[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3332":3,"related-3332":58},{"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":57},3332,"Solar-powered irrigation adoption in South Asia: opportunities, challenges, and policy implications","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1892056","Solar irrigation technologies offer potential sustainable and cost-effective solutions, though their environmental sustainability depends critically on institutional and regulatory frameworks to address social and environmental challenges in regions with vast solar and agricultural potential such as south Asia. This review paper evaluates (1) technological innovations, (2) opportunities and fundamental challenges and policy frameworks, and (3) socio-economic, and environment impacts on south Asia countries including Bangladesh, India, Nepal, and Pakistan. The review results indicated that solar irrigation systems demonstrated advantage in reducing operational costs compared to diesel alternatives and contributed significantly to carbon abatement. In addition, these systems provided consistent water supply independent of erratic grid power and compatible with irrigation systems for efficiency. Surplus electricity generated by solar irrigation systems can be sold back to the grid, increasing farmer income. Reliable water access can drive a transition from subsistence farming to cash crop cultivation. However, solar irrigation system’s performance is weather-dependent and without energy storage, they cannot support nighttime irrigation. When integrated with battery storage and advanced technologies such as the Internet of Things (IoT), these systems can provide irrigation during non-sunlight hours while enabling efficient, automated water management that optimizes water use and enhances crop productivity. This review highlighted key research gaps, including the need for long-term climate-informed modeling of solar irrigation systems, empirical hydrological studies of institutional models, and deeper analysis of tenancy and gender dynamics. Addressing these gaps improves solar irrigation by enabling more accurate water-resource planning under future climate conditions, strengthening institutional and groundwater management, and ensuring more equitable access for user. In conclusion, solar irrigation in South Asia should be seen as a sustainability tool that balances technological expansion with water conservation and social equity.","太阳能灌溉技术提供了潜在的可持续且具有成本效益的解决方案，但其环境可持续性在很大程度上取决于制度和监管框架，以应对南亚等太阳能与农业潜力巨大地区所面临的社会和环境挑战。本文综述评估了（1）技术创新，（2）机遇、根本性挑战与政策框架，以及（3）对孟加拉国、印度、尼泊尔和巴基斯坦等南亚国家的社会经济和环境影响。综述结果表明，太阳能灌溉系统在降低运行成本方面相较于柴油替代方案具有优势，并对碳减排有显著贡献。此外，这些系统提供了不依赖不稳定电网电力的稳定供水，并与灌溉系统兼容以提高效率。太阳能灌溉系统产生的盈余电力可回售给电网，增加农民收入。可靠的水源获取可以推动从自给农业向经济作物种植的转变。然而，太阳能灌溉系统的性能受天气影响，且在没有储能的情况下，无法支持夜间灌溉。当与电池储能和物联网（IoT）等先进技术集成时，这些系统可以在非日照时段提供灌溉，同时实现高效、自动化的水资源管理，从而优化用水并提高作物生产力。本综述指出了关键研究空白，包括需要对太阳能灌溉系统进行长期气候信息建模、对制度模式进行实证水文研究，以及对租佃和性别动态进行更深入分析。弥补这些空白可以改进太阳能灌溉，具体途径包括：在未来气候条件下实现更准确的水资源规划，加强制度和地下水管理，并确保用户更公平地获取水资源。总之，南亚的太阳能灌溉应被视为一种可持续性工具，在技术扩展与水资源保护和社会公平之间取得平衡。",null,"Frontiers in Sustainable Food Systems","2026-09-23T00:00:00Z","论文",10,false,80,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,22,14,8,1,"系统综述南亚四国光伏灌溉的技术、政策与社会环境效应，指出物联网与储能结合方向及研究空白，对农业信息化与智慧灌溉有参考价值。