[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2961":3,"related-2961":100},{"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":59,"slug":62,"view_count":63,"doi":64,"paper":65,"created_at":99},2961,"Advances in Bioacoustics Sensing and Signal Processing Technologies","https:\u002F\u002Fdoi.org\u002F10.1088\u002F2631-7990\u002Faea9da","Abstract Driven by advances in flexible electronics, microfabrication, and artificial intelligence, bioacoustics is revolutionizing sensing and signal processing across health, ecology, and intelligent systems. This review systematically examines key progress in high-performance acoustic sensors (e.g., piezoelectric, triboelectric, MEMS) and acquisition schemes for multi-scale biological sounds, ranging from heart tones to plant stress emissions. Furthermore, we delve into advanced methodologies for acoustic signal preprocessing, feature extraction, and classification, with a particular emphasis on the powerful analytical capabilities of multimodal data fusion and deep learning algorithms in complex real-world scenarios. Crucially, although the sensing requirements and biological targets vary drastically across medical, ecological, and agricultural domains, this review establishes a unifying framework centered on the shared physical and algorithmic challenges of bioacoustic information flow. By juxtaposing these diverse fields, the potential strategies for the interdisciplinary integration of sensing methodologies are highlighted. We also critically assess the essential roles of acoustic physical modeling, standardized testing frameworks, and gold-standard databases in evaluating system performance. Finally, the review highlights cutting-edge applications of bioacoustics in human health monitoring, medical diagnostics, human-computer interaction, and precision agriculture. By synthesizing current technological convergences and outlining future trajectories, we provide a comprehensive perspective on the promising directions and pivotal challenges facing bioacoustics research.","摘要 在柔性电子、微加工和人工智能进步的推动下，生物声学正在革新健康、生态和智能系统中的传感与信号处理。本文系统梳理了高性能声学传感器（如压电、摩擦电、MEMS）以及从心音到植物胁迫发射等多尺度生物声音采集方案的关键进展。此外，我们深入探讨了声学信号预处理、特征提取和分类的先进方法，特别强调了多模态数据融合和深度学习算法在复杂现实场景中的强大分析能力。至关重要的是，尽管医学、生态和农业领域在传感需求和生物目标上差异巨大，本文建立了一个以生物声学信息流中共同的物理和算法挑战为中心的统一框架。通过将这些不同领域并置比较，凸显了传感方法学跨学科整合的潜在策略。我们还批判性地评估了声学物理建模、标准化测试框架和金标准数据库在评估系统性能中的重要作用。最后，本文重点介绍了生物声学在人体健康监测、医学诊断、人机交互和精准农业中的前沿应用。通过综合当前技术汇聚趋势并勾勒未来轨迹，我们为生物声学研究面临的有前景方向和关键挑战提供了全面视角。",null,"International Journal of Extreme Manufacturing","2026-09-18T00:00:00Z","论文",10,false,78,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,20,14,8,1,"该综述系统梳理生物声学传感与信号处理技术，并明确指向精准农业与植物胁迫声发射监测，对农业信息化具有跨领域参考价值，但属综述类论文，非产业级突破。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","农业人工智能","精准农业","多模态融合","生物声学传感",{"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":46},[44,53],{"code":32,"score":45,"supported":46,"title":47,"abstract":48,"tags":52},11,true,[],[49,50,51],"深度学习","deep learning","artificial intelligence",[27,29],{"code":54,"score":20,"supported":46,"title":55,"abstract":56,"tags":58},"I1",[],[28,57],"precision agriculture",[26,28],[60,61],"生物声学 传感器 信号处理","植物胁迫 声发射 监测","生物声学传感器信号处理-2961",3,"10.1088\u002F2631-7990\u002Faea9da",{"doi":64,"openalex_id":66,"authors":67,"venue":10,"cited_by_count":90,"oa_url":6,"card":91,"direction":97,"ingested_from":98},"W7213590695",[68,71,74,76,79,82,84,87],{"name":69,"orcid":70},"Chengyu