[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2039":3,"related-2039":112},{"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,"primary_topic":31,"secondary_topics":37,"classification":38,"search_phrases":88,"slug":91,"view_count":92,"doi":93,"paper":94,"created_at":111},2039,"An Adaptive AI-DSS Framework for Real-Time Image Analysis and Decision-Making in Precision Agriculture","https:\u002F\u002Fdoi.org\u002F10.14445\u002F22315381\u002Fijett-v74i8p136","Environmental variability, pest infestation, and resource inefficiency have become increasingly problematic to agriculture, and on-line decision making using intelligent, adaptive technologies has been called for. This research is suggesting a novel Integrated Artificial Intelligence Decision Support System (AI-DSS) in precision agriculture so as to attain proper context-aware analysis and recommendation across dynamic field conditions. The framework is based on lightweight deep learning frameworks (MobileNetV2, Efficient Net-lite) and a newly designed Adaptive Feature Optimization (AFO) engine, which dynamically reweights convolutional features based on the temporal stability and environment consistency. Mathematically, the AFO mechanism is obtained via a weighted pooling of the instantaneous features of CNN with the temporal averaged prototypes using adaptive attention weights, which filter out transient noises due to illumination variations, occlusion, and sensor noises. The optimized features are fused with data from environmental and soil sensors in a hybrid Decision Support System (DSS) based on rule-based reasoning, Bayesian inference, and temporal tracking to construct explainable and region-specific recommendations to farmers. Experimental evaluations using the experimental data PlantVillage, DeepWeeds, and Fieldstream Sim demonstrate the superiority of the proposed AFO enhanced framework over the baseline CNN classifier in terms of classification accuracy (.95), macro F1 score (.95), and robustness to distortions (.15% improvement). Additionally, when deployed on edge devices like Raspberry Pi 4 and Nvidia Jetson Nano, the system achieves real-time inference latency (\u003C200 ms) and low energy consumption (~520 mJ\u002Fframe), which validates the scalability of the system in low-resource settings. In addition, the expert agreement was enhanced with the DSS module integration to 91.4% with 57% less false alarms. The obtained results validate the proposed AI-DSS framework with AFO as a promising solution to cover the distance between accuracy in a controlled lab environment and reliability in the field, providing a strong, explainable, and resource-efficient solution to the digital sustainable agriculture problem. This solution is further being expanded into proactive farm intelligence and climate resilience through multimodal sensing, satellite assisted crops monitoring, and adaptive decision-making-led solutions.","环境变异、病虫害侵袭和资源低效问题对农业的影响日益严重，亟需利用智能自适应技术进行在线决策。本研究提出了一种用于精准农业的新型集成人工智能决策支持系统（AI-DSS），旨在实现动态田间条件下的情境感知分析与推荐。该框架基于轻量级深度学习框架（MobileNetV2、EfficientNet-lite）和新设计的自适应特征优化（AFO）引擎，后者根据时间稳定性和环境一致性对卷积特征进行动态重加权。在数学上，AFO机制通过自适应注意力权重将CNN的瞬时特征与时间平均原型进行加权池化，从而滤除由光照变化、遮挡和传感器噪声引起的瞬态噪声。优化后的特征与环境和土壤传感器数据在混合决策支持系统（DSS）中融合，该系统基于规则推理、贝叶斯推断和时间追踪，为农民构建可解释的、区域特定的推荐方案。使用PlantVillage、DeepWeeds和Fieldstream Sim实验数据的评估表明，所提出的AFO增强框架在分类准确率（.95）、宏F1分数（.95）和抗失真鲁棒性（提升.15%）方面均优于基线CNN分类器。此外，在Raspberry Pi 4和Nvidia Jetson Nano等边缘设备上部署时，系统实现了实时推理延迟（\u003C200 ms）和低能耗（约520 mJ\u002F帧），验证了该系统在低资源环境下的可扩展性。同时，集成DSS模块后专家一致率提升至91.4%，误报率降低57%。所得结果验证了所提出的带有AFO的AI-DSS框架是一种有前景的解决方案，能够弥合受控实验室环境中的准确性与田间可靠性之间的差距，为数字可持续农业问题提供了稳健、可解释且资源高效的解决方案。该方案正进一步通过多模态传感、卫星辅助作物监测和自适应决策驱动的解决方案，扩展至主动式农场智能和气候韧性领域。",null,"International Journal of Engineering Trends and Technology","2026-09-09T00:00:00Z","论文",10,false,79,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,22,12,9,1,"提出融合自适应特征优化与多源传感器推理的轻量级AI决策支持框架，在多个公开数据集与边缘设备上验证了精度与实时性，方法新颖、结论可靠，对智慧农业落地有参考价值。