[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3047":3,"related-3047":77},{"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":23,"tags":25,"primary_topic":31,"secondary_topics":37,"classification":38,"search_phrases":62,"slug":65,"view_count":53,"doi":8,"paper":66,"created_at":76},3047,"基于CNN\u002FLR\u002FGA-BP的辣椒叶病识别稳定性与精度多维度视觉特征比较","https:\u002F\u002Fwww.mdpi.com\u002F2311-7524\u002F12\u002F9\u002F1176","塔里木大学Xueting Ma、Yifei Li等联合东北林业大学、南京农业大学，构建1260份辣椒叶数据集（健康、细菌性斑点病、黄化卷叶病），采用Lab b-channel、RGB super-green、Otsu-ACWE三种分割算法系统评估最优预处理方案，提取32个融合视觉特征并以随机森林消除7个低贡献冗余特征保留25个判别变量。20次独立重复试验表明：CNN测试平均精度97.67%、平均AUC 0.999，稳定性最佳；LR计算成本低适合资源受限场景；GA-BP非线性拟合能力弱、预测波动严重。该研究为辣椒叶病识别提供标准化实验框架。",null,"MDPI Horticulturae 12(9):1176","2026-09-19T00:00:00Z","论文",10,false,78,{"impact":16,"substance":17,"depth":18,"authority":19,"freshness":20,"relevant":21,"comment":22},16,22,18,14,8,1,"基于1260份辣椒叶数据集系统比较CNN\u002FLR\u002FGA-BP三种模型，方法规范、结论可靠，对作物病害智能识别有参考价值。",[24],{"name":9,"url":6},[26,27,28,29,30],"智慧农业","农业人工智能","图像识别","辣椒叶病","病害诊断",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"B1","vegetables","蔬菜","\u002Ftopic\u002Fhorticulture\u002Fvegetables","B",[],{"status":39,"locked":13,"version":40,"method":41,"evidence":42},"classified","2026-10-22.2","rules",{"candidates":43,"agricultural_evidence":46},[44,51,57],{"code":32,"score":45,"supported":46,"title":47,"abstract":49,"tags":50},11,true,[48],"辣椒",[48],[],{"code":52,"score":53,"supported":13,"title":54,"abstract":55,"tags":56},"I2",2,[],[],[27,28],{"code":58,"score":21,"supported":13,"title":59,"abstract":60,"tags":61},"I1",[],[],[26],[63,64],"塔里木大学 辣椒叶病 识别","CNN 辣椒叶病 视觉特征","塔里木大学辣椒叶病识别-3047",{"doi":8,"openalex_id":8,"authors":67,"venue":8,"cited_by_count":68,"oa_url":8,"card":69,"direction":73,"ingested_from":75},[],0,{"tldr":70,"method":71,"finding":72,"direction":73,"opportunity":74},"比较CNN、LR、GA-BP对辣椒叶病的识别精度与稳定性，并评估三种分割预处理方案。","1260份辣椒叶图像，Lab\u002FRGB\u002FOtsu-ACWE分割，32特征经随机森林","CNN平均精度97.67%、AUC 0.999且最稳定；LR成本低适合资源受限；GA-BP波动严重。","农业人工智能与决策模型","可探索轻量化CNN与LR融合的田间实时识别方案，并验证多作物、多病害下的泛化稳定性。","agent","2026-09-21T00:04:39.222838Z",{"total":78,"page":21,"page_size":78,"items":79},6,[80,148,214,275,371,430],{"id":81,"title":82,"url":83,"summary":84,"summary_zh":85,"content":8,"source_name":86,"source_url":83,"published_at":87,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":88,"score_detail":89,"sources":92,"tags":94,"primary_topic":97,"secondary_topics":98,"classification":104,"search_phrases":126,"slug":129,"view_count":68,"doi":130,"paper":131,"created_at":147},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":90,"substance":18,"depth":16,"authority":12,"freshness":20,"relevant":21,"comment":91},12,"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。",[93],{"name":86,"url":83},[26,27,95,96,28],"深度学习","番茄病害",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[99],{"code":52,"slug":100,"name":101,"path":102,"parent_code":103},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"status":39,"locked":13,"version":40,"method":41,"evidence":105},{"candidates":106,"agricultural_evidence":46},[107,114,120],{"code":32,"score":108,"supported":46,"title":109,"abstract":111,"tags":113},15,[110],"tomato",[112,110],"番茄",[96],{"code":52,"score":115,"supported":46,"title":116,"abstract":117,"tags":119},9,[],[95,118],"deep