[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4492":3,"related-4492":98},{"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":74,"slug":77,"view_count":78,"doi":79,"paper":80,"created_at":97},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。",null,"PLoS ONE","2026-10-08T00:00:00Z","论文",10,false,79,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,22,13,8,1,"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。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","农业人工智能","病虫害识别","精准农业","多模态融合",{"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},[44,53,59,67],{"code":32,"score":45,"supported":46,"title":47,"abstract":48,"tags":52},12,true,[],[49,50,51],"深度学习","deep learning","large language model",[27,30,28],{"code":54,"score":20,"supported":46,"title":55,"abstract":56,"tags":58},"I1",[],[29,57],"precision agriculture",[26,29],{"code":60,"score":61,"supported":46,"title":62,"abstract":63,"tags":66},"A3",6,[],[64,65],"玉米","maize",[],{"code":68,"score":61,"supported":46,"title":69,"abstract":70,"tags":73},"A8",[],[71,72],"粮食安全","food security",[],[75,76],"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",0,"10.1371\u002Fjournal.pone.0359596",{"doi":79,"openalex_id":81,"authors":82,"venue":10,"cited_by_count":78,"oa_url":6,"card":89,"direction":95,"ingested_from":96},"W7221033947",[83,86],{"name":84,"orcid":85},"John Telesphory Mhagama","https:\u002F\u002Forcid.org\u002F0009-0001-4103-5344",{"name":87,"orcid":88},"Kanwal Garg","https:\u002F\u002Forcid.org\u002F0000-0003-0048-9355",{"tldr":90,"method":91,"finding":92,"direction":93,"opportunity":94},"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。","农业人工智能与决策模型","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。","智慧农业 \u002F 农业物联网","openalex","2026-10-09T23:30:08.136081Z",{"total":61,"page":21,"page_size":61,"items":99},[100,173,280,349,415,509],{"id":101,"title":102,"url":103,"summary":104,"summary_zh":105,"content":9,"source_name":106,"source_url":103,"published_at":107,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":108,"score_detail":109,"sources":113,"tags":115,"primary_topic":117,"secondary_topics":123,"classification":125,"search_phrases":144,"slug":147,"view_count":78,"doi":148,"paper":149,"created_at":172},4818,"Hybrid Deep Feature Extraction and Ensemble Machine Learning for Potato Leaf Disease Classification in Uncontrolled Field Conditions","https:\u002F\u002Fdoi.org\u002F10.15832\u002Fankutbd.1837896","Early and accurate diagnosis of potato leaf diseases is critical for preventing yield losses under real field conditions. This study proposes a hybrid deep feature–based machine learning framework that combines transfer learning and ensemble classifiers to automatically identify seven potato leaf disease classes from images. A total of 4,260 images from the publicly available Potato Leaf Disease Dataset were organized into seven categories: Bacteria, Virus, Pest, Fungi, Nematode, Phytophthora, and Healthy. Real-time image augmentation was performed using Keras ImageDataGenerator to improve strength to field variability. Deep feature vectors were extracted from three pre-trained convolutional neural networks (ResNet50, EfficientNetB0, and MobileNetV2), each followed by a lightweight dense head, and concatenated into 768-dimensional feature representations. After standardization, dimensionality reduction was applied via principal component analysis, and class imbalance in the training set was addressed using the SMOTE-ENN hybrid resampling method. On these deep features, 32 classical machine