[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3360":3,"related-3360":117},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":24,"tags":26,"primary_topic":32,"secondary_topics":37,"classification":38,"search_phrases":86,"slug":89,"view_count":90,"doi":91,"paper":92,"created_at":116},3360,"Quantum Machine Learning for Hyperspectral Soil Nutrient Estimation in Precision Agriculture: A Review and Roadmap","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fagriculture16192069","Accurate soil nutrient estimation underpins fertility assessment, precision agriculture, and sustainable land management, yet conventional laboratory analysis is slow, costly, and impractical at scale. Hyperspectral imaging (HSI) captures detailed spectral signatures linked to soil properties, but its high dimensionality and limited ground truth samples strain classical machine learning. This review examines quantum machine learning (QML) as an emerging rather than established direction for high-dimensional, low-sample hyperspectral soil analysis. We outline where classical machine learning and deep learning fall short and then assess how quantum kernel methods, variational quantum circuits, and hybrid quantum–classical architectures might improve feature representation and nonlinear modeling. Of the 150 studies reviewed, 26 report quantum machine learning results of any kind, five use soil data, and only one evaluates soil spectra directly for nutrient or property estimation. The remainder contribute transferable evidence from adjacent soil and remote sensing tasks, together with soil applications that remain proposals. Accordingly, the review weighs both the promise and the practical constraints of QML on current noisy intermediate-scale quantum hardware, including the encoding cost, measurement overhead, circuit depth, and trainability limits. Future research priorities are identified: quantum-ready hyperspectral soil datasets, reproducible benchmarking against well-tuned classical baselines, scalable hybrid pipelines, hardware-aware reporting, and field validation. Pairing QML with hyperspectral soil sensing may eventually support soil fertility assessment, variable-rate fertilization, and sustainable precision agriculture, but any such benefit must be demonstrated experimentally rather than inferred and will depend on continued progress in quantum hardware.","准确的土壤养分估算是肥力评估、精准农业和可持续土地管理的基础，然而传统实验室分析速度慢、成本高，难以大规模应用。高光谱成像（HSI）能够捕获与土壤属性相关的精细光谱特征，但其高维度和有限的真实标注样本给经典机器学习带来了挑战。本综述将量子机器学习（QML）视为面向高维、小样本高光谱土壤分析的一个新兴而非成熟的方向。我们梳理了经典机器学习和深度学习的不足之处，进而评估量子核方法、变分量子电路以及量子—经典混合架构如何可能改进特征表示与非线性建模。在综述的150项研究中，26项报告了某种形式的量子机器学习结果，5项使用了土壤数据，仅1项直接评估土壤光谱以估算养分或属性。其余研究提供了来自相邻土壤和遥感任务的可迁移证据，以及仍处于提案阶段的土壤应用。据此，本综述权衡了QML在当前含噪中等规模量子硬件上的前景与实际约束，包括编码成本、测量开销、电路深度和可训练性限制。未来研究重点包括：量子就绪的高光谱土壤数据集、针对精心调优的经典基线的可复现基准测试、可扩展的混合流程、硬件感知的报告以及田间验证。将QML与高光谱土壤传感相结合，最终可能支持土壤肥力评估、变量施肥和可持续精准农业，但任何此类收益都必须通过实验证明而非推断，并将取决于量子硬件的持续进步。",null,"Agriculture","2026-09-23T00:00:00Z","论文",10,false,77,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},16,21,18,13,9,1,"系统梳理量子机器学习用于高光谱土壤养分估算的现状与路线图，指出仅1项研究直接验证土壤光谱，兼具前沿性与现实约束，值得进入每日精选。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","农业人工智能","高光谱遥感","土壤养分","量子计算",{"code":33,"slug":34,"name":35,"path":36,"parent_code":9},"I","digital-agriculture","数字农业与前沿技术","\u002Ftopic\u002Fdigital-agriculture",[],{"status":39,"locked":14,"version":40,"method":41,"evidence":42},"partial","2026-10-22.2","rules",{"candidates":43,"agricultural_evidence":46,"reason":85},[44,54,62,70,77],{"code":45,"score":19,"supported":46,"title":47,"abstract":49,"tags":53},"I2",true,[48],"machine learning",[50,51,48,52],"深度学习","机器学习","deep learning",[28],{"code":55,"score":19,"supported":46,"title":56,"abstract":58,"tags":61},"I3",[57],"hyperspectral",[59,60,57],"遥感","remote sensing",[29],{"code":63,"score":64,"supported":46,"title":65,"abstract":67,"tags":69},"H2",15,[66],"soil",[68,66],"土壤",[30],{"code":71,"score":64,"supported":46,"title":72,"abstract":74,"tags":76},"I1",[73],"precision