[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4157":3,"related-4157":85},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":8,"source_name":9,"source_url":8,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":15,"sources":22,"tags":24,"primary_topic":30,"secondary_topics":36,"classification":37,"search_phrases":69,"slug":72,"view_count":73,"doi":8,"paper":74,"created_at":84},4157,"种子逐粒精检分选机的设计与试验——基于YOLO11n的辣椒种子在线检测","http:\u002F\u002Fwww.qikanvip.com\u002Fqkml\u002F166252.html","梁晨阳、杨天宇、范晓飞、彭勃（农机化研究2026年第11期；河北农业大学机电工程学院、华北作物改良与调控国家重点实验室）。设计了一款结合机器视觉的高通量种子逐粒精检分选机，识别系统采用高速线扫描工业相机并融合YOLO11n目标检测视觉模型，以ARM Cortex-M4芯片为核心的控制系统驱动控制8路吹气阀完成无损剔除。以辣椒种子为对象，在振动供料频率5.0 Hz、传送带速0.5 m\u002Fs工作模式下，坏种识别率94.5%、分选准确率90.1%、好种误吹率仅4.4%，单机处理通量6000粒\u002Fmin。",null,"农机化研究2026年第11期","2026-10-01T00:00:00Z","论文",10,false,80,{"impact":16,"substance":17,"depth":16,"authority":18,"freshness":19,"relevant":20,"comment":21},18,22,13,9,1,"基于YOLO11n与ARM Cortex-M4的辣椒种子高通量在线检测分选装备，数据翔实、指标明确，具备较强工程参考价值。",[23],{"name":9,"url":6},[25,26,27,28,29],"智慧农业","农业人工智能","辣椒育种","机器视觉","种子分选",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},"B1","vegetables","蔬菜","\u002Ftopic\u002Fhorticulture\u002Fvegetables","B",[],{"status":38,"locked":13,"version":39,"method":40,"evidence":41},"classified","2026-10-22.2","rules",{"candidates":42,"agricultural_evidence":45},[43,50,57,64],{"code":31,"score":44,"supported":45,"title":46,"abstract":48,"tags":49},12,true,[47],"辣椒",[47],[27],{"code":51,"score":52,"supported":45,"title":53,"abstract":54,"tags":56},"I2",5,[],[55],"目标检测",[26,28],{"code":58,"score":59,"supported":45,"title":60,"abstract":61,"tags":63},"F5",3,[],[62],"农机",[],{"code":65,"score":20,"supported":13,"title":66,"abstract":67,"tags":68},"I1",[],[],[25],[70,71],"河北农业大学 辣椒种子 分选机","YOLO11n 种子检测","河北农业大学辣椒种子分选机-4157",7,{"doi":8,"openalex_id":8,"authors":75,"venue":8,"cited_by_count":76,"oa_url":8,"card":77,"direction":81,"ingested_from":83},[],0,{"tldr":78,"method":79,"finding":80,"direction":81,"opportunity":82},"设计基于YOLO11n的辣椒种子逐粒精检分选机，实现高通量在线检测与无损剔除。","高速线扫描相机+YOLO11n目标检测，ARM Cortex-M4控制8路吹气阀","坏种识别率94.5%、分选准确率90.1%、误吹率4.4%，通量6000粒\u002Fmin。","农业人工智能与决策模型","可拓展至多作物种子及微小缺陷检测，探索轻量化模型与多光谱融合提升精度。","agent","2026-10-04T00:09:10.031951Z",{"total":86,"page":20,"page_size":86,"items":87},6,[88,158,215,276,375,433],{"id":89,"title":90,"url":91,"summary":92,"summary_zh":93,"content":8,"source_name":94,"source_url":91,"published_at":95,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":96,"score_detail":97,"sources":101,"tags":103,"primary_topic":106,"secondary_topics":111,"classification":113,"search_phrases":137,"slug":140,"view_count":59,"doi":141,"paper":142,"created_at":157},3284,"Computer vision-based size estimation module for selective harvesting of lettuce","https:\u002F\u002Fdoi.org\u002F10.14719\u002Fpst.14546","Selective harvesting of leafy vegetables remains a major challenge in agricultural automation, particularly for lettuce that meet predefined size or maturity criteria. Vision-based perception systems play a critical role in enabling selective harvesting by identifying plants that meet predefined size or maturity criteria. This study presents the development and validation of computer vision-based size estimation software designed to