Li","https:\u002F\u002Forcid.org\u002F0000-0002-2128-3420",{"name":72,"orcid":73},"Wenbo Li","https:\u002F\u002Forcid.org\u002F0000-0003-3599-3324",{"name":75,"orcid":9},"Jingyang Wu",{"name":77,"orcid":78},"Shuo Wang","https:\u002F\u002Forcid.org\u002F0000-0002-5411-7269",{"name":80,"orcid":81},"Han Liao","https:\u002F\u002Forcid.org\u002F0009-0001-6256-7438",{"name":83,"orcid":9},"xiang Yu",{"name":85,"orcid":86},"Cheng Li","https:\u002F\u002Forcid.org\u002F0000-0003-3424-2414",{"name":88,"orcid":89},"Xiaoming Tao","https:\u002F\u002Forcid.org\u002F0000-0002-2406-0695",0,{"tldr":92,"method":93,"finding":94,"direction":95,"opportunity":96},"综述生物声学传感与信号处理进展，涵盖医疗、生态与农业应用。","综述压电\u002F摩擦电\u002FMEMS声传感器、深度学习与多模态融合方法。","建立跨领域统一框架，强调物理建模、标准测试与数据库的关键作用。","智慧农业 \u002F 农业物联网","植物胁迫声发射的标准化采集与深度学习分类，可填补农业声学监测空白。","农业人工智能与决策模型","openalex","2026-09-19T23:30:51.998646Z",{"total":101,"page":21,"page_size":101,"items":102},6,[103,169,279,360,437,503],{"id":104,"title":105,"url":106,"summary":107,"summary_zh":108,"content":9,"source_name":109,"source_url":106,"published_at":110,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":111,"score_detail":112,"sources":116,"tags":118,"primary_topic":120,"secondary_topics":121,"classification":122,"search_phrases":149,"slug":152,"view_count":90,"doi":153,"paper":154,"created_at":168},4492,"A multimodal context-aware AI recommender for smart farming","https:\u002F\u002Fdoi.org\u002F10.1371\u002Fjournal.pone.0359596","Agricultural productivity in developing regions is significantly affected by plant diseases and pest infestations, making early detection and timely intervention essential for improving crop yield and food security. This study proposes a multimodal context-aware AI recommendation framework that integrates maize leaf image analysis and environmental conditions to support intelligent agricultural decision-making. The framework combines a deep learning-based image classification model with weather variables, including temperature, humidity, rainfall, and solar radiation, through a multimodal fusion model for maize disease diagnosis. Based on the predicted crop condition, a context-aware recommendation engine integrates rule-based agronomic knowledge with the Qwen2.5:7B large language model to generate reliable and actionable natural-language recommendations for farmers. The generated recommendations are subsequently evaluated using Llama 3.1:8B in a blind LLM-based evaluation framework. Experiments were conducted using maize leaf images from the YEESI Lab dataset and a 61-day environmental dataset from the NASA POWER database, obtained for the geographic coordinates of Morogoro, Tanzania. Three maize conditions were considered: healthy plants, aphid infestation, and maize streak virus infection. The image-based model achieved an accuracy of 91%, while the weather-based model showed lower performance due to overlapping environmental characteristics among disease classes. The proposed multimodal fusion model achieved a classification accuracy of 94%, demonstrating the effectiveness of integrating visual and environmental information for disease diagnosis. The recommendation engine achieved an overall evaluation score of 4.80\u002F5.00 across five quality dimensions—Safety, Technical Accuracy, Relevance, Actionability, and Clarity—indicating that the generated recommendations were agronomically consistent, context-aware, and actionable. The proposed framework provides an end-to-end AI-driven decision-support solution that integrates multimodal disease diagnosis, and context-aware recommendation for precision agriculture.","发展中国家地区的农业生产力受植物病害和虫害侵染的显著影响，因此早期检测和及时干预对于提高作物产量和保障粮食安全至关重要。