agriculture",[75,73],"精准农业",[27],{"code":78,"score":79,"supported":46,"title":80,"abstract":81,"tags":84},"F3",6,[],[82,83],"施肥","fertilization",[],"一级领域明确，二级方向需要细分",[87,88],"量子机器学习 高光谱 土壤养分","精准农业 变量施肥 土壤肥力","量子机器学习高光谱土壤养分-3360",7,"10.3390\u002Fagriculture16192069",{"doi":91,"openalex_id":93,"authors":94,"venue":10,"cited_by_count":108,"oa_url":6,"card":109,"direction":113,"ingested_from":115},"W7214101561",[95,98,100,103,106],{"name":96,"orcid":97},"Dristi Datta","https:\u002F\u002Forcid.org\u002F0000-0002-9426-9750",{"name":99,"orcid":9},"Dipti Biswas",{"name":101,"orcid":102},"Uttam Sinha Mahapatra","https:\u002F\u002Forcid.org\u002F0009-0001-0619-0381",{"name":104,"orcid":105},"Manoranjan Paul","https:\u002F\u002Forcid.org\u002F0000-0001-6870-5056",{"name":107,"orcid":9},"Davina White",0,{"tldr":110,"method":111,"finding":112,"direction":113,"opportunity":114},"综述量子机器学习用于高光谱土壤养分估算，指出其尚处早期并给出研究路线图。","综述150项研究，评估量子核方法、变分量子电路与混合量子-经典架构。","仅1项研究直接评估土壤光谱养分估算，QML优势尚未实验证实。","农业遥感与作物表型","构建量子就绪高光谱土壤数据集，并与调优经典基线做可复现基准对比。","openalex","2026-09-24T23:30:20.797372Z",{"total":79,"page":22,"page_size":79,"items":118},[119,190,279,351,411,471],{"id":120,"title":121,"url":122,"summary":123,"summary_zh":124,"content":9,"source_name":125,"source_url":122,"published_at":126,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":127,"score_detail":128,"sources":131,"tags":133,"primary_topic":137,"secondary_topics":138,"classification":139,"search_phrases":172,"slug":175,"view_count":108,"doi":176,"paper":177,"created_at":189},5563,"AI-Powered Early Detection of Crop Diseases and Pest Infestations: A New Paradigm for Plantation Risk Management","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23531525","Abstract: Crop diseases and pest infestations remain persistent threats to plantation productivity, with pests estimated to destroy up to 40% of global crop production and plant diseases and invasive insects imposing substantial economic losses worldwide (Food and Agriculture Organization of the United Nations [FAO], 2019). Climate change is intensifying these risks by altering the geographic range, seasonality, and population dynamics of pathogens and pests (Gullino et al., 2021; Skendžić et al., 2021). This paper examines how artificial intelligence (AI) — including convolutional neural networks (CNNs), vision transformers (ViTs), unmanned aerial vehicle (UAV)-based multispectral and hyperspectral sensing, and Internet of Things (IoT)-enabled monitoring — can strengthen the early detection of biotic stress in plantation crops and thereby support plantation risk management. Drawing on recent peer-reviewed and industry literature, the paper synthesises evidence on detection performance, deployment modalities, and pathways for integration into enterprise risk-management systems. It proposes a conceptual framework in which AI-enabled early-warning systems operate as a continuous, spatially explicit risk-sensing layer linking agronomic observations with operational, financial, and insurance decision-making. AI can improve the timeliness, spatial precision, and consistency of surveillance; however, its risk-management value depends on field validation, representative training data, reliable infrastructure, affordable deployment, and appropriate governance. AI should therefore currently be positioned as decision support that augments, rather than replaces, agronomic expertise and ground-truthing. Keywords: artificial intelligence; plant disease detection; pest infestation; precision agriculture; plantation risk management; machine learning; remote sensing. Title: AI-Powered Early Detection of Crop Diseases and Pest Infestations: A New Paradigm for Plantation Risk Management Author: Dr. Geetha Govindan K Gopalakrishnan International Journal of Management and Commerce Innovations ISSN 2348-7585 (Online) Vol. 14, Issue 2, October 2026 - March 2027 Page No: 54-59 Research Publish Journals Website: www.researchpublish.com Published Date: 21-October-2026 DOI: https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23531526 Paper Download Link (Source) https:\u002F\u002Fwww.researchpublish.com\u002Fpapers\u002Fai-powered-early-detection-of-crop-diseases-and-pest-infestations-a-new-paradigm-for-plantation-risk-management","摘要：作物病害与虫害侵染始终是种植园生产力的持续威胁，据估计，害虫每年可摧毁全球高达40%的作物产量，植物病害与入侵昆虫在全球范围内造成重大经济损失（联合国粮食及农业组织[FAO]，2019）。