[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2540":3,"related-2540":75},{"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":18,"tags":20,"primary_topic":25,"secondary_topics":31,"classification":32,"search_phrases":56,"slug":59,"view_count":60,"doi":61,"paper":62,"created_at":74},2540,"Artificial Intelligence, Machine Learning, and Deep Learning Concepts, Methods, and Real-World Impact","https:\u002F\u002Fdoi.org\u002F10.64823\u002Fijcsa.2601011","Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) have evolved from rule-based symbolic systems into data-driven computational methods capable of perception, prediction, decision support, and content generation. This chapter presents an accessible yet technically grounded overview of the relationship among AI, ML, and DL, tracing their historical development from early symbolic reasoning and theoretical foundations to modern neural networks and transformer-based systems. It explains the principal ML paradigms supervised, unsupervised, reinforcement, semi-supervised, and self-supervised learning and introduces widely used algorithms including regression, decision trees, ensemble methods, support vector machines, k-nearest neighbors, Naive Bayes, gradient boosting, and k-means clustering. The chapter then examines deep-learning architectures such as Convolutional neural networks, recurrent and long short-term memory networks, transformers, generative adversarial networks, and diffusion models, together with training concepts including back propagation, gradient descent, regularization, transfer learning, and evaluation metrics. Applications across healthcare, finance, transportation, manufacturing, agriculture, education, cyber security, and creative work are discussed, alongside challenges involving bias, explainability, privacy, misinformation, employment, and computational and environmental costs. The chapter concludes by emphasizing human-centered deployment, responsible governance, interpretability, and continuous evaluation as AI systems become increasingly integrated into high-impact domains. Keywords: artificial intelligence; machine learning; deep learning; natural language processing; computer vision; generative AI; reinforcement learning; neural networks; Responsible AI; Transformers","人工智能（Artificial Intelligence, AI）、机器学习（Machine Learning, ML）和深度学习（Deep Learning, DL）已从基于规则的符号系统演变为能够感知、预测、决策支持和内容生成的数据驱动计算方法。本章对AI、ML和DL之间的关系进行了通俗易懂且具有技术深度的概述，追溯了它们从早期符号推理和理论基础到现代神经网络和基于Transformer的系统的历史发展。本章阐释了主要的机器学习范式——监督学习、无监督学习、强化学习、半监督学习和自监督学习——并介绍了广泛使用的算法，包括回归、决策树、集成方法、支持向量机、k近邻、朴素贝叶斯、梯度提升和k均值聚类。随后，本章考察了深度学习架构，如卷积神经网络、循环神经网络和长短期记忆网络、Transformer、生成对抗网络和扩散模型，以及包括反向传播、梯度下降、正则化、迁移学习和评估指标在内的训练概念。本章讨论了AI在医疗健康、金融、交通、制造、农业、教育、网络安全和创意工作等领域的应用，同时探讨了涉及偏见、可解释性、隐私、虚假信息、就业以及计算和环境成本等方面的挑战。本章最后强调，随着AI系统日益融入高影响力领域，应以人为中心进行部署，实施负责任的治理，注重可解释性，并持续进行评估。关键词：人工智能；机器学习；深度学习；自然语言处理；计算机视觉；生成式AI；强化学习；神经网络；负责任AI；Transformer",null,"International Journal of Computer Science and Artificial Intelligence","2026-09-14T00:00:00Z","论文",10,false,0,{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":17},"AI\u002FML\u002FDL通用综述章节，仅在应用部分顺带提及农业，非三农或农业信息化主题，相关性不足，不建议进入每日精选。",[19],{"name":10,"url":6},[21,22,23,24],"智慧农业","农业人工智能","机器学习","深度学习",{"code":26,"slug":27,"name":28,"path":29,"parent_code":30},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[],{"status":33,"locked":14,"version":34,"method":35,"evidence":36},"classified","2026-10-22.2","rules",{"candidates":37,"agricultural_evidence":40},[38,50],{"code":26,"score":39,"supported":40,"title":41,"abstract":45,"tags":49},36,true,[42,43,44],"machine learning","deep