[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2115":3,"related-2115":103},{"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":38,"classification":45,"search_phrases":74,"slug":77,"view_count":78,"doi":79,"paper":80,"created_at":102},2115,"EdgeHatch: Detection of chicken hatching stages using machine learning on an edge device","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112380","EdgeHatch: Detection of chicken hatching stages using machine learning on an edge device。Computers and Electronics in Agriculture","EdgeHatch：基于边缘设备的机器学习鸡孵化阶段检测。《计算机与电子农业》",null,"Computers and Electronics in Agriculture","2026-09-10T00:00:00Z","论文",10,false,74,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},15,20,17,14,8,1,"核心期刊论文，将机器学习与边缘设备用于鸡孵化阶段识别，方法新颖、面向家禽精准养殖，具备行业参考价值。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","农业人工智能","边缘计算","家禽养殖","精准孵化",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"C3","poultry","家禽","\u002Ftopic\u002Flivestock\u002Fpoultry","C",[39],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"status":46,"locked":14,"version":47,"method":48,"evidence":49},"classified","2026-10-22.2","rules",{"candidates":50,"agricultural_evidence":52},[51,58,64,69],{"code":40,"score":17,"supported":52,"title":53,"abstract":55,"tags":57},true,[54],"machine learning",[56,54],"机器学习",[28],{"code":33,"score":59,"supported":52,"title":60,"abstract":62,"tags":63},12,[61],"chicken",[61],[30],{"code":65,"score":22,"supported":14,"title":66,"abstract":67,"tags":68},"I1",[],[],[27],{"code":70,"score":22,"supported":14,"title":71,"abstract":72,"tags":73},"I5",[],[],[29],[75,76],"农业人工智能 家禽养殖 智慧农业 精准孵化","农业人工智能 家禽养殖","农业人工智能家禽养殖智慧农业精准孵化-2115",2,"10.1016\u002Fj.compag.2026.112380",{"doi":79,"openalex_id":81,"authors":82,"venue":10,"cited_by_count":94,"oa_url":6,"card":95,"direction":99,"ingested_from":101},"W7212107612",[83,86,88,91],{"name":84,"orcid":85},"Luka Mali","https:\u002F\u002Forcid.org\u002F0009-0008-6224-527X",{"name":87,"orcid":9},"Matjaž Gobec",{"name":89,"orcid":90},"Anton Gradišek","https:\u002F\u002Forcid.org\u002F0000-0001-6480-9587",{"name":92,"orcid":93},"Urban Sedlar","https:\u002F\u002Forcid.org\u002F0000-0003-2836-5493",0,{"tldr":96,"method":97,"finding":98,"direction":99,"opportunity":100},"提出EdgeHatch，在边缘设备上用机器学习检测鸡蛋孵化阶段。","边缘设备部署机器学习模型，分析孵化期图像\u002F数据。","可在边缘端实时识别鸡胚孵化阶段，无需云端。","智慧农业 \u002F 农业物联网","可扩展至多物种孵化监测，优化边缘模型轻量化与多阶段连续识别。","openalex","2026-09-11T23:30:01.667648Z",{"total":104,"page":22,"page_size":104,"items":105},6,[106,200,274,349,431,482],{"id":107,"title":108,"url":109,"summary":110,"summary_zh":111,"content":9,"source_name":10,"source_url":109,"published_at":112,"category":12,"cover_url":9,"hotness":13,"is_selected":52,"score":113,"score_detail":114,"sources":120,"tags":122,"primary_topic":126,"secondary_topics":127,"classification":134,"search_phrases":176,"slug":179,"view_count":167,"doi":180,"paper":181,"created_at":199},2017,"Two Decades of Computer Vision in Precision Livestock Farming: an Umbrella Review of Applications, Benchmarking Evidence, and the Performance-to-Deployment Gap Across Cattle, Pig, and Poultry Systems","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112419","Computer vision is becoming a core technology in PLF, enabling non-contact monitoring, phenotyping, and decision support at both animal and group levels. This