support selective harvesting decisions in leafy vegetable production. A C #-based image processing application was developed to perform red, green and blue (RGB) colour segmentation, contour detection and pixel-to-metric size estimation using images acquired from a standard Universal Serial Bus (USB) webcam. The system converts pixel measurements into real-world dimensions through calibrated scaling factors that account for variations in camera-to-plant distance. Field experiments were conducted using three commercial lettuce cultivars Romaine, Iceberg and Curly. Vision-based canopy width measurements showed strong agreement with manual measurements (N = 60) and exhibited very strong correlations with plant weight (Romaine type: r = 0.9803; Iceberg type: r = 0.9935; Curly type: r = 0.9707; p \u003C 0.001). Linear regression models achieved high predictive performance (R² = 0.942–0.987), while error metrics indicated low prediction errors (Mean absolute error (MAE): 5.62–8.64 g; root mean square error (RMSE): 7.30–11.06 g). Bland-Altman analysis further confirmed strong agreement between vision-based estimates and manual measurements. This study primarily validates a lightweight vision-based measurement algorithm intended for future integration into robotic harvesting systems. The results demonstrate that simple RGB-based image processing can provide reliable size estimation for selective harvesting decisions in leafy vegetable production systems.","叶菜类蔬菜的选择性收获仍是农业自动化领域的一项重大挑战，尤其是对符合预定大小或成熟度标准的生菜而言。基于视觉的感知系统通过识别符合预定大小或成熟度标准的植株，在实现选择性收获中发挥着关键作用。本研究介绍了一款基于计算机视觉的大小估算软件的开发与验证，该软件旨在为叶菜类蔬菜生产中的选择性收获决策提供支持。研究开发了一款基于C#的图像处理应用程序，利用标准通用串行总线（USB）网络摄像头采集的图像，执行红绿蓝（RGB）颜色分割、轮廓检测以及像素到实际尺寸的估算。该系统通过经过校准的缩放因子将像素测量值转换为实际尺寸，并考虑了摄像头与植株之间距离的变化。田间试验采用三个商业化生菜品种——罗马生菜、结球生菜和皱叶生菜。基于视觉的冠幅测量结果与人工测量结果高度一致（N = 60），并与植株重量呈现极强的相关性（罗马型：r = 0.9803；结球型：r = 0.9935；皱叶型：r = 0.9707；p \u003C 0.001）。线性回归模型取得了较高的预测性能（R² = 0.942–0.987），误差指标显示预测误差较低（平均绝对误差（MAE）：5.62–8.64 g；均方根误差（RMSE）：7.30–11.06 g）。Bland-Altman分析进一步证实了基于视觉的估算值与人工测量值之间的高度一致性。本研究主要验证了一种轻量级视觉测量算法，旨在未来集成到机器人收获系统中。结果表明，简单的基于RGB的图像处理能够为叶菜类蔬菜生产系统中的选择性收获决策提供可靠的大小估算。","Plant Science Today","2026-09-22T00:00:00Z",69,{"impact":44,"substance":98,"depth":99,"authority":44,"freshness":19,"relevant":20,"comment":100},20,16,"该研究开发了基于RGB图像的轻量级生菜尺寸估计模块，与人工测量高度一致，为机器人选择性采收提供了可靠感知方案，具有较高的技术参考价值。",[102],{"name":94,"url":91},[25,26,104,28,105],"生菜","选择性采收",{"code":51,"slug":107,"name":108,"path":109,"parent_code":110},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[112],{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},{"status":38,"locked":13,"version":39,"method":40,"evidence":114},{"candidates":115,"agricultural_evidence":45},[116,122,127,133],{"code":51,"score":99,"supported":45,"title":117,"abstract":119,"tags":121},[118],"computer vision",[120,118],"计算机视觉",[26,28],{"code":31,"score":12,"supported":45,"title":123,"abstract":124,"tags":126},[],[33,104,125],"vegetable",[104],{"code":128,"score":59,"supported":45,"title":129,"abstract":130,"tags":132},"F6",[],[131],"robotic harvesting",[],{"code":65,"score":20,"supported":13,"title":134,"abstract":135,"tags":136},[],[],[25],[138,139],"生菜 选择性采收 机器视觉","计算机视觉 生菜 