learning and ensemble models including SVMs, tree-based methods, boosting algorithms, multilayer perceptrons, and four ensemble schemes, were trained. Model performance was assessed by three-fold cross-validation on the training set and final evaluation on the held-out validation set using accuracy, precision, recall, and macro F1-score. Ensemble models outperformed individual classifiers. The best performance was obtained with the StackingClassifier_L2 model, which combines XGBoost, LightGBM, Random Forest, SVM with RBF kernel, and Extra Trees within a stacking framework. This model achieved an accuracy of 81.34% and a macro F1-score of 81.74% on the validation set. The proposed hybrid system, which integrates multi-backbone deep feature extraction with comprehensive classical and ensemble classifiers, provides a robust and practical solution for automatic potato leaf disease classification in uncontrolled field conditions. The results suggest that stacking-based ensembles on top of deep features can capture complex visual patterns more effectively than single models and may support decision-making in precision agriculture.","在马铃薯实际田间条件下，早期准确地诊断叶片病害对于防止产量损失至关重要。本研究提出了一种基于混合深度特征的机器学习框架，该框架结合迁移学习和集成分类器，从图像中自动识别七类马铃薯叶片病害。来自公开可用的马铃薯叶片病害数据集的4,260张图像被分为七个类别：细菌（Bacteria）、病毒（Virus）、害虫（Pest）、真菌（Fungi）、线虫（Nematode）、疫霉（Phytophthora）和健康（Healthy）。使用Keras ImageDataGenerator进行实时图像增强，以提高对田间变异的鲁棒性。从三个预训练的卷积神经网络（ResNet50、EfficientNetB0和MobileNetV2）中提取深度特征向量，每个网络后接一个轻量级密集头，并将其拼接为768维特征表示。经过标准化后，通过主成分分析进行降维，并使用SMOTE-ENN混合重采样方法解决训练集中的类别不平衡问题。在这些深度特征上，训练了32种经典机器学习和集成模型，包括支持向量机、基于树的方法、提升算法、多层感知机以及四种集成方案。通过训练集上的三折交叉验证和保留验证集上的最终评估，使用准确率、精确率、召回率和宏F1分数评估模型性能。集成模型优于单个分类器。最佳性能由StackingClassifier_L2模型获得，该模型在堆叠框架内结合了XGBoost、LightGBM、随机森林、带RBF核的支持向量机和极端随机树。该模型在验证集上达到了81.34%的准确率和81.74%的宏F1分数。所提出的混合系统将多骨干深度特征提取与全面的经典分类器和集成分类器相结合，为无控制田间条件下的马铃薯叶片病害自动分类提供了一种稳健且实用的解决方案。结果表明，基于深度特征的堆叠集成能够比单一模型更有效地捕捉复杂的视觉模式，并可能支持精准农业中的决策。","Tarım Bilimleri Dergisi","2026-10-12T00:00:00Z",65,{"impact":20,"substance":110,"depth":111,"authority":19,"freshness":20,"relevant":21,"comment":112},20,16,"方法组合较全面、数据规模明确，但验证集准确率仅81.34%，属细分领域技术进展，可作主题聚合素材而非头条精选。",[114],{"name":106,"url":103},[26,27,116,28,29],"马铃薯",{"code":118,"slug":119,"name":120,"path":121,"parent_code":122},"A5","tubers-grains","薯类与杂粮","\u002Ftopic\u002Ffield-crops\u002Ftubers-grains","A",[124],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":126},{"candidates":127,"agricultural_evidence":46},[128,134,140],{"code":32,"score":111,"supported":46,"title":129,"abstract":131,"tags":133},[130],"machine learning",[132,130],"机器学习",[27,28],{"code":118,"score":135,"supported":46,"title":136,"abstract":138,"tags":139},15,[137],"potato",[116,137],[116],{"code":54,"score":20,"supported":46,"title":141,"abstract":142,"tags":143},[],[29,57],[26,29],[145,146],"马铃薯 叶片病害 深度学习","Potato Leaf Disease Dataset 集成学习","马铃薯叶片病害深度学习-4818","10.15832\u002Fankutbd.1837896",{"doi":148,"openalex_id":150,"authors":151,"venue":106,"cited_by_count":78,"oa_url":166,"card":167,"direction":95,"ingested_from":96},"W7221546496",[152,155,157,160,163],{"name":153,"orcid":154},"Cihan Ünal","https:\u002F\u002Forcid.org\u002F0000-0002-5255-4078",{"name":156,"orcid":9},"Cengizhan Tumer",{"name":158,"orcid":159},"Elham Tahsin Yasin","https:\u002F\u002Forcid.org\u002F0000-0003-3246-6000",{"name":161,"orcid":162},"İlkay Çınar","https:\u002F\u002Forcid.org\u002F0000-0003-0611-3316",{"name":164,"orcid":165},"Murat