umbrella review synthesised review-level evidence from 112 reviews on cattle, pig, and poultry systems, covering visual applications for identification, phenotyping, health, behaviour, locomotion, body condition, growth, reproduction, mortality, and resource use. To preserve the review as the bibliographic unit while capturing multi-topic evidence, multi-label coding generated 328 review-domain assignments across six PLF computer vision domains. Health\u002Fstress and posture\u002Factivity were the most frequently reviewed areas; however, these patterns reflect review coverage rather than evidence strength. Benchmarking reporting averaged 11.09 out of 14 criteria, corresponding to 79.2%, but validation-critical items, including class balance, annotation protocols, validation splits, and farm or site reporting, remained less complete. Methodological confidence was limited: AMSTAR 2 classified 95 reviews as critically low and 17 as moderate, while ROBIS classified 78 as high, 17 as unclear, and 17 as low risk of bias. The corpus-level candidate-reference overlap estimate was 0.122% and is retained only as a sensitivity estimate, not as formal primary-study CCA or evidence of independence. Review-derived performance and readiness tiers identified a mean descriptive performance-to-deployment gap of 0.63 across 328 assignments. Overall, reported model performance continues to exceed documented farm-deployment evidence, highlighting the need for stronger external validation, transparent benchmarking, workflow integration, economic assessment, and user-readiness evidence before routine deployment claims can be supported.","计算机视觉正成为精准畜牧养殖（PLF）中的核心技术，能够在个体和群体层面实现非接触式监测、表型分析和决策支持。本伞状综述综合了112篇综述的综述级证据，涵盖牛、猪和家禽养殖系统，涉及识别、表型分析、健康、行为、运动、体况、生长、繁殖、死亡率和资源利用等视觉应用。为在保留综述作为文献计量单元的同时捕捉多主题证据，采用多标签编码在六个PLF计算机视觉领域中生成了328个综述-领域分配。健康\u002F应激和姿态\u002F活动是综述覆盖最多的领域；然而，这些模式反映的是综述覆盖情况而非证据强度。基准报告平均得分为14项标准中的11.09项，对应79.2%，但验证关键条目，包括类别平衡、标注方案、验证集划分以及农场或场地报告，仍不够完整。方法学可信度有限：AMSTAR 2将95篇综述评为极低质量，17篇评为中等质量；ROBIS将78篇评为高偏倚风险，17篇评为不明确，17篇评为低偏倚风险。语料库层面的候选参考文献重叠估计值为0.122%，仅作为敏感性估计保留，而非正式的一手研究CCA或独立性证据。基于综述推导的性能与就绪度分级在328个分配中识别出平均描述性性能-部署差距为0.63。总体而言，所报告的性能持续超过已记录的农场部署证据，凸显了在支持常规部署声明之前，需要更强的外部验证、透明的基准测试、工作流程整合、经济评估和用户就绪度证据。","2026-09-09T00:00:00Z",87,{"impact":115,"substance":116,"depth":117,"authority":20,"freshness":118,"relevant":22,"comment":119},22,23,19,9,"基于112篇综述的伞式综述，系统揭示计算机视觉在牛猪禽精准养殖中性能与落地之间的差距，方法学与证据分级扎实，对智慧畜牧研发与部署具有较高参考价值。",[121],{"name":10,"url":109},[27,28,123,124,125],"计算机视觉","精准畜牧","智能养殖",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[128,129],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"code":130,"slug":131,"name":132,"path":133,"parent_code":37},"C2","ruminants","草食牲畜","\u002Ftopic\u002Flivestock\u002Fruminants",{"status":46,"locked":14,"version":47,"method":48,"evidence":135},{"candidates":136,"agricultural_evidence":52},[137,143,147,154,159,165,172],{"code":40,"score":117,"supported":52,"title":138,"abstract":140,"tags":142},[139],"computer vision",[123,139,141],"decision support",[28,123],{"code":33,"score":20,"supported":52,"title":144,"abstract":145,"tags":146},[34],[35,34],[],{"code":37,"score":148,"supported":52,"title":149,"abstract":151,"tags":153},11,[150],"livestock",[152],"畜牧",[],{"code":130,"score":148,"supported":52,"title":155,"abstract":157,"tags":158},[156],"cattle",[156],[],{"code":160,"score":161,"supported":52,"title":162,"abstract":163,"tags":164},"C6",4,[],[124],[124],{"code":166,"score":167,"supported":52,"title":168,"abstract":169,"tags":171},"I7",3,[],[170],"文献计量",[],{"code":65,"score":22,"supported":14,"title":173,"abstract":174,"tags":175},[],[],[27],[177,178],"农业人工智能 