learning","artificial intelligence",[24,23,46,42,43,44,47,48],"计算机视觉","computer vision","decision support",[22,23,24],{"code":51,"score":52,"supported":14,"title":53,"abstract":54,"tags":55},"I1",1,[],[],[21],[57,58],"农业人工智能 智慧农业 机器学习 深度学习","农业人工智能 智慧农业","农业人工智能智慧农业机器学习深度学习-2540",3,"10.64823\u002Fijcsa.2601011",{"doi":61,"openalex_id":63,"authors":64,"venue":10,"cited_by_count":15,"oa_url":6,"card":67,"direction":71,"ingested_from":73},"W7212755989",[65],{"name":66,"orcid":9},"Dr Nellutla Sasikala",{"tldr":68,"method":69,"finding":70,"direction":71,"opportunity":72},"系统综述AI、机器学习与深度学习的概念、方法、应用及伦理挑战。","文献综述，梳理符号推理到Transformer的算法与训练技术。","AI已渗透多领域，但需重视可解释性、偏见与负责任治理。","农业人工智能与决策模型","可聚焦农业场景下可解释AI与轻量化模型，解决小样本、边缘部署与伦理治理空白。","openalex","2026-09-15T23:30:33.611886Z",{"total":76,"page":52,"page_size":76,"items":77},6,[78,170,237,289,370,453],{"id":79,"title":80,"url":81,"summary":82,"summary_zh":83,"content":9,"source_name":84,"source_url":81,"published_at":85,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":86,"score_detail":87,"sources":93,"tags":95,"primary_topic":98,"secondary_topics":99,"classification":106,"search_phrases":143,"slug":146,"view_count":15,"doi":147,"paper":148,"created_at":169},5400,"Artificial intelligence in food safety: a bibliometric and empirical synthesis of emerging technologies and research frontiers","https:\u002F\u002Fdoi.org\u002F10.1108\u002Fbfj-06-2025-0819","Purpose This study aimed to identify recent research trends in the integration of artificial intelligence (AI) with food safety and security through a comprehensive bibliometric analysis. Design\u002Fmethodology\u002Fapproach A total of 2,782 documents published between 2021 and 2025 were retrieved from the Web of Science Core Collection. The dataset was refined by removing duplicates, filtering document types and excluding irrelevant records. VOSviewer was then used to conduct co-authorship, keyword co-occurrence, citation, co-citation and bibliographic coupling analyses. Findings Machine learning and deep learning emerged as core technologies associated with food security, contamination detection and quality prediction. Keyword clustering demonstrated interdisciplinary connections with agriculture, climate change and public health. Citation and co-citation analyses identified three major intellectual foundations: traditional machine learning, computer vision-based deep learning and time-series modeling. China, India and the United States were the most influential contributors in terms of research output and technological development. Bibliographic coupling and source co-citation analyses further indicated increasing convergence between food science and computational science. Originality\u002Fvalue This study provides a comprehensive overview of the intellectual structure, collaborative networks and thematic evolution of AI-driven food safety research. Unlike previous studies focusing on specific technologies or applications, it maps global research patterns and interdisciplinary linkages. The findings provide strategic insights for researchers, policymakers and industry stakeholders. Future studies should prioritize explainable AI integration into regulatory frameworks and the development of larger, more diverse datasets to improve model generalizability across food systems.","目的 本研究旨在通过全面的文献计量分析，识别人工智能（AI）与食品安全及保障融合领域的最新研究趋势。设计与方法 从Web of Science核心合集中检索了2021年至2025年间发表的2，782篇文献。通过去除重复记录、筛选文献类型并排除不相关记录对数据集进行精炼。随后使用VOSviewer进行合著、关键词共现、引文、共被引及文献耦合分析。研究发现 机器学习和深度学习成为与食品安全、污染检测和质量预测相关的核心技术。关键词聚类显示了与农业、气候变化和公共卫生的跨学科联系。引文和共被引分析识别出三大主要知识基础：传统机器学习、基于计算机视觉的深度学习和时间序列建模。