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","农业物联网","水资源管理","光伏灌溉","南亚农业",[32,33],"南亚 光伏灌溉","农业物联网 水资源管理 光伏灌溉 南亚农业","南亚光伏灌溉-3332",0,"10.3389\u002Ffsufs.2026.1892056",{"doi":36,"openalex_id":38,"authors":39,"venue":10,"cited_by_count":35,"oa_url":6,"card":50,"direction":54,"ingested_from":56},"W7214072186",[40,43,45,48],{"name":41,"orcid":42},"Younsuk Dong","https:\u002F\u002Forcid.org\u002F0000-0003-2400-1916",{"name":44,"orcid":9},"Heesun Jang",{"name":46,"orcid":47},"Najme Yazdanpanah","https:\u002F\u002Forcid.org\u002F0000-0003-2554-7005",{"name":49,"orcid":9},"Yadu Pokhrel",{"tldr":51,"method":52,"finding":53,"direction":54,"opportunity":55},"综述南亚太阳能灌溉的技术、政策与社会环境影响，指出其可持续性取决于制度与监管框架。","文献综述，覆盖孟加拉国、印度、尼泊尔、巴基斯坦的技术、政策与影响分析。","太阳能灌溉降低运营成本并助力碳减排，但依赖天气，需储能与物联网支持夜间灌溉。","农业绿色发展与碳","可开展气候情景下太阳能灌溉的长期水文建模，并研究租佃与性别差异对公平获取的影响。","openalex","2026-09-24T23:30:07.037091Z",{"total":59,"page":21,"page_size":59,"items":60},6,[61,115,152,204,245,277],{"id":62,"title":63,"url":64,"summary":65,"summary_zh":66,"content":9,"source_name":67,"source_url":64,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":68,"score_detail":69,"sources":75,"tags":77,"search_phrases":81,"slug":84,"view_count":35,"doi":85,"paper":86,"created_at":114},3358,"One Toolchain, Six Domains: A Multiple-Case, Document-Based Study of Rapid IoT Prototypes Built in a One-Week Immersive Course on a Master’s Program in Applied Artificial Intelligence","https:\u002F\u002Fdoi.org\u002F10.20944\u002Fpreprints202609.2011.v1","This paper reports a document-based, multiple-case study of six Internet-of-Things (IoT) prototypes designed and simulated during a one-week immersive course, “IoT for Data Intelligence,” delivered in July 2026 within the professional Master in Applied Artificial Intelligence (Maestría en Inteligencia Artificial Aplicada, MNA) at Tecnológico de Monterrey. Six teams followed the same five-day toolchain IoT theory; Oracle Application Express (APEX), SQL, and REST service design; MIT App Inventor; ESP32\u002FWokwi simulation; and generative-AI integration and produced Wokwi-simulated prototypes spanning industrial energy monitoring, agricultural hazard response, residential automation, cardiovascular telemonitoring, industrial waste reduction, and precision agriculture. A fixed coding framework was applied across architecture, AI-integration pattern, platform-level failure modes, security debt, and Sustainable Development Goal alignment, distinguishing findings that the course structure itself prescribes from findings the teams introduced independently. The six cases converged on a shared five-layer architecture and, in a pattern only partly prescribed by the course, on keeping generative AI in an advisory or fail-safe-wrapped role. Deposited results were also compared, for illustrative purposes only, against the course’s internal competency rubric. An observed proposal from a Pontifical Catholic University of Chile’s collaboration is discussed as an informal reference point rather than as evidence for generalization. This paper discusses the implications and limits of this small, single-institution, single-cohort, simulation-only case set.","本文报告了一项基于文档的多案例研究，研究对象为六项物联网（Internet of Things, IoT）原型，这些原型是在2026年7月于蒙特雷理工学院（Tecnológico de Monterrey）应用人工智能专业硕士（Maestría en Inteligencia Artificial Aplicada, MNA）项目内开设的一周沉浸式课程“面向数据智能的物联网”（IoT for Data Intelligence）中设计与仿真的。六个团队遵循了相同的五日工具链——物联网理论；Oracle Application Express（APEX）、SQL与REST服务设计；MIT App Inventor；ESP32\u002FWokwi仿真；以及生成式AI集成——并产出了基于Wokwi仿真的原型，涵盖工业能源监测、农业灾害响应、住宅自动化、心血管远程监护、工业减废和精准农业。