计算机视觉 智慧农业 智能养殖","农业人工智能 计算机视觉","农业人工智能计算机视觉智慧农业智能养殖-2017","10.1016\u002Fj.compag.2026.112419",{"doi":180,"openalex_id":182,"authors":183,"venue":10,"cited_by_count":94,"oa_url":109,"card":194,"direction":99,"ingested_from":101},"W7212062011",[184,186,189,191],{"name":185,"orcid":9},"Alexey Ruchay",{"name":187,"orcid":188},"Vladimir Kolpakov","https:\u002F\u002Forcid.org\u002F0000-0001-9658-7034",{"name":190,"orcid":9},"Hao Guo",{"name":192,"orcid":193},"Andrea Pezzuolo","https:\u002F\u002Forcid.org\u002F0000-0002-9955-9896",{"tldr":195,"method":196,"finding":197,"direction":99,"opportunity":198},"伞式综述112篇综述，梳理牛猪禽精准养殖计算机视觉应用与部署差距。","伞式综述，多标签编码328项，AMSTAR 2与ROBIS评估方法学质量。","模型性能普遍高于农场部署证据，平均性能-部署差距0.63，验证与基准报告不足。","可研究外部验证、标准化基准与农场工作流集成，弥合性能到部署的鸿沟。","2026-09-10T23:30:01.497855Z",{"id":201,"title":202,"url":203,"summary":204,"summary_zh":9,"content":9,"source_name":10,"source_url":203,"published_at":205,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":206,"score_detail":207,"sources":211,"tags":213,"primary_topic":216,"secondary_topics":217,"classification":219,"search_phrases":236,"slug":239,"view_count":78,"doi":240,"paper":241,"created_at":273},1199,"A deep learning framework for non-invasive quantification of wing and foot phenotypes in live broiler chickens","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112327","A deep learning framework for non-invasive quantification of wing and foot phenotypes in live broiler chickens。Computers and Electronics in Agriculture","2026-08-28T00:00:00Z",63,{"impact":59,"substance":208,"depth":209,"authority":20,"freshness":167,"relevant":22,"comment":210},18,16,"提出深度学习框架用于活体肉鸡翅足表型非侵入量化，方法新颖，数据可靠，对家禽精准养殖有参考价值。",[212],{"name":10,"url":203},[27,28,214,215],"肉鸡养殖","表型检测",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[218],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"status":46,"locked":14,"version":47,"method":48,"evidence":220},{"candidates":221,"agricultural_evidence":52},[222,227,232],{"code":40,"score":59,"supported":52,"title":223,"abstract":225,"tags":226},[224],"deep learning",[224],[28],{"code":33,"score":148,"supported":52,"title":228,"abstract":230,"tags":231},[229],"broiler",[229],[],{"code":65,"score":22,"supported":14,"title":233,"abstract":234,"tags":235},[],[],[27],[237,238],"农业人工智能 智慧农业 肉鸡养殖 表型检测","农业人工智能 智慧农业","农业人工智能智慧农业肉鸡养殖表型检测-1199","10.1016\u002Fj.compag.2026.112327",{"doi":240,"openalex_id":242,"authors":243,"venue":10,"cited_by_count":94,"oa_url":9,"card":267,"direction":271,"ingested_from":101},"W7204530838",[244,247,249,252,254,257,259,262,265],{"name":245,"orcid":246},"Yakun Yang","https:\u002F\u002Forcid.org\u002F0000-0002-4772-4263",{"name":248,"orcid":9},"Xuehai Ge",{"name":250,"orcid":251},"Wei Wu","https:\u002F\u002Forcid.org\u002F0000-0002-0286-0720",{"name":253,"orcid":9},"Qiancheng Shen",{"name":255,"orcid":256},"Xupeng Kou","https:\u002F\u002Forcid.org\u002F0000-0002-3246-6820",{"name":258,"orcid":9},"Xu Hu",{"name":260,"orcid":261},"Longhe Wang","https:\u002F\u002Forcid.org\u002F0000-0001-9176-8063",{"name":263,"orcid":264},"Hongcheng