显著性（p\u003C0.05）。完全调整的固定效应模型识别出较高可持续性状态与产量之间的正相关关系，而回溯性数字孪生重放表明预测结果与观测结果在时间上高度一致。基于模型的情景模拟显示，在所评估的可持续性状态扰动下，预测产量增幅最高可达12.4%；这些估计代表的是反事实敏感性而非保证的因果效应。TSAFI-DT为可持续性感知的农业预测、比较情景探索和数据驱动决策支持提供了一个可复现的框架。","AI",76,{"impact":455,"substance":136,"depth":17,"authority":317,"freshness":21,"relevant":22,"comment":456},16,"提出融合数字孪生、反事实分析与贝叶斯优化XGBoost的花生产量预测框架，方法新颖、数据跨度长且验证充分，对可持续农业决策支持有参考价值。",[458],{"name":452,"url":449},[30,31,460,461,462],"产量预测","数字孪生","花生种植",{"code":58,"slug":251,"name":252,"path":253,"parent_code":151},[465],{"code":36,"slug":37,"name":38,"path":39,"parent_code":40},{"status":43,"locked":14,"version":44,"method":45,"evidence":467},{"candidates":468,"agricultural_evidence":50},[469,473,479,484,489,495],{"code":58,"score":317,"supported":50,"title":470,"abstract":471,"tags":472},[63],[63],[31],{"code":36,"score":49,"supported":50,"title":474,"abstract":475,"tags":478},[],[53,476,477],"再生农业","regenerative agriculture",[],{"code":73,"score":21,"supported":50,"title":480,"abstract":481,"tags":483},[],[461,482],"digital twin",[461,30],{"code":191,"score":342,"supported":50,"title":485,"abstract":486,"tags":488},[],[487],"花生",[462],{"code":490,"score":67,"supported":50,"title":491,"abstract":492,"tags":494},"J1",[],[493],"农业规划",[],{"code":78,"score":22,"supported":14,"title":496,"abstract":497,"tags":498},[],[],[460],[500,501],"农业人工智能 产量预测 数字孪生 智慧农业","农业人工智能 产量预测","农业人工智能产量预测数字孪生智慧农业-2537","10.3390\u002Fai7090364",{"doi":503,"openalex_id":505,"authors":506,"venue":452,"cited_by_count":113,"oa_url":449,"card":518,"direction":524,"ingested_from":120},"W7212812029",[507,510,513,515],{"name":508,"orcid":509},"Rekha R Nair","https:\u002F\u002Forcid.org\u002F0000-0002-7207-2877",{"name":511,"orcid":512},"Tina Babu","https:\u002F\u002Forcid.org\u002F0000-0001-7846-3679",{"name":514,"orcid":9},"Sumendra Yogarayan",{"name":516,"orcid":517},"Abdul Razak","https:\u002F\u002Forcid.org\u002F0000-0002-6108-3183",{"tldr":519,"method":520,"finding":521,"direction":522,"opportunity":523},"提出TSAFI-DT数字孪生框架，用贝叶斯优化XGBoost预测印度花生产量并做反事实情景分析。","基于1997-2023年印度县级花生产量数据，构建eRAI可持续性指数，采用贝叶","模型RMSE为0.171 t\u002Fha、R²=0.92，高可持续状态与产量正相关，情景模拟产量最高提升1","农业人工智能与决策模型","可延伸至实时物联网数据驱动的数字孪生，并验证反事实情景的因果效应与跨作物泛化能力。","数字乡村与农业信息化","2026-09-15T23:30:26.594281Z",{"id":527,"title":528,"url":529,"summary":530,"summary_zh":531,"content":9,"source_name":532,"source_url":529,"published_at":11,"category":12,"cover_url":9,"hotness":133,"is_selected":14,"score":453,"score_detail":533,"sources":535,"tags":537,"primary_topic":539,"secondary_topics":540,"classification":543,"search_phrases":609,"slug":611,"view_count":86,"doi":612,"paper":613,"created_at":623},2518,"AI-Driven Transformation in Sustainable Agriculture: A Systematic Review","https:\u002F\u002Fdoi.org\u002F10.18805\u002Fijare.a-6627","The agriculture sector is facing unprecedented pressure from a rapidly growing global population, erratic weather conditions and declining water availability. Traditional farming methods are increasingly proving insufficient in meeting the rising food demand, necessitating a shift towards technology-driven solutions. This paper provides a critical and systematic review of the application of Artificial Intelligence (AI) in agriculture, analysing its impact on productivity and sustainability, while identifying potential barriers hindering adoption. Employing a Systematic Literature Review approach, this study synthesizes recent research organized around the core domains of agricultural production - crop, soil and nutrient, water and irrigation and crop-protection (pest, disease and weed) management, alongside yield forecasting - examining how data-driven tools such as machine learning, the Internet of Things, robotics and computer vision are applied within each and prioritizing studies that reported agronomically meaningful outcomes such as yield