[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3350":3,"related-3350":67},{"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":23,"tags":25,"search_phrases":31,"slug":34,"view_count":35,"doi":36,"paper":37,"created_at":66},3350,"Precision agriculture for water saving: The case of processing tomato and table grape","https:\u002F\u002Fdoi.org\u002F10.1002\u002Fppp3.70253","Societal Impact Statement Agriculture faces increasing pressure to produce high‐quality food while reducing water use under intensifying climate change and water scarcity. This study compared sensor‐based precision irrigation, drone monitoring, and near‐infrared spectroscopy with conventional farmer management in table grape and processing tomato production systems. Precision agriculture improved crop water status, photosynthetic performance, yield, and marketable quality, while reducing irrigation by 8%–15% in table grape and approximately 15% in processing tomato. These findings support wider adoption of integrated digital tools through targeted incentives and farmer training, offering a scalable strategy to strengthen long‐term water security, farm resilience, and sustainable food production globally. Summary The study aimed to evaluate the effectiveness of precision agriculture (PA) technologies, specifically sensor‐based irrigation and drones, on table grape (cv. Allison ) and processing tomato (cv. Taylor ) production compared to traditional farming methods (control). The research involved field experiments in two locations in the Puglia region, southeastern Italy, in 2023 and 2024. For table grape and processing tomato, two different irrigation managements (PA vs. Control\u002FFarmer) were compared, monitoring physiological, morphological, yield, and quality parameters. For processing tomato, drone imagery, and ground measurements were also conducted. Predictive models for fruit ripeness and quality traits of both species were also developed using near‐infrared (NIR) spectroscopy data, preprocessing techniques, and PLS regression. For table grape, the PA vines showed greater water potential stability, more uniform stomatal conductance, and higher chlorophyll content, resulting in higher and more consistent production with 8%–15% water savings. For processing tomato, PA management improved plant vegetative indicators, total and marketable yields, and reduced water consumption by approximately 15%. Three out of four calibrated models using NIR showed predictive performance suitable for future practical applications. The findings highlight the potential of PA to improve water resource utilization, crop development, yield, and fruit quality, contributing to more sustainable agricultural systems in regions facing water scarcity and climate change. Moreover, this work demonstrates how sensor‐driven irrigation of the two crops (with more equilibrated plants) directly influenced high‐accuracy predictive modelling. The integration of environmental sensors (for irrigation) and optical sensors (for quality) will represent the core of modern smart farming.","社会影响声明 在气候变化加剧和水资源短缺的背景下，农业面临着在生产高质量食品的同时减少用水量的日益增大的压力。