[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1987":3,"related-1987":100},{"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":22,"tags":24,"primary_topic":29,"secondary_topics":35,"classification":36,"search_phrases":62,"slug":65,"view_count":66,"doi":67,"paper":68,"created_at":99},1987,"Quantile normalisation enables interpretable spatial prioritisation of persistence-related mechanistic suitability under uncertainty for tomato red spider mite (Tetranychus evansi) in Africa","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffagro.2026.1840571","Mechanistic trait models are increasingly considered to estimate persistence-related suitability for invasive species, although translating reproduction metrics into operational indicators for spatial decision-making remains challenging. Branching-process transformations are widely applied to convert mechanistic reproduction numbers into probabilities of establishment; however, such transformations may compress spatial gradients when persistence is broadly feasible. Using tomato red spider mite ( Tetranychus evansi ) as a case study across Africa, this study developed an uncertainty-aware, physiologically-based model and generated spatially explicit estimates of expected reproduction (R 0 ). We compared three representations: raw mechanistic suitability, branching-process persistence probability, and quantile-normalised suitability. Although all representations preserved rank ordering, branching probability showed strong spatial saturation (>93% of pixels have P = 1 − 1 R 0 close to 1), reducing effective resolution for prioritisation. In contrast, quantile normalisation retained uniform spatial contrast and stable hotspot delineation under fixed-effort targeting. Our findings demonstrate that indicator choice fundamentally affects interpretability, and that rank-based transformations may provide more robust ecological indicators for spatial and spatiotemporal decision support under uncertainty.","机制性性状模型越来越多地被用于评估入侵物种的持续适应性，尽管将繁殖指标转化为空间决策的可操作指标仍具挑战性。分枝过程变换被广泛用于将机制性繁殖数转化为建立概率；然而，当持续存在广泛可行时，此类变换可能压缩空间梯度。本研究以番茄红蜘蛛（*Tetranychus evansi*）为非洲案例，开发了一种考虑不确定性的生理基础模型，并生成了预期繁殖量（R₀）的空间显式估计。我们比较了三种表征方式：原始机制性适宜性、分枝过程持续概率和分位数归一化适宜性。尽管所有表征均保持了秩次排序，但分枝概率表现出强烈的空间饱和（>93%的像元其P = 1 − 1\u002FR₀接近1），降低了优先排序的有效分辨率。相比之下，分位数归一化在固定投入目标下保持了均匀的空间对比度和稳定的热点区域划定。我们的研究结果表明，指标选择从根本上影响可解释性，基于秩次的变换可能为不确定条件下的空间和时空决策支持提供更稳健的生态指标。",null,"Frontiers in Agronomy","2026-09-08T00:00:00Z","论文",10,false,67,{"impact":17,"substance":18,"depth":18,"authority":17,"freshness":19,"relevant":20,"comment":21},12,18,7,1,"论文提出分位数归一化方法改进入侵物种适生区空间优先级排序，对农业病虫害精准防控有参考价值。",[23],{"name":10,"url":6},[25,26,27,28],"智慧农业","农业人工智能","非洲","病虫害防治",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},"B1","vegetables","蔬菜","\u002Ftopic\u002Fhorticulture\u002Fvegetables","B",[],{"status":37,"locked":14,"version":38,"method":39,"evidence":40},"classified","2026-10-22.2","rules",{"candidates":41,"agricultural_evidence":44},[42,50,57],{"code":30,"score":43,"supported":44,"title":45,"abstract":47,"tags":49},14,true,[46],"tomato",[48,46],"番茄",[],{"code":51,"score":52,"supported":44,"title":53,"abstract":54,"tags":56},"I2",4,[],[55],"decision support",[26],{"code":58,"score":20,"supported":14,"title":59,"abstract":60,"tags":61},"I1",[],[],[25],[63,64],"农业人工智能 病虫害防治 智慧农业 非洲","农业人工智能 病虫害防治","农业人工智能病虫害防治智慧农业非洲-1987",0,"10.3389\u002Ffagro.2026.1840571",{"doi":67,"openalex_id":69,"authors":70,"venue":10,"cited_by_count":66,"oa_url":90,"card":91,"direction":97,"ingested_from":98},"W7211971144",[71,74,77,80,83,85,88],{"name":72,"orcid":73},"Komi Agboka","https:\u002F\u002Forcid.org\u002F0000-0002-8287-6203",{"name":75,"orcid":76},"Luca Rossini","https:\u002F\u002Forcid.org\u002F0000-0003-2558-7111",{"name":78,"orcid":79},"Quinto Juma Meltus","https:\u002F\u002Forcid.org\u002F0009-0008-8712-0465",{"name":81,"orcid":82},"Bonoukpoè