[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4010":3,"related-4010":122},{"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":76,"slug":79,"view_count":80,"doi":81,"paper":82,"created_at":121},4010,"Intelligent Automated Crop Monitoring System Based on Unmanned Aerial Vehicles and Deep Learning for Smart Agriculture","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fapp16199747","The rapid development of precision agriculture technologies requires intelligent systems capable of automatically analyzing unmanned aerial vehicle (UAV) imagery and transforming image-processing results into structured information for crop-monitoring decision support. This study develops and computationally validates an integrated framework combining deep learning-based semantic segmentation, RGB-based spatial interpretation, image-space vectorization of candidate RGB heterogeneity zones, and monitoring-priority assessment. Experimental validation was performed using the open-access dataset “High-Resolution RGB Images and Corresponding Masks of Agricultural Fields”, comprising mixed agricultural vegetation scenes. Following data-integrity verification, 326 valid RGB image–reference mask pairs were retained and divided into 21,324 non-overlapping 512 × 512-pixel patches. U-Net-ResNet50, DeepLabV3-ResNet50, and FCN-ResNet50 were evaluated in three repeated source-image-grouped holdout experiments using the full dataset. U-Net-ResNet50 achieved the highest held-out performance, with mean Precision = 0.9946 ± 0.0008, Recall = 0.9931 ± 0.0021, vegetation IoU = 0.9877 ± 0.0020, and Dice = 0.9938 ± 0.0010, and was therefore selected for subsequent spatial analysis. Its segmentation outputs were combined with VARI, ExG, and GLI indices to identify candidate RGB heterogeneity zones, which were converted into image-space vector objects in source-image pixel coordinates. The revised full-data analysis generated 58,206 candidate RGB heterogeneity zones. These regions represent visible canopy heterogeneity rather than physiologically confirmed crop stress and are intended to support targeted field verification and monitoring prioritization. The results demonstrate the feasibility of an end-to-end UAV RGB processing workflow that transforms pixel-level segmentation outputs into structured spatial information for precision-agriculture monitoring and decision support.","精准农业技术的快速发展需要智能系统能够自动分析无人机（UAV）影像，并将图像处理结果转化为结构化信息，以支持作物监测决策。本研究开发并通过计算验证了一个集成框架，该框架结合了基于深度学习的语义分割、基于RGB的空间解译、候选RGB异质性区域的图像空间矢量化以及监测优先级评估。实验验证使用开放获取数据集“High-Resolution RGB Images and Corresponding Masks of Agricultural Fields”进行，该数据集包含混合农业植被场景。经数据完整性验证后，保留了326对有效RGB图像-参考掩膜对，并划分为21,324个不重叠的512 × 512像素图像块。使用完整数据集，在三次重复的以源图像分组的留出实验中评估了U-Net-ResNet50、DeepLabV3-ResNet50和FCN-ResNet50。U-Net-ResNet50取得了最高的留出性能，平均精确率 = 0.9946 ± 0.0008，召回率 = 0.9931 ± 0.0021，植被IoU = 0.9877 ± 0.0020，Dice = 0.9938 ± 0.0010，因此被选用于后续空间分析。其分割输出与VARI、ExG和GLI指数相结合，用于识别候选RGB异质性区域，并将其转换为源图像像素坐标下的图像空间矢量对象。修订后的全数据分析生成了58,206个候选RGB异质性区域。这些区域代表可见的冠层异质性，而非经生理学确认的作物胁迫，旨在支持有针对性的田间验证和监测优先级排序。结果表明，端到端无人机RGB处理工作流程具有可行性，该流程可将像素级分割输出转化为结构化空间信息，用于精准农业监测与决策支持。",null,"Applied Sciences","2026-10-01T00:00:00Z","论文",10,false,80,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,22,13,9,1,"基于无人机RGB影像与深度学习语义分割的端到端作物监测框架，数据规模大、指标可靠，对精准农业决策支持有实质参考价值。