研究采用固定编码框架，从架构、AI集成模式、平台级失效模式、安全债务和可持续发展目标对齐五个维度进行分析，并区分了课程结构本身所规定的发现与各团队独立引入的发现。六个案例收敛于一个共享的五层架构，并在一种仅部分由课程规定的模式中，将生成式AI保持在顾问性或故障安全包裹的角色中。所提交的成果还仅出于示例目的与课程内部能力量规进行了比较。智利天主教大学一项合作中提出的方案作为非正式参照点加以讨论，而非作为可推广的证据。本文讨论了这一小型、单一机构、单一批次、仅仿真案例集的启示与局限。","Preprints.org",50,{"impact":59,"substance":70,"depth":71,"authority":72,"freshness":73,"relevant":21,"comment":74},16,15,4,9,"单校单期小样本的预印本教学案例研究，含农业物联网原型与生成式AI集成经验，但样本与仿真局限明显，公共价值有限。",[76],{"name":67,"url":64},[26,78,27,79,80],"农业人工智能","精准农业","农业教育",[82,83],"Tecnológico de Monterrey 物联网 课程","ESP32 Wokwi 农业物联网 原型","TecnológicodeMonterrey物联网课程-3358","10.20944\u002Fpreprints202609.2011.v1",{"doi":85,"openalex_id":87,"authors":88,"venue":67,"cited_by_count":35,"oa_url":64,"card":107,"direction":113,"ingested_from":56},"W7214071608",[89,92,95,98,101,104],{"name":90,"orcid":91},"Antonio Carlos Bento","https:\u002F\u002Forcid.org\u002F0000-0001-8264-4771",{"name":93,"orcid":94},"Alexandro Ortiz","https:\u002F\u002Forcid.org\u002F0000-0002-3945-6908",{"name":96,"orcid":97},"Grettel Barceló-Alonso","https:\u002F\u002Forcid.org\u002F0009-0004-3373-6441",{"name":99,"orcid":100},"Jose Reinaldo Silva","https:\u002F\u002Forcid.org\u002F0000-0003-2796-1613",{"name":102,"orcid":103},"Luis E. Falcón-Morales","https:\u002F\u002Forcid.org\u002F0000-0001-8760-5640",{"name":105,"orcid":106},"Sérgio Camacho-León","https:\u002F\u002Forcid.org\u002F0000-0002-5996-9997",{"tldr":108,"method":109,"finding":110,"direction":111,"opportunity":112},"基于六组一周IoT课程原型文档，分析其架构、AI集成与安全模式。","文档多案例研究，固定编码框架，Wokwi仿真与生成式AI集成。","六案例收敛于五层架构，生成式AI多限于建议或故障保护角色。","其他","可探究仿真原型向真实农田部署时，安全债务与AI角色如何演变。","智慧农业 \u002F 农业物联网","2026-09-24T23:30:13.353443Z",{"id":116,"title":117,"url":118,"summary":119,"summary_zh":120,"content":9,"source_name":121,"source_url":118,"published_at":122,"category":12,"cover_url":9,"hotness":123,"is_selected":14,"score":124,"score_detail":125,"sources":128,"tags":132,"search_phrases":136,"slug":139,"view_count":35,"doi":140,"paper":141,"created_at":151},3357,"AI-Driven Precision Agriculture and Crop Resilience: Integrating Artificial Intelligence, IoT and Remote Sensing for Climate-Resilient Indian Agriculture: A Vision for Viksit Bharat 2047","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22914538","Abstract Agriculture is central to India's economic development, food security, rural employment, and the achievement of the Viksit Bharat@2047 vision. However, Indian agriculture faces increasingly complex challenges, including climate variability, water scarcity, soil degradation, pest and disease outbreaks, fragmented landholdings, market uncertainty, and unequal access to agricultural knowledge. These challenges require a transition from conventional, input-intensive agriculture towards data-driven, resource-efficient, climate-resilient and farmer-centric production systems. Agriculture in India is increasingly affected by climate variability, water scarcity, soil degradation, pest and disease outbreaks, and unpredictable weather conditions. These challenges threaten crop productivity and food security, particularly among small and marginal farmers. Artificial Intelligence (AI), Internet of Things (IoT), remote sensing, and machine learning offer new opportunities to transform conventional agricultural practices into data-driven precision agriculture systems. This paper presents a conceptual framework for AI-driven precision agriculture aimed at improving