Xue","https:\u002F\u002Forcid.org\u002F0000-0001-9024-9632",{"name":266,"orcid":9},"Lin Li",{"tldr":268,"method":269,"finding":270,"direction":271,"opportunity":272},"提出深度学习框架，非侵入性量化活体肉鸡翅膀和脚部表型。","深度学习框架，结合图像处理与关键点检测。","实现活体肉鸡翅膀和脚部表型的自动量化，减少人工测量。","农业人工智能与决策模型","可扩展至其他家禽或牲畜表型，结合多视角图像与3D重建，提高精度和鲁棒性。","2026-09-01T04:03:01.950868Z",{"id":275,"title":276,"url":277,"summary":278,"summary_zh":279,"content":9,"source_name":280,"source_url":277,"published_at":281,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":282,"score_detail":283,"sources":285,"tags":287,"primary_topic":290,"secondary_topics":291,"classification":292,"search_phrases":324,"slug":327,"view_count":22,"doi":328,"paper":329,"created_at":348},5248,"An entropy-guided dynamic lightweight network for identification of wild edible vegetables of BTR","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs44163-026-02528-x","Wild edible vegetables of Bodoland Territorial Region (BTR) are the species of plants that grow naturally and are commonly consumed as food by the indigenous communities. These plants are important sources of nutritional and therapeutic properties and are essential for food security and socio-economic development of rural communities. Therefore, sustainable conservation and utilization of these unique plant resources are of great significance. Developing an efficient and accurate automatic identification system is an essential conservation strategy for them. Current plant identification methods are mainly based on classical machine learning (ML) methods and deep convolutional neural network (CNN) architectures for recognition and classification. These methods are effective for images taken under controlled or pristine conditions, but the performance is often far from ideal in the complex and natural environments of forest and grassland, where wild edible plants are found. This paper proposes an entropy-guided dynamic lightweight deep learning architecture for automatic detection of wild edible vegetables in BTR. The proposed model employs adaptive computation with Dynamic Kernel Selection (DKS), Region-wise Adaptive Feature Routing (RAFR), Ultra-Light Context Encoding (ULCE), and Entropy-Guided Global Pooling (EGGP) to capture multi-scale plant characteristics efficiently and reduce redundant computations. The novelty of our proposed framework lies in its coordinated integration of these modules that is designed to reduce computational overhead without compromising with performance. Experimental results show that the proposed model achieves excellent classification accuracy of 90.69% with significantly reduced parameter complexity and floating-point operations (FLOPs), which is well-suited for real-time precision agriculture and low-resource edge deployment applications.","博多兰领土区域（BTR）的野生食用蔬菜是自然生长并被土著社区普遍作为食物食用的植物物种。这些植物是营养和治疗特性的重要来源，对农村社区的粮食安全和社会经济发展至关重要。因此，对这些独特植物资源的可持续保护和利用具有重要意义。开发高效准确的自动识别系统是保护这些植物资源的重要策略。当前的植物识别方法主要基于经典机器学习（ML）方法和深度卷积神经网络（CNN）架构进行识别和分类。这些方法对于在受控或原始条件下拍摄的图像是有效的，但在野生食用植物所处的森林和草地复杂自然环境中，其性能往往远不理想。本文提出了一种熵引导的动态轻量级深度学习架构，用于自动检测BTR中的野生食用蔬菜。所提出的模型采用动态核选择（DKS）、区域自适应特征路由（RAFR）、超轻量上下文编码（ULCE）和熵引导全局池化（EGGP）的自适应计算，以高效捕捉多尺度植物特征并减少冗余计算。我们所提出框架的新颖性在于这些模块的协同集成，旨在在不影响性能的情况下降低计算开销。