[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3482":3,"related-3482":99},{"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":28,"primary_topic":34,"secondary_topics":40,"classification":41,"search_phrases":79,"slug":82,"view_count":64,"doi":83,"paper":84,"created_at":98},3482,"Digital Transformation and the Reconfiguration of Farming Systems: Understanding How Tech-nological Adoption and Institutional Conditions Shape Sustainable and Inclusive Agriculture","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22932900","Digital agriculture is progressively reshaping farming through the integration of sensors, artificial intelligence, Internet of Things technologies, digital platforms, data analytics, robotics and automated decision-support systems. This paper examines this transformation by connecting three complementary dimensions: the socio-technical development of digital agriculture, the determinants and processes of technological adoption, and the economic, organizational and inclusive consequences of digitalization for farming systems. The literature indicates that agricultural digitalization cannot be reduced to the availability of increasingly sophisticated technologies. Adoption and sustained use depend on farmers’ perceptions of usefulness and compatibility, farm resources, human capital, infrastructure, institutional support, advisory systems and the capacity to integrate technologies into established production routines. Digital technologies may improve resource efficiency, information management, productivity and sustainability, while also modifying labor organization, farmer autonomy, data governance and relationships among actors within agricultural value chains. These effects remain uneven across farms and territories, particularly where smallholders face financial, infrastructural and capability constraints. The paper therefore interprets digital agriculture as a multidimensional transformation whose outcomes depend on the interaction between technological capabilities, farmer behavior and institutional conditions. Inclusive and sustainable digitalization requires attention not only to innovation diffusion but also to governance, skills, accessibility and the distribution of technological benefits.","数字农业正通过传感器、人工智能、物联网技术、数字平台、数据分析、机器人技术和自动化决策支持系统的整合，逐步重塑农业生产方式。本文通过连接三个互补维度来考察这一转型：数字农业的社会技术发展、技术采纳的决定因素与过程，以及数字化对农业系统产生的经济、组织和包容性后果。文献表明，农业数字化不能简化为日益复杂的技术供给。采纳和持续使用取决于农民对有用性和兼容性的认知、农场资源、人力资本、基础设施、制度支持、咨询系统以及将技术整合到既定生产惯例中的能力。数字技术可能提高资源效率、信息管理、生产力和可持续性，同时也会改变劳动组织、农民自主性、数据治理以及农业价值链中参与者之间的关系。这些影响在农场和区域之间仍不均衡，尤其是小农户面临资金、基础设施和能力约束的地方。因此，本文将数字农业解释为一种多维转型，其结果取决于技术能力、农民行为和制度条件之间的相互作用。包容和可持续的数字化不仅需要关注创新扩散，还需要关注治理、技能、可及性和技术收益的分配。",null,"Zenodo (CERN European Organization for Nuclear Research)","2026-09-24T00:00:00Z","论文",25,false,77,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},18,20,17,13,9,1,"系统梳理数字农业技术采纳与制度条件的研究综述，对智慧农业政策与推广有参考价值，但属文献综述类，非重大突破。",[25,26],{"name":10,"url":6},{"name":10,"url":27},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22932899",[29,30,31,32,33],"数字农业","智慧农业","农业人工智能","小农户","技术采纳",{"code":35,"slug":36,"name":37,"path":38,"parent_code":39},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot","I",[],{"status":42,"locked":14,"version":43,"method":44,"evidence":45},"classified","2026-10-22.2","rules",{"candidates":46,"agricultural_evidence":50},[47,55,62,68,74],{"code":48,"score":49,"supported":50,"title":51,"abstract":52,"tags":54},"I1",8,true,[],[29,53],"digital agriculture",[29,30],{"code":35,"score":56,"supported":50,"title":57,"abstract":58,"tags":61},6,[],[59,60],"物联网","internet of things",[],{"code":63,"score":64,"supported":50,"title":65,"abstract":66,"tags":67},"G2",4,[],[32],[32],{"code":69,"score":64,"supported":50,"title":70,"abstract":71,"tags":73},"I2",[],[72],"artificial intelligence",[31],{"code":75,"score":22,"supported":14,"title":76,"abstract":77,"tags":78},"G1",[],[],[33],[80,81],"农业人工智能 