气候变化通过改变病原体与害虫的地理分布范围、季节性规律及种群动态，正在加剧这些风险（Gullino等，2021；Skendžić等，2021）。本文探讨人工智能（AI）——包括卷积神经网络（CNN）、视觉变换器（ViT）、基于无人机（UAV）的多光谱与高光谱遥感，以及物联网（IoT）赋能的监测技术——如何强化种植园作物生物胁迫的早期检测，从而支撑种植园风险管理。本文基于近期同行评审文献与行业文献，综合梳理了检测性能、部署模式及其融入企业风险管理系统的路径等方面的证据。文章提出一个概念框架，其中AI赋能的预警系统作为持续运行、空间显性的风险感知层，将农艺观测与运营、财务及保险决策相衔接。AI能够提升监测的及时性、空间精度与一致性；然而，其风险管理价值取决于田间验证、具有代表性的训练数据、可靠的基础设施、可负担的部署成本以及适当的治理。因此，当前应将AI定位为决策支持工具，用以增强而非替代农艺专业知识与实地核验。关键词：人工智能；植物病害检测；虫害侵染；精准农业；种植园风险管理；机器学习；遥感。题目：AI驱动的作物病害与虫害侵染早期检测：种植园风险管理的新范式。作者：Geetha Govindan K Gopalakrishnan博士。《国际管理与商业创新期刊》ISSN 2348-7585（在线）第14卷第2期，2026年10月—2027年3月，第54-59页。研究出版期刊网站：www.researchpublish.com。发布日期：2026年10月21日。DOI：https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23531526。论文下载链接（来源）：https:\u002F\u002Fwww.researchpublish.com\u002Fpapers\u002Fai-powered-early-detection-of-crop-diseases-and-pest-infestations-a-new-paradigm-for-plantation-risk-ma","Zenodo (CERN European Organization for Nuclear Research)","2026-10-21T00:00:00Z",56,{"impact":129,"substance":17,"depth":20,"authority":79,"freshness":21,"relevant":22,"comment":130},12,"综述性论文，系统梳理AI与遥感在作物病虫害早期预警中的应用并提出风险管理框架，有参考价值但方法创新与数据规模有限，信源为普通出版平台。",[132],{"name":125,"url":122},[27,28,134,135,136],"遥感监测","风险管理","病虫害监测",{"code":33,"slug":34,"name":35,"path":36,"parent_code":9},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":140},{"candidates":141,"agricultural_evidence":46,"reason":85},[142,148,153,161,165],{"code":45,"score":13,"supported":46,"title":143,"abstract":144,"tags":147},[],[51,48,145,146],"artificial intelligence","decision support",[28],{"code":55,"score":13,"supported":46,"title":149,"abstract":150,"tags":152},[],[59,60,151,57],"multispectral",[134],{"code":154,"score":21,"supported":46,"title":155,"abstract":156,"tags":160},"I5",[],[157,158,159],"物联网","internet of things","IoT",[],{"code":71,"score":90,"supported":46,"title":162,"abstract":163,"tags":164},[],[75,73],[27],{"code":166,"score":79,"supported":46,"title":167,"abstract":168,"tags":171},"H7",[],[169,170],"气候变化","climate change",[],[173,174],"AI 作物病害 早期检测","无人机 多光谱 病虫害监测","AI作物病害早期检测-5563","10.5281\u002Fzenodo.23531525",{"doi":176,"openalex_id":178,"authors":179,"venue":125,"cited_by_count":108,"oa_url":122,"card":182,"direction":188,"ingested_from":115},"W7223085400",[180],{"name":181,"orcid":9},"Dr. Geetha Govindan K Gopalakrishnan",{"tldr":183,"method":184,"finding":185,"direction":186,"opportunity":187},"综述AI（CNN、ViT、无人机遥感、物联网）用于作物病虫害早期检测并融入种植园风险管理。","文献综述，整合CNN、ViT、无人机多\u002F高光谱与IoT监测的检测性能与部署方式。","AI可提升监测及时性与空间精度，但价值取决于田间验证、代表性数据与治理，应作决策支持而非替代专家。","农业人工智能与决策模型","可将AI预警层与保险、金融风控系统对接，研究跨域数据融合与可解释风险定价的空白。","智慧农业 \u002F 农业物联网","2026-10-22T11:20:24.428080Z",{"id":191,"title":192,"url":193,"summary":194,"summary_zh":195,"content":9,"source_name":196,"source_url":193,"published_at":126,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":197,"score_detail":198,"sources":203,"tags":205,"primary_topic":207,"secondary_topics":208,"classification":209,"search_phrases":265,"slug":268,"view_count":108,"doi":269,"paper":270,"created_at":278},5556,"AI Powered IoT-Based System for Weed and Pest Control in Agriculture Using Deep Learning","https:\u002F\u002Fdoi.org\u002F10.25258\u002Fijddt.16.76s.32","Weeds, pest attacks, labour shortage and over-dependence on chemical pesticides are the serious challenges faced by the agricultural sector in the wake of ever growing weed infestation.Manual weed and pest control has potential negative environmental impacts, increased production costs and reduced yields.The application of Artificial Intelligence (AI), Internet of Things (IoT), Deep Learning, robotics, and laser technologies are a promising way of developing sustainable and autonomous agricultural systems.This study introduces an intelligent agricultural system based on Artificial Intelligence (AI) and Internet of Things (IoT) for weed identification, harmful insect identification, crop condition monitoring and automatic weed removal using a laser beam.The system will be based on Internet of Things (IoT) sensors, Computer vision modules, Convolutional neural networks (CNN), Unmanned robotic platforms, Environmental sensing systems, and Electronic Swarm Suppression (ESS) mechanisms to enhance agricultural productivity and reduce crop losses.The system uses deep learning algorithms for real time weed, crop, beneficial and harmful insect classification.High-resolution cameras and sensing devices collect environmental and crop data continuously and feed the data into an IoT system for processing in a cloud-edge computing system.High precision weed elimination with a robotic platform containing laser emitters which do not harm adjacent crops.Further, computer vision and environmental monitoring are employed to identify aerial insect swarms and to enable automatic attacks of insect swarms by means of a laser beam and ESS technology.Multiple agricultural blocks were used for experimental evaluation of the maize, wheat, soybean and vegetable crops.The results show substantial increases in the accuracy of weed detection, pest identification, crop protection and cost savings for operations over current practices.The proposed system achieved a 97.6% accuracy in the weed classification, 96.8% accuracy in the detection of the insect and a 42.3% reduction in pesticide use.In addition, the crop yield rose by 18.7% and the operational costs dropped by 28.4%.Overall, the results suggest that AI-powered IoT can significantly enhance precision agriculture and promote sustainable food production.","在杂草侵扰日益加剧的背景下，杂草、病虫害侵袭、劳动力短缺以及过度依赖化学农药是农业部门面临的严峻挑战。人工除草和病虫害防治具有潜在的环境负面影响，会增加生产成本并降低产量。人工智能（AI）、物联网（IoT）、深度学习、机器人技术及激光技术的应用，是发展可持续自主农业系统的有前景的途径。本研究提出一种基于人工智能（AI）和物联网（IoT）的智能农业系统，用于杂草识别、有害昆虫识别、作物状况监测以及利用激光束自动除草。该系统将基于物联网（IoT）传感器、计算机视觉模块、卷积神经网络（CNN）、无人机器人平台、环境感知系统及电子蜂群抑制（ESS）机制，以提高农业生产力并减少作物损失。该系统采用深度学习算法对杂草、作物、有益昆虫和有害昆虫进行实时分类。高分辨率摄像头和传感设备持续采集环境与作物数据，并将数据输入物联网系统，在云边计算系统中进行处理。通过配备激光发射器的机器人平台实现高精度除草，且不伤害邻近作物。此外，利用计算机视觉和环境监测来识别空中昆虫蜂群，并通过激光束和ESS技术实现对昆虫蜂群的自动攻击。研究采用多个农业区块对玉米、小麦、大豆和蔬菜作物进行了实验评估。结果表明，与当前做法相比，杂草检测、病虫害识别、作物保护的准确性显著提高，作业成本得以节省。所提出的系统在杂草分类中达到97.6%的准确率，昆虫检测准确率为96.8%，农药使用量减少42.3%。此外，作物产量提高18.7%，作业成本下降28.4%。总体而言，结果表明人工智能驱动的物联网能够显著增强精准农业并促进可持续粮食生产。","International Journal of Drug Delivery Technology",75,{"impact":19,"substance":199,"depth":200,"authority":201,"freshness":13,"relevant":22,"comment":202},22,17,8,"论文给出97.6%杂草识别率、42.3%农药减量等量化结论，方法新颖且数据完整，但期刊权威性一般，属细分领域技术进展。",[204],{"name":196,"url":193},[27,28,157,206,136],"杂草识别",{"code":33,"slug":34,"name":35,"path":36,"parent_code":9},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":210},{"candidates":211,"agricultural_evidence":46,"reason":85},[212,219,224,233,237,244,251,258],{"code":45,"score":19,"supported":46,"title":213,"abstract":214,"tags":218},[52],[50,215,216,52,145,217],"计算机视觉","病虫害识别","computer vision",[28],{"code":154,"score":19,"supported":46,"title":220,"abstract":221,"tags":223},[159],[157,158,159,222],"edge computing",[157],{"code":225,"score":13,"supported":46,"title":226,"abstract":227,"tags":232},"F4",[],[228,229,230,231],"农药","除草","pesticide","crop protection",[206],{"code":71,"score":90,"supported":46,"title":234,"abstract":235,"tags":236},[],[75,73],[27],{"code":238,"score":79,"supported":46,"title":239,"abstract":240,"tags":243},"A2",[],[241,242],"小麦","wheat",[],{"code":245,"score":79,"supported":46,"title":246,"abstract":247,"tags":250},"A3",[],[248,249],"玉米","maize",[],{"code":252,"score":79,"supported":46,"title":253,"abstract":254,"tags":257},"A4",[],[255,256],"大豆","soybean",[],{"code":259,"score":79,"supported":46,"title":260,"abstract":261,"tags":264},"B1",[],[262,263],"蔬菜","vegetable",[],[266,267],"AI