Mawuko Sokame","https:\u002F\u002Forcid.org\u002F0000-0001-5874-3243",{"name":84,"orcid":9},"Abdulmutallab G. A. Azrag",{"name":86,"orcid":87},"Thomas Dubois","https:\u002F\u002Forcid.org\u002F0000-0002-4865-843X",{"name":89,"orcid":9},"Elfatih M. Abdel-Rahman","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fagronomy\u002Farticles\u002F10.3389\u002Ffagro.2026.1840571\u002Fpdf",{"tldr":92,"method":93,"finding":94,"direction":95,"opportunity":96},"提出分位数归一化方法，改进非洲番茄红蜘蛛螨持久性适宜性的空间优先排序。","基于生理的机理模型，结合分支过程与分位数归一化，生成R0空间估计。","分位数归一化保持空间对比度，避免分支概率的空间饱和，提高决策可解释性。","农业人工智能与决策模型","可探索将分位数归一化应用于其他入侵物种或农业害虫的时空动态预测与优先防控区域划定。","智慧农业 \u002F 农业物联网","openalex","2026-09-09T23:30:08.949411Z",{"total":101,"page":20,"page_size":101,"items":102},6,[103,175,233,330,390,454],{"id":104,"title":105,"url":106,"summary":107,"summary_zh":108,"content":9,"source_name":109,"source_url":106,"published_at":110,"category":12,"cover_url":9,"hotness":13,"is_selected":44,"score":111,"score_detail":112,"sources":117,"tags":119,"primary_topic":120,"secondary_topics":121,"classification":127,"search_phrases":149,"slug":151,"view_count":52,"doi":152,"paper":153,"created_at":174},1762,"Deep learning and computer vision framework for tomato insect classification with XAI insights","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-66844-z","Pest infestations pose a life-threatening challenge to global tomato production, often resulting in devastating yield losses and economic instability. Traditional manual identification practices are subjective, labor-intensive, and prone to error. To address this, within the paradigm of Agriculture 5.0, we introduce a novel hybrid deep learning model, ResNet50-Fusion (ViT), designed for the precise detection of six economically relevant tomato pests. The proposed architecture integrates a ResNet50 backbone with a Vision Transformer (ViT) branch via a Multi-Head Cross-Attention mechanism that facilitates asymmetric semantic alignment, enabling the simultaneous extraction of fine-grained morphological features and global contextual patterns. To ensure scientific rigor and eliminate the risk of data leakage, a ‘Split-then-Augment’ protocol was implemented, keeping the test set entirely independent and original. The framework achieved a state-of-the-art test accuracy of 97.00% ± 0.12%, significantly outperforming baseline models such as ResNet50 (96.44%) and DenseNet169 (96.00%), while demonstrating high computational efficiency with an average inference latency of 26.6 ms per image for real-time edge deployment. Beyond predictive accuracy, the model’s reliability was validated using a multi-method quantitative Explainable AI (XAI) framework integrating semantic indicators: Semantic Localization Score (SLS) for feature alignment, Robustness Score (RS) for logical stability, and Interpretability Reliability Coefficient (IRC) for decision consistency. Our results demonstrate a 96.5% Pointing Game Accuracy and a low Deletion AUC (0.142), indicating that the model’s attention patterns strongly align with biologically relevant morphological features rather than background artifacts. Systematic occlusion sensitivity analysis further confirmed model robustness with a stability score of 0.988. Finally, external validation on world-scale datasets, including IP102 and Pest24, yielded accuracies exceeding 90% via direct inference, demonstrating the generalizability of the framework to diverse automated field monitoring conditions. This research provides a precise, robust, and scalable solution for real-time pest identification in global precision agriculture.","虫害侵袭对全球番茄生产构成致命威胁，常导致毁灭性的产量损失和经济不稳定。传统的人工识别方法主观性强、劳动密集且易出错。为此，在农业5.0（Agriculture 5.0）范式下，我们提出了一种新颖的混合深度学习模型ResNet50-Fusion（ViT），专用于精准检测六种具有经济重要性的番茄害虫。