技术采纳 数字农业 智慧农业","农业人工智能 技术采纳","农业人工智能技术采纳数字农业智慧农业-3482","10.5281\u002Fzenodo.22932900",{"doi":83,"openalex_id":85,"authors":86,"venue":10,"cited_by_count":89,"oa_url":6,"card":90,"direction":96,"ingested_from":97},"W7214187008",[87],{"name":88,"orcid":9},"Chilufya Banda",0,{"tldr":91,"method":92,"finding":93,"direction":94,"opportunity":95},"综述数字农业转型，分析技术采纳与制度条件如何共同塑造可持续包容性农业。","文献综述，整合社会技术、技术采纳与数字化经济组织后果三维度。","数字化成效取决于技术能力、农户行为与制度条件的交互，小农面临多重约束。","数字乡村与农业信息化","可实证检验制度支持与农户能力如何调节数字技术对包容性和可持续性的影响。","智慧农业 \u002F 农业物联网","openalex","2026-09-25T23:30:18.463733Z",{"total":56,"page":22,"page_size":56,"items":100},[101,159,252,342,411,523],{"id":102,"title":103,"url":104,"summary":105,"summary_zh":106,"content":9,"source_name":107,"source_url":104,"published_at":108,"category":12,"cover_url":9,"hotness":109,"is_selected":14,"score":110,"score_detail":111,"sources":113,"tags":115,"primary_topic":118,"secondary_topics":119,"classification":120,"search_phrases":144,"slug":147,"view_count":22,"doi":9,"paper":148,"created_at":158},4070,"Digital Transformation of Agribusiness: Pathways Towards Smart and Sustainable Food Systems in Nordic and Indian Agricultural Contexts","https:\u002F\u002Fballisticsjournal.com\u002Findex.php\u002Fjournal\u002Farticle\u002Fview\u002F720","Digital transformation is reshaping agribusiness by integrating advanced technologies into production, processing, marketing, and supply chain management, thereby creating more efficient, resilient, and sustainable food systems. In India, increasing demand for food, climate variability, shrinking landholdings, and resource constraints have accelerated the need for smart agricultural solutions. This review examines the emerging role of digital technologies, including artificial intelligence, precision agriculture, the Internet of Things, blockchain, big data analytics, drones, and digital platforms, in modernizing agribusiness and strengthening food value chains. It synthesizes recent scientific evidence on technological innovations, adoption patterns, opportunities, barriers, and sustainability outcomes across the agricultural sector. The review highlights that digital technologies improve productivity, resource-use efficiency, traceability, market connectivity, and climate resilience while supporting informed decision-making. However, challenges related to digital infrastructure, affordability, data governance, and digital literacy remain significant. The paper concludes that supportive policies, inclusive innovation, capacity building, and multi-stakeholder collaboration are essential to accelerate India's transition toward smart, resilient, and sustainable food systems.","数字化转型正通过将先进技术融入生产、加工、营销和供应链管理，重塑农业综合企业，从而构建更高效、更具韧性且可持续的粮食系统。在印度，粮食需求不断增长、气候多变、土地持有规模缩小以及资源约束，加速了对智慧农业解决方案的需求。本文综述了人工智能、精准农业、物联网、区块链、大数据分析、无人机和数字平台等数字技术在推动农业综合企业现代化和强化食品价值链中的新兴作用。文章综合了近期关于农业领域技术创新、采用模式、机遇、障碍和可持续性成果的科学证据。综述指出，数字技术提高了生产力、资源利用效率、可追溯性、市场连通性和气候韧性，同时支持知情决策。然而，数字基础设施、可负担性、数据治理和数字素养方面的挑战依然显著。本文结论认为，支持性政策、包容性创新、能力建设和多利益相关方合作对于加速印度向智慧、有韧性且可持续的粮食系统转型至关重要。","Dandao Xuebao\u002FJournal of Ballistics","2026-10-01T00:00:00Z",10,76,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":49,"relevant":22,"comment":112},"综述性论文，系统梳理数字技术在印度与北欧农业的应用与障碍，具行业参考价值但非突发资讯。",[114],{"name":107,"url":104},[29,30,31,116,117],"精准农业","农业区块链",{"code":35,"slug":36,"name":37,"path":38,"parent_code":39},[],{"status":42,"locked":14,"version":43,"method":44,"evidence":121},{"candidates":122,"agricultural_evidence":50},[123,129,133,137],{"code":48,"score":124,"supported":50,"title":125,"abstract":126,"tags":128},12,[],[30,116,127],"precision agriculture",[29,30,116],{"code":35,"score":56,"supported":50,"title":130,"abstract":131,"tags":132},[],[59,60],[],{"code":69,"score":64,"supported":50,"title":134,"abstract":135,"tags":136},[],[72],[31],{"code":138,"score":139,"supported":50,"title":140,"abstract":141,"tags":143},"H7",3,[],[142],"climate