learning",[27,28,95],{"code":58,"score":121,"supported":46,"title":122,"abstract":123,"tags":125},7,[],[26,124],"smart agriculture",[26],[127,128],"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":130,"openalex_id":132,"authors":133,"venue":86,"cited_by_count":68,"oa_url":139,"card":140,"direction":145,"ingested_from":146},"W7222977034",[134,136],{"name":135,"orcid":8},"Mrs. Vijetalaxmi A Hegde",{"name":137,"orcid":138},"Abhay Ramakant Kasetwar","https:\u002F\u002Forcid.org\u002F0000-0002-5800-8764","https:\u002F\u002Factainnovations.com\u002Findex.php\u002Fpub\u002Farticle\u002Fdownload\u002F1918\u002F981",{"tldr":141,"method":142,"finding":143,"direction":73,"opportunity":144},"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","openalex","2026-10-20T23:30:10.424511Z",{"id":149,"title":150,"url":151,"summary":152,"summary_zh":153,"content":8,"source_name":154,"source_url":151,"published_at":155,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":156,"score_detail":157,"sources":161,"tags":163,"primary_topic":165,"secondary_topics":166,"classification":168,"search_phrases":185,"slug":188,"view_count":21,"doi":189,"paper":190,"created_at":213},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":20,"substance":158,"depth":159,"authority":90,"freshness":115,"relevant":21,"comment":160},20,17,"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。",[162],{"name":154,"url":151},[26,27,112,28,164],"自动采摘",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[167],{"code":52,"slug":100,"name":101,"path":102,"parent_code":103},{"status":39,"locked":13,"version":40,"method":41,"evidence":169},{"candidates":170,"agricultural_evidence":46},[171,175,181],{"code":32,"score":108,"supported":46,"title":172,"abstract":173,"tags":174},[110],[112,110],[112],{"code":52,"score":20,"supported":46,"title":176,"abstract":177,"tags":180},[],[178,179],"计算机视觉","computer vision",[27,28],{"code":58,"score":21,"supported":13,"title":182,"abstract":183,"tags":184},[],[],[26],[186,187],"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":189,"openalex_id":191,"authors":192,"venue":154,"cited_by_count":68,"oa_url":207,"card":208,"direction":145,"ingested_from":146},"W7220840158",[193,195,197,199,202,204],{"name":194,"orcid":8},"Rut Yatigul",{"name":196,"orcid":8},"Wisanu Jitviriya",{"name":198,"orcid":8},"Eiji Hayashi",{"name":200,"orcid":201},"Aran Blattler","https:\u002F\u002Forcid.org\u002F0000-0002-8684-6239",{"name":203,"orcid":8},"Watcharin Tangsuksant",{"name":205,"orcid":206},"Teppakorn Sittiwanchai","https:\u002F\u002Forcid.org\u002F0000-0001-9653-8750","https:\u002F\u002Fph01.tci-thaijo.org\u002Findex.php\u002Feasr\u002Farticle\u002Fdownload\u002F266223\u002F176569",{"tldr":209,"method":210,"finding":211,"direction":73,"opportunity":212},"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",{"id":215,"title":216,"url":217,"summary":218,"summary_zh":219,"content":8,"source_name":220,"source_url":217,"published_at":221,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":222,"score_detail":223,"sources":225,"tags":227,"primary_topic":229,"secondary_topics":230,"classification":231,"search_phrases":247,"slug":250,"view_count":251,"doi":252,"paper":253,"created_at":274},4074,"Automated