本研究在鲜食葡萄和加工番茄生产系统中，将基于传感器的精准灌溉、无人机监测和近红外光谱技术与传统农户管理进行了比较。精准农业改善了作物水分状况、光合性能、产量和商品品质，同时在鲜食葡萄中减少了8%–15%的灌溉用水，在加工番茄中减少了约15%。这些发现支持通过有针对性的激励措施和农户培训来更广泛地采用集成数字工具，为增强全球长期水资源安全、农场韧性和可持续食品生产提供了一种可推广的策略。摘要 本研究旨在评估精准农业（PA）技术，特别是基于传感器的灌溉和无人机，在鲜食葡萄（品种Allison）和加工番茄（品种Taylor）生产中相较于传统耕作方法（对照）的有效性。研究于2023年和2024年在意大利东南部普利亚地区的两个地点进行了田间试验。对于鲜食葡萄和加工番茄，比较了两种不同的灌溉管理方式（PA vs. 对照\u002F农户），监测了生理、形态、产量和品质参数。对于加工番茄，还进行了无人机影像采集和地面测量。研究还利用近红外（NIR）光谱数据、预处理技术和PLS回归，开发了两种作物果实成熟度和品质性状的预测模型。对于鲜食葡萄，PA处理的葡萄藤表现出更强的水势稳定性、更均匀的气孔导度和更高的叶绿素含量，从而实现了更高且更稳定的产量，并节约了8%–15%的用水。对于加工番茄，PA管理改善了植株营养指标、总产量和商品产量，并减少了约15%的耗水量。使用NIR校准的四个模型中有三个显示出适合未来实际应用的预测性能。研究结果凸显了PA在改善水资源利用、作物发育、产量和果实品质方面的潜力，有助于在水资源短缺和气候变化地区构建更可持续的农业系统。此外，本研究还表明，两种作物的传感器驱动灌溉（使植株更加均衡）……",null,"Plants People Planet","2026-09-23T00:00:00Z","论文",10,false,81,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,22,14,9,1,"意大利田间试验证实传感器与无人机精准灌溉可节水8%–15%并提升产量品质，对缺水地区智慧农业推广有实证参考价值。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","农业遥感","精准灌溉","节水农业","近红外光谱",[32,33],"加工番茄 精准灌溉 节水","鲜食葡萄 无人机 灌溉","加工番茄精准灌溉节水-3350",0,"10.1002\u002Fppp3.70253",{"doi":36,"openalex_id":38,"authors":39,"venue":10,"cited_by_count":35,"oa_url":6,"card":59,"direction":63,"ingested_from":65},"W7214079746",[40,43,46,48,50,53,56],{"name":41,"orcid":42},"Giuseppe Ferrara","https:\u002F\u002Forcid.org\u002F0000-0002-2129-6723",{"name":44,"orcid":45},"Alessandro Pesole","https:\u002F\u002Forcid.org\u002F0009-0007-1292-4517",{"name":47,"orcid":9},"Rita De Marco",{"name":49,"orcid":9},"Sara Bisceglie",{"name":51,"orcid":52},"Giovanni Popeo","https:\u002F\u002Forcid.org\u002F0009-0003-6796-3880",{"name":54,"orcid":55},"Simone Pascuzzi","https:\u002F\u002Forcid.org\u002F0000-0002-6699-3485",{"name":57,"orcid":58},"Luigi Tedone","https:\u002F\u002Forcid.org\u002F0000-0003-4398-3820",{"tldr":60,"method":61,"finding":62,"direction":63,"opportunity":64},"对比传感器精准灌溉、无人机与近红外光谱和传统管理在葡萄与番茄上的节水增产效果。","意大利普利亚两年田间试验，传感器灌溉、无人机监测与NIR光谱PLS建模。","精准农业改善水分与光合状态，葡萄节水8%–15%、番茄约15%，并提高产量与品质。","智慧农业 \u002F 农业物联网","可探索多源传感与NIR模型跨品种跨区域迁移，并量化农户采纳激励与培训的长期节水效益。","openalex","2026-09-24T23:30:10.070697Z",{"total":68,"page":21,"page_size":68,"items":69},6,[70,110,147,189,213,277],{"id":71,"title":72,"url":73,"summary":74,"summary_zh":75,"content":9,"source_name":76,"source_url":73,"published_at":77,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":78,"score_detail":79,"sources":84,"tags":86,"search_phrases":89,"slug":92,"view_count":35,"doi":93,"paper":94,"created_at":109},3184,"Soil Compaction and Irrigation Management: Implications of Soil Hydraulic Changes for Precision Agriculture","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fagronomy16181853","Soil compaction is one of the main forms of physical soil degradation affecting agricultural productivity. Although its effects on soil structure and crop growth have been extensively investigated, their