Köklü","https:\u002F\u002Forcid.org\u002F0000-0002-2737-2360","https:\u002F\u002Fdergipark.org.tr\u002Fen\u002Fdownload\u002Farticle-file\u002F5487015",{"tldr":168,"method":169,"finding":170,"direction":93,"opportunity":171},"融合三种CNN深度特征与集成学习，实现田间马铃薯叶片七类病害分类。","ResNet50\u002FEfficientNetB0\u002FMobileNetV2特征拼接+","StackingClassifier_L2最优，验证集准确率81.34%、宏F1为81.74%。","可探索轻量化模型与多模态数据融合，提升非受控田间小样本病害识别鲁棒性。","2026-10-13T23:30:16.021066Z",{"id":174,"title":175,"url":176,"summary":177,"summary_zh":178,"content":9,"source_name":179,"source_url":176,"published_at":180,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":181,"score_detail":182,"sources":185,"tags":187,"primary_topic":189,"secondary_topics":195,"classification":202,"search_phrases":258,"slug":261,"view_count":21,"doi":262,"paper":263,"created_at":279},4038,"Use of artificial intelligence (AI) in vegetable crops: A review","https:\u002F\u002Fdoi.org\u002F10.33545\u002F2664844x.2026.v8.i10a.1995","Artificial Intelligence (AI) is emerging technology in vegetable production, enabling data-driven decision making and precision agriculture practices. AI refers to the intelligence exhibited by machines that can perceive their environment, process large datasets, and make decisions to optimize agricultural productivity. With the integration of technologies such as machine learning, sensors, drones, satellites, Geographic Information Systems (GIS), and Global Positioning Systems (GPS), AI enables efficient monitoring and management of crop production systems In vegetable cultivation, AI technologies assist farmers in several critical operations including soil monitoring, climate assessment, pest and disease diagnosis, irrigation scheduling, and yield prediction. Sensors such as wind vane, cup count anemometer, rain gauge, lux meter, soil moisture sensor, and crop canopy sensors collect real-time environmental and crop data. These data was processed through data loggers and machine learning algorithms to provide accurate recommendations for crop management. Drone-based imaging systems and multispectral cameras further support precision farming by identifying weeds, nutrient deficiencies and disease symptoms at early stages. The use of AI in vegetables also highlights image-based predictive agriculture systems where captured images are processed through stages such as. image cleaning, feature extraction, and model training to detect pests and diseases. Several case studies demonstrate the use of AI in vegetable crops such as tomato, capsicum, and chilli for monitoring canopy growth, irrigation management. Additionally, robotic technologies in harvesting address labor shortages in agriculture. Crop yield prediction of AI in vegetables increases by 40% sensors and algorithms reduces labor cost by 30% moisture sensor increases water use efficiency by 40% spraying done reduces use of pesticide by 60% robotic arms increases harvesting accuracy by 40% artificial neural network data integration saves 25% cost in disease monitoring.","人工智能（AI）正在成为蔬菜生产领域的新兴技术，推动数据驱动决策与精准农业实践。AI是指机器所表现出的智能，能够感知环境、处理大规模数据集并做出决策以优化农业生产力。通过整合机器学习、传感器、无人机、卫星、地理信息系统（GIS）和全球定位系统（GPS）等技术，AI实现了对作物生产系统的高效监测与管理。在蔬菜种植中，AI技术可协助农民完成多项关键作业，包括土壤监测、气候评估、病虫害诊断、灌溉调度和产量预测。风向标、杯式风速计、雨量计、照度计、土壤水分传感器和作物冠层传感器等设备可采集实时的环境与作物数据。这些数据通过数据记录器和机器学习算法进行处理，为作物管理提供精准建议。基于无人机的成像系统与多光谱相机通过早期识别杂草、养分缺乏和病害症状，进一步支持精准农业。AI在蔬菜中的应用还突出了基于图像的预测性农业系统，该系统将采集的图像经过图像清洗、特征提取和模型训练等阶段处理，以检测病虫害。多个案例研究展示了AI在番茄、辣椒和尖椒等蔬菜作物中用于监测冠层生长和灌溉管理的应用。此外，收获机器人技术可缓解农业劳动力短缺问题。