中国、印度和美国在研究产出和技术发展方面是最具影响力的贡献者。文献耦合和来源共被引分析进一步表明食品科学与计算科学之间的融合日益加深。原创性\u002F价值 本研究全面概述了AI驱动的食品安全研究的知识结构、合作网络和主题演变。与以往聚焦特定技术或应用的研究不同，本研究绘制了全球研究模式和跨学科联系。研究结果为研究人员、政策制定者和行业利益相关者提供了战略性见解。未来研究应优先关注可解释AI融入监管框架以及开发更大、更多样化的数据集，以提高模型在食品系统中的泛化能力。","British Food Journal","2026-10-19T00:00:00Z",80,{"impact":88,"substance":89,"depth":88,"authority":90,"freshness":91,"relevant":52,"comment":92},18,22,14,8,"基于2782篇文献的AI食品安全计量分析，方法规范、结论有参考价值，但属综述性研究，产业落地影响有限。",[94],{"name":84,"url":81},[21,22,23,96,97],"食品安全","文献计量",{"code":26,"slug":27,"name":28,"path":29,"parent_code":30},[100],{"code":101,"slug":102,"name":103,"path":104,"parent_code":105},"E4","food-safety","质量安全与溯源","\u002Ftopic\u002Ffood-supply\u002Ffood-safety","E",{"status":33,"locked":14,"version":34,"method":35,"evidence":107},{"candidates":108,"agricultural_evidence":40},[109,114,120,126,133,139],{"code":26,"score":110,"supported":40,"title":111,"abstract":112,"tags":113},19,[44],[24,23,46,42,43,44,47],[22,23],{"code":101,"score":115,"supported":40,"title":116,"abstract":118,"tags":119},15,[117],"food safety",[96,117],[96],{"code":121,"score":115,"supported":40,"title":122,"abstract":124,"tags":125},"I7",[123],"bibliometric",[97,123],[97],{"code":127,"score":76,"supported":40,"title":128,"abstract":129,"tags":132},"H7",[],[130,131],"气候变化","climate change",[],{"code":134,"score":60,"supported":40,"title":135,"abstract":136,"tags":138},"A8",[],[137],"food security",[],{"code":51,"score":52,"supported":14,"title":140,"abstract":141,"tags":142},[],[],[21],[144,145],"AI 食品安全 文献计量","机器学习 食品质量预测","AI食品安全文献计量-5400","10.1108\u002Fbfj-06-2025-0819",{"doi":147,"openalex_id":149,"authors":150,"venue":84,"cited_by_count":15,"oa_url":9,"card":164,"direction":71,"ingested_from":73},"W7222920742",[151,153,155,158,161],{"name":152,"orcid":9},"Harang Lee",{"name":154,"orcid":9},"지은서",{"name":156,"orcid":157},"Yoonbin Kim","https:\u002F\u002Forcid.org\u002F0000-0002-5625-2560",{"name":159,"orcid":160},"Joon-Goo Lee","https:\u002F\u002Forcid.org\u002F0000-0003-3617-5518",{"name":162,"orcid":163},"Woo‐Ju Kim","https:\u002F\u002Forcid.org\u002F0000-0002-6954-1580",{"tldr":165,"method":166,"finding":167,"direction":71,"opportunity":168},"对2021-2025年2782篇文献做文献计量，梳理AI与食品安全交叉研究趋势。","Web of Science文献计量，VOSviewer共被引、关键词共现与耦合","机器学习与深度学习是核心，形成传统ML、计算机视觉、时序建模三大知识基础。","可解释AI嵌入监管框架、构建更大更多样数据集以提升跨食物系统泛化能力。","2026-10-20T23:30:31.567802Z",{"id":171,"title":172,"url":173,"summary":174,"summary_zh":175,"content":9,"source_name":176,"source_url":173,"published_at":177,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":178,"score_detail":179,"sources":183,"tags":185,"primary_topic":188,"secondary_topics":194,"classification":196,"search_phrases":216,"slug":219,"view_count":15,"doi":220,"paper":221,"created_at":236},5387,"LIGHTWEIGHT FEATURE FUSED ATTENTION DRIVEN MULTI LEVEL HYBRID MOBILENET V2 FRAMEWORK FOR EARLY TOMATO LEAF DISEASE DETECTION","https:\u002F\u002Fdoi.org\u002F10.62441\u002Factainnovations.vi.1918","It is crucial to find and treat tomato crop diseases early and accurately to improve crop efficiency and make sure there is no scarcity of crop. However, computerized analysis is provocative due to lighting differences, compound backgrounds, and graphic likenesses among infection regions. Research work proposed a lightweight Attention Driven Multi Level Hybrid MobileNet-V2 algorithm that fuses the handcrafted texture structures with Deep Learning (DL) features, then image preprocessing is incorporated using Gaussian, Bilateral, and Guided filters to minimize noise by preserving the disease lesions. LAB color model along with Contrast