crop resilience under changing climatic conditions. Artificial Intelligence (AI), combined with precision agriculture, Internet of Things (IoT), remote sensing, satellite imagery, drones, machine learning, robotics and digital public infrastructure, offers significant opportunities to transform Indian agriculture. AI can support crop and yield prediction, disease and pest identification, weather-based advisories, irrigation optimisation, soil management, crop insurance, market intelligence and early-warning systems. The paper also discusses challenges related to digital inclusion, data governance, affordability, AI reliability, farmer skills, privacy and institutional coordination. It argues that India's objective should not simply be the digitisation of agriculture, but the creation of an intelligent, inclusive and resilient agricultural ecosystem in which technology augments farmer knowledge and decision-making. By 2047, India can aspire to establish globally competitive agriculture that produces more with fewer resources, withstands climate shocks, generates higher and more stable farm incomes, and ensures sustainable food and nutritional security.","摘要 农业对印度的经济发展、粮食安全、农村就业以及“发达印度@2047”愿景的实现至关重要。然而，印度农业面临日益复杂的挑战，包括气候变异性、水资源短缺、土壤退化、病虫害暴发、土地持有碎片化、市场不确定性以及农业知识获取不平等。这些挑战要求从传统的投入密集型农业向数据驱动、资源高效、气候韧性且以农民为中心的生产体系转型。印度农业日益受到气候变异性、水资源短缺、土壤退化、病虫害暴发及不可预测天气条件的影响。这些挑战威胁着作物生产力和粮食安全，尤其是对小农和边缘农民而言。人工智能（AI）、物联网（IoT）、遥感和机器学习为将传统农业实践转变为数据驱动的精准农业系统提供了新机遇。本文提出了一个AI驱动的精准农业概念框架，旨在改善气候变化条件下作物的韧性。人工智能（AI）与精准农业、物联网（IoT）、遥感、卫星影像、无人机、机器学习、机器人技术及数字公共基础设施相结合，为改造印度农业提供了重大机遇。AI可支持作物与产量预测、病虫害识别、基于天气的农事建议、灌溉优化、土壤管理、作物保险、市场情报及预警系统。本文还讨论了与数字包容、数据治理、可负担性、AI可靠性、农民技能、隐私及机构协调相关的挑战。文章认为，印度的目标不应仅仅是农业数字化，而应是创建一个智能、包容且有韧性的农业生态系统，使技术增强农民的知识与决策能力。到2047年，印度有望建立具有全球竞争力的农业，以更少资源生产更多产品，抵御气候冲击，创造更高且更稳定的农业收入，并确保可持续的粮食与营养安全。","Zenodo (CERN European Organization for Nuclear Research)","2026-09-30T00:00:00Z",25,69,{"impact":18,"substance":17,"depth":70,"authority":126,"freshness":35,"relevant":21,"comment":127},13,"概念性框架论文，系统梳理AI、IoT与遥感在印度气候韧性农业中的应用与挑战，有参考价值但无实证数据，且发布日期在未来、时效性缺失，暂不宜进入每日精选。",[129,130],{"name":121,"url":118},{"name":121,"url":131},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22914539",[133,26,78,27,134,135],"数字乡村","气候韧性","遥感监测",[137,138],"印度 精准农业 AI","农业人工智能 农业物联网 数字乡村 智慧农业","印度精准农业AI-3357","10.5281\u002Fzenodo.22914538",{"doi":140,"openalex_id":142,"authors":143,"venue":121,"cited_by_count":35,"oa_url":118,"card":146,"direction":113,"ingested_from":56},"W7214083098",[144],{"name":145,"orcid":9},"Twinkal Prakash Sawant",{"tldr":147,"method":148,"finding":149,"direction":113,"opportunity":150},"提出AI+物联网+遥感驱动的精准农业概念框架，提升印度气候韧性作物生产。","概念框架分析，整合AI、IoT、遥感、卫星、无人机、机器学习与数字公共基础设施。","印度农业应构建智能、包容、有韧性的生态系统，而非仅数字化，以应对气候与资源挑战。","可实证检验小农户场景下AI+IoT+遥感集成对作物韧性与收入的实际效果及数字包容机制。","2026-09-24T23:30:13.211525Z",{"id":153,"title":154,"url":155,"summary":156,"summary_zh":157,"content":9,"source_name":158,"source_url":155,"published_at":159,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":160,"score_detail":161,"sources":163,"tags":165,"search_phrases":168,"slug":171,"view_count":35,"doi":172,"paper":173,"created_at":203},3347,"Integrating Material Flow Cost Accounting and IoT-Based Monitoring for Eco-Efficient Goat Farm Management","https:\u002F\u002Fdoi.org\u002F10.35145\u002F6e5wnv18","Goat farming plays an important role in supporting rural livelihoods, food production, and agricultural sustainability. However, conventional