AI在蔬菜中的作物产量预测通过传感器和算法提高了40%，劳动力成本降低了30%，土壤水分传感器使水分利用效率提高了40%，喷雾作业使农药使用量减少了60%，机械臂使收获准确率提高了40%，人工神经网络数据集成使病害监测成本节省了25%。","International Journal of Agriculture and Food Science","2026-10-01T00:00:00Z",75,{"impact":17,"substance":110,"depth":111,"authority":45,"freshness":183,"relevant":21,"comment":184},9,"系统综述AI在蔬菜生产中的应用，含产量、成本、水肥药效等量化数据，对智慧农业主题有聚合价值。",[186],{"name":179,"url":176},[26,27,28,29,188],"蔬菜种植",{"code":190,"slug":191,"name":192,"path":193,"parent_code":194},"B1","vegetables","蔬菜","\u002Ftopic\u002Fhorticulture\u002Fvegetables","B",[196,197],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"code":198,"slug":199,"name":200,"path":201,"parent_code":36},"I4","gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis",{"status":39,"locked":14,"version":40,"method":41,"evidence":203},{"candidates":204,"agricultural_evidence":46},[205,211,219,226,230,237,244,251],{"code":32,"score":206,"supported":46,"title":207,"abstract":209,"tags":210},19,[208],"artificial intelligence",[132,130,208],[27,28],{"code":190,"score":17,"supported":46,"title":212,"abstract":214,"tags":218},[213],"vegetable",[192,215,216,213,217],"番茄","辣椒","tomato",[188],{"code":198,"score":183,"supported":46,"title":220,"abstract":221,"tags":225},[],[222,223,224],"地理信息","GIS","geographic information",[],{"code":54,"score":20,"supported":46,"title":227,"abstract":228,"tags":229},[],[29,57],[26,29],{"code":231,"score":61,"supported":46,"title":232,"abstract":233,"tags":236},"F4",[],[234,235],"农药","pesticide",[],{"code":238,"score":61,"supported":46,"title":239,"abstract":240,"tags":243},"H2",[],[241,242],"土壤","soil",[],{"code":245,"score":61,"supported":46,"title":246,"abstract":247,"tags":250},"H3",[],[248,249],"灌溉","irrigation",[],{"code":252,"score":253,"supported":46,"title":254,"abstract":255,"tags":257},"I3",3,[],[256],"multispectral",[],[259,260],"AI 蔬菜 精准农业","蔬菜 病虫害 图像识别","AI蔬菜精准农业-4038","10.33545\u002F2664844x.2026.v8.i10a.1995",{"doi":262,"openalex_id":264,"authors":265,"venue":179,"cited_by_count":78,"oa_url":9,"card":274,"direction":93,"ingested_from":96},"W7215128283",[266,268,270,272],{"name":267,"orcid":9},"Hrishikesh Nirmala Balkrishna Thube",{"name":269,"orcid":9},"Dr. SK Kshirsagar",{"name":271,"orcid":9},"MG Agale",{"name":273,"orcid":9},"GS Shinde",{"tldr":275,"method":276,"finding":277,"direction":95,"opportunity":278},"综述人工智能在蔬菜作物生产中的应用，涵盖监测、诊断、灌溉、产量预测与采收机器人。","综述机器学习、传感器、无人机、卫星、GIS\u002FGPS及图像处理在蔬菜生产中的应用案","AI使蔬菜产量预测提高40%、节水40%、减农药60%、采收准确率提高40%。","可针对小农户蔬菜场景开发低成本多源数据融合模型，并验证AI长期经济与生态效益。","2026-10-02T23:30:40.356457Z",{"id":281,"title":282,"url":283,"summary":284,"summary_zh":285,"content":9,"source_name":286,"source_url":283,"published_at":287,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":288,"score_detail":289,"sources":292,"tags":294,"primary_topic":296,"secondary_topics":297,"classification":303,"search_phrases":331,"slug":334,"view_count":21,"doi":335,"paper":336,"created_at":348},3877,"Machine learning classification techniques for smart agriculture: a review of disease detection and crop recommendation","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffrai.2026.1878006","The adoption of Internet of Things (IoT) and Machine Learning (ML) in agriculture presents a revolutionary step toward efficient agricultural practices using data to address global issues surrounding food security. This review conducted a systematic search of the Scopus, IEEE Xplore, ScienceDirect, and SpringerLink databases for articles published between January 2023 and January 2026. It used keyword combinations like “IoT and machine learning and agriculture”, “crop recommendation”, “plant disease detection”, and “precision farming”. From the initial pool of articles found, 30 articles met the criteria for empirical