Diagnosis of Tomato Leaf Diseases with Attention-Enhanced Feature Aggregation","https:\u002F\u002Fdoi.org\u002F10.35377\u002Fsaucis...1826329","Tomato crop yield can be enhanced using agricultural technologies if the diseases associated with the plant leaves are detected early. This article proposes a new tomato disease classification model, known as the Multiscale Parallel Feature Aggregation Network with Attention Fusion (MPFAN-AF), which can classify diseases using leaf images. This model comprises parallel convolutional branches that extract multiscale features to retrieve rich data and fuse them through an attention-based fusion module, which performs global average pooling and weights the channels. This model enables the network to optimise patterns associated with the disease while disregarding the diseased area's background noise. The generalisation ability is increased using dropout and L2 weight decay. The refined features are passed through a lightweight multi-layer perceptron for classification. Evaluated on a benchmark tomato leaf disease dataset, MPFAN-AF outperforms conventional Convolutional Neural Networks (CNNs) and existing attention-based models across accuracy, precision, recall, and F1-score. Overall, MPFAN-AF delivers an efficient, accurate, and interpretable solution for automated disease diagnosis in precision agriculture.","如果及早检测出与植物叶片相关的病害，就可以利用农业技术提高番茄作物产量。本文提出了一种新的番茄病害分类模型，称为基于注意力融合的多尺度并行特征聚合网络（MPFAN-AF），可利用叶片图像对病害进行分类。该模型包含并行卷积分支，用于提取多尺度特征以获取丰富数据，并通过基于注意力的融合模块进行融合，该模块执行全局平均池化并对通道进行加权。该模型使网络能够优化与病害相关的模式，同时忽略病区背景噪声。通过dropout和L2权重衰减提高了泛化能力。精炼后的特征通过轻量级多层感知机进行分类。在基准番茄叶片病害数据集上评估时，MPFAN-AF在准确率、精确率、召回率和F1分数方面均优于传统卷积神经网络（CNN）和现有基于注意力的模型。总体而言，MPFAN-AF为精准农业中的自动化病害诊断提供了一种高效、准确且可解释的解决方案。","Sakarya University Journal of Computer and Information Sciences","2026-09-30T00:00:00Z",70,{"impact":90,"substance":158,"depth":159,"authority":90,"freshness":115,"relevant":21,"comment":224},"提出注意力融合多尺度特征网络用于番茄叶病自动诊断，方法有创新且指标优于现有模型，但属单篇学术论文，产业影响有限。",[226],{"name":220,"url":217},[26,27,228,96,28],"精准农业",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":13,"version":40,"method":41,"evidence":232},{"candidates":233,"agricultural_evidence":46},[234,238,243],{"code":32,"score":108,"supported":46,"title":235,"abstract":236,"tags":237},[110],[112,110],[96],{"code":58,"score":20,"supported":46,"title":239,"abstract":240,"tags":242},[],[228,241],"precision agriculture",[26,228],{"code":52,"score":53,"supported":13,"title":244,"abstract":245,"tags":246},[],[],[27,28],[248,249],"番茄叶片病害 识别 模型","MPFAN-AF 番茄病害","番茄叶片病害识别模型-4074",4,"10.35377\u002Fsaucis...1826329",{"doi":252,"openalex_id":254,"authors":255,"venue":220,"cited_by_count":68,"oa_url":268,"card":269,"direction":145,"ingested_from":146},"W7214991700",[256,259,262,265],{"name":257,"orcid":258},"Mohd