本研究提出了一种多模态情境感知AI推荐框架，该框架整合了玉米叶片图像分析与环境条件，以支持智能农业决策。该框架将基于深度学习的图像分类模型与天气变量（包括温度、湿度、降雨量和太阳辐射）相结合，通过多模态融合模型进行玉米病害诊断。基于预测的作物状况，情境感知推荐引擎将基于规则的农艺知识与Qwen2.5:7B大语言模型相结合，为农民生成可靠且可操作的自然语言建议。生成的建议随后通过Llama 3.1:8B在基于大语言模型的盲评框架中进行评估。实验使用来自YEESI Lab数据集的玉米叶片图像和来自NASA POWER数据库的61天环境数据集，该数据集获取自坦桑尼亚莫罗戈罗的地理坐标。研究考虑了三种玉米状况：健康植株、蚜虫侵染和玉米条纹病毒（maize streak virus）感染。基于图像的模型达到了91%的准确率，而基于天气的模型由于病害类别之间环境特征重叠而表现较低。所提出的多模态融合模型达到了94%的分类准确率，证明了整合视觉与环境信息进行病害诊断的有效性。推荐引擎在五个质量维度——安全性、技术准确性、相关性、可操作性和清晰度——上获得了4.80\u002F5.00的总体评估分数，表明生成的建议在农艺上一致、具有情境感知能力且可操作。所提出的框架提供了一种端到端的AI驱动决策支持解决方案，将多模态病害诊断与情境感知推荐相结合，服务于精准农业。","PLoS ONE","2026-10-08T00:00:00Z",79,{"impact":17,"substance":113,"depth":17,"authority":114,"freshness":20,"relevant":21,"comment":115},22,13,"多模态融合诊断玉米病虫害并生成可执行农事建议，方法新颖、数据与评估扎实，对智慧农业决策支持有参考价值。",[117],{"name":109,"url":106},[26,27,119,28,29],"病虫害识别",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":123},{"candidates":124,"agricultural_evidence":46},[125,131,135,142],{"code":32,"score":126,"supported":46,"title":127,"abstract":128,"tags":130},12,[],[49,50,129],"large language model",[27,29,119],{"code":54,"score":20,"supported":46,"title":132,"abstract":133,"tags":134},[],[28,57],[26,28],{"code":136,"score":101,"supported":46,"title":137,"abstract":138,"tags":141},"A3",[],[139,140],"玉米","maize",[],{"code":143,"score":101,"supported":46,"title":144,"abstract":145,"tags":148},"A8",[],[146,147],"粮食安全","food security",[],[150,151],"玉米叶病 多模态 诊断","Qwen2.5 农业推荐","玉米叶病多模态诊断-4492","10.1371\u002Fjournal.pone.0359596",{"doi":153,"openalex_id":155,"authors":156,"venue":109,"cited_by_count":90,"oa_url":106,"card":163,"direction":95,"ingested_from":98},"W7221033947",[157,160],{"name":158,"orcid":159},"John Telesphory Mhagama","https:\u002F\u002Forcid.org\u002F0009-0001-4103-5344",{"name":161,"orcid":162},"Kanwal Garg","https:\u002F\u002Forcid.org\u002F0000-0003-0048-9355",{"tldr":164,"method":165,"finding":166,"direction":97,"opportunity":167},"提出融合玉米叶片图像与气象数据的多模态AI推荐框架，实现病害诊断与农事建议生成。","深度学习图像分类+气象变量多模态融合，结合规则知识与Qwen2.5大模型生成建议","多模态融合诊断准确率达94%，推荐引擎在五个质量维度获4.80\u002F5.00分。","可探索多作物、多病害扩展及大模型推荐在真实田间场景的长期验证与本地化适配。","2026-10-09T23:30:08.136081Z",{"id":170,"title":171,"url":172,"summary":173,"summary_zh":174,"content":9,"source_name":175,"source_url":172,"published_at":176,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":177,"score_detail":178,"sources":181,"tags":183,"primary_topic":186,"secondary_topics":187,"classification":199,"search_phrases":255,"slug":258,"view_count":90,"doi":259,"paper":260,"created_at":278},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":113,"depth":17,"authority":114,"freshness":179,"relevant":21,"comment":180},9,"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。",[182],{"name":175,"url":172},[26,27,184,28,185],"物联网","气候韧性",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[188,193],{"code":189,"slug":190,"name":191,"path":192,"parent_code":36},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",{"code":194,"slug":195,"name":196,"path":197,"parent_code":198},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",{"status":39,"locked":14,"version":40,"method":41,"evidence":200},{"candidates":201,"agricultural_evidence":46},[202,210,215,222,228,236,243,249],{"code":32,"score":17,"supported":46,"title":203,"abstract":204,"tags":209},[51],[49,205,206,207,50,208],"机器学习","计算机视觉","machine