gains and resource-use efficiency. The strongest gains identified across the review fall within three agricultural areas - crop and soil monitoring, yield forecasting and robotic field operations such as targeted detection, weeding and harvesting. The technologies enabling them, chiefly machine-learning models such as convolutional neural networks combined with field and remote sensors, have delivered high in-field precision, with reported object-detection precision of up to 95.78% in field conditions. In practical terms, this translates into more efficient use of water, fertilizer and other inputs and into improved yield and crop quality. The review concludes that prioritizing Explainable AI and interoperable farm systems is central to building farmer trust and safeguarding food security under a changing climate.","农业部门正面临着来自全球人口快速增长、天气条件不稳定以及水资源日益减少的前所未有的压力。传统耕作方法在满足不断增长的粮食需求方面日益显得力不从心，因此有必要转向技术驱动的解决方案。本文对人工智能（Artificial Intelligence, AI）在农业中的应用进行了批判性和系统性的综述，分析了其对生产力和可持续性的影响，同时识别了阻碍其采用的潜在障碍。本研究采用系统性文献综述方法，围绕农业生产的核心领域——作物、土壤与养分、水资源与灌溉、作物保护（病虫害和杂草）管理以及产量预测——对近期研究进行了综合梳理，考察了机器学习、物联网、机器人技术和计算机视觉等数据驱动工具在各领域中的应用方式，并优先关注报告了具有农学意义成果（如产量提升和资源利用效率）的研究。综述中发现的最显著收益集中在三个农业领域——作物与土壤监测、产量预测以及机器人田间作业（如靶向检测、除草和收获）。实现这些收益的技术，主要是卷积神经网络等机器学习模型与田间及遥感传感器的结合，已在田间条件下实现了高精度，所报告的目标检测精度在田间条件下高达95.78%。在实际应用中，这意味着水、肥料及其他投入品的更高效利用，以及产量和作物品质的提升。综述得出结论：优先发展可解释人工智能（Explainable AI）和可互操作的农场系统，对于在气候变化背景下建立农民信任和保障粮食安全至关重要。","Indian Journal of Agricultural Research",{"impact":17,"substance":18,"depth":19,"authority":138,"freshness":21,"relevant":22,"comment":534},"系统综述梳理AI在作物土壤监测、产量预测与机器人田间作业中的应用成效与推广障碍，结论扎实，对智慧农业方向有参考价值。",[536],{"name":532,"url":529},[30,31,460,538,143],"可解释AI",{"code":58,"slug":251,"name":252,"path":253,"parent_code":151},[541,542],{"code":244,"slug":245,"name":246,"path":247,"parent_code":40},{"code":36,"slug":37,"name":38,"path":39,"parent_code":40},{"status":43,"locked":14,"version":44,"method":45,"evidence":544},{"candidates":545,"agricultural_evidence":50},[546,553,557,561,568,575,579,585,589,595,601,605],{"code":58,"score":332,"supported":50,"title":547,"abstract":548,"tags":552},[],[549,270,550,271,62,551],"目标检测","计算机视觉","computer vision",[31,538],{"code":244,"score":49,"supported":50,"title":554,"abstract":555,"tags":556},[],[260,261,245],[],{"code":36,"score":21,"supported":50,"title":558,"abstract":559,"tags":560},[55],[],[],{"code":562,"score":123,"supported":50,"title":563,"abstract":564,"tags":567},"A8",[],[565,566],"粮食安全","food security",[],{"code":569,"score":123,"supported":50,"title":570,"abstract":571,"tags":574},"F3",[],[572,573],"肥料","fertilizer",[],{"code":154,"score":123,"supported":50,"title":576,"abstract":577,"tags":578},[],[172,155],[],{"code":147,"score":123,"supported":50,"title":580,"abstract":581,"tags":584},[],[582,583],"物联网","internet of things",[],{"code":78,"score":342,"supported":50,"title":586,"abstract":587,"tags":588},[],[373],[460],{"code":590,"score":67,"supported":50,"title":591,"abstract":592,"tags":594},"A7",[],[593],"间作",[],{"code":596,"score":67,"supported":50,"title":597,"abstract":598,"tags":600},"F4",[],[599],"除草",[],{"code":398,"score":67,"supported":50,"title":602,"abstract":603,"tags":604},[],[401],[],{"code":73,"score":86,"supported":14,"title":606,"abstract":607,"tags":608},[],[],[30,143],[610,501],"农业人工智能 产量预测 智慧农业 精准农业","农业人工智能产量预测智慧农业精准农业-2518","10.18805\u002Fijare.a-6627",{"doi":612,"openalex_id":614,"authors":615,"venue":532,"cited_by_count":113,"oa_url":529,"card":618,"direction":223,"ingested_from":120},"W7212616413",[616],{"name":617,"orcid":9},"Manish Maan",{"tldr":619,"method":620,"finding":621,"direction":522,"opportunity":622},"系统综述AI在可持续农业中的应用，分析生产力与可持续性影响及推广障碍。","系统文献综述，围绕作物、土壤、水、植保和产量预测梳理ML、IoT、机器人、计算机","作物与土壤监测、产量预测和机器人田间作业收益最大，检测精度达95.78%，需可解释AI与互操作系统。","可解释AI与互操作农场系统在农户信任和气候适应中的作用尚缺实证，是值得深入的研究空白。","2026-09-15T23:30:13.705421Z"]