尺寸估计","生菜选择性采收机器视觉-3284","10.14719\u002Fpst.14546",{"doi":141,"openalex_id":143,"authors":144,"venue":94,"cited_by_count":76,"oa_url":91,"card":151,"direction":81,"ingested_from":156},"W7214000589",[145,148],{"name":146,"orcid":147},"Erhan Kahya","https:\u002F\u002Forcid.org\u002F0000-0001-7768-9190",{"name":149,"orcid":150},"Fatma Funda Özdüven","https:\u002F\u002Forcid.org\u002F0000-0003-4286-8943",{"tldr":152,"method":153,"finding":154,"direction":81,"opportunity":155},"开发基于RGB图像处理的生菜尺寸估计软件，用于选择性采收决策。","C#图像处理，USB摄像头采集，RGB分割与轮廓检测，标定像素-实际尺寸转换。","视觉冠幅与重量强相关（r=0.97-0.99），回归模型R²达0.94-0.99，误差低。","可集成到机器人采收系统，并探索多品种、多光照条件下的鲁棒性及实时边缘部署。","openalex","2026-09-23T23:30:38.203393Z",{"id":159,"title":160,"url":161,"summary":162,"summary_zh":8,"content":8,"source_name":163,"source_url":8,"published_at":95,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":164,"score_detail":165,"sources":168,"tags":170,"primary_topic":173,"secondary_topics":174,"classification":181,"search_phrases":203,"slug":206,"view_count":52,"doi":8,"paper":207,"created_at":214},3249,"设施番茄采摘机器人识别定位与采摘方法研究——江苏大学雷志龙等 基于改进YOLO v5-HSV融合算法识别准确率95.01%","http:\u002F\u002Fwww.qikanzj.com\u002Fhek\u002Fnyjxxb\u002Fmulu\u002F455758.html","智慧农业(中英文)期刊发表江苏大学雷志龙、刘畅、王权团队研究：针对设施单果番茄采摘需求，设计一款设施番茄智能采摘平台，主要由升降机构、采摘机构、识别与定位系统等部分组成。平台整体结构由低压一体化伺服滚珠丝杠副升降机构、六轴协作机械臂和力控末端执行器组成。基于改进YOLO v5-HSV融合算法来识别检测，通过对H分量进行图像阈值分割，提高对成熟目标果实识别的准确率，有效排除未成熟番茄和枝叶背景的干扰；通过眼在手外的标定方法，使用ZED双目相机进行定位。搭建的设施番茄采摘机样机平台在现场采摘试验中识别准确率达到95.01%，采摘成功率为87.96%，单果平均采摘时间为14.56s。","智慧农业(中英文)·江苏大学",82,{"impact":16,"substance":17,"depth":16,"authority":166,"freshness":12,"relevant":20,"comment":167},14,"核心期刊论文，方法有改进、数据完整，识别准确率与采摘成功率等指标明确，对设施农业智能装备研发有参考价值。",[169],{"name":163,"url":161},[25,26,171,28,172],"采摘机器人","设施番茄",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[175,176],{"code":51,"slug":107,"name":108,"path":109,"parent_code":110},{"code":128,"slug":177,"name":178,"path":179,"parent_code":180},"agricultural-robots","机器人与无人农场","\u002Ftopic\u002Finputs-equipment\u002Fagricultural-robots","F",{"status":38,"locked":13,"version":39,"method":40,"evidence":182},{"candidates":183,"agricultural_evidence":45},[184,189,194,198],{"code":51,"score":18,"supported":45,"title":185,"abstract":187,"tags":188},[186],"YOLO",[186],[26,28],{"code":31,"score":44,"supported":45,"title":190,"abstract":192,"tags":193},[191],"番茄",[191],[172],{"code":128,"score":19,"supported":45,"title":195,"abstract":196,"tags":197},[171],[],[171],{"code":65,"score":199,"supported":45,"title":200,"abstract":201,"tags":202},4,[],[25],[25],[204,205],"江苏大学 设施番茄 采摘机器人","YOLO v5 番茄 识别定位","江苏大学设施番茄采摘机器人-3249",{"doi":8,"openalex_id":8,"authors":208,"venue":8,"cited_by_count":76,"oa_url":8,"card":209,"direction":81,"ingested_from":83},[],{"tldr":210,"method":211,"finding":212,"direction":81,"opportunity":213},"设计设施番茄智能采摘平台，融合改进YOLO v5与HSV实现识别定位与采摘。","改进YOLO v5-HSV融合算法、ZED双目相机眼在手外标定、六轴机械臂力控末","识别准确率95.01%，采摘成功率87.96%，单果平均采摘时间14.56秒。","可探索多果簇、遮挡与弱光环境下识别定位鲁棒性，并优化采摘效率与末端力控。","2026-09-23T00:04:33.401768Z",{"id":216,"title":217,"url":218,"summary":219,"summary_zh":220,"content":8,"source_name":221,"source_url":218,"published_at":222,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":223,"score_detail":224,"sources":227,"tags":229,"primary_topic":233,"secondary_topics":234,"classification":236,"search_phrases":255,"slug":258,"view_count":76,"doi":259,"paper":260,"created_at":275},5387,"LIGHTWEIGHT