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","农业人工智能","边缘计算","决策支持系统","精准农业",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[],{"status":39,"locked":14,"version":40,"method":41,"evidence":42},"classified","2026-10-22.2","rules",{"candidates":43,"agricultural_evidence":47},[44,52,61,69,76,83],{"code":45,"score":46,"supported":47,"title":48,"abstract":50,"tags":51},"I1",16,true,[49],"precision agriculture",[30,49],[26,30],{"code":32,"score":53,"supported":47,"title":54,"abstract":55,"tags":60},11,[],[56,57,58,59],"深度学习","deep learning","artificial intelligence","decision support",[27,29],{"code":62,"score":63,"supported":47,"title":64,"abstract":65,"tags":68},"H2",6,[],[66,67],"土壤","soil",[],{"code":70,"score":63,"supported":47,"title":71,"abstract":72,"tags":75},"H8",[],[73,74],"可持续农业","sustainable agriculture",[],{"code":77,"score":78,"supported":47,"title":79,"abstract":80,"tags":82},"H7",3,[],[81],"climate resilience",[],{"code":84,"score":21,"supported":14,"title":85,"abstract":86,"tags":87},"I5",[],[],[28],[89,90],"农业人工智能 决策支持系统 智慧农业 精准农业","农业人工智能 决策支持系统","农业人工智能决策支持系统智慧农业精准农业-2039",2,"10.14445\u002F22315381\u002Fijett-v74i8p136",{"doi":93,"openalex_id":95,"authors":96,"venue":10,"cited_by_count":101,"oa_url":102,"card":103,"direction":109,"ingested_from":110},"W7212021859",[97,99],{"name":98,"orcid":9},"Yebhushi Prashanth",{"name":100,"orcid":9},"Dr.Manna SheelaRani Chetty",0,"https:\u002F\u002Fijettjournal.org\u002FVolume-74\u002FIssue-8\u002FIJETT-V74I8P136.pdf",{"tldr":104,"method":105,"finding":106,"direction":107,"opportunity":108},"提出自适应AI决策支持系统，用轻量网络与特征优化实现精准农业实时图像分析与决策。","MobileNetV2\u002FEfficientNet-lite加自适应特征优化引擎，","分类准确率与宏F1达0.95，边缘设备延迟低于200毫秒，专家一致率91.4%。","农业人工智能与决策模型","可探索多模态传感与卫星遥感融合，提升田间跨区域泛化与气候韧性决策能力。","智慧农业 \u002F 农业物联网","openalex","2026-09-10T23:30:09.239423Z",{"total":63,"page":21,"page_size":63,"items":113},[114,217,298,356,433,503],{"id":115,"title":116,"url":117,"summary":118,"summary_zh":119,"content":9,"source_name":120,"source_url":117,"published_at":121,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":122,"score_detail":123,"sources":126,"tags":128,"primary_topic":131,"secondary_topics":132,"classification":143,"search_phrases":193,"slug":196,"view_count":101,"doi":197,"paper":198,"created_at":216},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)","2026-10-21T00:00:00Z",80,{"impact":17,"substance":18,"depth":17,"authority":124,"freshness":20,"relevant":21,"comment":125},13,"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。",[127],{"name":120,"url":117},[26,27,129,30,130],"物联网","气候韧性",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[133,137],{"code":84,"slug":134,"name":135,"path":136,"parent_code":36},"agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",{"code":138,"slug":139,"name":140,"path":141,"parent_code":142},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",{"status":39,"locked":14,"version":40,"method":41,"evidence":144},{"candidates":145,"agricultural_evidence":47},[146,154,158,164,170,177,181,187],{"code":32,"score":17,"supported":47,"title":147,"abstract":148,"tags":153},[58],[56,149,150,151,57,152],"机器学习","计算机视觉","machine learning","computer vision",[27],{"code":45,"score":46,"supported":47,"title":155,"abstract":156,"tags":157},[49],[30,49],[26,30],{"code":84,"score":13,"supported":47,"title":159,"abstract":160,"tags":163},[],[129,28,161,162],"IoT","edge computing",[129],{"code":138,"score":20,"supported":47,"title":165,"abstract":166,"tags":169},[],[167,168,139],"灌溉","水资源",[],{"code":77,"score":171,"supported":47,"title":172,"abstract":173,"tags":176},7,[],[174,175],"气候变化","climate change",[130],{"code":62,"score":63,"supported":47,"title":178,"abstract":179,"tags":180},[],[66,67],[],{"code":182,"score":78,"supported":47,"title":183,"abstract":184,"tags":186},"A7",[],[185],"间作",[],{"code":188,"score":78,"supported":47,"title":189,"abstract":190,"tags":192},"F3",[],[191],"养分管理",[],[194,195],"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":197,"openalex_id":199,"authors":200,"venue":120,"cited_by_count":101,"oa_url":117,"card":211,"direction":109,"ingested_from":110},"W7223121456",[201,204,206,208],{"name":202,"orcid":203},"Olumayowa