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","农业人工智能","无人机遥感","作物监测","图像分割",{"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},[44,53,62,70],{"code":32,"score":45,"supported":46,"title":47,"abstract":49,"tags":52},19,true,[48],"deep learning",[50,48,51],"深度学习","decision support",[27,30],{"code":54,"score":55,"supported":46,"title":56,"abstract":58,"tags":61},"I1",15,[57],"smart agriculture",[59,60],"精准农业","precision agriculture",[26],{"code":63,"score":64,"supported":46,"title":65,"abstract":66,"tags":69},"I4",6,[],[67,68],"空间分析","spatial analysis",[],{"code":71,"score":72,"supported":14,"title":73,"abstract":74,"tags":75},"I3",2,[],[],[29,28],[77,78],"UAV 作物监测 深度学习","U-Net 植被分割 无人机","UAV作物监测深度学习-4010",4,"10.3390\u002Fapp16199747",{"doi":81,"openalex_id":83,"authors":84,"venue":10,"cited_by_count":112,"oa_url":6,"card":113,"direction":119,"ingested_from":120},"W7215074401",[85,88,91,94,96,98,101,103,106,109],{"name":86,"orcid":87},"Аnatoliy M. Тryhuba","https:\u002F\u002Forcid.org\u002F0000-0001-8014-5661",{"name":89,"orcid":90},"Paweł Kiełbasa","https:\u002F\u002Forcid.org\u002F0000-0003-0249-8626",{"name":92,"orcid":93},"Nazarii Ya. Koval","https:\u002F\u002Forcid.org\u002F0000-0001-7846-2924",{"name":95,"orcid":9},"Inna Tryhuba",{"name":97,"orcid":9},"Oleh Andrushkiv",{"name":99,"orcid":100},"Vitalij Grabovets","https:\u002F\u002Forcid.org\u002F0000-0002-0340-185X",{"name":102,"orcid":9},"Nataliia Kozak",{"name":104,"orcid":105},"Akinniyi Akinsunmade","https:\u002F\u002Forcid.org\u002F0000-0002-1602-1741",{"name":107,"orcid":108},"Anna Miernik","https:\u002F\u002Forcid.org\u002F0000-0003-0394-9185",{"name":110,"orcid":111},"Paweł Pysz","https:\u002F\u002Forcid.org\u002F0000-0001-7083-1078",0,{"tldr":114,"method":115,"finding":116,"direction":117,"opportunity":118},"构建无人机RGB影像深度学习分割与空间矢量化框架，实现作物监测优先级评估。","U-Net-ResNet50等模型在326张影像21,324个512×512块上","U-Net-ResNet50分割最优（IoU 0.9877），生成58,206个候选异质区用于田间验","农业遥感与作物表型","可延伸研究：将RGB异质区与多光谱\u002F热红外数据融合，验证生理胁迫并构建实时决策模型。","智慧农业 \u002F 农业物联网","openalex","2026-10-02T23:30:11.747839Z",{"total":64,"page":21,"page_size":64,"items":123},[124,186,257,309,370,442],{"id":125,"title":126,"url":127,"summary":128,"summary_zh":9,"content":9,"source_name":129,"source_url":9,"published_at":130,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":131,"score_detail":132,"sources":137,"tags":139,"primary_topic":142,"secondary_topics":148,"classification":150,"search_phrases":173,"slug":176,"view_count":112,"doi":9,"paper":177,"created_at":185},5532,"精准棉花病害管理：融合无人机影像与定制YOLO深度学习框架","https:\u002F\u002Farxiv.org\u002Fabs\u002F2610.24182","研究提出单阶段全卷积检测器Cotton-Vision，融合幽灵瓶颈通道注意力与跨阶段部分融合三项轻量化设计，参数减少20%。在巴基斯坦旁遮普省7个棉田采集的2700余幅高分辨率无人机影像上测试，精确率97.7%、召回率77.7%、mAP@50为85.6%，较主流基线提升10至20个百分点。