FEATURE FUSED ATTENTION DRIVEN MULTI LEVEL HYBRID MOBILENET V2 FRAMEWORK FOR EARLY TOMATO LEAF DISEASE DETECTION","https:\u002F\u002Fdoi.org\u002F10.62441\u002Factainnovations.vi.1918","It is crucial to find and treat tomato crop diseases early and accurately to improve crop efficiency and make sure there is no scarcity of crop. However, computerized analysis is provocative due to lighting differences, compound backgrounds, and graphic likenesses among infection regions. Research work proposed a lightweight Attention Driven Multi Level Hybrid MobileNet-V2 algorithm that fuses the handcrafted texture structures with Deep Learning (DL) features, then image preprocessing is incorporated using Gaussian, Bilateral, and Guided filters to minimize noise by preserving the disease lesions. LAB color model along with Contrast Limited Adaptive Histogram Equalization (CLAHE) are used to improve the contrast. Otsu thresholding is involved to effectively separate the leaf segment from diseased parts. Local Binary Pattern (LBP) and Gray Level Co-occurrence Matrix (GLCM) descriptors are used to obtain fine-grained features, while high level features are gained by pretrained MobileNet-V2 model. Features fusion is fed to the fully connected layers of deep learning model for training and validation purpose. Results have revealed that the suggested model attains better classification performance as compared to existing models, making it an effective explanation for real time tomato leaf disease detection for smart agriculture applications.","早期准确发现并治疗番茄作物病害，对于提高作物效率、确保作物不短缺至关重要。然而，由于光照差异、复杂背景以及感染区域之间的图形相似性，计算机化分析具有挑战性。研究提出了一种轻量级注意力驱动多层混合MobileNet-V2算法，该算法将手工纹理结构与深度学习（DL）特征相融合，然后结合高斯、双边和引导滤波器进行图像预处理，以在保留病害病变的同时最小化噪声。采用LAB颜色模型和对比度受限自适应直方图均衡化（CLAHE）来提高对比度。引入Otsu阈值分割以有效分离叶片片段与病害部分。使用局部二值模式（LBP）和灰度共生矩阵（GLCM）描述符获取细粒度特征，同时通过预训练的MobileNet-V2模型获取高层特征。特征融合后输入深度学习模型的全连接层进行训练和验证。结果表明，与现有模型相比，所提出的模型取得了更好的分类性能，使其成为智慧农业应用中实时番茄叶片病害检测的有效解决方案。","Acta Innovations","2026-10-18T00:00:00Z",64,{"impact":44,"substance":16,"depth":99,"authority":12,"freshness":225,"relevant":20,"comment":226},8,"提出融合手工纹理特征与MobileNet-V2的轻量番茄叶病检测方法，有一定方法新颖性，但属单篇论文、应用范围有限，未达每日精选门槛。",[228],{"name":221,"url":218},[25,26,230,231,232],"深度学习","番茄病害","图像识别",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[235],{"code":51,"slug":107,"name":108,"path":109,"parent_code":110},{"status":38,"locked":13,"version":39,"method":40,"evidence":237},{"candidates":238,"agricultural_evidence":45},[239,245,250],{"code":31,"score":240,"supported":45,"title":241,"abstract":243,"tags":244},15,[242],"tomato",[191,242],[231],{"code":51,"score":19,"supported":45,"title":246,"abstract":247,"tags":249},[],[230,248],"deep learning",[26,232,230],{"code":65,"score":73,"supported":45,"title":251,"abstract":252,"tags":254},[],[25,253],"smart agriculture",[25],[256,257],"MobileNet-V2 番茄叶片病害","番茄叶病 深度学习 检测","MobileNet-V2番茄叶片病害-5387","10.62441\u002Factainnovations.vi.1918",{"doi":259,"openalex_id":261,"authors":262,"venue":221,"cited_by_count":76,"oa_url":268,"card":269,"direction":274,"ingested_from":156},"W7222977034",[263,265],{"name":264,"orcid":8},"Mrs. Vijetalaxmi A Hegde",{"name":266,"orcid":267},"Abhay Ramakant