Limited Adaptive Histogram Equalization (CLAHE) are used to improve the contrast. Otsu thresholding is involved to effectively separate the leaf segment from diseased parts. Local Binary Pattern (LBP) and Gray Level Co-occurrence Matrix (GLCM) descriptors are used to obtain fine-grained features, while high level features are gained by pretrained MobileNet-V2 model. Features fusion is fed to the fully connected layers of deep learning model for training and validation purpose. Results have revealed that the suggested model attains better classification performance as compared to existing models, making it an effective explanation for real time tomato leaf disease detection for smart agriculture applications.","早期准确发现并治疗番茄作物病害，对于提高作物效率、确保作物不短缺至关重要。然而，由于光照差异、复杂背景以及感染区域之间的图形相似性，计算机化分析具有挑战性。研究提出了一种轻量级注意力驱动多层混合MobileNet-V2算法，该算法将手工纹理结构与深度学习（DL）特征相融合，然后结合高斯、双边和引导滤波器进行图像预处理，以在保留病害病变的同时最小化噪声。采用LAB颜色模型和对比度受限自适应直方图均衡化（CLAHE）来提高对比度。引入Otsu阈值分割以有效分离叶片片段与病害部分。使用局部二值模式（LBP）和灰度共生矩阵（GLCM）描述符获取细粒度特征，同时通过预训练的MobileNet-V2模型获取高层特征。特征融合后输入深度学习模型的全连接层进行训练和验证。结果表明，与现有模型相比，所提出的模型取得了更好的分类性能，使其成为智慧农业应用中实时番茄叶片病害检测的有效解决方案。","Acta Innovations","2026-10-18T00:00:00Z",64,{"impact":180,"substance":88,"depth":181,"authority":13,"freshness":91,"relevant":52,"comment":182},12,16,"提出融合手工纹理特征与MobileNet-V2的轻量番茄叶病检测方法，有一定方法新颖性，但属单篇论文、应用范围有限，未达每日精选门槛。",[184],{"name":176,"url":173},[21,22,24,186,187],"番茄病害","图像识别",{"code":189,"slug":190,"name":191,"path":192,"parent_code":193},"B1","vegetables","蔬菜","\u002Ftopic\u002Fhorticulture\u002Fvegetables","B",[195],{"code":26,"slug":27,"name":28,"path":29,"parent_code":30},{"status":33,"locked":14,"version":34,"method":35,"evidence":197},{"candidates":198,"agricultural_evidence":40},[199,205,210],{"code":189,"score":115,"supported":40,"title":200,"abstract":202,"tags":204},[201],"tomato",[203,201],"番茄",[186],{"code":26,"score":206,"supported":40,"title":207,"abstract":208,"tags":209},9,[],[24,43],[22,187,24],{"code":51,"score":211,"supported":40,"title":212,"abstract":213,"tags":215},7,[],[21,214],"smart agriculture",[21],[217,218],"MobileNet-V2 番茄叶片病害","番茄叶病 深度学习 检测","MobileNet-V2番茄叶片病害-5387","10.62441\u002Factainnovations.vi.1918",{"doi":220,"openalex_id":222,"authors":223,"venue":176,"cited_by_count":15,"oa_url":229,"card":230,"direction":235,"ingested_from":73},"W7222977034",[224,226],{"name":225,"orcid":9},"Mrs. Vijetalaxmi A Hegde",{"name":227,"orcid":228},"Abhay Ramakant Kasetwar","https:\u002F\u002Forcid.org\u002F0000-0002-5800-8764","https:\u002F\u002Factainnovations.com\u002Findex.php\u002Fpub\u002Farticle\u002Fdownload\u002F1918\u002F981",{"tldr":231,"method":232,"finding":233,"direction":71,"opportunity":234},"提出轻量注意力多级混合MobileNet-V2框架，融合手工纹理与深度学习特征实现番茄叶片病害早期检","高斯\u002F双边\u002F引导滤波去噪，LAB+CLAHE增强，Otsu分割，LBP与GLCM","所提模型分类性能优于现有模型，可有效用于智慧农业实时番茄叶片病害检测。","可探索多作物跨域泛化、田间复杂光照下轻量模型部署与可解释性，及病害早期分级检测。","智慧农业 \u002F 农业物联网","2026-10-20T23:30:10.424511Z",{"id":238,"title":239,"url":240,"summary":241,"summary_zh":242,"content":9,"source_name":243,"source_url":240,"published_at":177,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":244,"score_detail":245,"sources":247,"tags":249,"primary_topic":251,"secondary_topics":252,"classification":253,"search_phrases":274,"slug":277,"view_count":15,"doi":278,"paper":279,"created_at":288},5385,"A Comprehensive Review of Deep Learning and Computer Vision Techniques for Fruit Quality Assessment and Classification","https:\u002F\u002Fdoi.org\u002F10.25258\u002Fijddt.16.75s.151","Fruit image analysis has become an essential research area in precision agriculture, enabling automated fruit classification, quality