resilience",[],[145,146],"印度 数字农业 转型","北欧 智慧农业 食品系统","印度数字农业转型-4070",{"doi":9,"openalex_id":149,"authors":150,"venue":107,"cited_by_count":89,"oa_url":104,"card":153,"direction":96,"ingested_from":97},"W7217570283",[151],{"name":152,"orcid":9},"Anjali Adhikari",{"tldr":154,"method":155,"finding":156,"direction":94,"opportunity":157},"综述数字技术在印度农业综合企业中的应用，探讨迈向智慧可持续食品系统的路径。","综述AI、物联网、区块链、大数据、无人机等数字技术在农业价值链中的证据。","数字技术提升生产力、资源效率、可追溯性和气候韧性，但基础设施、成本和数字素养仍是障碍。","可对比北欧与印度情境，研究数字农业技术适配性与包容性创新政策。","2026-10-03T23:30:14.654788Z",{"id":160,"title":161,"url":162,"summary":163,"summary_zh":164,"content":9,"source_name":165,"source_url":162,"published_at":11,"category":12,"cover_url":9,"hotness":109,"is_selected":14,"score":166,"score_detail":167,"sources":170,"tags":172,"primary_topic":175,"secondary_topics":181,"classification":183,"search_phrases":215,"slug":218,"view_count":219,"doi":220,"paper":221,"created_at":251},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",62,{"impact":49,"substance":17,"depth":168,"authority":109,"freshness":109,"relevant":22,"comment":169},16,"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。",[171],{"name":165,"url":162},[29,30,31,173,174],"农业物联网","畜牧养殖",{"code":176,"slug":177,"name":178,"path":179,"parent_code":180},"C2","ruminants","草食牲畜","\u002Ftopic\u002Flivestock\u002Fruminants","C",[182],{"code":35,"slug":36,"name":37,"path":38,"parent_code":39},{"status":42,"locked":14,"version":43,"method":44,"evidence":184},{"candidates":185,"agricultural_evidence":50},[186,191,198,204,210],{"code":35,"score":17,"supported":50,"title":187,"abstract":189,"tags":190},[188],"IoT",[59,60,188],[173],{"code":176,"score":192,"supported":50,"title":193,"abstract":195,"tags":197},14,[194],"goat",[196,194],"山羊",[],{"code":69,"score":199,"supported":50,"title":200,"abstract":201,"tags":203},7,[],[72,202],"decision support",[31],{"code":180,"score":56,"supported":50,"title":205,"abstract":206,"tags":209},[],[207,208],"畜牧","livestock",[],{"code":48,"score":211,"supported":14,"title":212,"abstract":213,"tags":214},2,[],[],[29,30],[216,217],"GEMBALA 山羊养殖 物联网","MFCA 畜牧 环境监测","GEMBALA山羊养殖物联网-3347",5,"10.35145\u002F6e5wnv18",{"doi":220,"openalex_id":222,"authors":223,"venue":165,"cited_by_count":89,"oa_url":162,"card":246,"direction":96,"ingested_from":97},"W7214075234",[224,226,228,230,232,234,237,240,242,244],{"name":225,"orcid":9},"Nicholas Renaldo",{"name":227,"orcid":9},"Sulaiman Musa",{"name":229,"orcid":9},"Jaswar Koto",{"name":231,"orcid":9},"Kristy Veronica",{"name":233,"orcid":9},"Umar Faruq",{"name":235,"orcid":236},"Yulvia Nora Marlim","https:\u002F\u002Forcid.org\u002F0009-0007-8624-5023",{"name":238,"orcid":239},"Rangga Rahmadian Yuliendi","https:\u002F\u002Forcid.org\u002F0000-0003-2288-3580",{"name":241,"orcid":9},"Wilda Susanti",{"name":243,"orcid":9},"Achmad Tavip Junaedi",{"name":245,"orcid":9},"Nabila Wahid",{"tldr":247,"method":248,"finding":249,"direction":96,"opportunity":250},"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":253,"title":254,"url":255,"summary":256,"summary_zh":257,"content":9,"source_name":258,"source_url":255,"published_at":259,"category":12,"cover_url":9,"hotness":109,"is_selected":14,"score":260,"score_detail":261,"sources":264,"tags":266,"primary_topic":268,"secondary_topics":274,"classification":281,"search_phrases":322,"slug":325,"view_count":211,"doi":326,"paper":327,"created_at":341},3010,"A Multi-Task Stacked Ensemble and IoT-Enabled Decision Support System for Precision Fertigation in Smallholder Agriculture","https:\u002F\u002Fdoi.org\u002F10.5120\u002Fijca9a6e56b81235","Nigerian agriculture's fixed-schedule fertigation causes low efficiency and nutrient leaching.A stacked-ensemble model is developed for precision fertigation that jointly predicts fertigation need, rate (kg\u002Fha) and timing (Early\u002FOptimal\u002FLate).To train and evaluate the ensemble, a unified dataset was