J. Iyanda","https:\u002F\u002Forcid.org\u002F0000-0002-5340-9884",{"name":205,"orcid":9},"Oluwadare OYEBODE",{"name":207,"orcid":9},"Iretiayo Adelaiye",{"name":209,"orcid":210},"Ayomide Afolabi","https:\u002F\u002Forcid.org\u002F0000-0002-5171-886X",{"tldr":212,"method":213,"finding":214,"direction":107,"opportunity":215},"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。","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",{"id":218,"title":219,"url":220,"summary":221,"summary_zh":222,"content":9,"source_name":223,"source_url":220,"published_at":224,"category":12,"cover_url":9,"hotness":13,"is_selected":47,"score":225,"score_detail":226,"sources":232,"tags":234,"primary_topic":237,"secondary_topics":238,"classification":239,"search_phrases":266,"slug":269,"view_count":256,"doi":270,"paper":271,"created_at":297},5382,"Modeling the global potential of smart farming innovations","https:\u002F\u002Fdoi.org\u002F10.1073\u002Fpnas.2530081123","Smart farming innovations can improve agricultural productivity, resilience, and environmental sustainability, yet their real-world impact hinges on their diffusion dynamics, which in turn depend on their economic viability. We present here a global assessment of the profitability of smart farming innovation across croplands at a spatial resolution of ~5 km from the farmers’ and the societal perspectives. We cover application mapping for precision agriculture, automated application of inputs, autonomous weeding machines, GPS steering assistance systems, and autonomous irrigation systems. For this, we compiled a globally comprehensive dataset of smart farming innovation adoption locations (N = 4,422) based on a combination of a survey (N = 2,677) and a meta-analysis (N = 1,745) and combined them with remotely sensed and statistical data to train an ensemble of five geospatial machine learning models (generalized linear models, random forests, support vector machines, maximum entropy, and boosted regression trees), which show the global distribution of the different innovations’ profitability and its overlap with major environmental issues the innovations can help to mitigate. Simulations of future socioeconomic pathways combined with assumptions about future agri-environmental policies reveal that, especially in middle-income countries, the potential of smart farming will grow over time.","智慧农业创新能够提升农业生产力、韧性和环境可持续性，然而其现实影响取决于扩散动态，而扩散动态又取决于其经济可行性。本文从农民和社会两个视角，以约5公里的空间分辨率对全球农田范围内智慧农业创新的盈利能力进行了评估。研究涵盖精准农业的应用测绘、投入品的自动化施用、自主除草机械、GPS转向辅助系统以及自主灌溉系统。为此，我们基于一项调查（N = 2,677）和一项元分析（N = 1,745）相结合，编制了全球范围内较为全面的智慧农业创新采用地点数据集（N = 4,422），并将其与遥感和统计数据相结合，用于训练由五种地理空间机器学习模型（广义线性模型、随机森林、支持向量机、最大熵和提升回归树）组成的集成模型，这些模型揭示了不同创新盈利能力的全球分布及其与这些创新有助于缓解的主要环境问题的重叠情况。对未来社会经济路径的模拟结合对未来农业环境政策的假设表明，智慧农业的潜力将随时间推移而增长，尤其是在中等收入国家。","Proceedings of the National Academy of Sciences","2026-10-19T00:00:00Z",93,{"impact":227,"substance":228,"depth":229,"authority":230,"freshness":20,"relevant":21,"comment":231},26,24,19,15,"PNAS 全球尺度智慧农业盈利性评估，数据规模与方法新颖，结论对智慧农业推广具政策参考价值，值得进入每日精选。",[233],{"name":223,"url":220},[26,27,30,235,236],"遥感","全球评估",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":240},{"candidates":241,"agricultural_evidence":47},[242,246,250,254,260],{"code":45,"score":53,"supported":47,"title":243,"abstract":244,"tags":245},[],[26,30,49],[26,30],{"code":32,"score":171,"supported":47,"title":247,"abstract":248,"tags":249},[],[149,151],[27],{"code":138,"score":63,"supported":47,"title":251,"abstract":252,"tags":253},[],[167,139],[],{"code":255,"score":256,"supported":47,"title":257,"abstract":258,"tags":259},"I3",4,[],[235],[235],{"code":261,"score":78,"supported":47,"title":262,"abstract":263,"tags":265},"F4",[],[264],"除草",[],[267,268],"农业人工智能 全球评估 智慧农业 精准农业","农业人工智能 