grading, maturity estimation, disease diagnosis, yield prediction, and intelligent harvesting through advances in computer vision and artificial intelligence (AI).Recent progress in imaging technologies, large-scale benchmark datasets, and deep learning has substantially improved the accuracy, robustness, and scalability of automated fruit analysis systems.This survey presents a comprehensive and systematic review of image processing and AI techniques for fruit image analysis.It covers the complete image analysis pipeline, including image acquisition, preprocessing, enhancement, color space transformation, segmentation, feature extraction, and handcrafted texture descriptors.Furthermore, conventional machine learning algorithms are reviewed alongside state-of-the-art deep learning architectures, including Convolutional Neural Networks (CNNs), Vision Transformers (ViTs), and Vision Mamba models, for applications such as fruit recognition, quality assessment, disease detection, ripeness estimation, and object detection.Publicly available benchmark datasets, evaluation metrics, and performance comparison strategies are also summarized to facilitate objective assessment of existing approaches.In addition, the survey discusses key research challenges, including illumination variations, occlusion, complex orchard environments, limited annotated datasets, computational complexity, and domain adaptation, while highlighting emerging research trends such as self-supervised learning, multimodal learning, explainable AI, lightweight deep networks, foundation models, and edge intelligence for real-time deployment.By providing a unified review of methodologies, datasets, evaluation protocols, challenges, and future research directions, this survey offers a comprehensive reference for researchers and practitioners developing intelligent and scalable fruit image analysis systems for precision agriculture.","水果图像分析已成为精准农业中的一个重要研究领域，借助计算机视觉和人工智能（AI）的进步，实现了果实自动分类、品质分级、成熟度估计、病害诊断、产量预测和智能采收。成像技术、大规模基准数据集和深度学习的最新进展显著提升了自动化水果分析系统的准确性、鲁棒性和可扩展性。本综述对用于水果图像分析的图像处理和AI技术进行了全面而系统的回顾。内容涵盖完整的图像分析流程，包括图像采集、预处理、增强、颜色空间变换、分割、特征提取和手工纹理描述符。此外，综述了传统机器学习算法以及最先进的深度学习架构，包括卷积神经网络（CNN）、视觉Transformer（ViT）和视觉Mamba模型，用于果实识别、品质评估、病害检测、成熟度估计和目标检测等应用。同时总结了公开可用的基准数据集、评价指标和性能比较策略，以便对现有方法进行客观评估。此外，本综述讨论了关键研究挑战，包括光照变化、遮挡、复杂果园环境、标注数据集有限、计算复杂度和域适应，并重点介绍了新兴研究趋势，如自监督学习、多模态学习、可解释AI、轻量级深度网络、基础模型和面向实时部署的边缘智能。通过提供方法、数据集、评价协议、挑战和未来研究方向的统一综述，本综述为开发面向精准农业的智能且可扩展的水果图像分析系统的研究人员和实践者提供了全面的参考。","International Journal of Drug Delivery Technology",56,{"impact":91,"substance":88,"depth":181,"authority":76,"freshness":91,"relevant":52,"comment":246},"系统性综述覆盖CNN、ViT、Vision Mamba等前沿方法，信息量足，但期刊权威性一般且偏学术综述，公共价值有限，适合主题聚合而非每日精选头条。",[248],{"name":243,"url":240},[21,22,46,24,250],"水果品质检测",{"code":26,"slug":27,"name":28,"path":29,"parent_code":30},[],{"status":33,"locked":14,"version":34,"method":35,"evidence":254},{"candidates":255,"agricultural_evidence":40},[256,262,268],{"code":26,"score":257,"supported":40,"title":258,"abstract":259,"tags":261},28,[43,47],[260,24,23,46,42,43,44,47],"目标检测",[22,24,46],{"code":51,"score":211,"supported":40,"title":263,"abstract":264,"tags":267},[],[265,266],"精准农业","precision agriculture",[21],{"code":269,"score":60,"supported":40,"title":270,"abstract":271,"tags":273},"B2",[],[272],"orchard",[],[275,276],"水果品质 计算机视觉 深度学习","农业人工智能 水果品质检测 计算机视觉 智慧农业","水果品质计算机视觉深度学习-5385","10.25258\u002Fijddt.16.75s.151",{"doi":278,"openalex_id":280,"authors":281,"venue":243,"cited_by_count":15,"oa_url":240,"card":282,"direction":235,"ingested_from":73},"W7222892831",[],{"tldr":283,"method":284,"finding":285,"direction":286,"opportunity":287},"系统综述深度学习与计算机视觉在水果品质评估与分级中的全流程方法。","综述图像采集到分割、特征提取及CNN、ViT、Vision