IoT 激光除草 病虫害识别","深度学习 精准农业 农药减量","AIIoT激光除草病虫害识别-5556","10.25258\u002Fijddt.16.76s.32",{"doi":269,"openalex_id":271,"authors":272,"venue":196,"cited_by_count":108,"oa_url":193,"card":273,"direction":188,"ingested_from":115},"W7223145687",[],{"tldr":274,"method":275,"finding":276,"direction":188,"opportunity":277},"提出AI+IoT系统，用深度学习和激光机器人实现杂草与害虫识别及自动清除。","IoT传感器、CNN视觉分类、无人机\u002F机器人平台与激光除草，云边协同处理。","杂草分类准确率97.6%、害虫识别96.8%，农药减42.3%，增产18.7%。","可探索激光除草对非靶标生物与土壤的长期生态影响，及多作物跨域模型泛化。","2026-10-22T11:20:18.540359Z",{"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":14,"score":287,"score_detail":288,"sources":291,"tags":293,"primary_topic":295,"secondary_topics":296,"classification":297,"search_phrases":332,"slug":335,"view_count":22,"doi":336,"paper":337,"created_at":350},5074,"QISHLOQ XO‘JALIGI OLIY TA’LIM MUASSASALARIDA MAXSUS FANLARNI O‘QITISHDA “SMART AGRICULTURE” TEXNOLOGIYALARIDAN FOYDALANISH METODIKASI","https:\u002F\u002Fdoi.org\u002F10.67547\u002Ffanistiqbollari.2026.876","The article examines a methodology for integrating Smart Agriculture technologies into the teaching of specialized subjects at agricultural higher education institutions. The study analyzes the didactic potential of the Internet of Things, sensor monitoring, GIS, remote sensing, artificial intelligence, smart irrigation and digital data analytics.","本文探讨了将智慧农业技术融入农业高等院校专业课程教学的方法论。研究分析了物联网、传感器监测、地理信息系统、遥感、人工智能、智能灌溉和数字数据分析在教学中的潜力。","Ta limda Istiqbolli Izlanishlar xalqaro ilmiy metodik jurnal","2026-10-15T00:00:00Z",49,{"impact":201,"substance":289,"depth":20,"authority":79,"freshness":201,"relevant":22,"comment":290},14,"乌兹别克语论文探讨智慧农业技术融入农业高校专业教学的路径，属农业信息化人才培养方向，但为境外一般期刊、影响面有限，暂不宜进入每日精选。",[292],{"name":285,"url":282},[27,28,157,59,294],"农业教育",{"code":33,"slug":34,"name":35,"path":36,"parent_code":9},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":298},{"candidates":299,"agricultural_evidence":46,"reason":85},[300,305,309,313,320,327],{"code":71,"score":64,"supported":46,"title":301,"abstract":303,"tags":304},[302],"smart agriculture",[27,302],[27],{"code":55,"score":90,"supported":46,"title":306,"abstract":307,"tags":308},[],[59,60],[59],{"code":154,"score":90,"supported":46,"title":310,"abstract":311,"tags":312},[],[157,158],[157],{"code":314,"score":79,"supported":46,"title":315,"abstract":316,"tags":319},"H3",[],[317,318],"灌溉","irrigation",[],{"code":321,"score":79,"supported":46,"title":322,"abstract":323,"tags":326},"I4",[],[324,325],"地理信息","GIS",[],{"code":45,"score":328,"supported":46,"title":329,"abstract":330,"tags":331},4,[],[145],[28],[333,334],"农业人工智能 农业教育 智慧农业 物联网","农业人工智能 农业教育","农业人工智能农业教育智慧农业物联网-5074","10.67547\u002Ffanistiqbollari.2026.876",{"doi":336,"openalex_id":338,"authors":339,"venue":285,"cited_by_count":108,"oa_url":343,"card":344,"direction":188,"ingested_from":115},"W7221997102",[340],{"name":341,"orcid":342},"O'G'ILOY SADULLO QIZI SHAMSIYEV","https:\u002F\u002Forcid.org\u002F0009-0003-4706-6488","https:\u002F\u002Ffanistiqbollari.uz\u002Fstorage\u002Farticle876.pdf",{"tldr":345,"method":346,"finding":347,"direction":348,"opportunity":349},"研究将智慧农业技术融入农业高校专业课程教学的方法论。","分析物联网、传感器、GIS、遥感、AI等技术的教学潜力。","智慧农业技术可有效支撑农业专业课程的教学方法设计。","数字乡村与农业信息化","可实证检验该教学法对学生技能与就业的影响，并开发配套课程资源。","2026-10-16T23:30:07.890026Z",{"id":352,"title":353,"url":354,"summary":355,"summary_zh":356,"content":9,"source_name":357,"source_url":354,"published_at":358,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":359,"score_detail":360,"sources":363,"tags":365,"primary_topic":367,"secondary_topics":368,"classification":369,"search_phrases":389,"slug":392,"view_count":22,"doi":393,"paper":394,"created_at":410},4825,"Multi-Source Remote Sensing Fusion and Deep Learning for Intelligent Precision Agriculture","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fs26206434","The fusion of multi-source remote sensing data and deep learning technology has been increasingly