Kasetwar","https:\u002F\u002Forcid.org\u002F0000-0002-5800-8764","https:\u002F\u002Factainnovations.com\u002Findex.php\u002Fpub\u002Farticle\u002Fdownload\u002F1918\u002F981",{"tldr":270,"method":271,"finding":272,"direction":81,"opportunity":273},"提出轻量注意力多级混合MobileNet-V2框架，融合手工纹理与深度学习特征实现番茄叶片病害早期检","高斯\u002F双边\u002F引导滤波去噪，LAB+CLAHE增强，Otsu分割，LBP与GLCM","所提模型分类性能优于现有模型，可有效用于智慧农业实时番茄叶片病害检测。","可探索多作物跨域泛化、田间复杂光照下轻量模型部署与可解释性，及病害早期分级检测。","智慧农业 \u002F 农业物联网","2026-10-20T23:30:10.424511Z",{"id":277,"title":278,"url":279,"summary":280,"summary_zh":281,"content":8,"source_name":282,"source_url":279,"published_at":283,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":284,"score_detail":285,"sources":289,"tags":291,"primary_topic":295,"secondary_topics":300,"classification":307,"search_phrases":352,"slug":355,"view_count":76,"doi":8,"paper":356,"created_at":374},5313,"Real-Time IoT-Integrated Ensemble LSTM–Bi-LSTM Framework for Greenhouse Microclimate Forecasting","https:\u002F\u002Fwasrpublication.com\u002Findex.php\u002Findex\u002Farticle\u002Fview\u002F512","Accurate prediction of greenhouse microclimates is essential for optimizing crop growth and resource utilization in precision agriculture. The research presents an IoT-integrated ensemble deep learning framework for real-time greenhouse microclimate forecasting using Long Short-Term Memory (LSTM) and Bidirectional Long Short-Term Memory (Bi-LSTM) networks. Environmental variables, including temperature, light intensity, soil moisture, and humidity, were acquired through IoT sensor nodes deployed in a controlled greenhouse environment. A benchmark greenhouse dataset was first used for model development and hyperparameter optimization, while an original IoT-based dataset collected during cucumber, tomato, and pepper cultivation at the Federal University Wukari, Nigeria, was used for validation and real-world deployment. The data were preprocessed through noise filtering, missing value imputation, Z-score-based outlier removal, and Min–Max normalization. A weighted ensemble strategy was then applied to combine LSTM and Bi-LSTM predictions for improved robustness. Experimental results show that the Bi-LSTM model outperformed the LSTM model, achieving R² = 0.9981, MAE = 0.0088, RMSE = 0.0110, and MSE = 0.0001, compared to LSTM results of R² = 0.9956, MAE = 0.0133, RMSE = 0.0167, and MSE = 0.0003. Statistical validation using paired t-test, Wilcoxon signed-rank test, confidence intervals, and multi-run analysis confirmed that the performance improvement is statistically significant and robust. The proposed framework enables accurate real-time microclimate forecasting and intelligent environmental monitoring, providing a scalable solution for precision greenhouse management, improved crop productivity, optimized energy utilization, and efficient irrigation scheduling.","准确预测温室微气候对于精准农业中优化作物生长和资源利用至关重要。本研究提出了一种集成物联网的集成深度学习框架，利用长短期记忆网络（LSTM）和双向长短期记忆网络（Bi-LSTM）进行实时温室微气候预测。温度、光照强度、土壤湿度和湿度等环境变量通过部署在受控温室环境中的物联网传感器节点采集。研究首先使用基准温室数据集进行模型开发和超参数优化，随后使用在尼日利亚武卡里联邦大学黄瓜、番茄和辣椒种植期间采集的原始物联网数据集进行验证和实际部署。数据经过噪声滤波、缺失值插补、基于Z分数的异常值去除和最小—最大归一化预处理。然后采用加权集成策略融合LSTM和Bi-LSTM的预测结果以提高鲁棒性。实验结果表明，Bi-LSTM模型优于LSTM模型，其R² = 0.9981、MAE = 0.0088、RMSE = 0.0110、MSE = 0.0001，而LSTM的结果为R² = 0.9956、MAE = 0.0133、RMSE = 0.0167、MSE = 0.0003。采用配对t检验、Wilcoxon符号秩检验、置信区间和多轮运行分析进行的统计验证证实，性能提升具有统计显著性和鲁棒性。