Mamba等模型与公","深度学习显著提升水果分析精度与鲁棒性，但光照、遮挡和小样本仍是主要瓶颈。","农业遥感与作物表型","可探索自监督与轻量化模型在复杂果园遮挡、小样本场景下的边缘实时部署。","2026-10-20T23:30:10.066042Z",{"id":290,"title":291,"url":292,"summary":293,"summary_zh":294,"content":9,"source_name":295,"source_url":292,"published_at":177,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":296,"score_detail":297,"sources":301,"tags":303,"primary_topic":305,"secondary_topics":306,"classification":307,"search_phrases":327,"slug":330,"view_count":15,"doi":331,"paper":332,"created_at":369},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",77,{"impact":88,"substance":298,"depth":299,"authority":90,"freshness":91,"relevant":52,"comment":300},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。",[302],{"name":295,"url":292},[21,22,23,265,304],"农户认知",{"code":26,"slug":27,"name":28,"path":29,"parent_code":30},[],{"status":33,"locked":14,"version":34,"method":35,"evidence":308},{"candidates":309,"agricultural_evidence":40},[310,315,322],{"code":26,"score":311,"supported":40,"title":312,"abstract":313,"tags":314},11,[],[23,42,44],[22,23],{"code":316,"score":76,"supported":40,"title":317,"abstract":318,"tags":321},"H2",[],[319,320],"土壤","soil",[],{"code":51,"score":323,"supported":14,"title":324,"abstract":325,"tags":326},2,[],[],[21,265],[328,329],"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":331,"openalex_id":333,"authors":334,"venue":295,"cited_by_count":15,"oa_url":292,"card":364,"direction":71,"ingested_from":73},"W7222866367",[335,338,341,344,347,350,353,356,358,361],{"name":336,"orcid":337},"Jennifer Koch","https:\u002F\u002Forcid.org\u002F0000-0002-7067-2705",{"name":339,"orcid":340},"Andrew Fox","https:\u002F\u002Forcid.org\u002F0000-0001-6719-8902",{"name":342,"orcid":343},"Richa Bhattarai","https:\u002F\u002Forcid.org\u002F0000-0003-3827-7761",{"name":345,"orcid":346},"Hank C. Jenkins‐Smith","https:\u002F\u002Forcid.org\u002F0000-0002-5204-2726",{"name":348,"orcid":349},"Jonathan McFadden","https:\u002F\u002Forcid.org\u002F0000-0003-3762-1428",{"name":351,"orcid":352},"Audrey Reinert","https:\u002F\u002Forcid.org\u002F0000-0001-8951-702X",{"name":354,"orcid":355},"Juan Sesmero","https:\u002F\u002Forcid.org\u002F0000-0002-2669-4536",{"name":357,"orcid":9},"M. Z. Walkup",{"name":359,"orcid":360},"Wolfgang Jentner","https:\u002F\u002Forcid.org\u002F0000-0003-1045-6020",{"name":362,"orcid":363},"David S. Ebert","https:\u002F\u002Forcid.org\u002F0000-0001-6177-1296",{"tldr":365,"method":366,"finding":367,"direction":71,"opportunity":368},"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":371,"title":372,"url":373,"summary":374,"summary_zh":375,"content":9,"source_name":376,"source_url":373,"published_at":377,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":378,"score_detail":379,"sources":381,"tags":383,"primary_topic":386,"secondary_topics":392,"classification":394,"search_phrases":414,"slug":417,"view_count":52,"doi":418,"paper":419,"created_at":452},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":180,"substance":298,"depth":181,"authority":180,"freshness":206,"relevant":52,"comment":380},"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。",[382],{"name":376,"url":373},[21,22,384,24,385],"边缘计算","玉米病害",{"code":387,"slug":388,"name":389,"path":390,"parent_code":391},"A3","maize","玉米","\u002Ftopic\u002Ffield-crops\u002Fmaize","A",[393],{"code":26,"slug":27,"name":28,"path":29,"parent_code":30},{"status":33,"locked":14,"version":34,"method":35,"evidence":395},{"candidates":396,"agricultural_evidence