implications for irrigation management have received little attention. This review synthesises current knowledge on how soil compaction modifies the hydraulic functioning of agricultural soils and discusses the consequences for irrigation scheduling and precision irrigation. The available evidence shows that soil compaction reduces water infiltration and saturated hydraulic conductivity while altering soil water retention and plant-available water. Surface sealing and crust formation can further restrict infiltration, particularly when bare soil is exposed to high-energy rainfall or sprinkler irrigation. These changes modify water movement, redistribution and storage within the soil profile, affecting root development, plant physiological responses and crop productivity. The magnitude of these effects depends on soil texture, soil water status, crop species and environmental conditions. Conventional irrigation scheduling generally assumes stable soil hydraulic properties and may not adequately represent compacted soils. Recent advances in soil moisture sensing, crop modelling, remote sensing and decision support systems provide new opportunities to incorporate soil structural variability into irrigation management. Combining precision irrigation with preventive traffic management and practices that enhance soil structural and biological resilience may improve irrigation efficiency, optimise water use and enhance the long-term sustainability of irrigated agricultural systems.","土壤压实是影响农业生产力的土壤物理退化的主要形式之一。尽管其对土壤结构和作物生长的影响已得到广泛研究，但其对灌溉管理的影响却鲜受关注。本文综述了关于土壤压实如何改变农业土壤水力功能的现有知识，并探讨了其对灌溉制度和精准灌溉的影响。现有证据表明，土壤压实降低了水分入渗和饱和导水率，同时改变了土壤持水性和植物有效水量。地表封闭和结皮的形成会进一步限制入渗，尤其是当裸露土壤暴露于高能量降雨或喷灌时。这些变化改变了土壤剖面中水分的运动、再分布和储存，进而影响根系发育、植物生理响应和作物生产力。这些影响的程度取决于土壤质地、土壤水分状况、作物种类和环境条件。传统灌溉制度通常假设土壤水力性质稳定，可能无法充分反映压实土壤的情况。土壤水分传感、作物建模、遥感和决策支持系统方面的最新进展，为将土壤结构变异性纳入灌溉管理提供了新的机遇。将精准灌溉与预防性机械作业管理及增强土壤结构和生物韧性的措施相结合，有望提高灌溉效率、优化水资源利用，并增强灌溉农业系统的长期可持续性。","Agronomy","2026-09-20T00:00:00Z",78,{"impact":17,"substance":80,"depth":81,"authority":19,"freshness":82,"relevant":21,"comment":83},21,17,8,"系统综述土壤压实对土壤水力特性与灌溉管理的影响，提出将土壤结构变异纳入精准灌溉决策，对智慧农业与节水灌溉有参考价值。",[85],{"name":76,"url":73},[26,27,28,87,88],"土壤墒情监测","土壤压实",[90,91],"土壤压实 精准灌溉","土壤水力特性 灌溉管理","土壤压实精准灌溉-3184","10.3390\u002Fagronomy16181853",{"doi":93,"openalex_id":95,"authors":96,"venue":76,"cited_by_count":35,"oa_url":73,"card":103,"direction":108,"ingested_from":65},"W7213866178",[97,100],{"name":98,"orcid":99},"Alessia Cogato","https:\u002F\u002Forcid.org\u002F0000-0001-8354-7324",{"name":101,"orcid":102},"Lucia Bortolini","https:\u002F\u002Forcid.org\u002F0000-0001-6863-4542",{"tldr":104,"method":105,"finding":106,"direction":63,"opportunity":107},"综述土壤压实改变水力特性对灌溉管理的影响，提出将结构变异纳入精准灌溉。","文献综述，整合土壤水力、传感、作物模型与遥感技术。","压实降低入渗与导水率，传统灌溉调度假设不成立，需纳入结构变异。","可开发融合土壤压实空间变异与实时传感的精准灌溉决策模型，填补结构退化下的调度空白。","农业遥感与作物表型","2026-09-22T23:30:25.358657Z",{"id":111,"title":112,"url":113,"summary":114,"summary_zh":115,"content":9,"source_name":116,"source_url":113,"published_at":117,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":118,"score_detail":119,"sources":124,"tags":126,"search_phrases":129,"slug":132,"view_count":35,"doi":133,"paper":134,"created_at":146},1986,"AI-Enabled