integrated, comprising 12,840 records and 42 variables from a Nigerian soil-weather-yield dataset, a locally sourced Nigerian IoT sensor series and historical weather\u002FNDVI feeds.An LSTM soil-dynamics model, an XGBoost rate regressor and Random Forest need\u002Ftiming classifiers are fused through an XGBoost meta-learner trained on out-of-fold predictions.On held-out partitions, the ensemble reduced rate MAE from 0.55 to 0.49 kg\u002Fha (-10.9%) and RMSE from 0.68 to 0.61 kg\u002Fha (-10.3%;R² 0.88→0.92),raised need F1 from 0.83 to 0.86 (accuracy 0.87→0.89;AUC 0.89→0.93)and timing macro-F1 from 0.84 to 0.86, with well-calibrated probabilities (Brier 0.082).The trained ensemble was deployed through a RESTful API and responsive dashboard; under concurrent load, the system recorded 0% request errors with 1.88s median API latency, demonstrating practical deployability for Nigerian smallholder agriculture.","尼日利亚农业的固定日程水肥一体化导致效率低下和养分淋失。本研究开发了一种堆叠集成模型用于精准水肥管理，可联合预测灌溉施肥需求、施用量（kg\u002Fha）和时机（早\u002F最佳\u002F晚）。为训练和评估该集成模型，整合了一个统一数据集，包含来自尼日利亚土壤-天气-产量数据集、本地尼日利亚物联网传感器序列及历史天气\u002FNDVI数据的12,840条记录和42个变量。通过基于折外预测训练的XGBoost元学习器，将LSTM土壤动力学模型、XGBoost施用量回归器和随机森林需求\u002F时机分类器进行融合。在留出集上，该集成模型将施用量MAE从0.55降至0.49 kg\u002Fha（-10.9%），RMSE从0.68降至0.61 kg\u002Fha（-10.3%；R² 0.88→0.92），需求F1从0.83提升至0.86（准确率0.87→0.89；AUC 0.89→0.93），时机宏平均F1从0.84提升至0.86，且概率校准良好（Brier 0.082）。训练后的集成模型通过RESTful API和响应式仪表板部署；在并发负载下，系统录得0%请求错误，API延迟中位数为1.88秒，展示了在尼日利亚小农农业中的实际可部署性。","International Journal of Computer Applications","2026-09-18T00:00:00Z",79,{"impact":17,"substance":262,"depth":17,"authority":20,"freshness":49,"relevant":22,"comment":263},22,"面向小农户的精准水肥一体化多任务集成模型与物联网决策支持系统，数据规模与方法验证扎实，对智慧农业落地有参考价值。",[265],{"name":258,"url":255},[30,31,173,32,267],"精准灌溉",{"code":269,"slug":270,"name":271,"path":272,"parent_code":273},"F3","fertilizers","肥料与水肥一体化","\u002Ftopic\u002Finputs-equipment\u002Ffertilizers","F",[275,276],{"code":35,"slug":36,"name":37,"path":38,"parent_code":39},{"code":63,"slug":277,"name":278,"path":279,"parent_code":280},"farm-services","社会化服务与规模经营","\u002Ftopic\u002Frural-services\u002Ffarm-services","G",{"status":42,"locked":14,"version":43,"method":44,"evidence":282},{"candidates":283,"agricultural_evidence":50},[284,291,296,301,305,312,318],{"code":269,"score":19,"supported":50,"title":285,"abstract":287,"tags":290},[286],"fertigation",[288,289,286],"施肥","水肥一体化",[],{"code":35,"score":292,"supported":50,"title":293,"abstract":294,"tags":295},15,[188],[59,188],[173],{"code":63,"score":124,"supported":50,"title":297,"abstract":299,"tags":300},[298],"smallholder",[298],[32],{"code":69,"score":21,"supported":50,"title":302,"abstract":303,"tags":304},[202],[],[31],{"code":306,"score":56,"supported":50,"title":307,"abstract":308,"tags":311},"H2",[],[309,310],"土壤","soil",[],{"code":313,"score":64,"supported":50,"title":314,"abstract":315,"tags":317},"H3",[],[316],"灌溉",[267],{"code":48,"score":22,"supported":14,"title":319,"abstract":320,"tags":321},[],[],[30],[323,324],"尼日利亚 精准灌溉 物联网","堆叠集成 施肥决策 小农户","尼日利亚精准灌溉物联网-3010","10.5120\u002Fijca9a6e56b81235",{"doi":326,"openalex_id":328,"authors":329,"venue":258,"cited_by_count":89,"oa_url":255,"card":336,"direction":96,"ingested_from":97},"W7213663876",[330,332,334],{"name":331,"orcid":9},"Awojide S.",{"name":333,"orcid":9},"Ikpotokin F.O.",{"name":335,"orcid":9},"Sadiq F.I.",{"tldr":337,"method":338,"finding":339,"direction":96,"opportunity":340},"构建多任务堆叠集成模型与物联网决策支持系统，实现小农户精准水肥一体化。","LSTM、XGBoost、随机森林堆叠集成，融合尼日利亚土壤气象、IoT与NDV","集成模型将施肥量MAE降低10.9%，需求与时机分类F1提升，系统部署零错误。","可探索多任务集成模型在非洲小农户不同作物与气候区的迁移能力及低成本IoT部署。","2026-09-20T23:30:09.003454Z",{"id":343,"title":344,"url":345,"summary":346,"summary_zh":347,"content":9,"source_name":10,"source_url":345,"published_at":348,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":349,"score_detail":350,"sources":353,"tags":357,"primary_topic":359,"secondary_topics":360,"classification":361,"search_phrases":395,"slug":398,"view_count":211,"doi":399,"paper":400,"created_at":410},2312,"MridAI: Autonomous Edge-to-Conversational IoT for Democratising Precision Agriculture via Neuro-Symbolic Telemetry","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22724805","While sensor-guided precision agriculture improves water efficiency and curtails chemical run-off, adoption across smallholder farm land in the Global South remains under 1%. Commercial telemetry systems are constrained by high capital acquisition costs (>$300) and complex, dashboard-centric mobile applications that impose heavy cognitive burdens on low-literacy farmers. This paper proposes MridAI, a low-cost (\u003C$45 \u002F ₹3,420 COGS) autonomous in-ground agro-telemetry node coupled with a cloud-based neuro-symbolic artificial intelligence advisory pipeline. The physical layer integrates a multi-parameter Modbus RS485 sensor, an ESP32-C3 micro-controller, an Indian-band 4G LTE Cat-1 modem, and an energy harvesting subsystem within an IP67-rated solar stem. To eliminate measurement errors in high-clay tropical soils (Vertisols), an on-device digital signal-processing pipeline applies an adapted Topp dielectric correction alongside a one-dimensional discrete Kalman filter. To overcome rural telecommunication instability, the firmware implements an asynchronous, non-volatile store-and-forward ring buffer. At the cloud layer, an analytical solver calculates deterministic FAO-56 evapotranspiration deficits and enforces Indian Council of Agricultural Research (ICAR) chemical boundaries, strictly isolating numerical computation from an instruction-tuned Large Language Model (LLM). The generative model functions solely as a linguistic translator, delivering actionable, dialect-adapted recommendations directly via the Meta WhatsApp Cloud API without requiring third-party application downloads. Empirical power-budget modelling confirms indefinite operational autonomy (>240 days without solar irradiance), establishing a scalable paradigm for digital agriculture.","尽管传感器引导的精准农业提高了用水效率并减少了化学品径流，但在全球南方小农农田中的采用率仍不足1%。商业遥测系统受制于高昂的资本购置成本（超过300美元）以及复杂的、以仪表盘为中心的移动应用程序，后者给低识字率农民带来了沉重的认知负担。本文提出MridAI，一种低成本（物料清单成本低于45美元\u002F3，420印度卢比）的自主地下农业遥测节点，并耦合基于云的神经符号人工智能咨询流水线。物理层将多参数Modbus RS485传感器、ESP32-C3微控制器、印度频段4G LTE Cat-1调制解调器以及能量收集子系统集成于IP67防护等级的太阳能杆体内。为消除高黏土热带土壤（变性土）中的测量误差，设备端数字信号处理流水线采用适配的Topp介电校正与一维离散卡尔曼滤波器。为克服农村电信不稳定性，固件实现了异步、非易失性的存储转发环形缓冲区。在云层，分析求解器计算确定性的FAO-56蒸散亏缺，并执行印度农业研究理事会（ICAR）的化学品边界，将数值计算与指令微调的大语言模型（LLM）严格隔离。生成模型仅充当语言翻译器，通过Meta WhatsApp Cloud API直接提供可操作的、适配方言的建议，无需下载第三方应用程序。实证功率预算建模证实了无限期运行自主性（无太阳辐照下超过240天），为数字农业建立了一种可扩展的范式。","2026-09-12T00:00:00Z",85,{"impact":262,"substance":351,"depth":17,"authority":20,"freshness":21,"relevant":22,"comment":352},23,"面向全球南方小农户的低成本自主土壤遥测节点与神经符号AI咨询管线，方法新颖、成本与能耗数据具体，对农业信息化与普惠数字农业有实质参考价值。",[354,355],{"name":10,"url":345},{"name":10,"url":356},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22724804",[358,30,31,173,32,267],"数字乡村",{"code":35,"slug":36,"name":37,"path":38,"parent_code":39},[],{"status":42,"locked":14,"version":43,"method":44,"evidence":362},{"candidates":363,"agricultural_evidence":50},[364,368,372,377,381,386,390],{"code":48,"score":17,"supported":50,"title":365,"abstract":366,"tags":367},[127],[116,29,127,53],[30],{"code":35,"score":21,"supported":50,"title":369,"abstract":370,"tags":371},[188],[],[173],{"code":69,"score":199,"supported":50,"title":373,"abstract":374,"tags":376},[],[72,375],"large language model",[31],{"code":63,"score":64,"supported":50,"title":378,"abstract":379,"tags":380},[],[298],[32],{"code":313,"score":64,"supported":50,"title":382,"abstract":383,"tags":385},[],[384],"evapotranspiration",[267],{"code":306,"score":139,"supported":50,"title":387,"abstract":388,"tags":389},[],[309],[],{"code":391,"score":22,"supported":14,"title":392,"abstract":393,"tags":394},"I6",[],[],[358],[396,397],"农业人工智能 农业物联网 数字乡村 智慧农业","农业人工智能 