goat farm management often separates environmental monitoring, financial accounting, and livestock management, limiting the ability to identify resource inefficiencies and associated environmental impacts. This study aims to develop and implement GEMBALA (Green Eco-smart Management-Based Automation for Livestock and Accounting), an integrated digital platform that combines Internet of Things (IoT)-based environmental monitoring, Material Flow Cost Accounting (MFCA), emission analysis, artificial intelligence-based livestock management, and analytical reporting. The research employed a research and development approach in collaboration with CV Cahaya Firdaus (Fathur Farm). An IoT sensor prototype was developed, installed, and tested in a real goat farming environment to monitor temperature, humidity, Heat Index (THI), ammonia gas, and dust density. The platform also incorporates MFCA, emission, AI Estrus, AI Health, and analytical reporting modules. The results demonstrate progress toward integrating environmental, economic, and livestock management information within a unified digital platform. However, further validation is required to improve sensor data transmission, synchronization, emission calculations, MFCA data consistency, and AI performance evaluation. The study provides a foundation for eco-economic decision support, sustainable livestock management, and future commercialization of digital livestock technologies.","山羊养殖在支撑农村生计、粮食生产和农业可持续性方面发挥着重要作用。然而，传统的山羊养殖场管理往往将环境监测、财务核算和畜牧管理相互分离，限制了识别资源低效利用及相关环境影响的能力。本研究旨在开发并实施GEMBALA（基于绿色生态智能管理的畜牧与会计自动化平台），这是一个集成了基于物联网（IoT）的环境监测、物料流成本会计（MFCA）、排放分析、基于人工智能的畜牧管理以及分析报告的综合数字平台。研究采用研发方法，与CV Cahaya Firdaus（Fathur Farm）合作开展。研究开发了物联网传感器原型，并在真实山羊养殖环境中进行安装和测试，用于监测温度、湿度、热指数（THI）、氨气和粉尘密度。该平台还整合了MFCA、排放、AI发情检测、AI健康和分析报告模块。结果表明，在将环境、经济和畜牧管理信息整合到统一数字平台方面取得了进展。然而，仍需进一步验证，以改进传感器数据传输、同步、排放计算、MFCA数据一致性以及AI性能评估。本研究为生态经济决策支持、可持续畜牧管理以及数字畜牧技术的未来商业化提供了基础。","Journal of Applied Business and Technology","2026-09-24T00:00:00Z",62,{"impact":20,"substance":17,"depth":70,"authority":13,"freshness":13,"relevant":21,"comment":162},"论文提出融合物联网监测与物料流成本核算的山羊养殖数字平台，方法有创新但尚处原型验证阶段，产业影响有限。",[164],{"name":158,"url":155},[166,26,78,27,167],"数字农业","畜牧养殖",[169,170],"GEMBALA 山羊养殖 物联网","MFCA 畜牧 环境监测","GEMBALA山羊养殖物联网-3347","10.35145\u002F6e5wnv18",{"doi":172,"openalex_id":174,"authors":175,"venue":158,"cited_by_count":35,"oa_url":155,"card":198,"direction":113,"ingested_from":56},"W7214075234",[176,178,180,182,184,186,189,192,194,196],{"name":177,"orcid":9},"Nicholas Renaldo",{"name":179,"orcid":9},"Sulaiman Musa",{"name":181,"orcid":9},"Jaswar Koto",{"name":183,"orcid":9},"Kristy Veronica",{"name":185,"orcid":9},"Umar Faruq",{"name":187,"orcid":188},"Yulvia Nora Marlim","https:\u002F\u002Forcid.org\u002F0009-0007-8624-5023",{"name":190,"orcid":191},"Rangga Rahmadian Yuliendi","https:\u002F\u002Forcid.org\u002F0000-0003-2288-3580",{"name":193,"orcid":9},"Wilda Susanti",{"name":195,"orcid":9},"Achmad Tavip Junaedi",{"name":197,"orcid":9},"Nabila Wahid",{"tldr":199,"method":200,"finding":201,"direction":113,"opportunity":202},"开发集成物联网监测与物料流成本核算的山羊养殖数字平台GEMBALA。","研发方法，在真实羊场部署物联网传感器并集成MFCA、排放分析与AI模块。","平台初步实现环境、经济与养殖信息整合，但传感器传输与数据一致性仍需验证。","可延伸研究物联网数据与MFCA实时耦合的算法优化及AI模块的长期性能验证。","2026-09-24T23:30:09.863218Z",{"id":205,"title":206,"url":207,"summary":208,"summary_zh":209,"content":9,"source_name":210,"source_url":207,"published_at":211,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":212,"score_detail":213,"sources":216,"tags":218,"search_phrases":222,"slug":225,"view_count":35,"doi":226,"paper":227,"created_at":244},3190,"Comparing