所提出的框架能够实现准确的实时微气候预测和智能环境监测，为精准温室管理、提高作物生产力、优化能源利用和高效灌溉调度提供了可扩展的解决方案。","World Journal of Multidisciplinary Studies","2026-10-16T00:00:00Z",67,{"impact":44,"substance":286,"depth":287,"authority":19,"freshness":225,"relevant":20,"comment":288},21,17,"方法扎实、验证充分，但属单点应用研究，公共影响有限，适合作为技术前沿条目而非头条精选。",[290],{"name":282,"url":279},[25,26,292,293,294],"农业物联网","温室环境","小气候预测",{"code":296,"slug":297,"name":298,"path":299,"parent_code":35},"B7","protected-horticulture","设施园艺与植物工厂","\u002Ftopic\u002Fhorticulture\u002Fprotected-horticulture",[301,306],{"code":302,"slug":303,"name":304,"path":305,"parent_code":110},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},{"status":38,"locked":13,"version":39,"method":40,"evidence":308},{"candidates":309,"agricultural_evidence":45},[310,316,322,328,334,338,345],{"code":302,"score":240,"supported":45,"title":311,"abstract":313,"tags":315},[312],"IoT",[314,312],"物联网",[292],{"code":296,"score":166,"supported":45,"title":317,"abstract":319,"tags":321},[318],"greenhouse",[320,318],"温室",[],{"code":31,"score":19,"supported":45,"title":323,"abstract":324,"tags":327},[],[191,47,325,242,326],"黄瓜","cucumber",[],{"code":65,"score":73,"supported":45,"title":329,"abstract":330,"tags":333},[],[331,332],"精准农业","precision agriculture",[25],{"code":51,"score":73,"supported":45,"title":335,"abstract":336,"tags":337},[],[230,248],[26],{"code":339,"score":86,"supported":45,"title":340,"abstract":341,"tags":344},"H2",[],[342,343],"土壤","soil",[],{"code":346,"score":86,"supported":45,"title":347,"abstract":348,"tags":351},"H3",[],[349,350],"灌溉","irrigation",[],[353,354],"温室 小气候 LSTM 预测","尼日利亚 温室 IoT 黄瓜 番茄","温室小气候LSTM预测-5313",{"doi":8,"openalex_id":357,"authors":358,"venue":282,"cited_by_count":76,"oa_url":8,"card":369,"direction":274,"ingested_from":156},"W7222909837",[359,361,363,365,367],{"name":360,"orcid":8},"Siman Emmanuel*",{"name":362,"orcid":8},"Edward Onyebueke Agu",{"name":364,"orcid":8},"Musa Gani",{"name":366,"orcid":8},"Oladunjoye John Abiodun",{"name":368,"orcid":8},"Anagu Emmanuel John",{"tldr":370,"method":371,"finding":372,"direction":274,"opportunity":373},"提出物联网集成LSTM与Bi-LSTM加权集成框架，实时预测温室微气候。","IoT传感器采集温光湿数据，经预处理后用LSTM-Bi-LSTM加权集成建模。","Bi-LSTM优于LSTM，R²达0.9981，统计检验证实性能提升显著稳健。","可探索边缘端轻量化部署与多作物跨温室迁移学习，提升实时预测泛化性。","2026-10-19T23:30:13.295236Z",{"id":376,"title":377,"url":378,"summary":379,"summary_zh":8,"content":8,"source_name":380,"source_url":8,"published_at":381,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":382,"score_detail":383,"sources":385,"tags":387,"primary_topic":390,"secondary_topics":391,"classification":392,"search_phrases":420,"slug":423,"view_count":412,"doi":424,"paper":425,"created_at":432},5301,"MambaSeed:基于Mamba的轻量级混合视觉网络用于番茄叶病识别(PLOS ONE)","https:\u002F\u002Fjournals.plos.org\u002Fplosone\u002Farticle?id=10.1371\u002Fjournal.pone.0359350","PLOS ONE于2026-10-15发表论文MambaSeed,由Wang X, Zhang H, Chen Y, Li Y, Qin Y, Lv Z等团队提出基于MambaVisionMixer的轻量级混合视觉网络,配套局部连续分支注入(LCBI)机制;在番茄、玉米、马铃薯数据集上识别准确率分别达98.97%、98.06%和95.87%,模型仅1.13M参数、0.240G MACs,在RTX 4090上延迟3.39ms\u002F图像,为资源受限农田环境提供紧凑、高效、鲁棒的病害识别方案。","PLOS