农业物联网","农业人工智能农业物联网数字乡村智慧农业-2312","10.5281\u002Fzenodo.22724805",{"doi":399,"openalex_id":401,"authors":402,"venue":10,"cited_by_count":89,"oa_url":345,"card":405,"direction":96,"ingested_from":97},"W7212357737",[403],{"name":404,"orcid":9},"Pranit Kamble",{"tldr":406,"method":407,"finding":408,"direction":96,"opportunity":409},"提出低成本自主地下物联网节点与神经符号AI咨询管道，让小农户用WhatsApp获取精准农业建议。","ESP32-C3与Modbus传感器、Topp校正和卡尔曼滤波、FAO-56与L","系统成本低于45美元，可独立运行超240天，无需下载应用即可通过WhatsApp获得方言化建议。","可探索低资源语言与方言适配的LLM农业咨询，以及神经符号系统在更多作物和土壤类型中的泛化验证。","2026-09-13T23:30:14.323767Z",{"id":412,"title":413,"url":414,"summary":415,"summary_zh":416,"content":9,"source_name":417,"source_url":414,"published_at":418,"category":12,"cover_url":9,"hotness":109,"is_selected":14,"score":260,"score_detail":419,"sources":421,"tags":423,"primary_topic":426,"secondary_topics":431,"classification":437,"search_phrases":493,"slug":496,"view_count":89,"doi":497,"paper":498,"created_at":522},5564,"Dynamic thermal imaging for crop water stress detection across phenological stages: Enhancing irrigation scheduling precision","https:\u002F\u002Fdoi.org\u002F10.14719\u002Fpst.17728","Thermal remote sensing and derived indices such as the crop water stress index (CWSI) can assess crop water status and support precision irrigation. However, most operational and artificial intelligence (AI)-based approaches rely on fixed, season-long thresholds to interpret canopy–air temperature differences and often overlook the influence of crop phenology. This review synthesises evidence from grapevine, wheat, maize, potato and several other crops on how developmental stage affects the non-water-stressed baseline (NWSB), CWSI thresholds and irrigation-scheduling algorithms. It covers CWSI theory and estimation practices; crop-specific, phenology-dependent behaviour of baselines and thresholds; a comparison of threshold-based, machine learning, Internet of Things (IoT), digital twin and Food and Agriculture Organization (FAO)-56 approaches with respect to crop phenology; and a conceptual framework for phenology-aware scheduling. Across the reviewed crops, baseline behaviour generally varied systematically across the growing season, the relationship between CWSI and crop water status generally strengthened after canopy closure and CWSI measured during reproductive stages was generally most closely related to yield. A quantitative audit of 56 AI-, thermal-imaging-, or CWSI-based studies found that 78.6 % either did not explicitly incorporate crop phenology or relied on day of year as a temporal proxy, whereas only 5.4 % explicitly encoded phenological stage. The proposed framework considers phenology explicitly in baseline estimation, threshold setting and data fusion or machine learning for irrigation scheduling; the framework will require multi-year, multi-site field validation before operational use. Future research should prioritise standardised phenology definitions and long-term, multi-location studies to establish and validate stage-specific parameters.","热红外遥感及其衍生的作物水分胁迫指数（crop water stress index, CWSI）等指标可评估作物水分状况并支持精准灌溉。然而，大多数业务化方法和基于人工智能（artificial intelligence, AI）的方法依赖固定的全生育期阈值来解释冠层—空气温差，且往往忽视作物物候的影响。本文综述了葡萄、小麦、玉米、马铃薯及其他若干作物在发育阶段如何影响非水分胁迫基线（non-water-stressed baseline, NWSB）、CWSI阈值和灌溉调度算法的相关证据。综述内容涵盖CWSI理论与估算实践；基线和阈值的作物特异性、物候依赖性行为；基于阈值、机器学习、物联网（Internet of Things, IoT）、数字孪生及联合国粮农组织（Food and Agriculture Organization, FAO）-56等方法在作物物候方面的比较；以及物候感知调度的概念框架。在所综述的作物中，基线行为在整个生长季通常呈系统性变化，CWSI与作物水分状况之间的关系在冠层封垄后总体增强，且在生殖阶段测得的CWSI与产量的关系总体最为密切。对56项基于AI、热成像或CWSI的研究进行的定量审查发现，78.6%的研究要么未明确纳入作物物候，要么依赖年积日作为时间代理变量，而仅5.4%的研究明确编码了物候阶段。所提出的框架在基线估算、阈值设定以及用于灌溉调度的数据融合或机器学习中明确考虑物候；该框架在投入业务应用前尚需多年、多站点的田间验证。