Precision Irrigation for Water-Efficient and Sustainable Agriculture","https:\u002F\u002Fdoi.org\u002F10.22214\u002Fijraset.2026.84732","The diminishing availability of water for agriculture, sub optimal irrigation methods, climate uncertainty and rising food demands have led to a demand for intelligent and sustainable solutions to water management problems. This research introduces an AI Driven Arduino and Raspberry Pi irrigation system that maximizes agricultural water use by real time sensing, intelligent decision making, and automated irrigation control. The proposed system comprises soil moisture, soil temperature, soil relative humidity, rainfall, and water level sensors to continuously monitor the field conditions. Arduino is used for interfacing with sensors and actuators, Raspberry Pi for data processing, AI based analysis, monitoring and data logging. The sensor data collected is preprocessed and analyzed with an AI\u002FML model to calculate the irrigation needs depending on soil and environmental conditions. The water pump or irrigation valve is automatically turned on or off with a relay mechanism, based on the decision generated. The experimental results show the effectiveness of the proposed method in reducing unnecessary irrigation and the utilization of the resources. With a representative prototype set of results, the AI model was able to identify 94.2% of the events correctly, whereas the automated system was able to detect around 98.6% of events, with a mean response time of 2.4 seconds. About 35% less water was used than traditional irrigation methods and still kept the soil moisture level appropriate. The proposed system uses minimal manual intervention, it facilitates real time monitoring and offers adaptive irrigation management. The research shows the potential of the integration of artificial intelligence with low cost embedded platforms to create scalable, water efficient and sustainable smart agriculture solutions.","农业可用水资源的日益减少、灌溉方法的不尽完善、气候的不确定性以及不断增长的粮食需求，催生了对水资源管理问题智能化、可持续解决方案的需求。本研究提出了一种基于人工智能驱动的Arduino与Raspberry Pi灌溉系统，通过实时感知、智能决策和自动化灌溉控制，最大化农业用水效率。该系统包含土壤湿度、土壤温度、土壤相对湿度、降雨量及水位传感器，以持续监测田间状况。其中，Arduino用于连接传感器和执行器，Raspberry Pi则负责数据处理、基于人工智能的分析、监控及数据记录。采集到的传感器数据经过预处理，并利用AI\u002FML模型进行分析，根据土壤及环境条件计算灌溉需求。基于生成的决策，通过继电器机制自动开启或关闭水泵或灌溉阀门。实验结果表明，该方法在减少不必要灌溉及资源利用方面具有显著效果。以代表性原型测试结果为例，AI模型能够正确识别94.2%的事件，而自动化系统可检测约98.6%的事件，平均响应时间为2.4秒。与传统灌溉方法相比，用水量减少了约35%，同时仍能保持适宜的土壤湿度水平。该系统所需人工干预极少，支持实时监控，并提供适应性灌溉管理。