learning","computer vision",[27],{"code":54,"score":211,"supported":46,"title":212,"abstract":213,"tags":214},16,[57],[28,57],[26,28],{"code":189,"score":13,"supported":46,"title":216,"abstract":217,"tags":221},[],[184,218,219,220],"边缘计算","IoT","edge computing",[184],{"code":194,"score":179,"supported":46,"title":223,"abstract":224,"tags":227},[],[225,226,195],"灌溉","水资源",[],{"code":229,"score":230,"supported":46,"title":231,"abstract":232,"tags":235},"H7",7,[],[233,234],"气候变化","climate change",[185],{"code":237,"score":101,"supported":46,"title":238,"abstract":239,"tags":242},"H2",[],[240,241],"土壤","soil",[],{"code":244,"score":63,"supported":46,"title":245,"abstract":246,"tags":248},"A7",[],[247],"间作",[],{"code":250,"score":63,"supported":46,"title":251,"abstract":252,"tags":254},"F3",[],[253],"养分管理",[],[256,257],"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":259,"openalex_id":261,"authors":262,"venue":175,"cited_by_count":90,"oa_url":172,"card":273,"direction":95,"ingested_from":98},"W7223121456",[263,266,268,270],{"name":264,"orcid":265},"Olumayowa J. Iyanda","https:\u002F\u002Forcid.org\u002F0000-0002-5340-9884",{"name":267,"orcid":9},"Oluwadare OYEBODE",{"name":269,"orcid":9},"Iretiayo Adelaiye",{"name":271,"orcid":272},"Ayomide Afolabi","https:\u002F\u002Forcid.org\u002F0000-0002-5171-886X",{"tldr":274,"method":275,"finding":276,"direction":97,"opportunity":277},"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":280,"title":281,"url":282,"summary":283,"summary_zh":284,"content":9,"source_name":285,"source_url":282,"published_at":286,"category":12,"cover_url":9,"hotness":13,"is_selected":46,"score":287,"score_detail":288,"sources":294,"tags":296,"primary_topic":299,"secondary_topics":300,"classification":301,"search_phrases":328,"slug":331,"view_count":318,"doi":332,"paper":333,"created_at":359},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":289,"substance":290,"depth":291,"authority":292,"freshness":179,"relevant":21,"comment":293},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。",[295],{"name":285,"url":282},[26,27,28,297,298],"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":302},{"candidates":303,"agricultural_evidence":46},[304,308,312,316,322],{"code":54,"score":45,"supported":46,"title":305,"abstract":306,"tags":307},[],[26,28,57],[26,28],{"code":32,"score":230,"supported":46,"title":309,"abstract":310,"tags":311},[],[205,207],[27],{"code":194,"score":101,"supported":46,"title":313,"abstract":314,"tags":315},[],[225,195],[],{"code":317,"score":318,"supported":46,"title":319,"abstract":320,"tags":321},"I3",4,[],[297],[297],{"code":323,"score":63,"supported":46,"title":324,"abstract":325,"tags":327},"F4",[],[326],"除草",[],[329,330],"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":332,"openalex_id":334,"authors":335,"venue":285,"cited_by_count":90,"oa_url":282,"card":354,"direction":95,"ingested_from":98},"W7222974188",[336,339,342,345,348,351],{"name":337,"orcid":338},"Shahrear