未来研究应优先建立标准化的物候定义，并开展长期、多地点研究以建立和验证阶段特异性参数。","Plant Science Today","2026-10-21T00:00:00Z",{"impact":17,"substance":262,"depth":17,"authority":124,"freshness":21,"relevant":22,"comment":420},"系统综述热成像与CWSI在物候期感知灌溉中的应用，含56项研究量化审计，方法学价值突出，但属综述类论文，影响偏学术层面。",[422],{"name":417,"url":414},[30,31,424,425,267],"遥感","作物水分胁迫",{"code":313,"slug":427,"name":428,"path":429,"parent_code":430},"irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",[432,436],{"code":69,"slug":433,"name":434,"path":435,"parent_code":39},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"code":35,"slug":36,"name":37,"path":38,"parent_code":39},{"status":42,"locked":14,"version":43,"method":44,"evidence":438},{"candidates":439,"agricultural_evidence":50},[440,444,450,454,460,466,473,480,487],{"code":313,"score":292,"supported":50,"title":441,"abstract":442,"tags":443},[427],[316,427],[267],{"code":69,"score":109,"supported":50,"title":445,"abstract":446,"tags":449},[],[447,448,72],"机器学习","machine learning",[31],{"code":35,"score":21,"supported":50,"title":451,"abstract":452,"tags":453},[],[59,60,188],[],{"code":48,"score":199,"supported":50,"title":455,"abstract":456,"tags":459},[],[457,458],"数字孪生","digital twin",[30],{"code":461,"score":199,"supported":50,"title":462,"abstract":463,"tags":465},"I3",[],[424,464],"remote sensing",[424],{"code":467,"score":56,"supported":50,"title":468,"abstract":469,"tags":472},"A2",[],[470,471],"小麦","wheat",[],{"code":474,"score":56,"supported":50,"title":475,"abstract":476,"tags":479},"A3",[],[477,478],"玉米","maize",[],{"code":481,"score":56,"supported":50,"title":482,"abstract":483,"tags":486},"A5",[],[484,485],"马铃薯","potato",[],{"code":488,"score":139,"supported":50,"title":489,"abstract":490,"tags":492},"B2",[],[491],"葡萄",[],[494,495],"热成像 作物水分胁迫","CWSI 灌溉调度","热成像作物水分胁迫-5564","10.14719\u002Fpst.17728",{"doi":497,"openalex_id":499,"authors":500,"venue":417,"cited_by_count":89,"oa_url":414,"card":516,"direction":96,"ingested_from":97},"W7223088921",[501,504,507,510,513],{"name":502,"orcid":503},"R M Jeya","https:\u002F\u002Forcid.org\u002F0009-0001-3621-8533",{"name":505,"orcid":506},"P. C. Prabu","https:\u002F\u002Forcid.org\u002F0000-0002-7237-9481",{"name":508,"orcid":509},"K P Ragunath","https:\u002F\u002Forcid.org\u002F0000-0002-7851-4437",{"name":511,"orcid":512},"P. Kannan","https:\u002F\u002Forcid.org\u002F0000-0001-7003-3535",{"name":514,"orcid":515},"P. M. Shanmugam","https:\u002F\u002Forcid.org\u002F0000-0002-6706-8419",{"tldr":517,"method":518,"finding":519,"direction":520,"opportunity":521},"综述物候期对热成像CWSI阈值的影响，提出物候感知灌溉调度框架。","综述葡萄、小麦、玉米等作物的热遥感CWSI研究，并审计56项AI\u002F热成像研究。","78.6%研究未显式纳入物候，仅5.4%编码物候期；CWSI与产量在生殖期关联最强。","农业遥感与作物表型","可开展多站点多年物候期特异性CWSI阈值标定与标准化物候定义研究。","2026-10-22T11:20:24.526683Z",{"id":524,"title":525,"url":526,"summary":527,"summary_zh":528,"content":9,"source_name":529,"source_url":526,"published_at":418,"category":12,"cover_url":9,"hotness":109,"is_selected":14,"score":530,"score_detail":531,"sources":533,"tags":535,"primary_topic":537,"secondary_topics":538,"classification":541,"search_phrases":588,"slug":591,"view_count":89,"doi":592,"paper":593,"created_at":612},5553,"Artificial Intelligence–Driven Precision Agriculture Systems: Engineering Frameworks for Climate-Resilient Food Production","https:\u002F\u002Fdoi.org\u002F10.53982\u002Fajerd.2026.01.14-j","Climate change, soil issues, water scarcity, and the increasing need for food worldwide mean we need smart farming systems that can manage resources flexibly. Old farming methods used the same inputs and simple rules for decisions. AI can help us manage resources better by adapting and optimizing them as we deal with complex environmental factors. This paper looks closely at AI-powered precision agriculture systems designed to make food production more resilient to changing weather. The authors reviewed research from 2021 to 2026 in agricultural engineering, computer engineering, and environmental systems. The authors examined how machine learning, deep learning, reinforcement learning, computer vision, robotics, and IoT sensors are used for predicting crop yields, spotting diseases, improving irrigation, managing