methodology, quantitative performance reporting, and clear dataset descriptions. These were chosen for detailed analysis. It was discovered that the ensemble, as well as the traditional machine learning methods, such as Random Forest, Support Vector Machine (SVM), and Decision Trees, produce highly accurate predictions of the crop yields with an accuracy of 99.9%. Meanwhile, Convolutional Neural Networks (CNN) and ResNet-based deep learning models detect diseases with an accuracy above 98% while Gated Recurrent Units (GRU) and Long-Short Term Memory (LSTM) time-series models provide forecasts of weather conditions with an accuracy ranging from 94 to 98%, due to the complex nature of the environment dynamics. The application of IoT systems for precision agriculture produced a significant improvement in terms of resource utilization, resulting in 60–96% of water savings, 25% increased yield, and 36–38% reduced herbicide use. Although these advancements have been made, wider application is still hindered by high costs of implementation, lack of technological skills among farmers, data privacy concerns, issues of scalability within different agricultural settings, and consistent internet connection in rural areas. The current paper evaluates the use of IoT and ML in the agriculture sector from diverse data sources and studies to reveal the immense benefits offered by the two systems in enhancing farming activities.","物联网（IoT）与机器学习（ML）在农业中的应用标志着向高效农业实践迈出了革命性的一步，即利用数据来解决全球粮食安全相关问题。本综述对Scopus、IEEE Xplore、ScienceDirect和SpringerLink数据库中2023年1月至2026年1月间发表的文章进行了系统性检索，使用了“物联网与机器学习与农业”“作物推荐”“植物病害检测”和“精准农业”等关键词组合。从初步检索到的文章池中，30篇文章符合实证方法、定量性能报告和清晰数据集描述的标准，被选入进行详细分析。研究发现，集成方法以及随机森林、支持向量机（SVM）和决策树等传统机器学习方法对作物产量的预测精度高达99.9%。同时，卷积神经网络（CNN）和基于ResNet的深度学习模型检测病害的准确率超过98%，而门控循环单元（GRU）和长短期记忆（LSTM）时间序列模型由于环境动态的复杂性，对天气状况的预测准确率在94%至98%之间。物联网系统在精准农业中的应用在资源利用方面产生了显著改善，实现了60%至96%的节水、25%的产量提升以及36%至38%的除草剂用量减少。尽管取得了这些进展，更广泛的应用仍受到实施成本高昂、农民缺乏技术技能、数据隐私顾虑、不同农业场景下的可扩展性问题以及农村地区互联网连接不稳定等因素的制约。本文通过来自不同数据来源和研究，评估了物联网与机器学习在农业领域的应用，揭示了这两个系统在提升农业活动方面所带来的巨大效益。","Frontiers in Artificial Intelligence","2026-09-30T00:00:00Z",76,{"impact":17,"substance":110,"depth":290,"authority":19,"freshness":20,"relevant":21,"comment":291},17,"系统综述整合30篇实证研究，量化了IoT与机器学习在作物推荐、病害检测和精准灌溉上的性能与节水增产效益，对智慧农业技术选型有参考价值。",[293],{"name":286,"url":283},[26,27,28,29,295],"作物推荐",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[298],{"code":299,"slug":300,"name":301,"path":302,"parent_code":36},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",{"status":39,"locked":14,"version":40,"method":41,"evidence":304},{"candidates":305,"agricultural_evidence":46},[306,310,315,322,326],{"code":32,"score":206,"supported":46,"title":307,"abstract":308,"tags":309},[130],[49,132,130,50],[27,28],{"code":54,"score":111,"supported":46,"title":311,"abstract":313,"tags":314},[312],"smart agriculture",[29,57],[26,29],{"code":299,"score":183,"supported":46,"title":316,"abstract":317,"tags":321},[],[318,319,320],"物联网","internet of things","IoT",[],{"code":68,"score":61,"supported":46,"title":323,"abstract":324,"tags":325},[],[71,72],[],{"code":231,"score":253,"supported":46,"title":327,"abstract":328,"tags":330},[],[329],"除草",[],[332,333],"农业人工智能 病虫害识别 作物推荐 智慧农业","农业人工智能 病虫害识别","农业人工智能病虫害识别作物推荐智慧农业-3877","10.3389\u002Ffrai.2026.1878006",{"doi":335,"openalex_id":337,"authors":338,"venue":286,"cited_by_count":78,"oa_url":283,"card":343,"direction":95,"ingested_from":96},"W7214889993",[339,341],{"name":340,"orcid":9},"M. Umamaheswari",{"name":342,"orcid":9},"Kavitha