Roman","https:\u002F\u002Forcid.org\u002F0009-0006-5659-4292",{"name":340,"orcid":341},"Hadi Hadi","https:\u002F\u002Forcid.org\u002F0000-0002-2410-9751",{"name":343,"orcid":344},"Wyclife Agumba Oluoch","https:\u002F\u002Forcid.org\u002F0000-0003-0803-9279",{"name":346,"orcid":347},"David Zilberman","https:\u002F\u002Forcid.org\u002F0000-0001-5743-5134",{"name":349,"orcid":350},"Robert Finger","https:\u002F\u002Forcid.org\u002F0000-0002-0634-5742",{"name":352,"orcid":353},"David Wuepper","https:\u002F\u002Forcid.org\u002F0000-0002-1344-6023",{"tldr":355,"method":356,"finding":357,"direction":95,"opportunity":358},"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":361,"title":362,"url":363,"summary":364,"summary_zh":365,"content":9,"source_name":366,"source_url":363,"published_at":367,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":368,"score_detail":369,"sources":372,"tags":374,"primary_topic":376,"secondary_topics":377,"classification":378,"search_phrases":394,"slug":397,"view_count":90,"doi":398,"paper":399,"created_at":436},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":18,"depth":370,"authority":19,"freshness":20,"relevant":21,"comment":371},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。",[373],{"name":366,"url":363},[26,27,205,28,375],"农户认知",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":379},{"candidates":380,"agricultural_evidence":46},[381,385,389],{"code":32,"score":45,"supported":46,"title":382,"abstract":383,"tags":384},[],[205,207,51],[27,205],{"code":237,"score":101,"supported":46,"title":386,"abstract":387,"tags":388},[],[240,241],[],{"code":54,"score":390,"supported":14,"title":391,"abstract":392,"tags":393},2,[],[],[26,28],[395,396],"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":398,"openalex_id":400,"authors":401,"venue":366,"cited_by_count":90,"oa_url":363,"card":431,"direction":97,"ingested_from":98},"W7222866367",[402,405,408,411,414,417,420,423,425,428],{"name":403,"orcid":404},"Jennifer Koch","https:\u002F\u002Forcid.org\u002F0000-0002-7067-2705",{"name":406,"orcid":407},"Andrew Fox","https:\u002F\u002Forcid.org\u002F0000-0001-6719-8902",{"name":409,"orcid":410},"Richa Bhattarai","https:\u002F\u002Forcid.org\u002F0000-0003-3827-7761",{"name":412,"orcid":413},"Hank C. Jenkins‐Smith","https:\u002F\u002Forcid.org\u002F0000-0002-5204-2726",{"name":415,"orcid":416},"Jonathan McFadden","https:\u002F\u002Forcid.org\u002F0000-0003-3762-1428",{"name":418,"orcid":419},"Audrey Reinert","https:\u002F\u002Forcid.org\u002F0000-0001-8951-702X",{"name":421,"orcid":422},"Juan Sesmero","https:\u002F\u002Forcid.org\u002F0000-0002-2669-4536",{"name":424,"orcid":9},"M. Z. Walkup",{"name":426,"orcid":427},"Wolfgang Jentner","https:\u002F\u002Forcid.org\u002F0000-0003-1045-6020",{"name":429,"orcid":430},"David S. Ebert","https:\u002F\u002Forcid.org\u002F0000-0001-6177-1296",{"tldr":432,"method":433,"finding":434,"direction":97,"opportunity":435},"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":438,"title":439,"url":440,"summary":441,"summary_zh":442,"content":9,"source_name":443,"source_url":440,"published_at":444,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":445,"score_detail":446,"sources":449,"tags":451,"primary_topic":454,"secondary_topics":455,"classification":456,"search_phrases":467,"slug":470,"view_count":90,"doi":471,"paper":472,"created_at":502},5153,"Real-Time