nutrients, and automating field tasks. Our analysis shows that hybrid deep learning models (CNN-LSTM) are better than single models for predicting crop yields over space and time. Reinforcement learning algorithms are also better at adjusting irrigation cycles when weather is unpredictable. Using edge computing for system design reduces delays and improves real-time decision-making compared to traditional cloud-based systems. However, there are still big challenges with understanding how AI models work, having enough diverse data, ensuring cybersecurity, setting common standards, and making these systems affordable, especially in developing countries. To tackle these problems, we suggest a framework that combines AI analysis, distributed sensor networks, adaptive controls, and climate risk assessments. The authors provided critical insights into essential research gaps and outline a technological roadmap towards achieving sustainable, resilient and intelligent agricultural systems.","气候变化、土壤问题、水资源短缺以及全球日益增长的粮食需求，意味着我们需要能够灵活管理资源的智能农业系统。传统农业方法对相同的投入采用固定模式，并依赖简单规则进行决策。人工智能可以帮助我们更好地管理资源，通过适应和优化资源利用来应对复杂的环境因素。本文深入探讨了人工智能驱动的精准农业系统，旨在使粮食生产对不断变化的天气更具韧性。作者综述了2021年至2026年间农业工程、计算机工程和环境系统领域的研究。作者考察了机器学习、深度学习、强化学习、计算机视觉、机器人技术及物联网传感器如何被用于作物产量预测、病害识别、灌溉优化、养分管理和田间作业自动化。我们的分析表明，混合深度学习模型（CNN-LSTM）在时空作物产量预测方面优于单一模型。在天气不可预测的情况下，强化学习算法在调整灌溉周期方面也表现更优。与传统基于云的系统相比，采用边缘计算进行系统设计可降低延迟并改善实时决策能力。然而，在理解人工智能模型的工作原理、获取足够多样化的数据、确保网络安全、制定通用标准以及使这些系统具备可负担性方面，仍存在重大挑战，尤其是在发展中国家。为应对这些问题，我们提出了一个融合人工智能分析、分布式传感器网络、自适应控制和气候风险评估的框架。作者对关键研究空白提供了重要见解，并勾勒了实现可持续、韧性和智能农业系统的技术路线图。","ABUAD Journal of Engineering Research and Development (AJERD)",80,{"impact":17,"substance":262,"depth":17,"authority":20,"freshness":21,"relevant":22,"comment":532},"系统综述AI驱动精准农业的工程框架与技术路线，方法梳理扎实、结论有参考价值，但属综述类论文，产业落地影响有限。",[534],{"name":529,"url":526},[30,31,59,116,536],"气候韧性",{"code":69,"slug":433,"name":434,"path":435,"parent_code":39},[539,540],{"code":35,"slug":36,"name":37,"path":38,"parent_code":39},{"code":313,"slug":427,"name":428,"path":429,"parent_code":430},{"status":42,"locked":14,"version":43,"method":44,"evidence":542},{"candidates":543,"agricultural_evidence":50},[544,552,556,562,567,573,577,583],{"code":69,"score":17,"supported":50,"title":545,"abstract":546,"tags":551},[72],[547,447,548,448,549,550],"深度学习","计算机视觉","deep learning","computer vision",[31],{"code":48,"score":168,"supported":50,"title":553,"abstract":554,"tags":555},[127],[116,127],[30,116],{"code":35,"score":109,"supported":50,"title":557,"abstract":558,"tags":561},[],[59,559,188,560],"边缘计算","edge computing",[59],{"code":313,"score":21,"supported":50,"title":563,"abstract":564,"tags":566},[],[316,565,427],"水资源",[],{"code":138,"score":199,"supported":50,"title":568,"abstract":569,"tags":572},[],[570,571],"气候变化","climate change",[536],{"code":306,"score":56,"supported":50,"title":574,"abstract":575,"tags":576},[],[309,310],[],{"code":578,"score":139,"supported":50,"title":579,"abstract":580,"tags":582},"A7",[],[581],"间作",[],{"code":269,"score":139,"supported":50,"title":584,"abstract":585,"tags":587},[],[586],"养分管理",[],[589,590],"AI 精准农业 气候韧性","CNN-LSTM 作物产量预测","AI精准农业气候韧性-5553","10.53982\u002Fajerd.2026.01.14-j",{"doi":592,"openalex_id":594,"authors":595,"venue":529,"cited_by_count":89,"oa_url":526,"card":606,"direction":96,"ingested_from":97},"W7223121456",[596,599,601,603],{"name":597,"orcid":598},"Olumayowa J. Iyanda","https:\u002F\u002Forcid.org\u002F0000-0002-5340-9884",{"name":600,"orcid":9},"Oluwadare OYEBODE",{"name":602,"orcid":9},"Iretiayo Adelaiye",{"name":604,"orcid":605},"Ayomide Afolabi","https:\u002F\u002Forcid.org\u002F0000-0002-5171-886X",{"tldr":607,"method":608,"finding":609,"direction":610,"opportunity":611},"综述2021-2026年AI驱动精准农业系统，提出气候韧性粮食生产工程框架。","综述机器学习、深度学习、强化学习、计算机视觉、机器人与物联网在精准农业中的应用。","CNN-LSTM混合模型和强化学习在产量预测与灌溉调度上优于单一方法，边缘计算提升实时性。","农业人工智能与决策模型","可研究边缘计算与强化学习结合的轻量级灌溉决策系统，并探索发展中国家低成本部署与标准化路径。","2026-10-22T11:20:18.151376Z"]