本研究展示了将人工智能与低成本嵌入式平台相结合，以构建可扩展、节水且可持续的智慧农业解决方案的潜力。","International Journal for Research in Applied Science and Engineering Technology","2026-09-08T00:00:00Z",66,{"impact":17,"substance":120,"depth":121,"authority":13,"freshness":122,"relevant":21,"comment":123},20,16,2,"研究展示AI与低成本硬件结合的精准灌溉系统，节水35%，具实践价值，但发表于普通期刊且时效性低。",[125],{"name":116,"url":113},[26,127,128,28,29],"农业人工智能","传感器",[130,131],"农业人工智能 智慧农业 精准灌溉 节水农业","农业人工智能 智慧农业","农业人工智能智慧农业精准灌溉节水农业-1986","10.22214\u002Fijraset.2026.84732",{"doi":133,"openalex_id":135,"authors":136,"venue":116,"cited_by_count":35,"oa_url":113,"card":141,"direction":63,"ingested_from":65},"W7211968732",[137,139],{"name":138,"orcid":9},"Priyadharshini V",{"name":140,"orcid":9},"Hemapriya",{"tldr":142,"method":143,"finding":144,"direction":63,"opportunity":145},"提出AI驱动的Arduino和树莓派灌溉系统，实现实时监测与自动灌溉，节水35%。","集成多种传感器，Arduino采集，树莓派运行AI\u002FML模型决策，继电器控制灌溉","系统事件识别准确率94.2%，自动检测98.6%，响应2.4秒，节水35%且维持土壤湿度。","可探索AI模型在不同作物和气候下的泛化性，以及多节点协同和能耗优化，提升系统可扩展性。","2026-09-09T23:30:08.891805Z",{"id":148,"title":149,"url":150,"summary":151,"summary_zh":152,"content":9,"source_name":153,"source_url":150,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":154,"score_detail":155,"sources":158,"tags":160,"search_phrases":163,"slug":166,"view_count":35,"doi":167,"paper":168,"created_at":188},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)",77,{"impact":17,"substance":120,"depth":17,"authority":156,"freshness":82,"relevant":21,"comment":157},13,"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。",[159],{"name":153,"url":150},[26,127,161,29,162],"智能灌溉","成本效益分析",[164,165],"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":167,"openalex_id":169,"authors":170,"venue":153,"cited_by_count":35,"oa_url":150,"card":183,"direction":63,"ingested_from":65},"W7214115789",[171,173,175,177,179,181],{"name":172,"orcid":9},"Venkata Suman Jami",{"name":174,"orcid":9},"Purushotham Prasad Kalisetti",{"name":176,"orcid":9},"Sampath Dakshina Murthy A.",{"name":178,"orcid":9},"Gurunadha R.",{"name":180,"orcid":9},"Hema Mamidipaka",{"name":182,"orcid":9},"Gurrapu Omprakash",{"tldr":184,"method":185,"finding":186,"direction":63,"opportunity":187},"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":190,"title":191,"url":192,"summary":193,"summary_zh":9,"content":194,"source_name":195,"source_url":9,"published_at":196,"category":197,"cover_url":9,"hotness":13,"is_selected":14,"score":198,"score_detail":199,"sources":201,"tags":203,"search_phrases":208,"slug":211,"view_count":35,"doi":9,"paper":9,"created_at":212},3297,"山区油菜智能化监测成果在十堰国家农业科技园区落地推广——天空地一体化监测技术与山区油菜大数据平台","https:\u002F\u002Fwww.iarrp.cn\u002Fysdt\u002Fkydt\u002Fd6e5d598bf9f4f998f7f08d7c811699c.htm","近日，由中国农业科学院农业资源与农业区划研究所牵头实施的国家重点研发计划课题山区油菜产业智能化关键技术和机械装备研究示范成果应用推广会在十堰国家农业科技园区召开。项目首席科学家梅德圣研究员及行业专家，十堰市农技中心及下属各县农技中心、长阳县等秦巴山区县基层农技推广部门、区域内油菜种植大户代表参会。课题组精准锚定秦巴山区油菜生产实际需求，构建天空地一体化监测技术与山区油菜大数据平台，能够对区域气候环境、油菜长势、菌核病发生态势开展动态监测与智能研判；研发适配山地小地块、不规则地块的油菜播种、田间管护、无人植保等专用系统装备。","近日，由中国农业科学院农业资源与农业区划研究所牵头实施的国家重点研发计划课题“山区油菜产业智能化关键技术和机械装备研究示范”成果应用推广会在十堰国家农业科技园区顺利召开。本次会议聚焦山区油菜产业智能化转型痛点难点，集中交付项目核心科技成果，开展大数据平台实操培训，搭建成果落地、技术普及、产业赋能的产学研交流平台，助力秦巴山区油菜产业摆脱传统生产模式，全面迈向数字化、智能化、高效化高质量发展新阶段。项目首席科学家梅德圣研究员等行业专家，十堰市农技中心及下属各县农技中心、长阳县等秦巴山市区县基层农技推广部门，区域内油菜种植大户用户代表参会参训。\n\n我国秦巴等山区油菜种植体量较大，是保障区域油料供给、带动农民增收、助推乡村产业振兴的特色优势产业。