全球评估","农业人工智能全球评估智慧农业精准农业-5382","10.1073\u002Fpnas.2530081123",{"doi":270,"openalex_id":272,"authors":273,"venue":223,"cited_by_count":101,"oa_url":220,"card":292,"direction":109,"ingested_from":110},"W7222974188",[274,277,280,283,286,289],{"name":275,"orcid":276},"Shahrear Roman","https:\u002F\u002Forcid.org\u002F0009-0006-5659-4292",{"name":278,"orcid":279},"Hadi Hadi","https:\u002F\u002Forcid.org\u002F0000-0002-2410-9751",{"name":281,"orcid":282},"Wyclife Agumba Oluoch","https:\u002F\u002Forcid.org\u002F0000-0003-0803-9279",{"name":284,"orcid":285},"David Zilberman","https:\u002F\u002Forcid.org\u002F0000-0001-5743-5134",{"name":287,"orcid":288},"Robert Finger","https:\u002F\u002Forcid.org\u002F0000-0002-0634-5742",{"name":290,"orcid":291},"David Wuepper","https:\u002F\u002Forcid.org\u002F0000-0002-1344-6023",{"tldr":293,"method":294,"finding":295,"direction":109,"opportunity":296},"全球5公里尺度评估五类智慧农业创新在农田的盈利性与扩散潜力。","整合4422个采用点调查与元分析数据，用五种地理空间机器学习集成建模。","智慧农业盈利性空间差异显著，中等收入国家未来潜力将随社会经济路径增长。","可结合区域政策与农户异质性，细化盈利性预测并评估环境协同效益。","2026-10-20T23:30:08.754594Z",{"id":299,"title":300,"url":301,"summary":302,"summary_zh":303,"content":9,"source_name":304,"source_url":301,"published_at":305,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":306,"score_detail":307,"sources":311,"tags":313,"primary_topic":316,"secondary_topics":317,"classification":318,"search_phrases":342,"slug":345,"view_count":101,"doi":346,"paper":347,"created_at":355},5311,"AI SMART AGRO SYSTEM: A WEB-BASED AI-ASSISTED DECISION SUPPORT PLATFORM FOR FARMERS","https:\u002F\u002Fdoi.org\u002F10.56726\u002Firjmets103514","Farmers often have to decide on disease control, crop selection, soil management and irrigation without timely expert advice, which leads to yield loss and wastage of water and fertiliser.This paper presents AI Smart Agro System, a web-based decision-support platform that brings five services into one dashboard: leaf disease detection from an uploaded photograph, crop recommendation, soil health analysis, irrigation advisory and weather-based farming advice.The system is built with PHP, MySQL and JavaScript, while disease detection and crop recommendation are handled by an external Python Flask AI service that is called through a secured REST API.Soil, irrigation and weather advice are generated by transparent rule-based logic using soil pH, N-P-K values, soil moisture, rainfall, temperature and humidity.Every result is stored in the user's history, and notifications and an administrator panel support monitoring.The prototype is designed to return the disease name, confidence, treatment and prevention tips for an uploaded leaf image, to recommend the three most suitable crops, and to give clear advice for soil and water management.Quantitative evaluation of the AI models is left for future work.","农民常常需要在没有及时专家建议的情况下决定病害防治、作物选择、土壤管理和灌溉，这会导致产量损失以及水和肥料的浪费。本文提出AI Smart Agro System，这是一个基于Web的决策支持平台，将五项服务整合到一个仪表板中：根据上传照片进行叶片病害检测、作物推荐、土壤健康分析、灌溉建议以及基于天气的农事建议。该系统使用PHP、MySQL和JavaScript构建，而病害检测和作物推荐由外部Python Flask AI服务处理，并通过安全的REST API调用。土壤、灌溉和天气建议由透明的基于规则的逻辑生成，使用土壤pH、N-P-K值、土壤湿度、降雨量、温度和湿度。每个结果都存储在用户历史记录中，通知和管理员面板支持监控。该原型旨在为上传的叶片图像返回病害名称、置信度、治疗和预防提示，推荐三种最合适的作物，并为土壤和水管理提供明确建议。AI模型的定量评估留待未来工作。","International Research Journal of Modernization in Engineering Technology and Science","2026-10-17T00:00:00Z",49,{"impact":308,"substance":309,"depth":19,"authority":63,"freshness":20,"relevant":21,"comment":310},8,14,"原型系统整合病害识别、作物推荐与灌溉建议，思路清晰但缺乏定量评估，属细分技术进展，公共影响有限。",[312],{"name":304,"url":301},[26,27,29,314,315],"病害识别","灌溉管理",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":319},{"candidates":320,"agricultural_evidence":47},[321,325,329,333,338],{"code":32,"score":53,"supported":47,"title":322,"abstract":323,"tags":324},[59],[],[27,29,314],{"code":138,"score":171,"supported":47,"title":326,"abstract":327,"tags":328},[],[167,139],[315],{"code":62,"score":63,"supported":47,"title":330,"abstract":331,"tags":332},[],[66,67],[],{"code":188,"score":78,"supported":47,"title":334,"abstract":335,"tags":337},[],[336],"肥料",[],{"code":45,"score":21,"supported":14,"title":339,"abstract":340,"tags":341},[],[],[26],[343,344],"Web 