ONE(2026-10-15)","2026-10-15T00:00:00Z",79,{"impact":16,"substance":17,"depth":16,"authority":18,"freshness":225,"relevant":20,"comment":384},"方法新颖、指标扎实的轻量级病害识别论文，对资源受限农田场景有实用价值，值得进入每日精选。",[386],{"name":380,"url":378},[25,26,191,388,389],"病害识别","轻量级模型",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":13,"version":39,"method":40,"evidence":393},{"candidates":394,"agricultural_evidence":45},[395,399,405,411,416],{"code":31,"score":44,"supported":45,"title":396,"abstract":397,"tags":398},[191],[191],[191],{"code":400,"score":59,"supported":45,"title":401,"abstract":402,"tags":404},"A3",[],[403],"玉米",[],{"code":406,"score":59,"supported":45,"title":407,"abstract":408,"tags":410},"A5",[],[409],"马铃薯",[],{"code":51,"score":412,"supported":13,"title":413,"abstract":414,"tags":415},2,[],[],[26,388],{"code":65,"score":20,"supported":13,"title":417,"abstract":418,"tags":419},[],[],[25],[421,422],"MambaSeed 番茄叶病","MambaVisionMixer 病害识别","MambaSeed番茄叶病-5301","10.1371\u002Fjournal.pone.0359350",{"doi":424,"openalex_id":8,"authors":426,"venue":8,"cited_by_count":76,"oa_url":8,"card":427,"direction":81,"ingested_from":83},[],{"tldr":428,"method":429,"finding":430,"direction":81,"opportunity":431},"提出轻量级混合视觉网络MambaSeed，实现番茄等作物叶病高效识别。","基于MambaVisionMixer与局部连续分支注入机制，在三种作物病害数据集","番茄、玉米、马铃薯识别准确率达98.97%、98.06%、95.87%，仅1.13M参数、3.39m","可探索该轻量模型在移动端与边缘设备的田间部署，及多作物跨域泛化与病害早期识别。","2026-10-19T00:07:48.589614Z",{"id":434,"title":435,"url":436,"summary":437,"summary_zh":438,"content":8,"source_name":439,"source_url":436,"published_at":440,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":441,"score_detail":442,"sources":444,"tags":446,"primary_topic":448,"secondary_topics":449,"classification":451,"search_phrases":466,"slug":469,"view_count":20,"doi":470,"paper":471,"created_at":494},4494,"Tomato ripeness classification with weight-based and texture-adaptive pooling in CNNs","https:\u002F\u002Fdoi.org\u002F10.64960\u002Feasr.2026.266223","This research introduces a weight-based mixed pooling and adaptive fuzzy pooling mechanism based on feature map texture analysis for attention mechanism Squeeze-and-Excitation Networks (SE) and Convolutional Neural Networks (CNNs) in tomato ripeness classification. Traditional CNNs and SE networks rely on deterministic pooling strategies that can be suboptimal for capturing diverse visual features such as texture and color gradients in fruit. A novel fuzzy pooling system is proposed that dynamically adjusts the fusion ratio of min and max pooling based on real-time texture analysis of feature maps via a Laplacian filter. This mechanism was integrated into Darknet53, ResNet34, and ResNet101 architectures, both directly and within the SE attention block. The method was evaluated on a five-class tomato ripeness dataset using 10-fold cross-validation against traditional and fixed-weight pooling methods. The fuzzy and mixed pooling approach demonstrated superior performance on certain models which include ESEnet: Darknet53 + SE (Fuzzy pool) achieved an average F1 score across folds of 0.8429, outperforming Darknet53 + baseline SE (Average pool) at 0.8350; Extended Net: ResNet34 + Fuzzy pool achieved an average F1 score across folds of 0.8305, outperforming baseline ResNet34 at 0.8130 and ResNet101 + Fuzzy pool achieved an average F1 score across folds of 0.8284, outperforming baseline ResNet101 at 0.8267. This improvement was realized with minimal increase in computational