D",{"tldr":344,"method":345,"finding":346,"direction":93,"opportunity":347},"综述2023-2026年30篇文献，评估物联网与机器学习在作物病害检测和推荐中的应用。","系统检索Scopus等四个数据库，筛选30篇实证研究进行性能与数据集分析。","集成学习与SVM等预测产量准确率达99.9%，CNN病害检测超98%，IoT节水60-96%。","可研究低成本、低连接依赖的边缘ML模型，解决小农户部署与数据隐私问题。","2026-10-01T23:30:09.158196Z",{"id":350,"title":351,"url":352,"summary":353,"summary_zh":354,"content":9,"source_name":355,"source_url":352,"published_at":356,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":357,"score_detail":358,"sources":361,"tags":363,"primary_topic":365,"secondary_topics":366,"classification":367,"search_phrases":379,"slug":382,"view_count":253,"doi":383,"paper":384,"created_at":414},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。","International Journal of Extreme Manufacturing","2026-09-18T00:00:00Z",78,{"impact":17,"substance":110,"depth":17,"authority":359,"freshness":20,"relevant":21,"comment":360},14,"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。",[362],{"name":355,"url":352},[26,27,29,30,364],"生物声学传感",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":368},{"candidates":369,"agricultural_evidence":46},[370,375],{"code":32,"score":371,"supported":46,"title":372,"abstract":373,"tags":374},11,[],[49,50,208],[27,30],{"code":54,"score":20,"supported":46,"title":376,"abstract":377,"tags":378},[],[29,57],[26,29],[380,381],"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","10.1088\u002F2631-7990\u002Faea9da",{"doi":383,"openalex_id":385,"authors":386,"venue":355,"cited_by_count":78,"oa_url":352,"card":409,"direction":93,"ingested_from":96},"W7213590695",[387,390,393,395,398,401,403,406],{"name":388,"orcid":389},"Chengyu Li","https:\u002F\u002Forcid.org\u002F0000-0002-2128-3420",{"name":391,"orcid":392},"Wenbo Li","https:\u002F\u002Forcid.org\u002F0000-0003-3599-3324",{"name":394,"orcid":9},"Jingyang Wu",{"name":396,"orcid":397},"Shuo Wang","https:\u002F\u002Forcid.org\u002F0000-0002-5411-7269",{"name":399,"orcid":400},"Han Liao","https:\u002F\u002Forcid.org\u002F0009-0001-6256-7438",{"name":402,"orcid":9},"xiang Yu",{"name":404,"orcid":405},"Cheng Li","https:\u002F\u002Forcid.org\u002F0000-0003-3424-2414",{"name":407,"orcid":408},"Xiaoming Tao","https:\u002F\u002Forcid.org\u002F0000-0002-2406-0695",{"tldr":410,"method":411,"finding":412,"direction":95,"opportunity":413},"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。","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。","2026-09-19T23:30:51.998646Z",{"id":416,"title":417,"url":418,"summary":419,"summary_zh":420,"content":9,"source_name":421,"source_url":418,"published_at":422,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":423,"score_detail":424,"sources":426,"tags":428,"primary_topic":430,"secondary_topics":431,"classification":437,"search_phrases":485,"slug":488,"view_count":78,"doi":489,"paper":490,"created_at":508},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":19,"freshness":183,"relevant":21,"comment":425},"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。",[427],{"name":421,"url":418},[26,27,318,29,429],"气候韧性",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[432,433],{"code":299,"slug":300,"name":301,"path":302,"parent_code":36},{"code":245,"slug":249,"name":434,"path":435,"parent_code":436},"水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",{"status":39,"locked":14,"version":40,"method":41,"evidence":438},{"candidates":439,"agricultural_evidence":46},[440,446,450,456,461,469,473,479],{"code":32,"score":17,"supported":46,"title":441,"abstract":442,"tags":445},[208],[49,132,443,130,50,444],"计算机视觉","computer