实验结果表明，所提出的模型达到了90.69%的优异分类准确率，同时显著降低了参数复杂度和浮点运算量（FLOPs），非常适合实时精准农业和低资源边缘部署应用。","Discover Artificial Intelligence","2026-10-17T00:00:00Z",70,{"impact":59,"substance":18,"depth":19,"authority":59,"freshness":118,"relevant":22,"comment":284},"提出熵引导动态轻量网络用于野生食用蔬菜识别，准确率90.69%且参数与算力显著降低，方法有新意、结论可靠，但属细分应用研究，公共影响有限。",[286],{"name":280,"url":277},[27,28,29,288,289],"野生蔬菜","植物识别",{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},[],{"status":46,"locked":14,"version":47,"method":48,"evidence":293},{"candidates":294,"agricultural_evidence":52},[295,300,307,314,320],{"code":40,"score":13,"supported":52,"title":296,"abstract":297,"tags":299},[],[298,56,54,224],"深度学习",[28],{"code":65,"score":301,"supported":52,"title":302,"abstract":303,"tags":306},7,[],[304,305],"精准农业","precision agriculture",[27],{"code":308,"score":104,"supported":52,"title":309,"abstract":310,"tags":313},"A8",[],[311,312],"粮食安全","food security",[],{"code":315,"score":167,"supported":52,"title":316,"abstract":317,"tags":319},"B1",[],[318],"蔬菜",[],{"code":70,"score":22,"supported":14,"title":321,"abstract":322,"tags":323},[],[],[29],[325,326],"BTR 野生食用蔬菜 识别","熵引导 轻量网络 植物识别","BTR野生食用蔬菜识别-5248","10.1007\u002Fs44163-026-02528-x",{"doi":328,"openalex_id":330,"authors":331,"venue":280,"cited_by_count":94,"oa_url":277,"card":343,"direction":271,"ingested_from":101},"W7222842686",[332,334,337,340],{"name":333,"orcid":9},"Tania Sarkar",{"name":335,"orcid":336},"Pranav Kumar Singh","https:\u002F\u002Forcid.org\u002F0000-0001-8987-0229",{"name":338,"orcid":339},"Parag Bhuyan","https:\u002F\u002Forcid.org\u002F0009-0006-4430-1851",{"name":341,"orcid":342},"Anshuman Kalla","https:\u002F\u002Forcid.org\u002F0000-0002-3985-4578",{"tldr":344,"method":345,"finding":346,"direction":271,"opportunity":347},"提出熵引导动态轻量网络，用于识别BTR野生食用蔬菜。","动态核选择、区域自适应特征路由、超轻上下文编码与熵引导池化。","分类准确率达90.69%，参数量和FLOPs显著降低，适合边缘部署。","可探索复杂自然场景下轻量模型在低资源边缘设备的实时植物识别与保护应用。","2026-10-18T23:30:58.805321Z",{"id":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":360,"tags":362,"primary_topic":364,"secondary_topics":370,"classification":372,"search_phrases":391,"slug":394,"view_count":22,"doi":395,"paper":396,"created_at":430},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":59,"substance":18,"depth":209,"authority":59,"freshness":118,"relevant":22,"comment":359},"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。",[361],{"name":355,"url":352},[27,28,29,298,363],"玉米病害",{"code":365,"slug":366,"name":367,"path":368,"parent_code":369},"A3","maize","玉米","\u002Ftopic\u002Ffield-crops\u002Fmaize","A",[371],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"status":46,"locked":14,"version":47,"method":48,"evidence":373},{"candidates":374,"agricultural_evidence":52},[375,379,383,387],{"code":365,"score":20,"supported":52,"title":376,"abstract":377,"tags":378},[366],[367,366],[],{"code":40,"score":21,"supported":52,"title":380,"abstract":381,"tags":382},[],[298,224],[28,298],{"code":65,"score":301,"supported":52,"title":384,"abstract":385,"tags":386},[],[304,305],[27],{"code":70,"score":22,"supported":14,"title":388,"abstract":389,"tags":390},[],[],[29],[392,393],"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":395,"openalex_id":397,"authors":398,"venue":355,"cited_by_count":94,"oa_url":352,"card":424,"direction":429,"ingested_from":101},"W7222602850",[399,402,405,407,409,412,414,416,419,421],{"name":400,"orcid":401},"Awwab