但受山地、坡地为主的特殊地形制约，区域内耕地零散、地块不规则，长期存在机械化作业难、生产管理粗放、长势监测滞后、病虫害防控被动、生产数据缺失等突出问题，生产成本偏高、产量稳定性不足，严重制约山区油菜产业提质增效与绿色可持续发展。课题组精准锚定秦巴山区油菜生产实际需求，依托高水平产学研协同攻关体系，针对性开展智能技术改良、专用装备适配、数字体系构建与本地化示范推广，形成一套适配山地生产场景的成熟技术成果与应用模式。\n\n会上，课题组详细介绍了课题研发成果、技术创新亮点与本地化示范成效，顺利完成系列核心成果集中交付。中国农业科学院农业资源与农业区划研究所宋茜研究员表示，课题创新构建天空地一体化监测技术与山区油菜大数据平台，能够对区域气候环境、油菜长势、菌核病发生态势开展动态监测与智能研判，可为田间科学施肥、绿色防控、适时收获、精准植保提供精准数据支撑，改变山区油菜传统凭经验、粗放式的生产管理模式。同时，课题研发适配山地小地块、不规则地块的油菜播种、田间管护、无人植保等专用系统装备，有效破解山地农机作业难度大、精度低、损耗高的产业难题，全面提升山区油菜机械化、标准化作业水平。\n\n为切实打通科技成果落地“最后一公里”，会议同步开展山区油菜大数据平台专项实操培训。项目技术专家围绕平台功能架构、操作流程、数据填报、智能分析、后台运维等核心内容开展系统讲解，结合秦巴山区油菜生产真实场景进行实操演示，现场解答基层农技人员、种植大户在平台使用、技术落地、田间实操中的各类问题。培训内容贴合一线生产需求、实操性极强，有效提升了基层技术人员数字化应用能力，为成果规模化落地推广夯实人才基础。\n\n据了解，课题以十堰柳陂镇示范基地为核心辐射支点，持续拓展示范应用版图，辐射带动五峰、安阳、大柳等多个乡镇开展规模化示范种植，常态化开展技术培训、智能设备实操演示、田间观摩交流、入户指导等配套服务，不断优化完善技术应用模式，成功探索形成一套适配秦巴山区、可复制、可推广的智慧油菜绿色高效生产模式。通过智能化技术与装备的落地应用，有效实现油菜稳产提质、生产降本增效，显著提升山区油菜产业标准化、智能化、现代化发展水平。\n\n与会代表一致认为，该课题成果针对性解决了山区油菜生产的系列瓶颈问题，构建了适配山地特征的智慧油菜生产体系，为山区油料产业高质量发展提供了优秀示范样板。下一步，课题各方将持续深化产学研协同合作，依托十堰国家农业科技园区平台优势，持续迭代优化大数据平台功能与智能装备性能，持续扩大示范推广覆盖面，常态化开展基层技术赋能服务，全力推动智慧油菜生产技术在秦巴武陵山区落地普及、提质增效，持续助力区域油料稳产保供、农业绿色发展、农民增收致富与乡村全面振兴。","中国农业科学院农业资源与农业区划研究所 2026年09月18日","2026-09-18T00:00:00Z","报道",70,{"impact":17,"substance":17,"depth":19,"authority":19,"freshness":68,"relevant":21,"comment":200},"国家重点研发计划课题成果在十堰国家农业科技园区落地推广，天空地一体化监测与山区油菜大数据平台具备实质技术内容与示范价值，值得进入每日精选。",[202],{"name":195,"url":192},[26,27,204,205,206,207],"成果转化","油菜","天空地一体化","大数据平台",[209,210],"十堰 山区油菜 智能化监测","天空地一体化 油菜大数据平台","十堰山区油菜智能化监测-3297","2026-09-24T00:03:59.027363Z",{"id":214,"title":215,"url":216,"summary":217,"summary_zh":218,"content":9,"source_name":219,"source_url":216,"published_at":220,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":221,"score_detail":222,"sources":224,"tags":226,"search_phrases":230,"slug":233,"view_count":35,"doi":234,"paper":235,"created_at":276},3278,"Research on the optimal modeling path for inversion of Pb content in rice leaves based on hyperspectral data of ground objects and machine learning and cross-scale remote sensing monitoring","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs10661-026-15959-x","Research on the optimal modeling path for inversion of Pb content in rice leaves based on hyperspectral data of ground objects and machine learning and cross-scale remote sensing monitoring。Environmental Monitoring and Assessment","基于地物高光谱数据与机器学习的稻叶铅含量反演最优建模路径及跨尺度遥感监测研究。环境监测与评估","Environmental Monitoring and Assessment","2026-09-22T00:00:00Z",64,{"impact":82,"substance":17,"depth":121,"authority":156,"freshness":20,"relevant":21,"comment":223},"基于地物高光谱与机器学习的水稻叶片铅含量反演建模研究，方法有创新但属细分领域学术进展，公共影响有限。",[225],{"name":219,"url":216},[26,27,227,228,229],"水稻","机器学习","高光谱遥感",[231,232],"水稻叶片 铅含量 高光谱 反演","稻米 重金属 遥感 监测","水稻叶片铅含量高光谱反演-3278","10.1007\u002Fs10661-026-15959-x",{"doi":234,"openalex_id":236,"authors":237,"venue":219,"cited_by_count":35,"oa_url":9,"card":271,"direction":108,"ingested_from":65},"W7214027889",[238,241,244,247,250,253,255,257,260,263,265,267,269],{"name":239,"orcid":240},"Zhenlong