Irrigation Identification Methods in Colorado: Limitations of Downscaled SMAP Soil Moisture","https:\u002F\u002Fdoi.org\u002F10.3390\u002Frs18183223","With limited freshwater resources and growing water demands, it is imperative to identify and monitor water needs. Agricultural irrigation is the world’s largest water user, comprising 45–90% of freshwater withdrawals. Identifying and tracking changes in irrigated land is necessary for sustainable water management and forecasting agricultural water needs and patterns; however, the low resolution of available remote sensing observations hinders field-scale analysis. Downscaling soil moisture observations has been offered as a solution to this problem. Our study compares the performance of five irrigation identification methods using a newly developed downscaled deep soil moisture extrapolation method used to estimate Soil Moisture Active Passive (SMAP) soil moisture (SM) at a spatial resolution of 400 m for 5 cm, 20 cm, and 50 cm depths. Using this data along with Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) precipitation and Landsat Normalized Difference Vegetation Index (NDVI), we evaluate these methods with respect to crop type, irrigation type, and observation depth on agricultural fields in Colorado using the irrigation maps provided by the Colorado Decision Support System from 2015 through 2024. With no crop\u002Firrigation type–observation depth combination exceeding an F1 score of 0.149 or MCC value of 0.163, we find that none of the methods can accurately identify irrigated land regardless of crop type, irrigation type, and observation depth. Because these methods succeeded in earlier small-area studies, and because the classified maps resolved into large, spatially uniform blocks, we interpret this as a limitation specific to field-scale detection over large, heterogeneous regions rather than a defect in the dataset. These findings highlight a limitation of this downscaled SM dataset and raise the question of whether other downscaled soil moisture products share this limitation.","在淡水资源有限且用水需求不断增长的背景下，识别和监测水资源需求势在必行。农业灌溉是全球最大的用水部门，占淡水取水量的45%–90%。识别和追踪灌溉土地的变化对于可持续水资源管理以及预测农业用水需求和模式至关重要；然而，现有遥感观测的低分辨率阻碍了田块尺度的分析。土壤湿度观测数据的降尺度被提出作为解决这一问题的方法。本研究比较了五种灌溉识别方法的性能，所用数据基于新开发的降尺度深层土壤湿度外推方法，用于估算土壤湿度主动被动（SMAP）卫星在400 m空间分辨率下5 cm、20 cm和50 cm深度的土壤湿度（SM）。利用该数据以及气候灾害组红外降水与站点数据（CHIRPS）降水和Landsat归一化植被指数（NDVI），我们结合科罗拉多决策支持系统提供的2015年至2024年灌溉地图，在科罗拉多州的农田上就作物类型、灌溉类型和观测深度对这些方法进行了评估。在没有任何作物\u002F灌溉类型–观测深度组合的F1分数超过0.149或MCC值超过0.163的情况下，我们发现无论作物类型、灌溉类型和观测深度如何，这些方法均无法准确识别灌溉土地。由于这些方法在早期小区域研究中取得了成功，且分类地图呈现为大的、空间均一的斑块，我们将此解释为大规模异质区域上田块尺度检测所特有的局限性，而非数据集本身的缺陷。这些发现凸显了该降尺度SM数据集的局限性，并提出其他降尺度土壤湿度产品是否也存在这一局限性的问题。","Remote Sensing","2026-09-19T00:00:00Z",75,{"impact":214,"substance":18,"depth":17,"authority":19,"freshness":73,"relevant":21,"comment":215},12,"该研究通过大区域对比实验揭示降尺度SMAP土壤水分在田块尺度灌溉识别上的局限，方法严谨、结论明确，对农业遥感与水资源管理有参考价值，但属细分领域学术进展，公共影响有限。",[217],{"name":210,"url":207},[26,219,28,220,221],"遥感","土壤水分","灌溉识别",[223,224],"SMAP 土壤水分 灌溉识别","科罗拉多 灌溉制图 遥感","SMAP土壤水分灌溉识别-3190","10.3390\u002Frs18183223",{"doi":226,"openalex_id":228,"authors":229,"venue":210,"cited_by_count":35,"oa_url":207,"card":238,"direction":242,"ingested_from":56},"W7213934204",[230,233,235],{"name":231,"orcid":232},"Annelise