Aquib Ansari","https:\u002F\u002Forcid.org\u002F0000-0002-9083-1523",{"name":260,"orcid":261},"Shahnawaz Ahmad","https:\u002F\u002Forcid.org\u002F0000-0002-3989-9928",{"name":263,"orcid":264},"Arvind Mewada","https:\u002F\u002Forcid.org\u002F0000-0002-4680-611X",{"name":266,"orcid":267},"Harsh Pratap Singh","https:\u002F\u002Forcid.org\u002F0000-0001-5044-8258","https:\u002F\u002Fdergipark.org.tr\u002Fen\u002Fdownload\u002Farticle-file\u002F5432390",{"tldr":270,"method":271,"finding":272,"direction":73,"opportunity":273},"提出MPFAN-AF模型，用多尺度并行卷积和注意力融合实现番茄叶片病害自动分类。","多尺度并行卷积分支提取特征，注意力融合模块加权通道，dropout和L2正则化。","在基准数据集上，MPFAN-AF在准确率、精确率、召回率和F1上均优于传统CNN和现有注意力模型。","可探索轻量化模型在移动端或边缘设备的部署，以及多作物病害的跨域泛化能力。","2026-10-03T23:30:18.708398Z",{"id":276,"title":277,"url":278,"summary":279,"summary_zh":280,"content":8,"source_name":281,"source_url":278,"published_at":282,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":283,"score_detail":284,"sources":287,"tags":289,"primary_topic":293,"secondary_topics":298,"classification":305,"search_phrases":348,"slug":351,"view_count":68,"doi":8,"paper":352,"created_at":370},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":90,"substance":285,"depth":159,"authority":115,"freshness":20,"relevant":21,"comment":286},21,"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。",[288],{"name":281,"url":278},[26,27,290,291,292],"农业物联网","温室环境","小气候预测",{"code":294,"slug":295,"name":296,"path":297,"parent_code":36},"B7","protected-horticulture","设施园艺与植物工厂","\u002Ftopic\u002Fhorticulture\u002Fprotected-horticulture",[299,304],{"code":300,"slug":301,"name":302,"path":303,"parent_code":103},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":13,"version":40,"method":41,"evidence":306},{"candidates":307,"agricultural_evidence":46},[308,314,320,326,330,334,341],{"code":300,"score":108,"supported":46,"title":309,"abstract":311,"tags":313},[310],"IoT",[312,310],"物联网",[290],{"code":294,"score":19,"supported":46,"title":315,"abstract":317,"tags":319},[316],"greenhouse",[318,316],"温室",[],{"code":32,"score":115,"supported":46,"title":321,"abstract":322,"tags":325},[],[112,48,323,110,324],"黄瓜","cucumber",[],{"code":58,"score":121,"supported":46,"title":327,"abstract":328,"tags":329},[],[228,241],[26],{"code":52,"score":121,"supported":46,"title":331,"abstract":332,"tags":333},[],[95,118],[27],{"code":335,"score":78,"supported":46,"title":336,"abstract":337,"tags":340},"H2",[],[338,339],"土壤","soil",[],{"code":342,"score":78,"supported":46,"title":343,"abstract":344,"tags":347},"H3",[],[345,346],"灌溉","irrigation",[],[349,350],"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":353,"authors":354,"venue":281,"cited_by_count":68,"oa_url":8,"card":365,"direction":145,"ingested_from":146},"W7222909837",[355,357,359,361,363],{"name":356,"orcid":8},"Siman Emmanuel*",{"name":358,"orcid":8},"Edward Onyebueke Agu",{"name":360,"orcid":8},"Musa Gani",{"name":362,"orcid":8},"Oladunjoye John Abiodun",{"name":364,"orcid":8},"Anagu Emmanuel