Zhang","https:\u002F\u002Forcid.org\u002F0009-0008-2354-9123",{"name":242,"orcid":243},"Zhe Wang","https:\u002F\u002Forcid.org\u002F0000-0003-1266-7251",{"name":245,"orcid":246},"Chengxia Wang","https:\u002F\u002Forcid.org\u002F0009-0001-0820-2462",{"name":248,"orcid":249},"Wenxue Lin","https:\u002F\u002Forcid.org\u002F0000-0002-8245-9063",{"name":251,"orcid":252},"Jingyan Zhang","https:\u002F\u002Forcid.org\u002F0009-0004-4567-5316",{"name":254,"orcid":9},"Ying Luo",{"name":256,"orcid":9},"Jiaqian Zhang",{"name":258,"orcid":259},"Kai Ye","https:\u002F\u002Forcid.org\u002F0000-0002-2851-6741",{"name":261,"orcid":262},"Yiming Chen","https:\u002F\u002Forcid.org\u002F0000-0002-8121-3109",{"name":264,"orcid":9},"Chaoliang Peng",{"name":266,"orcid":9},"Duan Tian",{"name":268,"orcid":9},"Weihao Wang",{"name":270,"orcid":9},"Jiaxin Liu",{"tldr":272,"method":273,"finding":274,"direction":108,"opportunity":275},"研究基于地面高光谱与机器学习反演水稻叶片铅含量，并探索跨尺度遥感监测的最优建模路径。","地面高光谱数据结合机器学习建模，开展跨尺度遥感监测。","明确了水稻叶片铅含量反演的最优建模路径，实现跨尺度遥感监测。","可探索多尺度遥感数据融合与迁移学习，提升重金属胁迫反演的普适性与精度。","2026-09-23T23:30:19.291361Z",{"id":278,"title":279,"url":280,"summary":281,"summary_zh":282,"content":9,"source_name":116,"source_url":280,"published_at":283,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":284,"score_detail":285,"sources":287,"tags":289,"search_phrases":292,"slug":295,"view_count":35,"doi":296,"paper":297,"created_at":315},3160,"Determination of Water Stress in Okra Plants Using Canopy Temperature Measurement by Infrared Thermometer","https:\u002F\u002Fdoi.org\u002F10.22214\u002Fijraset.2026.84906","Water scarcity represents one of the most severe constraints on agricultural productivity in semi-arid and waterstressed cultivation zones. In traditional vegetable cultivation, farmers predominantly rely on visual symptoms of wilting to detect water deficit stress, which typically manifest only after substantial physiological damage, cellular dehydration, and irreversible yield loss have occurred. This study presents a non-destructive, precision agriculture framework for the early detection and classification of water stress in okra (Abelmoschus esculentus) using handheld infrared thermometry coupled with an ensemble Random Forest machine learning classifier. A primary field dataset comprising 100 observations was acquired under variable diurnal meteorological conditions in Jammu, India. The recorded and derived parameters encompassed canopy surface temperature (Tc), wet-bulb reference temperature (Twet), dry-bulb reference temperature (Tdry), ambient air temperature (Ta), relative humidity (RH), air saturation vapor pressure (SVPair), actual vapor pressure (AVP), leaf saturation vapor pressure (SVPleaf), leaf vapor pressure deficit (VPDleaf), canopy-air thermal differential (Tc – Ta), and the Crop Water Stress Index (CWSI). Using an empirical CWSI threshold of 0.30, samples were categorized into non-stressed (CWSI ≤ 0.30) and stressed (CWSI > 0.30) physiological states. The