预印本，仅在巴基斯坦单一区域验证。","arXiv 预印本 2026-10-16","2026-10-16T00:00:00Z",69,{"impact":133,"substance":134,"depth":135,"authority":20,"freshness":64,"relevant":21,"comment":136},16,21,17,"方法轻量化与实测数据有增量，但仅单区域预印本验证，属细分技术进展，可入精选备选。",[138],{"name":129,"url":127},[26,27,140,28,141],"目标检测","棉花病害",{"code":143,"slug":144,"name":145,"path":146,"parent_code":147},"A6","cash-crops","棉麻糖与特色经作","\u002Ftopic\u002Ffield-crops\u002Fcash-crops","A",[149],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":151},{"candidates":152,"agricultural_evidence":46},[153,158,165,169],{"code":32,"score":17,"supported":46,"title":154,"abstract":156,"tags":157},[50,155],"YOLO",[],[27,140],{"code":143,"score":159,"supported":46,"title":160,"abstract":162,"tags":164},11,[161],"棉花",[163],"cotton",[],{"code":54,"score":21,"supported":14,"title":166,"abstract":167,"tags":168},[],[],[26],{"code":71,"score":21,"supported":14,"title":170,"abstract":171,"tags":172},[],[],[28],[174,175],"Cotton-Vision 棉花病害","无人机影像 棉花 检测","Cotton-Vision棉花病害-5532",{"doi":9,"openalex_id":9,"authors":178,"venue":9,"cited_by_count":112,"oa_url":9,"card":179,"direction":117,"ingested_from":184},[],{"tldr":180,"method":181,"finding":182,"direction":117,"opportunity":183},"提出轻量化检测器Cotton-Vision，用无人机影像精准识别棉花病害。","基于YOLO改进，融合幽灵瓶颈通道注意力与跨阶段部分融合，用2700余幅无人机影","精确率97.7%、mAP@50达85.6%，较主流基线提升10至20个百分点。","仅在巴基斯坦单一区域验证，可扩展至多区域多品种，并探索边缘设备实时部署。","agent","2026-10-22T00:07:09.816790Z",{"id":187,"title":188,"url":189,"summary":190,"summary_zh":191,"content":9,"source_name":192,"source_url":189,"published_at":130,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":193,"score_detail":194,"sources":196,"tags":198,"primary_topic":201,"secondary_topics":202,"classification":203,"search_phrases":220,"slug":223,"view_count":112,"doi":224,"paper":225,"created_at":256},5147,"FDUNet-LRSA: efficient frequency-adaptive segmentation for automated crop disease inspection","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffpls.2026.1963106","The transition toward industrialized and automated agriculture requires reliable machine vision for high-throughput crop health monitoring and field decision support. Automated sweetpotato disease inspection, however, remains challenging because field images exhibit substantial variations in leaf scale, appearance, illumination, occlusion, and background complexity. Conventional U-shaped networks rely primarily on fixed convolutional kernels and local feature aggregation, whereas Transformer-based alternatives often introduce considerable computational overhead. This article proposes FDUNet-LRSA, a frequency-adaptive segmentation network that integrates frequency-domain dynamic convolution, low-resolution self-attention, and boundary-aware refinement. Frequency-domain dynamic convolution reconstructs input-dependent spatial kernels to improve feature adaptation under changing imaging conditions. Low-resolution self-attention captures long-range contextual dependencies in a fixed compact feature space, while boundary-aware refinement preserves irregular leaf contours during mask reconstruction. Under the reported evaluation protocol, FDUNet-LRSA achieved a mean test foreground IoU of 0.94 and a Dice coefficient of 0.97, with 3.72 GFLOPs. Additional experiments indicate that task-oriented moderate augmentation improves generalization compared with stronger augmentation. The proposed network provides an efficient visual perception approach for automated crop inspection systems.","向工业化与自动化农业转型，需要可靠的机器视觉技术，以支持高通量作物健康监测与田间决策。然而，甘薯病害自动检测仍具挑战性，因为田间图像在叶片尺度、外观、光照、遮挡和背景复杂度方面存在显著变化。传统U形网络主要依赖固定卷积核和局部特征聚合，而基于Transformer的替代方案往往带来较大的计算开销。