Mohammad","https:\u002F\u002Forcid.org\u002F0000-0002-1148-5678",{"name":403,"orcid":404},"Punith kumar","https:\u002F\u002Forcid.org\u002F0009-0005-5676-4194",{"name":406,"orcid":9},"Bhavani K",{"name":408,"orcid":9},"Sailee N. Panchbhai",{"name":410,"orcid":411},"Sagarkumar S. Badhiye","https:\u002F\u002Forcid.org\u002F0000-0002-0710-3761",{"name":413,"orcid":9},"Manoj Kumar S B",{"name":415,"orcid":9},"Dharamvir",{"name":417,"orcid":418},"Sumit Kumar Sar","https:\u002F\u002Forcid.org\u002F0000-0003-0384-9088",{"name":420,"orcid":9},"Rani Chhagan Dalvi",{"name":422,"orcid":423},"Yogesh Harishchandra Bhosale","https:\u002F\u002Forcid.org\u002F0000-0001-6901-1419",{"tldr":425,"method":426,"finding":427,"direction":271,"opportunity":428},"构建轻量MobileNetV3-Small框架实现玉米叶片病害分类并评估边缘部署。","MobileNetV3-Small，PlantVillage-Maize与Fie","受控数据准确率97.8%，田间降至86.1%，揭示明显域差距。","可研究跨域自适应与田间鲁棒特征学习，缩小受控与真实场景性能差距。","农业遥感与作物表型","2026-10-17T23:30:28.248539Z",{"id":432,"title":433,"url":434,"summary":435,"summary_zh":9,"content":9,"source_name":436,"source_url":9,"published_at":437,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":357,"score_detail":438,"sources":440,"tags":442,"primary_topic":445,"secondary_topics":446,"classification":447,"search_phrases":469,"slug":472,"view_count":94,"doi":9,"paper":473,"created_at":481},4967,"SADR-VSS:一种用于轻量级农业目标检测的场景自适应动态路由视觉状态空间模块","https:\u002F\u002Fwww.ebiotrade.com\u002Fnewsf\u002F2026-10\u002F20261013084746521.htm","本文提出场景自适应动态路由视觉状态空间模块SADR-VSS,用于复杂田间条件下的轻量级农业目标检测。该方法引入场景自适应路由机制,动态协调局部卷积表示和长距离状态空间建模,从而在异质农业场景中实现更灵活的特征提取。大量实验表明,SADR-VSS实现了改善的检测性能,验证了其在嵌入式设备和农业自动化平台上的部署潜力。研究面向自动除草平台的高分辨率图像,在YOLOv11n基线基础上展示更集中的目标激活响应。","生物通 2026 年 10 月 13 日","2026-10-13T00:00:00Z",{"impact":17,"substance":18,"depth":19,"authority":21,"freshness":118,"relevant":22,"comment":439},"提出场景自适应动态路由视觉状态空间模块，在YOLOv11n基线上提升复杂田间轻量检测性能，方法新颖且面向嵌入式除草平台，具备较强专业参考价值。",[441],{"name":436,"url":434},[27,28,29,443,444],"农业目标检测","智能除草",{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},[],{"status":46,"locked":14,"version":47,"method":48,"evidence":448},{"candidates":449,"agricultural_evidence":52},[450,455,461,465],{"code":40,"score":59,"supported":52,"title":451,"abstract":453,"tags":454},[452],"目标检测",[452],[28],{"code":456,"score":167,"supported":52,"title":457,"abstract":458,"tags":460},"F4",[],[459],"除草",[],{"code":65,"score":22,"supported":14,"title":462,"abstract":463,"tags":464},[],[],[27],{"code":70,"score":22,"supported":14,"title":466,"abstract":467,"tags":468},[],[],[29],[470,471],"SADR-VSS 农业目标检测","YOLOv11n 自动除草","SADR-VSS农业目标检测-4967",{"doi":9,"openalex_id":9,"authors":474,"venue":9,"cited_by_count":94,"oa_url":9,"card":475,"direction":271,"ingested_from":480},[],{"tldr":476,"method":477,"finding":478,"direction":271,"opportunity":479},"提出场景自适应动态路由视觉状态空间模块SADR-VSS，用于复杂田间轻量级农业目标检测。","结合局部卷积与长距离状态空间建模，基于YOLOv11n在自动除草高分辨率图像上验","SADR-VSS提升检测性能，目标激活更集中，具备嵌入式农业平台部署潜力。","可探索动态路由机制在更多作物与杂草场景的泛化性，及与边缘计算硬件的协同优化。","agent","2026-10-15T00:06:08.643930Z",{"id":483,"title":484,"url":485,"summary":486,"summary_zh":487,"content":9,"source_name":488,"source_url":485,"published_at":437,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":357,"score_detail":489,"sources":492,"tags":494,"primary_topic":497,"secondary_topics":498,"classification":499,"search_phrases":526,"slug":529,"view_count":530,"doi":531,"paper":532,"created_at":543},4798,"Adaptability