":40},[397,401,405,409],{"code":387,"score":90,"supported":40,"title":398,"abstract":399,"tags":400},[388],[389,388],[],{"code":26,"score":91,"supported":40,"title":402,"abstract":403,"tags":404},[],[24,43],[22,24],{"code":51,"score":211,"supported":40,"title":406,"abstract":407,"tags":408},[],[265,266],[21],{"code":410,"score":52,"supported":14,"title":411,"abstract":412,"tags":413},"I5",[],[],[384],[415,416],"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":418,"openalex_id":420,"authors":421,"venue":376,"cited_by_count":15,"oa_url":373,"card":447,"direction":286,"ingested_from":73},"W7222602850",[422,425,428,430,432,435,437,439,442,444],{"name":423,"orcid":424},"Awwab Mohammad","https:\u002F\u002Forcid.org\u002F0000-0002-1148-5678",{"name":426,"orcid":427},"Punith kumar","https:\u002F\u002Forcid.org\u002F0009-0005-5676-4194",{"name":429,"orcid":9},"Bhavani K",{"name":431,"orcid":9},"Sailee N. Panchbhai",{"name":433,"orcid":434},"Sagarkumar S. Badhiye","https:\u002F\u002Forcid.org\u002F0000-0002-0710-3761",{"name":436,"orcid":9},"Manoj Kumar S B",{"name":438,"orcid":9},"Dharamvir",{"name":440,"orcid":441},"Sumit Kumar Sar","https:\u002F\u002Forcid.org\u002F0000-0003-0384-9088",{"name":443,"orcid":9},"Rani Chhagan Dalvi",{"name":445,"orcid":446},"Yogesh Harishchandra Bhosale","https:\u002F\u002Forcid.org\u002F0000-0001-6901-1419",{"tldr":448,"method":449,"finding":450,"direction":71,"opportunity":451},"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。","2026-10-17T23:30:28.248539Z",{"id":454,"title":455,"url":456,"summary":457,"summary_zh":458,"content":9,"source_name":459,"source_url":456,"published_at":377,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":460,"score_detail":461,"sources":465,"tags":467,"primary_topic":470,"secondary_topics":475,"classification":477,"search_phrases":493,"slug":496,"view_count":52,"doi":497,"paper":498,"created_at":532},5150,"MSFANet: a multi-scale feature aggregation network for tobacco pest recognition","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffpls.2026.1871587","Introduction Tobacco is a globally significant economic crop, and pest infestation represents one of the primary factors contributing to declines in both yield and quality. However, tobacco pests encompass a wide variety of species that differ substantially in morphology and scale. Field-captured images are frequently subject to complex background interference and class imbalance, presenting considerable challenges for automated recognition. Existing deep learning approaches remain limited in their capacity for multi-scale feature extraction and discriminative feature representation under such conditions. To address these challenges, this study proposes a Multi-Scale Feature Aggregation Network (MSFANet) for tobacco pest recognition, formulated here as a single-label image classification task. Methods To address scale variation and complex field backgrounds, two task-oriented modules were developed. The Multi-Scale Pyramid Pooling (MSPP) module aggregates contextual information across multiple receptive fields through parallel feature extraction and adaptive cross-branch fusion. The Hierarchical Feature Fusion Block (HFFB) subsequently refines the feature representations through spatial gating and partial convolution. An auxiliary classification head and Focal Loss were incorporated during training to provide intermediate supervision and address class imbalance, respectively. Results The effectiveness of MSFANet was evaluated on a dataset comprising 13 tobacco-associated pest and natural-enemy categories. The model achieved