该架构通过多头交叉注意力（Multi-Head Cross-Attention）机制整合ResNet50主干与视觉Transformer（Vision Transformer, ViT）分支，实现非对称语义对齐，从而能够同时提取细粒度形态特征与全局上下文模式。为确保科学严谨性并消除数据泄漏风险，我们实施了“先分割后增强”（Split-then-Augment）协议，使测试集保持完全独立且原始状态。该框架实现了97.00%±0.12%的最先进测试准确率，显著优于ResNet50（96.44%）和DenseNet169（96.00%）等基线模型，同时展现出高计算效率，平均推理延迟为每张图像26.6毫秒，适用于实时边缘部署。除预测精度外，模型的可靠性还通过整合语义指标的多方法定量可解释人工智能（Explainable AI, XAI）框架得到验证，包括用于特征对齐的语义定位分数（Semantic Localization Score, SLS）、用于逻辑稳定性的鲁棒性分数（Robustness Score, RS）以及用于决策一致性的可解释性可靠性系数（Interpretability Reliability Coefficient, IRC）。结果表明，我们的模型实现了96.5%的指向游戏准确率（Pointing Game Accuracy）和较低的删除AUC（0.142），说明模型的注意力模式与生物学相关的形态特征高度一致，而非背景伪影。系统性遮挡敏感性分析进一步证实了模型的鲁棒性，稳定性得分为0.988。最后，在包括IP102和Pest24在内的世界规模数据集上进行的外部验证，通过直接推理获得了超过90%的准确率，证明了该框架在多样化自动化田间监测条件下的泛化能力。本研究为全球精准农业中的实时害虫识别提供了一种精准、稳健且可扩展的解决方案。","Scientific Reports","2026-09-04T00:00:00Z",76,{"impact":18,"substance":113,"depth":114,"authority":43,"freshness":115,"relevant":20,"comment":116},22,19,3,"提出高精度番茄害虫识别模型，兼具可解释性与泛化能力，对精准农业有实质推进。",[118],{"name":109,"url":106},[25,26,48,28],{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},[122],{"code":51,"slug":123,"name":124,"path":125,"parent_code":126},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"status":37,"locked":14,"version":38,"method":39,"evidence":128},{"candidates":129,"agricultural_evidence":44},[130,138,143],{"code":51,"score":131,"supported":44,"title":132,"abstract":135,"tags":137},23,[133,134],"deep learning","computer vision",[136,133],"深度学习",[26],{"code":30,"score":139,"supported":44,"title":140,"abstract":141,"tags":142},15,[46],[48,46],[48],{"code":58,"score":19,"supported":44,"title":144,"abstract":145,"tags":148},[],[146,147],"精准农业","precision agriculture",[25],[150,64],"农业人工智能 病虫害防治 智慧农业 番茄","农业人工智能病虫害防治智慧农业番茄-1762","10.1038\u002Fs41598-026-66844-z",{"doi":152,"openalex_id":154,"authors":155,"venue":109,"cited_by_count":66,"oa_url":168,"card":169,"direction":97,"ingested_from":98},"W7208742661",[156,158,160,163,165],{"name":157,"orcid":9},"Mohammad Monirul Islam",{"name":159,"orcid":9},"Md. Anisur Rahman",{"name":161,"orcid":162},"Md. Masum Billah","https:\u002F\u002Forcid.org\u002F0000-0003-1881-4275",{"name":164,"orcid":9},"Morium Akter",{"name":166,"orcid":167},"Mohammad B. Uddin","https:\u002F\u002Forcid.org\u002F0000-0001-9665-7442","https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41598-026-66844-z_reference.pdf",{"tldr":170,"method":171,"finding":172,"direction":95,"opportunity":173},"提出ResNet50-Fusion(ViT)混合深度学习模型，用于番茄害虫精准识别，准确率达97%。","ResNet50与ViT融合，多头交叉注意力，Split-then-Augmen","模型准确率97%，注意力与生物特征对齐，泛化性强，推理快，适合边缘部署。","可探索模型在更多作物害虫识别中的迁移，或结合边缘设备实现实时监测，并优化轻量化结构。","2026-09-06T23:30:09.870628Z",{"id":176,"title":177,"url":178,"summary":179,"summary_zh":180,"content":9,"source_name":181,"source_url":178,"published_at":182,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":183,"score_detail":184,"sources":188,"tags":190,"primary_topic":193,"secondary_topics":194,"classification":196,"search_phrases":213,"slug":216,"view_count":66,"doi":217,"paper":218,"created_at":232},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":17,"substance":18,"depth":185,"authority":13,"freshness":186,"relevant":20,"comment":187},16,8,"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。",[189],{"name":181,"url":178},[25,26,136,191,192],"番茄病害","图像识别",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},[195],{"code":51,"slug":123,"name":124,"path":125,"parent_code":126},{"status":37,"locked":14,"version":38,"method":39,"evidence":197},{"candidates":198,"agricultural_evidence":44},[199,203,208],{"code":30,"score":139,"supported":44,"title":200,"abstract":201,"tags":202},[46],[48,46],[191],{"code":51,"score":204,"supported":44,"title":205,"abstract":206,"tags":207},9,[],[136,133],[26,192,136],{"code":58,"score":19,"supported":44,"title":209,"abstract":210,"tags":212},[],[25,211],"smart