UAV scheduling under hazardous weather conditions using an integrated fuzzy logic and machine learning framework","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs43926-026-00489-2","The use of Unmanned Aerial Vehicles (UAVs) in precision agriculture, specifically palm farm management, is perpetually challenged by dynamic and extreme weather. From sudden wind gusts and thermal stress to destructive regional events like sandstorms and flash floods, these hazards severely constrict operational windows. This paper proposes a robust, hybrid scheduling framework that integrates fuzzy logic inference with an ensemble of machine learning classifiers to enhance real-time UAV flight reliability. The primary novelty of this research lies not in a new mathematical paradigm, but in its specialized application: the holistic integration of region-specific agricultural hazards into a real-time, decision-fusion architecture. The framework classifies takeoff viability into three categories (SAFE, CAUTION, and UNSAFE) automatically issuing alerts to trigger preventative measures and preserve hardware. By leveraging both short-term farm datasets and a decade of historical weather records, the fuzzy logic component reacts to rapid environmental shifts while establishing safety baselines. Simultaneously, tree-based ensemble models (Gradient Boosting, XGBoost, LightGBM, and CatBoost) analyze non-linear atmospheric interactions, achieving up to 99.67% accuracy in predicting safe windows. Comprehensive sensitivity analyses, including evaluations without SMOTE and seasonal temporal holdout validations, confirm the model’s robustness against class imbalance and unseen extremes. Empirical benchmarking demonstrates the framework's superiority over existing hybrid approaches, highlighting the necessity of combining adaptive fuzzy reasoning with deep historical learning for smart farming.","无人机（UAV）在精准农业尤其是棕榈农场管理中的应用，始终受到动态极端天气的困扰。从突发阵风和热应激到沙尘暴和山洪等破坏性区域事件，这些灾害严重压缩了作业窗口。本文提出了一种稳健的混合调度框架，将模糊逻辑推理与机器学习分类器集成相结合，以提升无人机实时飞行的可靠性。本研究的主要创新点不在于新的数学范式，而在于其专门化应用：将区域特定的农业灾害整体集成到实时决策融合架构中。该框架将起飞可行性分为三类（安全、 caution、不安全），自动发出警报以触发预防措施并保护硬件。通过利用短期农场数据集和十年历史天气记录，模糊逻辑组件能够对快速环境变化作出反应，同时建立安全基线。与此同时，基于树的集成模型（梯度提升、XGBoost、LightGBM和CatBoost）分析非线性大气相互作用，在预测安全窗口方面达到高达99.67%的准确率。全面的敏感性分析，包括无SMOTE的评估和季节性时间留出验证，证实了模型对类别不平衡和未见极端情况的稳健性。实证基准测试表明，该框架优于现有的混合方法，凸显了将自适应模糊推理与深度历史学习相结合对于智慧农业的必要性。","Discover Internet of Things","2026-10-15T00:00:00Z",76,{"impact":211,"substance":447,"depth":17,"authority":114,"freshness":20,"relevant":21,"comment":448},21,"将模糊逻辑与集成学习融合用于极端天气下的农业无人机实时调度，方法扎实、验证充分，对智慧农业作业安全有实际参考价值。",[450],{"name":443,"url":440},[26,27,452,28,453],"农业无人机","气象预警",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":457},{"candidates":458,"agricultural_evidence":46},[459,463],{"code":32,"score":292,"supported":46,"title":460,"abstract":461,"tags":462},[207],[205,207],[27],{"code":54,"score":45,"supported":46,"title":464,"abstract":465,"tags":466},[],[26,28,57],[26,28],[468,469],"无人机 精准农业 极端天气","UAV 棕榈园 