vision",[27],{"code":54,"score":111,"supported":46,"title":447,"abstract":448,"tags":449},[57],[29,57],[26,29],{"code":299,"score":13,"supported":46,"title":451,"abstract":452,"tags":455},[],[318,453,320,454],"边缘计算","edge computing",[318],{"code":245,"score":183,"supported":46,"title":457,"abstract":458,"tags":460},[],[248,459,249],"水资源",[],{"code":462,"score":463,"supported":46,"title":464,"abstract":465,"tags":468},"H7",7,[],[466,467],"气候变化","climate change",[429],{"code":238,"score":61,"supported":46,"title":470,"abstract":471,"tags":472},[],[241,242],[],{"code":474,"score":253,"supported":46,"title":475,"abstract":476,"tags":478},"A7",[],[477],"间作",[],{"code":480,"score":253,"supported":46,"title":481,"abstract":482,"tags":484},"F3",[],[483],"养分管理",[],[486,487],"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":489,"openalex_id":491,"authors":492,"venue":421,"cited_by_count":78,"oa_url":418,"card":503,"direction":95,"ingested_from":96},"W7223121456",[493,496,498,500],{"name":494,"orcid":495},"Olumayowa J. Iyanda","https:\u002F\u002Forcid.org\u002F0000-0002-5340-9884",{"name":497,"orcid":9},"Oluwadare OYEBODE",{"name":499,"orcid":9},"Iretiayo Adelaiye",{"name":501,"orcid":502},"Ayomide Afolabi","https:\u002F\u002Forcid.org\u002F0000-0002-5171-886X",{"tldr":504,"method":505,"finding":506,"direction":93,"opportunity":507},"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":510,"title":511,"url":512,"summary":513,"summary_zh":514,"content":9,"source_name":515,"source_url":512,"published_at":516,"category":12,"cover_url":9,"hotness":13,"is_selected":46,"score":517,"score_detail":518,"sources":522,"tags":524,"primary_topic":527,"secondary_topics":528,"classification":529,"search_phrases":553,"slug":556,"view_count":545,"doi":557,"paper":558,"created_at":584},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":519,"substance":520,"depth":206,"authority":135,"freshness":183,"relevant":21,"comment":521},26,24,"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。",[523],{"name":515,"url":512},[26,27,29,525,526],"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":530},{"candidates":531,"agricultural_evidence":46},[532,536,540,544,549],{"code":54,"score":371,"supported":46,"title":533,"abstract":534,"tags":535},[],[26,29,57],[26,29],{"code":32,"score":463,"supported":46,"title":537,"abstract":538,"tags":539},[],[132,130],[27],{"code":245,"score":61,"supported":46,"title":541,"abstract":542,"tags":543},[],[248,249],[],{"code":252,"score":545,"supported":46,"title":546,"abstract":547,"tags":548},4,[],[525],[525],{"code":231,"score":253,"supported":46,"title":550,"abstract":551,"tags":552},[],[329],[],[554,555],"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":557,"openalex_id":559,"authors":560,"venue":515,"cited_by_count":78,"oa_url":512,"card":579,"direction":95,"ingested_from":96},"W7222974188",[561,564,567,570,573,576],{"name":562,"orcid":563},"Shahrear Roman","https:\u002F\u002Forcid.org\u002F0009-0006-5659-4292",{"name":565,"orcid":566},"Hadi Hadi","https:\u002F\u002Forcid.org\u002F0000-0002-2410-9751",{"name":568,"orcid":569},"Wyclife Agumba Oluoch","https:\u002F\u002Forcid.org\u002F0000-0003-0803-9279",{"name":571,"orcid":572},"David Zilberman","https:\u002F\u002Forcid.org\u002F0000-0001-5743-5134",{"name":574,"orcid":575},"Robert Finger","https:\u002F\u002Forcid.org\u002F0000-0002-0634-5742",{"name":577,"orcid":578},"David Wuepper","https:\u002F\u002Forcid.org\u002F0000-0002-1344-6023",{"tldr":580,"method":581,"finding":582,"direction":95,"opportunity":583},"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"]