dataset was partitioned into an 80:20 training and testing split (80 training samples, 20 testing samples). The trained Random Forest classifier achieved an overall classification accuracy of 95.0% (19\u002F20 correct classifications) on the unseen test set. For the non-stressed class, the model demonstrated a precision of 1.00, recall of 0.92, and an F1-score of 0.96 (support = 12). For the water-stressed class, the model yielded a precision of 0.89, recall of 1.00, and an F1- score of 0.94 (support = 8), with zero false negatives (FN = 0), ensuring that no stressed crops were missed. Gini feature importance analysis revealed that dry-bulb reference temperature (Tdry, score = 0.187), leaf saturation vapor pressure (SVPleaf, score = 0.172), wet-bulb reference temperature (Twet, score = 0.160), leaf vapor pressure deficit (VPDleaf, score = 0.112), and Tc – Ta (score = 0.101) were the primary drivers governing classification. The findings confirm that coupling thermal radiometry with psychrometric feature engineering and ensemble learning provides a reliable, non-contact diagnostic mechanism for precision irrigation scheduling in smallholder horticulture.","水资源短缺是半干旱及水分胁迫耕作区农业生产力的最严重制约因素之一。在传统蔬菜种植中，农民主要依赖萎蔫的视觉症状来检测水分亏缺胁迫，而这类症状通常只有在发生大量生理损伤、细胞脱水及不可逆产量损失之后才会显现。本研究提出了一种非破坏性精准农业框架，利用手持式红外测温仪结合集成随机森林机器学习分类器，实现对秋葵（Abelmoschus esculentus）水分胁迫的早期检测与分类。在印度查谟地区多变的气象日变化条件下，采集了包含100个观测值的初始田间数据集。记录及衍生的参数包括冠层表面温度（Tc）、湿球参考温度（Twet）、干球参考温度（Tdry）、环境气温（Ta）、相对湿度（RH）、空气饱和水汽压（SVPair）、实际水汽压（AVP）、叶片饱和水汽压（SVPleaf）、叶片水汽压亏缺（VPDleaf）、冠层-空气温差（Tc – Ta）以及作物水分胁迫指数（CWSI）。采用经验性CWSI阈值0.30，将样本划分为非胁迫（CWSI ≤ 0.30）和胁迫（CWSI > 0.30）生理状态。数据集按80:20划分为训练集和测试集（80个训练样本，20个测试样本）。训练后的随机森林分类器在未见测试集上实现了95.0%的总体分类准确率（20个中正确分类19个）。对于非胁迫类别，模型精确率为1.00，召回率为0.92，F1分数为0.96（支持样本数=12）。对于水分胁迫类别，模型精确率为0.89，召回率为1.00，F1分数为0.94（支持样本数=8），假阴性为零（FN = 0），确保无胁迫作物被漏检。基尼特征重要性分析表明，干球参考温度（Tdry，得分=0.187）、叶片饱和水汽压（SVPleaf，得分=0.172）、湿球参考温度（Twet，得分=0.160）、叶片水汽压亏缺（VPDleaf，得分=0.112）以及Tc – Ta（得分=0.101）是主导分类的主要驱动因素。研究结果证实，将热辐射测量与湿度特征工程及集成学习相结合，可提供一种可靠的非接触式诊断机制","2026-09-21T00:00:00Z",63,{"impact":82,"substance":120,"depth":121,"authority":13,"freshness":20,"relevant":21,"comment":286},"印度查谟地区小样本田间研究，红外测温结合随机森林实现秋葵水分胁迫早期识别，方法可迁移至小农精准灌溉，但样本量仅100条、地域局限，产业影响有限。",[288],{"name":116,"url":280},[26,228,290,28,291],"作物水分胁迫","红外测温",[293,294],"秋葵 冠层温度 水分胁迫","红外测温 作物水分胁迫指数","秋葵冠层温度水分胁迫-3160","10.22214\u002Fijraset.2026.84906",{"doi":296,"openalex_id":298,"authors":299,"venue":116,"cited_by_count":35,"oa_url":280,"card":310,"direction":63,"ingested_from":65},"W7213933715",[300,302,304,306,308],{"name":301,"orcid":9},"Muneeb Ajmer",{"name":303,"orcid":9},"Sayam Prajapati",{"name":305,"orcid":9},"Somil Narang",{"name":307,"orcid":9},"Rudraksh Sharma",{"name":309,"orcid":9},"Saksham Khajuria",{"tldr":311,"method":312,"finding":313,"direction":108,"opportunity":314},"用红外测温仪测秋葵冠层温度并结合随机森林，实现水分胁迫的早期无损分类。","手持红外测温获取冠层温度与气象参数，计算CWSI，用随机森林分类。","随机森林测试集准确率95%，无漏判胁迫样本，Tdry、SVPleaf等为关键特征。","可扩展到多作物、多生育期及无人机热红外尺度，验证CWSI阈值与模型迁移性。","2026-09-22T23:30:11.117810Z"]