农业决策支持平台","农业人工智能 决策支持系统 智慧农业 灌溉管理","Web农业决策支持平台-5311","10.56726\u002Firjmets103514",{"doi":346,"openalex_id":348,"authors":349,"venue":304,"cited_by_count":101,"oa_url":301,"card":350,"direction":109,"ingested_from":110},"W7222785888",[],{"tldr":351,"method":352,"finding":353,"direction":107,"opportunity":354},"构建一个面向农民的网页版AI决策支持平台，集成病害识别、选种、土壤、灌溉与天气建议。","PHP\u002FMySQL\u002FJS前端，Python Flask提供病害与选种AI服务，规","原型可输出病害名称、置信度与防治建议，推荐三种适宜作物并给出水土管理建议。","AI模型尚未定量评估，可补充精度验证与田间实测，并扩展多语言、离线与本地化作物模型。","2026-10-19T23:30:08.043132Z",{"id":357,"title":358,"url":359,"summary":360,"summary_zh":361,"content":9,"source_name":362,"source_url":359,"published_at":363,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":364,"score_detail":365,"sources":369,"tags":371,"primary_topic":373,"secondary_topics":374,"classification":375,"search_phrases":390,"slug":393,"view_count":101,"doi":394,"paper":395,"created_at":432},5306,"A growing challenge: stakeholder perceptions of AI and ML applications in U.S. agriculture","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11119-026-10459-x","Abstract Context The global population is expected to exceed ten billion by 2050 and food demand is projected to increase by up to 100% in the same timeframe. Agricultural management must adapt to meet the increasing global demands. Artificial intelligence (AI) and machine learning (ML) technologies can help the agricultural community respond to these needs. Aims An online survey and follow-up interviews were conducted to examine patterns of use, perceptions, and barriers among early adopting stakeholders regarding AI and ML. Methods The online survey with agricultural stakeholders (n=129) included questions regarding the participants’ views on incorporating AI and ML techniques into farming practices and individual follow-up interviews with agricultural stakeholders (n=10) were conducted to better understand their concerns toward technology use and adoption of AI-enabled tools and technologies. Key results Using a mixed-methods approach, four challenges to establishing AI-\u002FML-enabled technology in agriculture were identified. Namely, challenges related to (1) systems of systems (SoS), (2) sub-seasonal to seasonal weather (S2S) forecasts, (3) precision soil management, and (4) policy and economics. Each challenge was discussed in the context of recent literature. Conclusion Concerns were voiced clustered around four broad research themes. This brief communication brings the perspective of agricultural stakeholders to the discussion of the potential of AI\u002FML-enabled technology and the direction of their development in the agricultural sector. Implications Data collected in 2021, establish an important baseline for the early phase of AI\u002FML use in U.S. agriculture, enabling comparison of future perceptions and developments in the sector. Impact This brief communication describes a survey and follow-up interviews conducted with agricultural stakeholders on factors influencing their willingness and reluctance to incorporate AI and ML techniques into farming practices. The findings, summarized and discussed as four key challenges for AI and ML in the agricultural sector, have the potential to streamline research activities.","摘要\n\n背景 预计到2050年全球人口将超过100亿，同期粮食需求预计将增加多达100%。农业管理必须作出调整以满足不断增长的全球需求。人工智能（AI）和机器学习（ML）技术可帮助农业界应对这些需求。\n\n目的 通过开展在线调查和后续访谈，考察早期采用相关技术的利益相关者在AI和ML方面的使用模式、认知及障碍。\n\n方法 针对农业利益相关者（n=129）的在线调查包含有关参与者对将AI和ML技术纳入农业实践看法的问题，并对农业利益相关者（n=10）进行个体后续访谈，以更好地了解他们对技术使用及采用AI赋能工具和技术的顾虑。\n\n主要结果 采用混合方法，识别出在农业中建立AI\u002FML赋能技术面临的四项挑战，即与以下方面相关的挑战：（1）系统之系统（SoS），（2）次季节至季节天气（S2S）预报，（3）精准土壤管理，以及（4）政策与经济。每项挑战均结合近期文献进行了讨论。\n\n结论 所表达的顾虑集中于四个广泛的研究主题。本简报将农业利益相关者的视角引入关于AI\u002FML赋能技术潜力及其在农业部门发展方向讨论中。\n\n意义 2021年收集的数据为美国农业中AI\u002FML应用的早期阶段建立了重要基线，有助于比较该部门未来的认知和发展。