applied in precision agriculture. This review focuses on the perception–decision–execution framework and summarizes the current application status, technical routes, and development trends of multi-source remote sensing data fusion and deep learning in precision agriculture. It examines the complementary advantages of multimodal data such as optical, thermal infrared (TIR), Light Detection and Ranging (LiDAR), and synthetic aperture radar (SAR) in characterizing crop physiological traits and structural characteristics. At the algorithmic level, this review analyzes the evolution of deep learning architectures from single convolutional neural networks (CNNs) to spatiotemporal fusion networks (STFNs), Transformer-based architectures, and emerging state-space models, with emphasis on their capabilities for heterogeneous feature extraction and cross-modal information integration. This review discusses the transformation mechanism from intelligent perception to executable decision-making and explores the application effectiveness of prescription maps and variable-rate task technology in improving resource utilization. This review proposes forward-looking views on core bottlenecks such as model generalization, physical interpretability, and edge computing. This review not only provides a theoretical analysis of precision agriculture but also provides academic recommendations for the development of intelligent agricultural equipment in the future.","多源遥感数据与深度学习技术的融合已在精准农业中得到日益广泛的应用。本文以“感知—决策—执行”框架为主线，综述了多源遥感数据融合与深度学习在精准农业中的应用现状、技术路线与发展趋势。文章考察了光学、热红外（TIR）、激光雷达（LiDAR）和合成孔径雷达（SAR）等多模态数据在表征作物生理性状与结构特征方面的互补优势。在算法层面，本文分析了深度学习架构从单一卷积神经网络（CNN）向时空融合网络（STFN）、基于Transformer的架构以及新兴状态空间模型的演进，重点探讨了其在异构特征提取与跨模态信息整合方面的能力。本文讨论了从智能感知到可执行决策的转化机制，并探究了处方图与变量作业技术在提升资源利用率方面的应用效果。针对模型泛化能力、物理可解释性与边缘计算等核心瓶颈，本文提出了前瞻性观点。本文不仅为精准农业提供了理论分析，也为未来智能农业装备的发展提供了学术建议。","Sensors","2026-10-12T00:00:00Z",78,{"impact":19,"substance":361,"depth":19,"authority":289,"freshness":201,"relevant":22,"comment":362},20,"系统综述多源遥感融合与深度学习在精准农业中的感知—决策—执行框架，方法脉络清晰、前瞻性强，对智慧农业装备研发有参考价值。",[364],{"name":357,"url":354},[27,28,75,59,366],"多源数据融合",{"code":33,"slug":34,"name":35,"path":36,"parent_code":9},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":370},{"candidates":371,"agricultural_evidence":46,"reason":85},[372,376,380,384],{"code":71,"score":17,"supported":46,"title":373,"abstract":374,"tags":375},[73],[75,73],[27,75],{"code":45,"score":64,"supported":46,"title":377,"abstract":378,"tags":379},[52],[50,52],[28],{"code":55,"score":64,"supported":46,"title":381,"abstract":382,"tags":383},[60],[59,60],[59],{"code":154,"score":90,"supported":46,"title":385,"abstract":386,"tags":388},[],[387,222],"边缘计算",[366],[390,391],"多源遥感 深度学习 精准农业","Transformer 遥感 作物表型","多源遥感深度学习精准农业-4825","10.3390\u002Fs26206434",{"doi":393,"openalex_id":395,"authors":396,"venue":357,"cited_by_count":108,"oa_url":354,"card":405,"direction":113,"ingested_from":115},"W7221565940",[397,400,403],{"name":398,"orcid":399},"Zhen Ma","https:\u002F\u002Forcid.org\u002F0000-0003-4377-9651",{"name":401,"orcid":402},"Xiyuan Chen","https:\u002F\u002Forcid.org\u002F0000-0002-1770-8529",{"name":404,"orcid":9},"Cundeng Wang",{"tldr":406,"method":407,"finding":408,"direction":113,"opportunity":409},"综述多源遥感融合与深度学习在精准农业感知-决策-执行框架中的应用与趋势。","综述光学、热红外、LiDAR、SAR融合及CNN到Transformer、状态空","多模态融合与先进深度学习架构可提升作物感知与处方图决策，但泛化性、可解释性和边缘计算仍是瓶颈。","可探索轻量化状态空间模型与物理约束融合，提升跨区域泛化及边缘端实时决策能力。","2026-10-13T23:30:24.008972Z",{"id":412,"title":413,"url":414,"summary":415,"summary_zh":416,"content":9,"source_name":417,"source_url":414,"published_at":418,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":419,"score_detail":420,"sources":422,"tags":424,"primary_topic":427,"secondary_topics":428,"classification":429,"search_phrases":450,"slug":453,"view_count":108,"doi":454,"paper":455,"created_at":470},4564,"Enhancing Long-Tailed Macaque Pest Identification Using a Two-Stage YOLO-EfficientNet Pipeline","https:\u002F\u002Fdoi.org\u002F10.36456\u002F1hrtgc63","Long-tailed