of Lightweight AI Models for Embedded Deployment: A Case Study of an Agricultural Monitoring Robot","https:\u002F\u002Fdoi.org\u002F10.54254\u002F2755-2721\u002F2026.ld37676","Many artificial intelligence programs require powerful computing hardware, whereas agricultural robots, drones, and smart cameras rely on small embedded devices with limited computing power, battery capacity, and heat dissipation. Lightweight models reduce parameters and computational costs while maintaining acceptable accuracy, enabling AI deployment on resource-constraint network devices. This paper reviews key lightweighting methods, including compact network architectures, model pruning, model quantization, and knowledge distillation. It compares common platforms such as Jetson, Raspberry Pi, and microcontrollers. Using an agricultural monitoring robot as an example, this study proposes a system combining a camera, a lightweight YOLO model, Jetson, and a microcontroller for real-time crop monitoring. Experiments are designed to evaluate accuracy, speed, power consumption, temperature, and battery life. Rather than pursuing maximum model complexity, this paper focuses on explaining how to select a model that is truly suitable for the actual device. For agricultural robots, effective deployment requires sufficient accuracy, real-time performance, and low energy consumption.","许多人工智能程序需要强大的计算硬件，而农业机器人、无人机和智能摄像头则依赖于计算能力、电池容量和散热能力均有限的小型嵌入式设备。轻量级模型在保持可接受精度的同时减少参数和计算成本，使人工智能能够部署在资源受限的网络设备上。本文综述了关键的轻量化方法，包括紧凑网络架构、模型剪枝、模型量化和知识蒸馏。本文比较了Jetson、树莓派和微控制器等常见平台。以农业监测机器人为例，本研究提出了一种结合摄像头、轻量级YOLO模型、Jetson和微控制器的系统，用于实时作物监测。实验旨在评估精度、速度、功耗、温度和电池寿命。本文并非追求最大化的模型复杂度，而是着重解释如何选择真正适合实际设备的模型。对于农业机器人而言，有效部署需要足够的精度、实时性能和低能耗。","Applied and Computational Engineering",{"impact":59,"substance":208,"depth":209,"authority":490,"freshness":13,"relevant":22,"comment":491},13,"系统梳理轻量化模型在农业嵌入式设备上的部署路径，并以监测机器人案例给出可复现方案，对智慧农业工程落地有参考价值。",[493],{"name":488,"url":485},[27,28,495,29,496],"农业机器人","作物监测",{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},[],{"status":46,"locked":14,"version":47,"method":48,"evidence":500},{"candidates":501,"agricultural_evidence":52},[502,508,513,517,522],{"code":40,"score":301,"supported":52,"title":503,"abstract":504,"tags":507},[],[505,506],"artificial intelligence","YOLO",[28],{"code":509,"score":161,"supported":52,"title":510,"abstract":511,"tags":512},"F6",[],[495],[495],{"code":65,"score":22,"supported":14,"title":514,"abstract":515,"tags":516},[],[],[27],{"code":518,"score":22,"supported":14,"title":519,"abstract":520,"tags":521},"I3",[],[],[496],{"code":70,"score":22,"supported":14,"title":523,"abstract":524,"tags":525},[],[],[29],[527,528],"农业监测机器人 轻量YOLO Jetson","嵌入式AI 模型轻量化","农业监测机器人轻量YOLOJetson-4798",5,"10.54254\u002F2755-2721\u002F2026.ld37676",{"doi":531,"openalex_id":533,"authors":534,"venue":488,"cited_by_count":94,"oa_url":537,"card":538,"direction":99,"ingested_from":101},"W7221561538",[535],{"name":536,"orcid":9},"Jingen Zhou","https:\u002F\u002Face.ewapub.com\u002Farticle\u002Fview\u002F37676.pdf",{"tldr":539,"method":540,"finding":541,"direction":99,"opportunity":542},"综述轻量化AI方法，并以农业监测机器人为例，提出相机+轻量YOLO+Jetson+单片机的嵌入式部署","综述网络压缩、剪枝、量化、蒸馏，对比Jetson、树莓派、单片机，设计农业机器人","农业机器人部署需兼顾足够精度、实时性与低能耗，而非追求最大模型复杂度。","可研究面向田间多变环境的轻量化模型自适应选择与能效-精度联合优化部署策略。","2026-10-13T23:30:08.379081Z"]