agriculture",[25],[214,215],"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":217,"openalex_id":219,"authors":220,"venue":181,"cited_by_count":66,"oa_url":226,"card":227,"direction":97,"ingested_from":98},"W7222977034",[221,223],{"name":222,"orcid":9},"Mrs. Vijetalaxmi A Hegde",{"name":224,"orcid":225},"Abhay Ramakant Kasetwar","https:\u002F\u002Forcid.org\u002F0000-0002-5800-8764","https:\u002F\u002Factainnovations.com\u002Findex.php\u002Fpub\u002Farticle\u002Fdownload\u002F1918\u002F981",{"tldr":228,"method":229,"finding":230,"direction":95,"opportunity":231},"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。","2026-10-20T23:30:10.424511Z",{"id":234,"title":235,"url":236,"summary":237,"summary_zh":238,"content":9,"source_name":239,"source_url":236,"published_at":240,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":241,"sources":245,"tags":247,"primary_topic":251,"secondary_topics":256,"classification":263,"search_phrases":307,"slug":310,"view_count":66,"doi":9,"paper":311,"created_at":329},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",{"impact":17,"substance":242,"depth":243,"authority":204,"freshness":186,"relevant":20,"comment":244},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。",[246],{"name":239,"url":236},[25,26,248,249,250],"农业物联网","温室环境","小气候预测",{"code":252,"slug":253,"name":254,"path":255,"parent_code":34},"B7","protected-horticulture","设施园艺与植物工厂","\u002Ftopic\u002Fhorticulture\u002Fprotected-horticulture",[257,262],{"code":258,"slug":259,"name":260,"path":261,"parent_code":126},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},{"status":37,"locked":14,"version":38,"method":39,"evidence":264},{"candidates":265,"agricultural_evidence":44},[266,272,278,285,289,293,300],{"code":258,"score":139,"supported":44,"title":267,"abstract":269,"tags":271},[268],"IoT",[270,268],"物联网",[248],{"code":252,"score":43,"supported":44,"title":273,"abstract":275,"tags":277},[274],"greenhouse",[276,274],"温室",[],{"code":30,"score":204,"supported":44,"title":279,"abstract":280,"tags":284},[],[48,281,282,46,283],"辣椒","黄瓜","cucumber",[],{"code":58,"score":19,"supported":44,"title":286,"abstract":287,"tags":288},[],[146,147],[25],{"code":51,"score":19,"supported":44,"title":290,"abstract":291,"tags":292},[],[136,133],[26],{"code":294,"score":101,"supported":44,"title":295,"abstract":296,"tags":299},"H2",[],[297,298],"土壤","soil",[],{"code":301,"score":101,"supported":44,"title":302,"abstract":303,"tags":306},"H3",[],[304,305],"灌溉","irrigation",[],[308,309],"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":9,"openalex_id":312,"authors":313,"venue":239,"cited_by_count":66,"oa_url":9,"card":324,"direction":97,"ingested_from":98},"W7222909837",[314,316,318,320,322],{"name":315,"orcid":9},"Siman Emmanuel*",{"name":317,"orcid":9},"Edward Onyebueke Agu",{"name":319,"orcid":9},"Musa Gani",{"name":321,"orcid":9},"Oladunjoye John Abiodun",{"name":323,"orcid":9},"Anagu Emmanuel John",{"tldr":325,"method":326,"finding":327,"direction":97,"opportunity":328},"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":331,"title":332,"url":333,"summary":334,"summary_zh":9,"content":9,"source_name":335,"source_url":9,"published_at":336,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":337,"score_detail":338,"sources":341,"tags":343,"primary_topic":346,"secondary_topics":347,"classification":348,"search_phrases":376,"slug":379,"view_count":368,"doi":380,"paper":381,"created_at":389},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":113,"depth":18,"authority":339,"freshness":186,"relevant":20,"comment":340},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。",[342],{"name":335,"url":333},[25,26,48,344,345],"病害识别","轻量级模型",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},[],{"status":37,"locked":14,"version":38,"method":39,"evidence":349},{"candidates":350,"agricultural_evidence":44},[351,355,361,367,372],{"code":30,"score":17,"supported":44,"title":352,"abstract":353,"tags":354},[48],[48],[48],{"code":356,"score":115,"supported":44,"title":357,"abstract":358,"tags":360},"A3",[],[359],"玉米",[],{"code":362,"score":115,"supported":44,"title":363,"abstract":364,"tags":366},"A5",[],[365],"马铃薯",[],{"code":51,"score":368,"supported":14,"title":369,"abstract":370,"tags":371},2,[],[],[26,344],{"code":58,"score":20,"supported":14,"title":373,"abstract":374,"tags":375},[],[],[25],[377,378],"MambaSeed 