\n\n影响 本简报描述了针对农业利益相关者开展的一项调查和后续访谈，内容涉及影响其愿意和不愿将AI和ML技术纳入农业实践的因素。研究结果总结并讨论为农业部门AI和ML的四项关键挑战，有望优化研究活动。","Precision Agriculture","2026-10-18T00:00:00Z",77,{"impact":17,"substance":366,"depth":367,"authority":309,"freshness":308,"relevant":21,"comment":368},20,17,"基于129份问卷与10次访谈的混合方法研究，系统梳理美国农业AI\u002FML应用的四大挑战，为早期采用者认知提供基线数据，对智慧农业研究有参考价值。",[370],{"name":362,"url":359},[26,27,149,30,372],"农户认知",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":376},{"candidates":377,"agricultural_evidence":47},[378,382,386],{"code":32,"score":53,"supported":47,"title":379,"abstract":380,"tags":381},[],[149,151,58],[27,149],{"code":62,"score":63,"supported":47,"title":383,"abstract":384,"tags":385},[],[66,67],[],{"code":45,"score":92,"supported":14,"title":387,"abstract":388,"tags":389},[],[],[26,30],[391,392],"美国农业 AI ML 调查","农业人工智能 应用障碍","美国农业AIML调查-5306","10.1007\u002Fs11119-026-10459-x",{"doi":394,"openalex_id":396,"authors":397,"venue":362,"cited_by_count":101,"oa_url":359,"card":427,"direction":107,"ingested_from":110},"W7222866367",[398,401,404,407,410,413,416,419,421,424],{"name":399,"orcid":400},"Jennifer Koch","https:\u002F\u002Forcid.org\u002F0000-0002-7067-2705",{"name":402,"orcid":403},"Andrew Fox","https:\u002F\u002Forcid.org\u002F0000-0001-6719-8902",{"name":405,"orcid":406},"Richa Bhattarai","https:\u002F\u002Forcid.org\u002F0000-0003-3827-7761",{"name":408,"orcid":409},"Hank C. Jenkins‐Smith","https:\u002F\u002Forcid.org\u002F0000-0002-5204-2726",{"name":411,"orcid":412},"Jonathan McFadden","https:\u002F\u002Forcid.org\u002F0000-0003-3762-1428",{"name":414,"orcid":415},"Audrey Reinert","https:\u002F\u002Forcid.org\u002F0000-0001-8951-702X",{"name":417,"orcid":418},"Juan Sesmero","https:\u002F\u002Forcid.org\u002F0000-0002-2669-4536",{"name":420,"orcid":9},"M. Z. Walkup",{"name":422,"orcid":423},"Wolfgang Jentner","https:\u002F\u002Forcid.org\u002F0000-0003-1045-6020",{"name":425,"orcid":426},"David S. Ebert","https:\u002F\u002Forcid.org\u002F0000-0001-6177-1296",{"tldr":428,"method":429,"finding":430,"direction":107,"opportunity":431},"通过问卷与访谈调查美国农业利益相关者对AI\u002FML应用的认知与障碍。","在线问卷129人加10人深度访谈的混合方法。","识别出系统之系统、次季节天气预报、精准土壤管理和政策经济四大挑战。","可针对四类障碍开展分区域、分作物类型的采纳意愿追踪与干预研究。","2026-10-19T23:30:02.345893Z",{"id":434,"title":435,"url":436,"summary":437,"summary_zh":438,"content":9,"source_name":439,"source_url":436,"published_at":305,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":440,"score_detail":441,"sources":443,"tags":445,"primary_topic":448,"secondary_topics":449,"classification":450,"search_phrases":478,"slug":481,"view_count":21,"doi":482,"paper":483,"created_at":502},5248,"An entropy-guided dynamic lightweight network for identification of wild edible vegetables of BTR","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs44163-026-02528-x","Wild edible vegetables of Bodoland Territorial Region (BTR) are the species of plants that grow naturally and are commonly consumed as food by the indigenous communities. These plants are important sources of nutritional and therapeutic properties and are essential for food security and socio-economic development of rural communities. Therefore, sustainable conservation and utilization of these unique plant resources are of great significance. Developing an efficient and accurate automatic identification system is an essential conservation strategy for them. Current plant identification methods are mainly based on classical machine learning (ML) methods and deep convolutional neural network (CNN) architectures for recognition and classification. These methods are effective for images taken under controlled or pristine conditions, but the performance is often far from ideal in the complex and natural environments of forest and grassland, where wild edible plants are found. This paper proposes an entropy-guided dynamic lightweight deep learning architecture for automatic detection of wild edible vegetables in BTR. The proposed model employs adaptive