本文提出FDUNet-LRSA，一种频率自适应分割网络，融合了频域动态卷积、低分辨率自注意力与边界感知细化。频域动态卷积重建依赖输入的空间卷积核，以提升成像条件变化下的特征适应性。低分辨率自注意力在固定的紧凑特征空间中捕获长程上下文依赖，而边界感知细化在掩码重建过程中保留不规则叶片轮廓。在所报告的评估协议下，FDUNet-LRSA取得了0.94的平均测试前景IoU和0.97的Dice系数，计算量为3.72 GFLOPs。额外实验表明，与更强的增强相比，面向任务的中等增强可提升泛化能力。所提出的网络为自动化作物检测系统提供了一种高效的视觉感知方法。","Frontiers in Plant Science",76,{"impact":133,"substance":134,"depth":135,"authority":19,"freshness":20,"relevant":21,"comment":195},"提出频域自适应分割网络，在甘薯病害检测上取得高精度且计算量低，方法有新意、结论可靠，属细分领域实质进展，值得进入每日精选。",[197],{"name":192,"url":189},[26,27,199,200,30],"作物病害","甘薯",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":204},{"candidates":205,"agricultural_evidence":46},[206,211,216],{"code":32,"score":207,"supported":46,"title":208,"abstract":209,"tags":210},5,[],[51],[27,30],{"code":212,"score":80,"supported":46,"title":213,"abstract":214,"tags":215},"A5",[],[200],[200],{"code":54,"score":21,"supported":14,"title":217,"abstract":218,"tags":219},[],[],[26],[221,222],"FDUNet-LRSA 甘薯病害","甘薯病害 图像分割","FDUNet-LRSA甘薯病害-5147","10.3389\u002Ffpls.2026.1963106",{"doi":224,"openalex_id":226,"authors":227,"venue":192,"cited_by_count":112,"oa_url":250,"card":251,"direction":119,"ingested_from":120},"W7222502318",[228,231,234,237,239,242,244,246,248],{"name":229,"orcid":230},"Ru Han","https:\u002F\u002Forcid.org\u002F0009-0000-5128-1314",{"name":232,"orcid":233},"Lei Shu","https:\u002F\u002Forcid.org\u002F0000-0002-6700-9347",{"name":235,"orcid":236},"Grzegorz Cielniak","https:\u002F\u002Forcid.org\u002F0000-0002-6299-8465",{"name":238,"orcid":9},"Yan Zhu",{"name":240,"orcid":241},"Jucheng Yang","https:\u002F\u002Forcid.org\u002F0000-0002-5046-2663",{"name":243,"orcid":9},"Fan Yang",{"name":245,"orcid":9},"Weihan Song",{"name":247,"orcid":9},"Qian Zhang",{"name":249,"orcid":9},"Kai Huang","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fplant-science\u002Farticles\u002F10.3389\u002Ffpls.2026.1963106\u002Fpdf",{"tldr":252,"method":253,"finding":254,"direction":117,"opportunity":255},"提出频域自适应分割网络FDUNet-LRSA，用于甘薯病害自动巡检。","频域动态卷积、低分辨率自注意力与边界感知细化，结合适度数据增强。","测试前景IoU达0.94、Dice达0.97，仅3.72 GFLOPs，轻量高效。","可探索频域动态卷积在多种作物病害与复杂田间光照下的跨域泛化与边缘部署。","2026-10-17T23:30:08.487857Z",{"id":258,"title":259,"url":260,"summary":261,"summary_zh":9,"content":9,"source_name":262,"source_url":9,"published_at":263,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":264,"score_detail":265,"sources":268,"tags":270,"primary_topic":273,"secondary_topics":274,"classification":275,"search_phrases":296,"slug":299,"view_count":21,"doi":300,"paper":301,"created_at":308},4968,"A real-time deep learning method for detection and localization of non-emerged cabbage seedlings","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fplant-science\u002Farticles\u002F10.3389\u002Ffpls.2026.1984989\u002Ffull","Chen, Han, Liu, Kong, Maity, Peng, Di, Yu and Yu(《Frontiers in Plant Science》, 2026-10-14发表)提出基于无人机的精确、快速、实时未出苗甘蓝幼苗检测与定位方法。Enhanced YOLOv8n通过更高效骨干网络和双向特征融合颈部结构,达到81.4% mAP50-95,较基线提升8%,同时保持实时推理能力。