John",{"tldr":366,"method":367,"finding":368,"direction":145,"opportunity":369},"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":372,"title":373,"url":374,"summary":375,"summary_zh":8,"content":8,"source_name":376,"source_url":8,"published_at":377,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":378,"score_detail":379,"sources":382,"tags":384,"primary_topic":387,"secondary_topics":388,"classification":389,"search_phrases":417,"slug":420,"view_count":53,"doi":421,"paper":422,"created_at":429},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":18,"substance":17,"depth":18,"authority":380,"freshness":20,"relevant":21,"comment":381},13,"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。",[383],{"name":376,"url":374},[26,27,112,385,386],"病害识别","轻量级模型",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":13,"version":40,"method":41,"evidence":390},{"candidates":391,"agricultural_evidence":46},[392,396,403,409,413],{"code":32,"score":90,"supported":46,"title":393,"abstract":394,"tags":395},[112],[112],[112],{"code":397,"score":398,"supported":46,"title":399,"abstract":400,"tags":402},"A3",3,[],[401],"玉米",[],{"code":404,"score":398,"supported":46,"title":405,"abstract":406,"tags":408},"A5",[],[407],"马铃薯",[],{"code":52,"score":53,"supported":13,"title":410,"abstract":411,"tags":412},[],[],[27,385],{"code":58,"score":21,"supported":13,"title":414,"abstract":415,"tags":416},[],[],[26],[418,419],"MambaSeed 番茄叶病","MambaVisionMixer 病害识别","MambaSeed番茄叶病-5301","10.1371\u002Fjournal.pone.0359350",{"doi":421,"openalex_id":8,"authors":423,"venue":8,"cited_by_count":68,"oa_url":8,"card":424,"direction":73,"ingested_from":75},[],{"tldr":425,"method":426,"finding":427,"direction":73,"opportunity":428},"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":431,"title":432,"url":433,"summary":434,"summary_zh":8,"content":435,"source_name":436,"source_url":8,"published_at":437,"category":438,"cover_url":8,"hotness":12,"is_selected":13,"score":439,"score_detail":440,"sources":442,"tags":444,"primary_topic":448,"secondary_topics":449,"classification":451,"search_phrases":470,"slug":473,"view_count":53,"doi":8,"paper":8,"created_at":474},4459,"中国农科院环发所构建知识与数据双驱动模型精准预测温室番茄生长态势","https:\u002F\u002Fnews.sciencenet.cn\u002Fhtmlnews\u002F2026\u002F10\u002F572492.shtm","中国农业科学院农业环境与可持续发展研究所设施植物环境工程创新团队以番茄为对象，构建知识与数据双驱动的尺度自适应时序卷积混合模型，根据番茄不同生育期动态调整时序特征提取尺度，结合机理模型合成数据预训练与生理阈值约束，实现番茄不同器官干物质及叶面积指数的精准预测；研究成果发表在Computers and Electronics in Agriculture；仅依靠常规温室环境数据即可无损估算生长态势，对推动温室智能环控、产量预测具重要应用价值。","[![Image 1: 科学网新闻频道](https:\u002F\u002Fnews.sciencenet.cn\u002Fimages\u002Fnews.jpg)](https:\u002F\u002Fnews.sciencenet.cn\u002F)\n\n[生命科学](https:\u002F\u002Fwww.sciencenet.cn\u002Flife\u002F) | [医学科学](https:\u002F\u002Fwww.sciencenet.cn\u002Fmedicine\u002F) | [化学科学](https:\u002F\u002Fwww.sciencenet.cn\u002Fchemistry\u002F) | [工程材料](https:\u002F\u002Fwww.sciencenet.cn\u002Fmaterial\u002F) | [信息科学](https:\u002F\u002Fwww.sciencenet.cn\u002Finformation\u002F) | [地球科学](https:\u002F\u002Fwww.sciencenet.cn\u002Fearth\u002F) | [数理科学](https:\u002F\u002Fwww.sciencenet.cn\u002Fmathematics\u002F) | [管理综合](https:\u002F\u002Fwww.sciencenet.cn\u002Fpolicy\u002F)[站内规定](https:\u002F\u