overhead. Specifically, measurements of model parameters (M) and FLOPs (GMACs) showed only a slight increase across all architectures after integrating SEnet into the system, and zero increase after integrating the Laplacian filter and fuzzy logic. The study concludes that logic-based adaptive pooling is a robust strategy that improves feature representation in CNNs, achieving a balance between classification accuracy and computational cost. These findings offer practical value for developing more reliable automated harvesting systems and provide a generalizable enhancement for a wide range of computer vision classification tasks.","本研究提出了一种基于特征图纹理分析的权重混合池化与自适应模糊池化机制，用于番茄成熟度分类中的注意力机制挤压激励网络（Squeeze-and-Excitation Networks, SE）和卷积神经网络（Convolutional Neural Networks, CNNs）。传统的CNN和SE网络依赖确定性池化策略，在捕捉果实纹理和颜色梯度等多样化视觉特征时可能并非最优。本文提出了一种新颖的模糊池化系统，通过拉普拉斯滤波器对特征图进行实时纹理分析，动态调整最小池化和最大池化的融合比例。该机制被集成到Darknet53、ResNet34和ResNet101架构中，既可直接集成，也可嵌入SE注意力模块内。该方法在五类番茄成熟度数据集上采用10折交叉验证，与传统池化及固定权重池化方法进行了对比评估。模糊池化和混合池化方法在部分模型上表现出优越性能，包括：ESEnet：Darknet53 + SE（模糊池化）在各折上的平均F1得分为0.8429，优于Darknet53 + 基线SE（平均池化）的0.8350；扩展网络：ResNet34 + 模糊池化在各折上的平均F1得分为0.8305，优于基线ResNet34的0.8130；ResNet101 + 模糊池化在各折上的平均F1得分为0.8284，优于基线ResNet101的0.8267。这一改进仅带来了极小的计算开销增加。具体而言，模型参数量（M）和浮点运算次数（GMACs）的测量结果表明，将SEnet集成到系统后所有架构仅有轻微增加，而集成拉普拉斯滤波器和模糊逻辑后则无任何增加。研究结论认为，基于逻辑的自适应池化是一种鲁棒策略，能够改善CNN中的特征表示，在分类精度和计算成本之间实现平衡。这些发现为开发更可靠的自动化收获系统提供了实用价值，并为广泛的计算机视觉分类任务提供了一种可推广的增强方法。","Engineering and Applied Science Research","2026-10-07T00:00:00Z",66,{"impact":225,"substance":98,"depth":287,"authority":44,"freshness":19,"relevant":20,"comment":443},"提出基于纹理自适应模糊池化的CNN改进方法，在番茄成熟度分类上取得小幅但稳定的F1提升且算力开销几乎不增，对自动采摘与农业视觉识别有参考价值，但属细分方法类论文，影响面有限。",[445],{"name":439,"url":436},[25,26,191,232,447],"自动采摘",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[450],{"code":51,"slug":107,"name":108,"path":109,"parent_code":110},{"status":38,"locked":13,"version":39,"method":40,"evidence":452},{"candidates":453,"agricultural_evidence":45},[454,458,462],{"code":31,"score":240,"supported":45,"title":455,"abstract":456,"tags":457},[242],[191,242],[191],{"code":51,"score":225,"supported":45,"title":459,"abstract":460,"tags":461},[],[120,118],[26,232],{"code":65,"score":20,"supported":13,"title":463,"abstract":464,"tags":465},[],[],[25],[467,468],"番茄 成熟度 分类 卷积神经网络","Darknet53 ResNet 番茄 成熟度","番茄成熟度分类卷积神经网络-4494","10.64960\u002Feasr.2026.266223",{"doi":470,"openalex_id":472,"authors":473,"venue":439,"cited_by_count":76,"oa_url":488,"card":489,"direction":274,"ingested_from":156},"W7220840158",[474,476,478,480,483,485],{"name":475,"orcid":8},"Rut Yatigul",{"name":477,"orcid":8},"Wisanu Jitviriya",{"name":479,"orcid":8},"Eiji Hayashi",{"name":481,"orcid":482},"Aran Blattler","https:\u002F\u002Forcid.org\u002F0000-0002-8684-6239",{"name":484,"orcid":8},"Watcharin Tangsuksant",{"name":486,"orcid":487},"Teppakorn Sittiwanchai","https:\u002F\u002Forcid.org\u002F0000-0001-9653-8750","https:\u002F\u002Fph01.tci-thaijo.org\u002Findex.php\u002Feasr\u002Farticle\u002Fdownload\u002F266223\u002F176569",{"tldr":490,"method":491,"finding":492,"direction":81,"opportunity":493},"提出基于纹理自适应模糊池化机制，提升CNN番茄成熟度分类性能。","用Laplacian滤波分析特征图纹理，动态调整最大最小池化融合比，集成到Dar","模糊池化在多个模型上F1提升，且计算开销几乎不增加。","可探索自适应池化在其它果蔬成熟度或田间复杂光照下的泛化能力。","2026-10-09T23:30:09.993787Z"]