调度","无人机精准农业极端天气-5153","10.1007\u002Fs43926-026-00489-2",{"doi":471,"openalex_id":473,"authors":474,"venue":443,"cited_by_count":90,"oa_url":496,"card":497,"direction":95,"ingested_from":98},"W7166891579",[475,478,481,483,485,488,490,493],{"name":476,"orcid":477},"Ahmad B. Alkhodre","https:\u002F\u002Forcid.org\u002F0000-0001-6168-3552",{"name":479,"orcid":480},"Adnan Ahmed Abi Sen","https:\u002F\u002Forcid.org\u002F0000-0003-2960-5807",{"name":482,"orcid":9},"Abdelouahab Abid",{"name":484,"orcid":9},"Nour Mahmoud Bahbouh",{"name":486,"orcid":487},"Adel Ben Mnaouer","https:\u002F\u002Forcid.org\u002F0000-0003-3617-7636",{"name":489,"orcid":9},"Mohamed Benaida",{"name":491,"orcid":492},"Yazed Alsaawy","https:\u002F\u002Forcid.org\u002F0000-0001-5031-3388",{"name":494,"orcid":495},"Hani Almoamari","https:\u002F\u002Forcid.org\u002F0000-0002-3550-8977","https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002Fs43926-026-00489-2.pdf",{"tldr":498,"method":499,"finding":500,"direction":95,"opportunity":501},"提出融合模糊逻辑与机器学习分类器的无人机实时调度框架，在恶劣天气下保障精准农业飞行安全。","模糊逻辑推理结合梯度提升、XGBoost、LightGBM、CatBoost集成","框架将起飞可行性分为安全、警告、不安全三级，预测安全窗口准确率最高达99.67%，优于现有混合方法。","可将区域特定灾害（沙尘暴、山洪）纳入实时决策融合，并扩展到多机协同调度与边缘端轻量化部署研究。","2026-10-17T23:30:08.889571Z",{"id":504,"title":505,"url":506,"summary":507,"summary_zh":508,"content":9,"source_name":509,"source_url":506,"published_at":444,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":445,"score_detail":510,"sources":512,"tags":514,"primary_topic":517,"secondary_topics":518,"classification":519,"search_phrases":543,"slug":546,"view_count":90,"doi":547,"paper":548,"created_at":576},5080,"Explainable deep learning for Mango leaf disease recognition using AlexNet and grad-CAM","https:\u002F\u002Fdoi.org\u002F10.1177\u002F1088467x261495708","Early and accurate detection of plant diseases is essential for improving crop productivity and supporting sustainable agricultural practices. Mango (Mangifera indica), one of the most economically important tropical fruit crops worldwide, is highly susceptible to several leaf diseases that can significantly reduce yield and fruit quality. This study presents an explainable deep learning framework for automated ten-class mango leaf disease classification using a publicly available dataset containing approximately 5000 images distributed across Anthracnose, Bacterial Canker, Cutting Weevil, Dag Disease, Die Back, Galls, Healthy, Powdery Mildew, Red Rust, and Sooty Mould classes. Unlike conventional AlexNet-based approaches, the proposed framework integrates Bayesian hyperparameter optimization, two-stage fine-tuning, test-time augmentation (TTA), and Grad-CAM-based explainability to improve both predictive performance and interpretability. Experimental results demonstrate that the proposed framework achieves a final test accuracy of 99.2% using eight-view test-time augmentation (TTA), with corresponding precision, recall, and F1-score of approximately 99.1%. The model also achieves micro-average and macro-average ROC–AUC scores of 1.00 on the curated test set, indicating strong discriminative capability across the considered disease categories. Comparative evaluations against MobileNetV2, DenseNet201, ConvNeXt, and LeafNet demonstrate competitive or superior classification performance while maintaining moderate computational complexity and architectural simplicity. Furthermore, Grad-CAM visualizations