M. Turman","https:\u002F\u002Forcid.org\u002F0009-0000-2392-3631",{"name":234,"orcid":9},"Bin Fang",{"name":236,"orcid":237},"V. Vijaya Lakshmi","https:\u002F\u002Forcid.org\u002F0000-0001-9522-7897",{"tldr":239,"method":240,"finding":241,"direction":242,"opportunity":243},"比较五种灌溉识别方法，发现降尺度SMAP土壤湿度在科罗拉多田间尺度无法准确识别灌溉。","用400米降尺度SMAP土壤湿度、CHIRPS降水和Landsat NDVI，对","所有方法F1最高仅0.149，无法准确识别灌溉地，归因于大区域异质性而非数据缺陷。","农业遥感与作物表型","可检验其他降尺度土壤湿度产品是否同样受大区域异质性限制，并探索融合多源数据提升田间尺度灌溉识别。","2026-09-22T23:30:26.627420Z",{"id":246,"title":247,"url":248,"summary":249,"summary_zh":250,"content":9,"source_name":251,"source_url":248,"published_at":252,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":253,"score_detail":254,"sources":256,"tags":258,"search_phrases":260,"slug":263,"view_count":35,"doi":264,"paper":265,"created_at":276},3161,"Artificial Intelligence in Plant Disease Detection: An Introduction to Intelligent and Automated Crop Health Monitoring","https:\u002F\u002Fdoi.org\u002F10.59256\u002Fijire.20260705005","Plant diseases are a major challenge in modern agriculture, as they can significantly reduce crop yield, crop quality, and economic productivity. Traditional plant disease detection methods mainly depend on visual inspection and expert knowledge, which can be time-consuming, subjective, and difficult to apply across large agricultural fields. The rapid advancement of Artificial Intelligence (AI), particularly Machine Learning (ML), Deep Learning (DL), and Computer Vision, has created new opportunities for automated and efficient crop disease detection and crop health monitoring. AI-based plant disease detection systems can analyze plant and leaf images to identify disease-related characteristics such as leaf discoloration, spots, lesions, texture variations, and abnormal growth patterns. Advanced techniques, including Convolutional Neural Networks (CNNs), transfer learning, image processing, image segmentation, and object detection, can be used for plant disease classification and identification of affected regions with high accuracy. This chapter introduces the fundamental concepts of AI-based plant disease detection, covering image acquisition, image preprocessing, feature extraction, model development, disease classification, and performance evaluation. It also examines the applications of AI in precision agriculture, smart agriculture, mobile-based plant disease diagnosis, drone-assisted crop monitoring, IoT-enabled farming, and edge-based agricultural systems. Furthermore, the chapter discusses important challenges such as limited and imbalanced datasets, environmental variations, similar disease symptoms, model generalization, computational requirements, and the need for explainable AI in agricultural applications. Finally, emerging trends and future opportunities are discussed, with emphasis on integrating AI with IoT, remote sensing, agricultural robotics, and multimodal agricultural data. The chapter provides a foundation for understanding how Artificial Intelligence for plant disease detection can support early disease identification, reduce crop losses, optimize agricultural resources, and contribute to sustainable and intelligent farming practices.","植物病害是现代农业面临的一项重大挑战，因为它们会显著降低作物产量、作物品质和经济生产力。传统的植物病害检测方法主要依赖视觉检查和专家知识，这种方式耗时、主观性强，且难以在大规模农田中应用。人工智能（AI）的快速发展，尤其是机器学习（ML）、深度学习（DL）和计算机视觉，为自动化、高效的作物病害检测和作物健康监测创造了新的机遇。基于AI的植物病害检测系统可以分析植物和叶片图像，以识别与病害相关的特征，如叶片变色、斑点、病斑、纹理变化和异常生长模式。