整合Enhanced YOLOv8n、YOLO跟踪算法和图像处理算法,实现鲁棒的实时未出苗幼苗检测与定位方法,未出苗甘蓝幼苗识别准确率达96.9%,且可区分连续未出苗幼苗数量,人工成本降至原1\u002F5。","Frontiers in Plant Science 2026 年 10 月 14 日","2026-10-14T00:00:00Z",82,{"impact":17,"substance":18,"depth":17,"authority":266,"freshness":13,"relevant":21,"comment":267},14,"方法新颖、数据扎实且落地价值明确，但属细分作物技术进展，未达重大突破层级。",[269],{"name":262,"url":260},[26,27,271,28,272],"甘蓝","幼苗检测",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":276},{"candidates":277,"agricultural_evidence":46},[278,283,288,292],{"code":32,"score":279,"supported":46,"title":280,"abstract":281,"tags":282},12,[48],[155],[27],{"code":284,"score":80,"supported":46,"title":285,"abstract":286,"tags":287},"B1",[],[271],[271],{"code":54,"score":21,"supported":14,"title":289,"abstract":290,"tags":291},[],[],[26],{"code":71,"score":21,"supported":14,"title":293,"abstract":294,"tags":295},[],[],[28],[297,298],"甘蓝 未出苗 幼苗检测","无人机 甘蓝 实时检测","甘蓝未出苗幼苗检测-4968","10.3389\u002Ffpls.2026.1984989\u002Ffull",{"doi":300,"openalex_id":9,"authors":302,"venue":9,"cited_by_count":112,"oa_url":9,"card":303,"direction":117,"ingested_from":184},[],{"tldr":304,"method":305,"finding":306,"direction":117,"opportunity":307},"提出无人机实时检测定位未出苗甘蓝幼苗的深度学习方法，准确率96.9%。","改进YOLOv8n骨干与双向特征融合颈部，结合YOLO跟踪和图像处理。","Enhanced YOLOv8n的mAP50-95达81.4%，未出苗识别准确率96.9%，人工成本","可迁移到其他作物出苗监测，并探索多光谱融合与边缘端轻量化实时部署。","2026-10-15T00:06:08.716058Z",{"id":310,"title":311,"url":312,"summary":313,"summary_zh":314,"content":9,"source_name":315,"source_url":312,"published_at":316,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":131,"score_detail":317,"sources":319,"tags":321,"primary_topic":324,"secondary_topics":325,"classification":326,"search_phrases":353,"slug":356,"view_count":207,"doi":357,"paper":358,"created_at":369},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","2026-10-13T00:00:00Z",{"impact":279,"substance":17,"depth":133,"authority":19,"freshness":13,"relevant":21,"comment":318},"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。",[320],{"name":315,"url":312},[26,27,322,323,29],"农业机器人","边缘计算",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":327},{"candidates":328,"agricultural_evidence":46},[329,335,340,344,348],{"code":32,"score":330,"supported":46,"title":331,"abstract":332,"tags":334},7,[],[333,155],"artificial intelligence",[27],{"code":336,"score":80,"supported":46,"title":337,"abstract":338,"tags":339},"F6",[],[322],[322],{"code":54,"score":21,"supported":14,"title":341,"abstract":342,"tags":343},[],[],[26],{"code":71,"score":21,"supported":14,"title":345,"abstract":346,"tags":347},[],[],[29],{"code":349,"score":21,"supported":14,"title":350,"abstract":351,"tags":352},"I5",[],[],[323],[354,355],"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","10.54254\u002F2755-2721\u002F2026.ld37676",{"doi":357,"openalex_id":359,"authors":360,"venue":315,"cited_by_count":112,"oa_url":363,"card":364,"direction":119,"ingested_from":120},"W7221561538",[361],{"name":362,"orcid":9},"Jingen Zhou","https:\u002F\u002Face.ewapub.com\u002Farticle\u002Fview\u002F37676.pdf",{"tldr":365,"method":366,"finding":367,"direction":119,"opportunity":368},"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",{"id":371,"title":372,"url":373,"summary":374,"summary_zh":375,"content":9,"source_name":376,"source_url":373,"published_at":377,"category":12,"cover_url":9,"hotness":378,"is_selected":14,"score":379,"score_detail":380,"sources":383,"tags":387,"primary_topic":389,"secondary_topics":393,"classification":395,"search_phrases":424,"slug":427,"view_count":428,"doi":429,"paper":430,"created_at":441},3913,"Lightweight