002Fblog.sciencenet.cn\u002Fblog-45-1064777.html) | [手机版](https:\u002F\u002Fwap.sciencenet.cn\u002F)\n\n[首页](https:\u002F\u002Fwww.sciencenet.cn\u002F) | [新闻](https:\u002F\u002Fnews.sciencenet.cn\u002F) | [博客](https:\u002F\u002Fblog.sciencenet.cn\u002Fblog.php) | [院士](https:\u002F\u002Fnews.sciencenet.cn\u002Fys\u002F) | [人才](https:\u002F\u002Ftalent.sciencenet.cn\u002F) | [会议](https:\u002F\u002Fmeeting.sciencenet.cn\u002F) | [基金·项目](https:\u002F\u002Ffund.sciencenet.cn\u002F) | [论文](https:\u002F\u002Fpaper.sciencenet.cn\u002F) | [绘图](https:\u002F\u002Fvisual-web.sciencenet.cn\u002F) | [视频·直播](https:\u002F\u002Fblog.sciencenet.cn\u002Fblog.php?mod=video) | [小柯机器人](https:\u002F\u002Fpaper.sciencenet.cn\u002FAInews) | [医学科普](https:\u002F\u002Fyxkp.sciencenet.cn\u002F)\n\n作者：李晨,王佳 来源：中国科学报 发布时间：2026\u002F10\u002F4 22:09:23 选择字号：小 中 大 双驱动模型可精准预测温室番茄生长态势 近日，中国农业科学院农业环境与可持续发展研究所设施植物环境工程创新团队构建了知识与数据双驱动模型，实现温室番茄生长的高精度无损预测，相关研究成果发表在《农业计算机与电子学》（_**Computers and Electronics in Agriculture**_）上。 作物生长精准估算是温室智能控制的核心前提。然而，传统机理模型参数获取难、泛化有限，而纯数据驱动模型常因缺乏生理约束致预测失真。因此，如何融合环境时序数据与作物生长规律，实现高精度且生理合理的生长预测仍是关键挑战。 科研团队以番茄为对象，提出一种知识与数据双驱动的尺度自适应时序卷积混合模型。该模型根据番茄不同生育期动态调整时序特征提取尺度，结合机理模型合成数据预训练与生理阈值约束，实现番茄不同器官干物质及叶面积指数的精准预测。该模型依靠常规温室环境数据即可无损估算生长态势，对推动温室智能环控、产量预测具有重要应用价值。 相关论文信息：https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112281 版权声明：凡本网注明“来源：中国科学报、科学网、科学新闻杂志”的所有作品，网站转载，请在正文上方注明来源和作者，且不得对内容作实质性改动；微信公众号、头条号等新媒体平台，转载请联系授权。邮箱：shouquan@stimes.cn。\n\n打印 发E-mail给：\n\n以下评论只代表网友个人观点，不代表科学网观点。\n\n 无标题页 \n\n[![Image 2](https:\u002F\u002Fnews.sciencenet.cn\u002Fimages\u002Fnewcomm.gif)](https:\u002F\u002Fnews.sciencenet.cn\u002Fhtml\u002Fcomment.aspx?id=572492)\n\n相关新闻 相关论文\n*   1\n*   [《科学》（20261001出版）一周论文导读](https:\u002F\u002Fnews.sciencenet.cn\u002Fhtmlnews\u002F2026\u002F10\u002F572487.shtm)\n\n*   2\n*   [2026年度湖南省科学技术奖提名工作启动](https:\u002F\u002Fnews.sciencenet.cn\u002Fhtmlnews\u002F2026\u002F10\u002F572486.shtm)\n\n*   3\n*   [河南中央引导地方科技发展资金项目结项验收结果公示](https:\u002F\u002Fnews.sciencenet.cn\u002Fhtmlnews\u002F2026\u002F10\u002F572485.shtm)\n\n*   4\n*   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2026-10-04","2026-10-04T00:00:00Z","报道",80,{"impact":17,"substance":285,"depth":159,"authority":19,"freshness":78,"relevant":21,"comment":441},"国家级科研机构在智慧农业领域的模型方法突破，方法新颖、结论可靠，对温室智能环控与产量预测有应用价值，值得进入每日精选。",[443],{"name":436,"url":433},[26,27,445,446,447],"作物生长模型","设施番茄","温室环控",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[450],{"code":294,"slug":295,"name":296,"path":297,"parent_code":36},{"status":39,"locked":13,"version":40,"method":41,"evidence":452},{"candidates":453,"agricultural_evidence":46},[454,458,462,466],{"code":32,"score":90,"supported":46,"title":455,"abstract":456,"tags":457},[112],[112],[446],{"code":294,"score":45,"supported":46,"title":459,"abstract":460,"tags":461},[318],[318],[],{"code":52,"score":53,"supported":13,"title":463,"abstract":464,"tags":465},[],[],[445,27],{"code":58,"score":21,"supported":13,"title":467,"abstract":468,"tags":469},[],[],[26],[471,472],"中国农科院环发所 温室番茄 生长预测","知识与数据双驱动 番茄 干物质","中国农科院环发所温室番茄生长预测-4459","2026-10-09T00:04:56.060677Z"]