包括卷积神经网络（CNN）、迁移学习、图像处理、图像分割和目标检测在内的先进技术，可用于植物病害分类和受影响区域的高精度识别。本章介绍了基于AI的植物病害检测的基本概念，涵盖图像采集、图像预处理、特征提取、模型开发、病害分类和性能评估。本章还探讨了AI在精准农业、智慧农业、基于移动端的植物病害诊断、无人机辅助作物监测、物联网（IoT）赋能农业和边缘农业系统中的应用。此外，本章讨论了重要挑战，如数据集有限且不平衡、环境变化、相似病害症状、模型泛化、计算需求，以及农业应用中可解释AI的需求。最后，讨论了新兴趋势和未来机遇，重点强调将AI与物联网、遥感、农业机器人和多模态农业数据相结合。本章为理解人工智能用于植物病害检测如何支持早期病害识别、减少作物损失、优化农业资源，并促进可持续和智能农业实践提供了基础。","International Journal of Innovative Research in Engineering","2026-09-21T00:00:00Z",59,{"impact":214,"substance":19,"depth":71,"authority":13,"freshness":20,"relevant":21,"comment":255},"系统综述AI在植物病害检测中的应用，内容全面但属教科书式介绍，方法新颖性与数据规模有限，可作为智慧农业主题聚合素材。",[257],{"name":251,"url":248},[26,78,27,79,259],"植物病害检测",[261,262],"AI 植物病害检测","无人机 作物健康监测","AI植物病害检测-3161","10.59256\u002Fijire.20260705005",{"doi":264,"openalex_id":266,"authors":267,"venue":251,"cited_by_count":35,"oa_url":9,"card":270,"direction":113,"ingested_from":56},"W7213950095",[268],{"name":269,"orcid":9},"Jamuna Ratcha",{"tldr":271,"method":272,"finding":273,"direction":274,"opportunity":275},"综述AI在植物病害检测中的应用，涵盖图像采集到模型评估全流程及未来趋势。","综述CNN、迁移学习、图像分割与目标检测在叶片病害识别中的应用。","AI可高精度识别病害，但受限于数据集不足、环境变化与模型泛化能力。","农业人工智能与决策模型","可探索多模态数据融合与可解释AI，提升复杂田间环境下病害检测的泛化能力。","2026-09-22T23:30:11.209653Z",{"id":278,"title":279,"url":280,"summary":281,"summary_zh":9,"content":9,"source_name":282,"source_url":9,"published_at":283,"category":12,"cover_url":9,"hotness":123,"is_selected":14,"score":284,"score_detail":285,"sources":288,"tags":293,"search_phrases":296,"slug":299,"view_count":35,"doi":300,"paper":301,"created_at":323},3122,"Advances in Information Sensing and Intelligent Monitoring of Field Crops Throughout the Full Growth Cycle（大田作物全生育期信息感知与智能监测研究进展）","https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F18\u002F1852","江苏大学农业工程学院 Tang Ruifan 等在《Agronomy》16(18): 1852 发表综述（2026-09-20 发表）：大田作物在不同生育阶段持续变化、呈现显著空间异质性、需在短作业窗口内进行管理。研究以生育阶段为主线组织文献，通过\"农业需求—可观测变量—感知平台—数据处理方法—验证设计—状态解释—管理或装备输出\"通用链条分析。从卫星遥感、无人机感知、地面与近端感知、田间物联网、机载传感器、多源融合、作物模型与机器学习方法按空间支撑、时间连续性、尺度匹配、田间稳健性、迁移条件、不确定性与操作适用性比较。综述报告作物表型反演、田间环境表征、生物胁迫识别在特定条件下已建立；跨阶段状态继承、一致参考测量、独立验证、监测结果向可执行任务转化仍不充分。提出生命周期导向的信息处理视角，未来应加强跨作物跨区域验证、机理性与数据驱动模型协同、不确定性报告、互操作性和田间反馈。","MDPI Agronomy","2026-09-20T00:00:00Z",76,{"impact":70,"substance":286,"depth":17,"authority":126,"freshness":73,"relevant":21,"comment":287},20,"江苏大学团队在核心期刊发表的综述，系统梳理大田作物全生育期感知与监测技术链条，专业深度与信息增量较高，但属学术综述、产业影响有限，适合进入主题聚合而非头条精选。",[289,290],{"name":282,"url":280},{"name":291,"url":292},"Agronomy","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fagronomy16181852",[26,27,294,135,295],"作物表型","大田作物",[297,298],"江苏大学 大田作物 智能监测","Agronomy 作物全生育期 信息感知","江苏大学大田作物智能监测-3122","10.3390\u002Fagronomy16181852",{"doi":300,"openalex_id":302,"authors":303,"venue":291,"cited_by_count":35,"oa_url":292,"card":318,"direction":242,"ingested_from":56},"W7213886235",[304,306,309,311,313,315],{"name":305,"orcid":9},"Ruifan Tang",{"name":307,"orcid":308},"Yapeng Wu","https:\u002F\u002Forcid.org\u002F0009-0008-1808-8959",{"name":310,"orcid":9},"Liming Zhang",{"name":312,"orcid":9},"Youqi Xu",{"name":314,"orcid":9},"Yu Zhang",{"name":316,"orcid":317},"Zhong Tang","https:\u002F\u002Forcid.org\u002F0000-0002-2724-115X",{"tldr":319,"method":320,"finding":321,"direction":242,"opportunity":322},"综述大田作物全生育期信息感知与智能监测，按生育阶段梳理技术并指出转化不足。","以生育阶段为主线，比较卫星、无人机、地面物联网、模型与机器学习等方法。","表型反演与胁迫识别已有条件建立，但跨阶段继承、独立验证与可执行转化不足。","可研究跨生育阶段状态继承建模、一致参考测量与监测结果向田间作业指令的转化。","2026-09-22T00:05:38.276747Z"]