Edge-AI Model for Real-Time Sorghum Disease Detection on Resource-Constrained Mobile Devices","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23054218","Lightweight Edge-AI Model for Real-Time Sorghum Disease Detection on Resource-Constrained Mobile Devices presents an efficient artificial intelligence approach for identifying sorghum diseases directly on mobile and edge-computing platforms. The study focuses on developing a lightweight machine learning model capable of performing rapid and accurate disease detection while operating under limited computational resources, memory, and power constraints. Computer vision and deep learning techniques are used to analyse sorghum leaf images and distinguish healthy plants from diseased conditions. Model optimization strategies are considered to reduce computational complexity and improve inference speed without substantially compromising detection performance. The proposed approach supports real-time, on-device disease diagnosis, reducing dependence on cloud connectivity and enabling timely intervention in agricultural environments. The work highlights the potential of Edge AI and mobile computing to make intelligent crop monitoring more accessible, practical, and scalable, particularly for farmers and agricultural users operating in resource-constrained settings.","面向资源受限移动设备的实时高粱病害检测轻量级边缘人工智能模型提出了一种高效的人工智能方法，用于直接在移动和边缘计算平台上识别高粱病害。该研究聚焦于开发一种轻量级机器学习模型，能够在有限的计算资源、内存和功耗约束下进行快速、准确的病害检测。研究采用计算机视觉和深度学习技术分析高粱叶片图像，区分健康植株与患病状态。研究考虑了模型优化策略，以降低计算复杂度并提高推理速度，同时不过度损害检测性能。所提出的方法支持实时、设备端病害诊断，减少了对云连接的依赖，并能够在农业环境中实现及时干预。该工作凸显了边缘人工智能和移动计算在使智能作物监测更加可及、实用和可扩展方面的潜力，特别是对于在资源受限环境中作业的农民和农业用户而言。","Zenodo (CERN European Organization for Nuclear Research)","2026-09-30T00:00:00Z",25,75,{"impact":133,"substance":381,"depth":135,"authority":19,"freshness":20,"relevant":21,"comment":382},20,"面向资源受限移动端的轻量边缘AI高粱病害实时检测研究，方法实用、契合智慧农业场景，但属细分作物技术进展，影响力有限。",[384,385],{"name":376,"url":373},{"name":376,"url":386},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23054219",[26,27,323,29,388],"高粱病害",{"code":212,"slug":390,"name":391,"path":392,"parent_code":147},"tubers-grains","薯类与杂粮","\u002Ftopic\u002Ffield-crops\u002Ftubers-grains",[394],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":396},{"candidates":397,"agricultural_evidence":46},[398,404,412,416,420],{"code":212,"score":266,"supported":46,"title":399,"abstract":401,"tags":403},[400],"sorghum",[402,400],"高粱",[],{"code":32,"score":13,"supported":46,"title":405,"abstract":406,"tags":411},[],[50,407,408,409,48,333,410],"机器学习","计算机视觉","machine learning","computer vision",[27],{"code":349,"score":80,"supported":46,"title":413,"abstract":414,"tags":415},[],[323],[323],{"code":54,"score":21,"supported":14,"title":417,"abstract":418,"tags":419},[],[],[26],{"code":71,"score":21,"supported":14,"title":421,"abstract":422,"tags":423},[],[],[29],[425,426],"高粱病害 边缘AI 检测","高粱叶片 病害识别 移动端","高粱病害边缘AI检测-3913",3,"10.5281\u002Fzenodo.23054218",{"doi":429,"openalex_id":431,"authors":432,"venue":376,"cited_by_count":112,"oa_url":373,"card":435,"direction":439,"ingested_from":120},"W7214912247",[433],{"name":434,"orcid":9},"Anantjit