macaque (Macaca fascicularis) infestations have reduced corn production from a potential 7–9 tons to only 4–5 tons per growing season (±40%), while conventional deterrence methods such as loud noise and shooting have proven ineffective. This study developed an Internet of Things (IoT)-based pest repellent system using audiosonic waves, an HC-SR501 sensor, and an ESP32-CAM module, incorporating YOLOv8 and EfficientNet-B0 to replace the previously used YOLOv3 Tiny model, with the aim of improving detection accuracy and distinguishing long-tailed macaques from humans. Since population density at the partner's farmland was insufficient to construct an adequate dataset, image data were collected at Kembang Island, South Kalimantan, which serves as a natural habitat supporting a large and observable population of long-tailed macaques, thereby enabling the acquisition of visually representative and diverse training data. A total of 3,083 images were annotated using Label Studio under two classes (human and long-tailed macaque), then divided at an 80:10:10 ratio without augmentation. The YOLOv8n model was trained for 50 epochs, after which validation detections were cropped into 920 images to fine-tune EfficientNet-B0 over 10 epochs. Evaluation results show that YOLOv8 achieved a precision of 0.959, recall of 0.965, mAP50 of 0.979, and mAP50-95 of 0.723, while EfficientNet-B0 reached validation accuracies of 98.4–100% with no significant overfitting. The two-stage detection-classification pipeline was successfully tested end-to-end on 121 field images, demonstrating substantial performance improvements over the prior model and contributing toward IoT and AI-based smart agriculture solutions for effective, sustainable corn field protection.","长尾猕猴（Macaca fascicularis）的侵害使玉米产量从潜在的每生长季7–9吨降至仅4–5吨（±40%），而诸如大声喧哗和射击等传统驱赶方法已被证明无效。本研究开发了一种基于物联网（IoT）的驱虫系统，采用声波、HC-SR501传感器和ESP32-CAM模块，并引入YOLOv8和EfficientNet-B0以替代先前使用的YOLOv3 Tiny模型，旨在提高检测精度并区分长尾猕猴与人类。由于合作农田的种群密度不足以构建足够的数据集，图像数据采集于南加里曼丹的Kembang岛，该岛作为自然栖息地，拥有大量且可观察的长尾猕猴种群，从而能够获取具有视觉代表性和多样性的训练数据。使用Label Studio对共计3,083张图像进行标注，分为两个类别（人类和长尾猕猴），然后按80:10:10的比例划分，未进行数据增强。YOLOv8n模型训练了50个epoch，之后将验证检测结果裁剪为920张图像，用于对EfficientNet-B0进行10个epoch的微调。评估结果表明，YOLOv8达到了0.959的精确率、0.965的召回率、0.979的mAP50和0.723的mAP50-95，而EfficientNet-B0达到了98.4–100%的验证准确率，且无明显过拟合。该两阶段检测-分类流水线在121张实地图像上成功进行了端到端测试，展示出相较于先前模型的显著性能提升，为基于物联网和人工智能的智慧农业解决方案做出了贡献，可有效、可持续地保护玉米田。","BEST Journal of Applied Electrical Science & Technology","2026-10-09T00:00:00Z",69,{"impact":129,"substance":199,"depth":200,"authority":21,"freshness":21,"relevant":22,"comment":421},"论文提出YOLOv8+EfficientNet-B0两阶段猕猴识别与声波驱避系统，数据规模与指标扎实，对智慧农业AI应用有参考价值。",[423],{"name":417,"url":414},[27,28,157,425,426],"目标检测","玉米病虫害",{"code":33,"slug":34,"name":35,"path":36,"parent_code":9},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":430},{"candidates":431,"agricultural_evidence":46,"reason":85},[432,437,441,445],{"code":45,"score":13,"supported":46,"title":433,"abstract":435,"tags":436},[434],"YOLO",[],[28,425],{"code":154,"score":13,"supported":46,"title":438,"abstract":439,"tags":440},[],[157,158,159],[157],{"code":71,"score":90,"supported":46,"title":442,"abstract":443,"tags":444},[],[27,302],[27],{"code":245,"score":79,"supported":46,"title":446,"abstract":447,"tags":449},[],[248,448],"corn",[],[451,452],"长尾猕猴 玉米 识别","农业人工智能 玉米病虫害 智慧农业 目标检测","长尾猕猴玉米识别-4564","10.36456\u002F1hrtgc63",{"doi":454,"openalex_id":456,"authors":457,"venue":417,"cited_by_count":108,"oa_url":414,"card":465,"direction":188,"ingested_from":115},"W7221187629",[458,460,463],{"name":459,"orcid":9},"Hammami Rifqi Darmawan",{"name":461,"orcid":462},"Finki Dona Marleny","https:\u002F\u002Forcid.org\u002F0000-0002-3437-5594",{"name":464,"orcid":9},"Ayu Ahadi Ningrum",{"tldr":466,"method":467,"finding":468,"direction":188,"opportunity":469},"用YOLOv8+EfficientNet-B0两阶段流水线识别长尾猕猴，提升玉米田驱猴系统检测精度。","3083张标注图像，YOLOv8n检测后裁剪920张微调EfficientNet","YOLOv8 