computation with Dynamic Kernel Selection (DKS), Region-wise Adaptive Feature Routing (RAFR), Ultra-Light Context Encoding (ULCE), and Entropy-Guided Global Pooling (EGGP) to capture multi-scale plant characteristics efficiently and reduce redundant computations. The novelty of our proposed framework lies in its coordinated integration of these modules that is designed to reduce computational overhead without compromising with performance. Experimental results show that the proposed model achieves excellent classification accuracy of 90.69% with significantly reduced parameter complexity and floating-point operations (FLOPs), which is well-suited for real-time precision agriculture and low-resource edge deployment applications.","博多兰领土区域（BTR）的野生食用蔬菜是自然生长并被土著社区普遍作为食物食用的植物物种。这些植物是营养和治疗特性的重要来源，对农村社区的粮食安全和社会经济发展至关重要。因此，对这些独特植物资源的可持续保护和利用具有重要意义。开发高效准确的自动识别系统是保护这些植物资源的重要策略。当前的植物识别方法主要基于经典机器学习（ML）方法和深度卷积神经网络（CNN）架构进行识别和分类。这些方法对于在受控或原始条件下拍摄的图像是有效的，但在野生食用植物所处的森林和草地复杂自然环境中，其性能往往远不理想。本文提出了一种熵引导的动态轻量级深度学习架构，用于自动检测BTR中的野生食用蔬菜。所提出的模型采用动态核选择（DKS）、区域自适应特征路由（RAFR）、超轻量上下文编码（ULCE）和熵引导全局池化（EGGP）的自适应计算，以高效捕捉多尺度植物特征并减少冗余计算。我们所提出框架的新颖性在于这些模块的协同集成，旨在在不影响性能的情况下降低计算开销。实验结果表明，所提出的模型达到了90.69%的优异分类准确率，同时显著降低了参数复杂度和浮点运算量（FLOPs），非常适合实时精准农业和低资源边缘部署应用。","Discover Artificial Intelligence",70,{"impact":19,"substance":366,"depth":367,"authority":19,"freshness":20,"relevant":21,"comment":442},"提出熵引导动态轻量网络用于野生食用蔬菜识别，准确率90.69%且参数与算力显著降低，方法有新意、结论可靠，但属细分应用研究，公共影响有限。",[444],{"name":439,"url":436},[26,27,28,446,447],"野生蔬菜","植物识别",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":451},{"candidates":452,"agricultural_evidence":47},[453,457,461,468,474],{"code":32,"score":13,"supported":47,"title":454,"abstract":455,"tags":456},[],[56,149,151,57],[27],{"code":45,"score":171,"supported":47,"title":458,"abstract":459,"tags":460},[],[30,49],[26],{"code":462,"score":63,"supported":47,"title":463,"abstract":464,"tags":467},"A8",[],[465,466],"粮食安全","food security",[],{"code":469,"score":78,"supported":47,"title":470,"abstract":471,"tags":473},"B1",[],[472],"蔬菜",[],{"code":84,"score":21,"supported":14,"title":475,"abstract":476,"tags":477},[],[],[28],[479,480],"BTR 野生食用蔬菜 识别","熵引导 轻量网络 植物识别","BTR野生食用蔬菜识别-5248","10.1007\u002Fs44163-026-02528-x",{"doi":482,"openalex_id":484,"authors":485,"venue":439,"cited_by_count":101,"oa_url":436,"card":497,"direction":107,"ingested_from":110},"W7222842686",[486,488,491,494],{"name":487,"orcid":9},"Tania Sarkar",{"name":489,"orcid":490},"Pranav Kumar Singh","https:\u002F\u002Forcid.org\u002F0000-0001-8987-0229",{"name":492,"orcid":493},"Parag Bhuyan","https:\u002F\u002Forcid.org\u002F0009-0006-4430-1851",{"name":495,"orcid":496},"Anshuman Kalla","https:\u002F\u002Forcid.org\u002F0000-0002-3985-4578",{"tldr":498,"method":499,"finding":500,"direction":107,"opportunity":501},"提出熵引导动态轻量网络，用于识别BTR野生食用蔬菜。","动态核选择、区域自适应特征路由、超轻上下文编码与熵引导池化。","分类准确率达90.69%，参数量和FLOPs显著降低，适合边缘部署。","可探索复杂自然场景下轻量模型在低资源边缘设备的实时植物识别与保护应用。","2026-10-18T23:30:58.805321Z",{"id":504,"title":505,"url":506,"summary":507,"summary_zh":508,"content":9,"source_name":509,"source_url":506,"published_at":510,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":511,"score_detail":512,"sources":514,"tags":516,"primary_topic":518,"secondary_topics":524,"classification":526,"search_phrases":545,"slug":548,"view_count":21,"doi":549,"paper":550,"created_at":584},5164,"Lightweight Hybrid CNN-Transformer Network for Robust Maize Leaf Disease Detection and Edge Deployment","https:\u002F\u002Fdoi.org\u002F10.22266\u002Fijies2026.1130.50","Early and accurate detection of maize leaf diseases is essential for improving crop productivity and supporting precision agriculture.However, deep learning models trained on controlled leaf-image datasets often experience performance degradation under real-world agricultural conditions because of illumination variation, background clutter, occlusion, leaf orientation, and imaging noise.To address these challenges, this study