Publication",{"tldr":436,"method":437,"finding":438,"direction":439,"opportunity":440},"开发轻量级边缘AI模型，在资源受限移动设备上实时检测高粱病害。","计算机视觉与深度学习分析高粱叶片图像，并做模型轻量化优化。","轻量模型可在移动端实时准确识别病害，减少对云连接依赖。","农业人工智能与决策模型","可探索多作物、多病害通用轻量模型及田间复杂光照下的鲁棒性。","2026-10-01T23:30:48.902282Z",{"id":443,"title":444,"url":445,"summary":446,"summary_zh":447,"content":9,"source_name":448,"source_url":445,"published_at":449,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":450,"score_detail":451,"sources":454,"tags":456,"primary_topic":459,"secondary_topics":460,"classification":461,"search_phrases":472,"slug":475,"view_count":72,"doi":476,"paper":477,"created_at":490},3195,"Segmentation of Cactus Diseases Using Machine and Deep Learning)","https:\u002F\u002Fdoi.org\u002F10.37394\u002F232018.2026.14.50","Artificial intelligence and machine learning play a critical role in plant disease detection and management. This study proposes an approach for diagnosing and segmenting cactus diseases using Random Forest (RF) and compares its performance with SVM, ANN, KNN, CNN, and U-Net models. A dataset of 20 cactus images (10 cochineal and 10 black bacterial soft rot), resized to 720×720 pixels, was used. Experimental results show that U-Net achieved the highest accuracy, reaching 98% for cochineal and 94% for black bacterial soft rot. RF also demonstrated strong performance, achieving 92% and 91%, respectively, outperforming several traditional machine learning models. These results confirm the effectiveness of U-Net for accurate segmentation and the suitability of RF for reliable disease detection with limited datasets, supporting their application in precision agriculture.","人工智能与机器学习在植物病害检测与管理中发挥着关键作用。本研究提出了一种利用随机森林（Random Forest, RF）诊断和分割仙人掌病害的方法，并将其性能与SVM、ANN、KNN、CNN和U-Net模型进行了比较。研究使用了20张仙人掌图像数据集（10张胭脂虫病害和10张黑细菌性软腐病），图像尺寸调整为720×720像素。实验结果表明，U-Net取得了最高准确率，胭脂虫病害达到98%，黑细菌性软腐病达到94%。随机森林同样表现出强劲性能，分别达到92%和91%，优于多种传统机器学习模型。这些结果证实了U-Net在精确分割方面的有效性，以及随机森林在有限数据集下进行可靠病害检测的适用性，支持其在精准农业中的应用。","WSEAS TRANSACTIONS ON COMPUTER RESEARCH","2026-09-21T00:00:00Z",62,{"impact":452,"substance":17,"depth":266,"authority":279,"freshness":13,"relevant":21,"comment":453},8,"方法对比扎实但数据集仅20张图像，属细分作物病害识别的技术验证，产业影响有限。",[455],{"name":448,"url":445},[26,27,457,30,458],"植物病害识别","仙人掌",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":462},{"candidates":463,"agricultural_evidence":46},[464,468],{"code":32,"score":381,"supported":46,"title":465,"abstract":466,"tags":467},[48],[407,409,333],[27,30,457],{"code":54,"score":330,"supported":46,"title":469,"abstract":470,"tags":471},[],[59,60],[26],[473,474],"仙人掌 病害 图像分割","U-Net 胭脂虫 黑腐病","仙人掌病害图像分割-3195","10.37394\u002F232018.2026.14.50",{"doi":476,"openalex_id":478,"authors":479,"venue":448,"cited_by_count":112,"oa_url":445,"card":485,"direction":439,"ingested_from":120},"W7213945610",[480,482],{"name":481,"orcid":9},"Aissam El Ibrahimi",{"name":483,"orcid":484},"Nabil El Akchioui","https:\u002F\u002Forcid.org\u002F0000-0001-9808-932X",{"tldr":486,"method":487,"finding":488,"direction":439,"opportunity":489},"该研究用随机森林和多种深度学习模型对仙人掌病害进行分割诊断，并比较性能。","使用20张仙人掌图像，对比RF、SVM、ANN、KNN、CNN和U-Net模型。","U-Net分割准确率最高（胭脂虫98%，黑细菌软腐病94%），RF在有限数据下表现良好。","可探索小样本条件下传统机器学习与深度学习融合的病害分割方法，并扩展至更多作物和病害类型。","2026-09-22T23:30:39.786818Z"]