mAP50达0.979，EfficientNet-B0验证准确率98.4–100%，1","可探索将两阶段检测-分类流水线轻量化部署到边缘设备，并扩展至多物种、多作物场景的实时驱避决策。","2026-10-10T23:30:07.974290Z",{"id":472,"title":473,"url":474,"summary":475,"summary_zh":476,"content":9,"source_name":477,"source_url":474,"published_at":478,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":479,"score_detail":480,"sources":483,"tags":485,"primary_topic":488,"secondary_topics":489,"classification":490,"search_phrases":529,"slug":532,"view_count":533,"doi":534,"paper":535,"created_at":553},4297,"IoT -Based Smart Agriculture Using Machine Learning","https:\u002F\u002Fdoi.org\u002F10.47392\u002Firjaeh.2026.0711","Agriculture plays a crucial role in economic development and food security. Traditional farming methods often face challenges such as inefficient water usage, unpredictable environmental conditions, and limited real-time monitoring. This paper proposes an IoT-Based Smart Agriculture system integrated with Machine Learning to enhance farm productivity and resource management. The system employs IoT sensors to collect data related to soil moisture, temperature, humidity, and light intensity. These data are transmitted to a cloud platform where machine learning algorithms analyze environmental conditions, predict crop requirements, and generate intelligent recommendations for irrigation and crop management. The proposed solution enables continuous monitoring, reduces water wastage, improves crop health assessment, and supports data-driven decision-making. By combining IoT and Machine Learning, the system offers a cost-effective and scalable approach to precision farming, helping farmers increase yield, reduce operational costs, and promote sustainable agricultural practices.","农业在经济发展和粮食安全中发挥着至关重要的作用。传统耕作方式往往面临水资源利用效率低、环境条件不可预测以及实时监测能力有限等挑战。本文提出了一种基于物联网（IoT）并与机器学习相结合的智慧农业系统，以提升农业生产率和资源管理水平。该系统采用物联网传感器采集与土壤湿度、温度、湿度和光照强度相关的数据。这些数据被传输至云平台，由机器学习算法分析环境条件、预测作物需求，并生成灌溉和作物管理的智能建议。所提出的解决方案能够实现持续监测、减少水资源浪费、改进作物健康评估，并支持数据驱动的决策。通过将物联网与机器学习相结合，该系统为精准农业提供了一种具有成本效益且可扩展的方法，帮助农民提高产量、降低运营成本并促进可持续农业实践。","International Research Journal on Advanced Engineering Hub (IRJAEH)","2026-10-05T00:00:00Z",46,{"impact":201,"substance":129,"depth":481,"authority":79,"freshness":21,"relevant":22,"comment":482},11,"论文提出IoT与机器学习结合的智慧农业系统，思路常规、缺乏实测数据与规模验证，学术权威性一般，仅具参考价值。",[484],{"name":477,"url":474},[27,28,486,487,75],"农业物联网","智能灌溉",{"code":33,"slug":34,"name":35,"path":36,"parent_code":9},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":491},{"candidates":492,"agricultural_evidence":46,"reason":85},[493,498,502,506,511,518,522],{"code":71,"score":494,"supported":46,"title":495,"abstract":496,"tags":497},19,[302],[27,75,302],[27,75],{"code":45,"score":64,"supported":46,"title":499,"abstract":500,"tags":501},[48],[51,48],[28],{"code":154,"score":64,"supported":46,"title":503,"abstract":504,"tags":505},[159],[157,159],[486],{"code":314,"score":13,"supported":46,"title":507,"abstract":508,"tags":510},[],[317,509,318],"水资源",[487],{"code":512,"score":79,"supported":46,"title":513,"abstract":514,"tags":517},"A8",[],[515,516],"粮食安全","food security",[],{"code":63,"score":79,"supported":46,"title":519,"abstract":520,"tags":521},[],[68,66],[],{"code":523,"score":524,"supported":46,"title":525,"abstract":526,"tags":528},"H8",3,[],[527],"可持续农业",[],[530,531],"IoT 智慧农业 机器学习","土壤墒情 智能灌溉 云平台","IoT智慧农业机器学习-4297",2,"10.47392\u002Firjaeh.2026.0711",{"doi":534,"openalex_id":536,"authors":537,"venue":477,"cited_by_count":108,"oa_url":474,"card":548,"direction":188,"ingested_from":115},"W7220440757",[538,540,542,544,546],{"name":539,"orcid":9},"Priyanka Behera",{"name":541,"orcid":9},"Satyabrata Satapathy",{"name":543,"orcid":9},"Nakshytrajeet Kar",{"name":545,"orcid":9},"Sucharita NathSharma",{"name":547,"orcid":9},"Priyanka Shit",{"tldr":549,"method":550,"finding":551,"direction":188,"opportunity":552},"提出物联网结合机器学习的智慧农业系统，实现环境监测与智能灌溉决策。","部署土壤湿度、温湿度、光照等IoT传感器，云端机器学习分析预测作物需求。","系统可连续监测、减少水资源浪费、改善作物健康评估并支持数据驱动决策。","可探索边缘计算与轻量模型在田间实时决策中的应用，降低云端依赖与延迟。","2026-10-06T23:30:08.015155Z"]