develops a lightweight deep learning framework for robust maize leaf disease classification and edge-oriented deployment.The framework employs MobileNetV3-Small for efficient extraction of disease-discriminative visual features and investigates lightweight feature-learning strategies for improving classification under challenging field conditions.Experiments were conducted using PlantVillage-Maize and Field-Maize datasets comprising 6,188 images from four categories: Maize Leaf Blight (MLB), Common Rust, Healthy, and Leaf Spot.The image-only MobileNetV3-Small model achieved 97.8% accuracy and a 97.4% F1-score on the controlled PlantVillage-Maize dataset, while its performance decreased to 86.1% accuracy and an 83.9% F1-score on Field-Maize, highlighting the domain gap between controlled and real-field imagery.Robustness was further evaluated under Gaussian noise, illumination variation, and partial occlusion, together with computational-efficiency and edge-deployment analyses.The findings demonstrate the importance of lightweight and robust visual feature learning for practical maize disease diagnosis under real-world agricultural imaging conditions.","早期准确检测玉米叶片病害对于提高作物生产力和支持精准农业至关重要。然而，在受控叶片图像数据集上训练的深度学习模型，在真实农业条件下往往会出现性能下降，原因包括光照变化、背景杂乱、遮挡、叶片朝向以及成像噪声。为应对这些挑战，本研究开发了一个轻量级深度学习框架，用于稳健的玉米叶片病害分类和面向边缘的部署。该框架采用MobileNetV3-Small高效提取具有病害判别力的视觉特征，并研究轻量级特征学习策略，以提升在复杂田间条件下的分类性能。实验使用PlantVillage-Maize和Field-Maize数据集进行，共包含来自四个类别的6，188张图像：玉米大斑病（Maize Leaf Blight，MLB）、普通锈病（Common Rust）、健康（Healthy）和叶斑病（Leaf Spot）。仅使用图像的MobileNetV3-Small模型在受控的PlantVillage-Maize数据集上达到了97.8%的准确率和97.4%的F1分数，而在Field-Maize上的性能下降至86.1%的准确率和83.9%的F1分数，凸显了受控图像与真实田间图像之间的域差距。研究进一步在高斯噪声、光照变化和部分遮挡条件下评估了稳健性，并进行了计算效率和边缘部署分析。研究结果表明，轻量级且稳健的视觉特征学习对于真实农业成像条件下的实际玉米病害诊断具有重要意义。","International journal of intelligent engineering and systems","2026-10-16T00:00:00Z",69,{"impact":19,"substance":366,"depth":46,"authority":19,"freshness":20,"relevant":21,"comment":513},"该研究提出轻量级玉米叶病检测框架，并揭示受控数据集与田间数据之间的性能差距，方法扎实、结论有参考价值，但属细分技术论文，公共影响有限。",[515],{"name":509,"url":506},[26,27,28,56,517],"玉米病害",{"code":519,"slug":520,"name":521,"path":522,"parent_code":523},"A3","maize","玉米","\u002Ftopic\u002Ffield-crops\u002Fmaize","A",[525],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":527},{"candidates":528,"agricultural_evidence":47},[529,533,537,541],{"code":519,"score":309,"supported":47,"title":530,"abstract":531,"tags":532},[520],[521,520],[],{"code":32,"score":308,"supported":47,"title":534,"abstract":535,"tags":536},[],[56,57],[27,56],{"code":45,"score":171,"supported":47,"title":538,"abstract":539,"tags":540},[],[30,49],[26],{"code":84,"score":21,"supported":14,"title":542,"abstract":543,"tags":544},[],[],[28],[546,547],"MobileNetV3 玉米叶部病害","玉米叶片病害 边缘部署","MobileNetV3玉米叶部病害-5164","10.22266\u002Fijies2026.1130.50",{"doi":549,"openalex_id":551,"authors":552,"venue":509,"cited_by_count":101,"oa_url":506,"card":578,"direction":583,"ingested_from":110},"W7222602850",[553,556,559,561,563,566,568,570,573,575],{"name":554,"orcid":555},"Awwab Mohammad","https:\u002F\u002Forcid.org\u002F0000-0002-1148-5678",{"name":557,"orcid":558},"Punith kumar","https:\u002F\u002Forcid.org\u002F0009-0005-5676-4194",{"name":560,"orcid":9},"Bhavani K",{"name":562,"orcid":9},"Sailee N. Panchbhai",{"name":564,"orcid":565},"Sagarkumar S. Badhiye","https:\u002F\u002Forcid.org\u002F0000-0002-0710-3761",{"name":567,"orcid":9},"Manoj Kumar S B",{"name":569,"orcid":9},"Dharamvir",{"name":571,"orcid":572},"Sumit Kumar Sar","https:\u002F\u002Forcid.org\u002F0000-0003-0384-9088",{"name":574,"orcid":9},"Rani Chhagan Dalvi",{"name":576,"orcid":577},"Yogesh Harishchandra Bhosale","https:\u002F\u002Forcid.org\u002F0000-0001-6901-1419",{"tldr":579,"method":580,"finding":581,"direction":107,"opportunity":582},"构建轻量MobileNetV3-Small框架实现玉米叶片病害分类并评估边缘部署。","MobileNetV3-Small，PlantVillage-Maize与Fie","受控数据准确率97.8%，田间降至86.1%，揭示明显域差距。","可研究跨域自适应与田间鲁棒特征学习，缩小受控与真实场景性能差距。","农业遥感与作物表型","2026-10-17T23:30:28.248539Z"]