accuracy, precision, recall, and F1-score of 97.63%, 97.89%, 97.59%, and 97.71%, respectively, with a parameter count of 1.21 M. Among the models trained from random initialization, MSFANet shared the highest accuracy while using the fewest parameters. Discussion By combining multi-scale contextual aggregation with hierarchical feature refinement, MSFANet achieved accurate classification across the 13 tobacco-associated categories under complex field conditions. The proposed method provides effective technical support for automated and precision-oriented pest monitoring in tobacco fields, offering practical value for advancing precise pest management and the broader development of smart agriculture.","引言 烟草是全球重要的经济作物，虫害侵染是导致其产量和品质下降的主要因素之一。然而，烟草害虫种类繁多，形态和尺度差异显著。田间采集的图像常受到复杂背景干扰和类别不平衡的影响，给自动化识别带来了相当大的挑战。现有深度学习方法在此类条件下进行多尺度特征提取和判别性特征表示的能力仍然有限。为应对这些挑战，本研究提出了一种用于烟草害虫识别的多尺度特征聚合网络（Multi-Scale Feature Aggregation Network，MSFANet），并将其形式化为单标签图像分类任务。方法 针对尺度变化和复杂田间背景，开发了两个面向任务的模块。多尺度金字塔池化（Multi-Scale Pyramid Pooling，MSPP）模块通过并行特征提取和自适应跨分支融合，聚合多个感受野的上下文信息。分层特征融合模块（Hierarchical Feature Fusion Block，HFFB）随后通过空间门控和部分卷积对特征表示进行细化。训练过程中引入辅助分类头和Focal Loss，分别提供中间监督和处理类别不平衡。结果 在包含13个烟草相关害虫及天敌类别的数据集上评估了MSFANet的有效性。模型的准确率、精确率、召回率和F1分数分别为97.63%、97.89%、97.59%和97.71%，参数量为1.21 M。在随机初始化训练的模型中，MSFANet以最少的参数量取得了最高准确率。讨论 通过结合多尺度上下文聚合与分层特征细化，MSFANet在复杂田间条件下实现了对13个烟草相关类别的准确分类。所提方法为烟草田间害虫的自动化和精准监测提供了有效的技术支持，对推进精准害虫管理和智慧农业的更广泛发展具有实用价值。","Frontiers in Plant Science",76,{"impact":181,"substance":462,"depth":299,"authority":463,"freshness":206,"relevant":52,"comment":464},21,13,"该论文提出轻量化多尺度特征聚合网络，在烟草害虫识别上取得97%以上精度且参数量仅1.21M，方法新颖、数据扎实，对智慧农业虫情监测有实用价值，值得进入每日精选。",[466],{"name":459,"url":456},[21,22,24,468,469],"病虫害识别","烟草",{"code":471,"slug":472,"name":473,"path":474,"parent_code":391},"A6","cash-crops","棉麻糖与特色经作","\u002Ftopic\u002Ffield-crops\u002Fcash-crops",[476],{"code":26,"slug":27,"name":28,"path":29,"parent_code":30},{"status":33,"locked":14,"version":34,"method":35,"evidence":478},{"candidates":479,"agricultural_evidence":40},[480,485,489],{"code":471,"score":115,"supported":40,"title":481,"abstract":483,"tags":484},[482],"tobacco",[469,482],[469],{"code":26,"score":206,"supported":40,"title":486,"abstract":487,"tags":488},[],[24,43],[22,24,468],{"code":51,"score":211,"supported":40,"title":490,"abstract":491,"tags":492},[],[21,214],[21],[494,495],"MSFANet 烟草害虫识别","多尺度特征聚合 害虫识别","MSFANet烟草害虫识别-5150","10.3389\u002Ffpls.2026.1871587",{"doi":497,"openalex_id":499,"authors":500,"venue":459,"cited_by_count":15,"oa_url":526,"card":527,"direction":235,"ingested_from":73},"W7222603139",[501,503,505,507,509,511,513,515,518,521,524],{"name":502,"orcid":9},"Shun He",{"name":504,"orcid":9},"Sifan Li",{"name":506,"orcid":9},"Xinjian Li",{"name":508,"orcid":9},"Qianqian Luo",{"name":510,"orcid":9},"Dong Sufen",{"name":512,"orcid":9},"Lu Lei",{"name":514,"orcid":9},"Xingjiang Chen",{"name":516,"orcid":517},"Liuti Cai","https:\u002F\u002Forcid.org\u002F0000-0002-9151-8002",{"name":519,"orcid":520},"Jian‐Yu Meng","https:\u002F\u002Forcid.org\u002F0000-0002-5796-1086",{"name":522,"orcid":523},"Ning Lu","https:\u002F\u002Forcid.org\u002F0000-0003-0214-5038",{"name":525,"orcid":9},"Hancheng Wang","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fplant-science\u002Farticles\u002F10.3389\u002Ffpls.2026.1871587\u002Fpdf",{"tldr":528,"method":529,"finding":530,"direction":71,"opportunity":531},"提出MSFANet多尺度特征聚合网络，实现复杂田间背景下烟草害虫的自动识别。","多尺度金字塔池化MSPP、分层特征融合HFFB、辅助分类头与Focal Loss","模型准确率97.63%、参数量仅1.21M，在随机初始化模型中精度最高且参数最少。","可探索轻量多尺度网络在边缘设备部署及跨作物害虫迁移识别，结合类不平衡与复杂背景的鲁棒性研究。","2026-10-17T23:30:08.695455Z"]