qualitatively highlight biologically relevant disease symptoms, including lesions, necrotic regions, rust spots, fungal growth, and tissue discoloration, thereby enhancing model interpretability. Although field-scale validation and quantitative explainability assessment remain subjects for future investigation, the proposed framework provides an accurate, computationally efficient, and interpretable approach for automated mango leaf disease diagnosis with strong potential for smart agriculture and precision farming applications.","植物病害的早期准确检测对于提高作物生产力和支持可持续农业实践至关重要。芒果（Mangifera indica）是全球最具经济重要性的热带水果作物之一，极易感染多种叶部病害，这些病害可显著降低产量和果实品质。本研究提出了一种可解释的深度学习框架，用于自动化十类芒果叶部病害分类，所使用的公开数据集包含约5000张图像，分布于炭疽病（Anthracnose）、细菌性溃疡病（Bacterial Canker）、切叶象甲（Cutting Weevil）、枯枝病（Dag Disease）、回枯病（Die Back）、虫瘿（Galls）、健康（Healthy）、白粉病（Powdery Mildew）、红锈病（Red Rust）和煤烟病（Sooty Mould）十个类别。与传统的基于AlexNet的方法不同，所提出的框架集成了贝叶斯超参数优化、两阶段微调、测试时增强（TTA）以及基于Grad-CAM的可解释性，以同时提升预测性能和可解释性。实验结果表明，所提出的框架在使用八视图测试时增强（TTA）时达到了99.2%的最终测试准确率，相应的精确率、召回率和F1分数约为99.1%。该模型在精选测试集上还取得了1.00的微平均和宏平均ROC–AUC分数，表明在所考虑的病害类别间具有较强的判别能力。与MobileNetV2、DenseNet201、ConvNeXt和LeafNet的对比评估表明，该框架在保持适中计算复杂度和架构简洁性的同时，具有竞争性或更优的分类性能。此外，Grad-CAM可视化定性地突出了具有生物学相关性的病害症状，包括病斑、坏死区域、锈斑、真菌生长和组织变色，从而增强了模型的可解释性。尽管田间规模验证和定量可解释性评估仍有待未来研究，但所提出的框架为自动化芒果叶部病害诊断提供了一种准确、计算高效且可解释的方法，在智慧农业和精准农业应用中具有强大潜力。","Intelligent Data Analysis",{"impact":211,"substance":447,"depth":370,"authority":114,"freshness":179,"relevant":21,"comment":511},"将贝叶斯调参、两阶段微调与Grad-CAM结合用于十类芒果叶病识别，准确率99.2%，方法新颖且可解释性强，对智慧农业植保应用有参考价值。",[513],{"name":509,"url":506},[26,27,515,28,516],"可解释AI","芒果病害",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":520},{"candidates":521,"agricultural_evidence":46},[522,526,531,537],{"code":32,"score":211,"supported":46,"title":523,"abstract":524,"tags":525},[50],[49,50],[27,515],{"code":54,"score":45,"supported":46,"title":527,"abstract":528,"tags":530},[],[26,28,529],"smart agriculture",[26,28],{"code":532,"score":63,"supported":46,"title":533,"abstract":534,"tags":536},"B2",[],[535],"芒果",[],{"code":538,"score":63,"supported":46,"title":539,"abstract":540,"tags":542},"H8",[],[541],"可持续农业",[],[544,545],"芒果叶片病害 AlexNet","Grad-CAM 芒果病害识别","芒果叶片病害AlexNet-5080","10.1177\u002F1088467x261495708",{"doi":547,"openalex_id":549,"authors":550,"venue":509,"cited_by_count":90,"oa_url":9,"card":571,"direction":95,"ingested_from":98},"W7222328160",[551,554,557,559,562,565,568],{"name":552,"orcid":553},"Anay Ghosh","https:\u002F\u002Forcid.org\u002F0009-0006-6237-7905",{"name":555,"orcid":556},"Tawfikur Rahman","https:\u002F\u002Forcid.org\u002F0000-0002-0456-5143",{"name":558,"orcid":9},"Md Moniruzzaman",{"name":560,"orcid":561},"Nibedita Deb","https:\u002F\u002Forcid.org\u002F0000-0002-1579-1138",{"name":563,"orcid":564},"Hanan Abdullah Mengash","https:\u002F\u002Forcid.org\u002F0000-0002-4103-2434",{"name":566,"orcid":567},"Noorlindawaty Md Jizat","https:\u002F\u002Forcid.org\u002F0000-0001-7618-2283",{"name":569,"orcid":570},"Samir Salem Al‐Bawri","https:\u002F\u002Forcid.org\u002F0000-0003-2852-575X",{"tldr":572,"method":573,"finding":574,"direction":97,"opportunity":575},"提出可解释深度学习框架，用AlexNet结合贝叶斯调参、微调和Grad-CAM实现芒果叶病十分类。","AlexNet+贝叶斯超参优化、两阶段微调、八视图TTA、Grad-CAM，约5","测试准确率达99.2%，ROC-AUC为1.00，Grad-CAM能定位病斑等生物相关症状。","可延伸至田间复杂背景验证、可解释性定量评估及轻量化边缘部署研究。","2026-10-16T23:30:08.303796Z"]