番茄叶病","MambaVisionMixer 病害识别","MambaSeed番茄叶病-5301","10.1371\u002Fjournal.pone.0359350",{"doi":380,"openalex_id":9,"authors":382,"venue":9,"cited_by_count":66,"oa_url":9,"card":383,"direction":95,"ingested_from":388},[],{"tldr":384,"method":385,"finding":386,"direction":95,"opportunity":387},"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。","agent","2026-10-19T00:07:48.589614Z",{"id":391,"title":392,"url":393,"summary":394,"summary_zh":395,"content":9,"source_name":396,"source_url":393,"published_at":397,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":398,"score_detail":399,"sources":402,"tags":404,"primary_topic":406,"secondary_topics":407,"classification":409,"search_phrases":425,"slug":428,"view_count":20,"doi":429,"paper":430,"created_at":453},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":186,"substance":400,"depth":243,"authority":17,"freshness":204,"relevant":20,"comment":401},20,"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。",[403],{"name":396,"url":393},[25,26,48,192,405],"自动采摘",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},[408],{"code":51,"slug":123,"name":124,"path":125,"parent_code":126},{"status":37,"locked":14,"version":38,"method":39,"evidence":410},{"candidates":411,"agricultural_evidence":44},[412,416,421],{"code":30,"score":139,"supported":44,"title":413,"abstract":414,"tags":415},[46],[48,46],[48],{"code":51,"score":186,"supported":44,"title":417,"abstract":418,"tags":420},[],[419,134],"计算机视觉",[26,192],{"code":58,"score":20,"supported":14,"title":422,"abstract":423,"tags":424},[],[],[25],[426,427],"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":429,"openalex_id":431,"authors":432,"venue":396,"cited_by_count":66,"oa_url":447,"card":448,"direction":97,"ingested_from":98},"W7220840158",[433,435,437,439,442,444],{"name":434,"orcid":9},"Rut Yatigul",{"name":436,"orcid":9},"Wisanu Jitviriya",{"name":438,"orcid":9},"Eiji Hayashi",{"name":440,"orcid":441},"Aran Blattler","https:\u002F\u002Forcid.org\u002F0000-0002-8684-6239",{"name":443,"orcid":9},"Watcharin Tangsuksant",{"name":445,"orcid":446},"Teppakorn Sittiwanchai","https:\u002F\u002Forcid.org\u002F0000-0001-9653-8750","https:\u002F\u002Fph01.tci-thaijo.org\u002Findex.php\u002Feasr\u002Farticle\u002Fdownload\u002F266223\u002F176569",{"tldr":449,"method":450,"finding":451,"direction":95,"opportunity":452},"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":455,"title":456,"url":457,"summary":458,"summary_zh":9,"content":459,"source_name":460,"source_url":9,"published_at":461,"category":462,"cover_url":9,"hotness":13,"is_selected":14,"score":463,"score_detail":464,"sources":466,"tags":468,"primary_topic":472,"secondary_topics":473,"classification":475,"search_phrases":495,"slug":498,"view_count":368,"doi":9,"paper":9,"created_at":499},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":113,"substance":242,"depth":243,"authority":43,"freshness":101,"relevant":20,"comment":465},"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。",[467],{"name":460,"url":457},[25,26,469,470,471],"作物生长模型","设施番茄","温室环控",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},[474],{"code":252,"slug":253,"name":254,"path":255,"parent_code":34},{"status":37,"locked":14,"version":38,"method":39,"evidence":476},{"candidates":477,"agricultural_evidence":44},[478,482,487,491],{"code":30,"score":17,"supported":44,"title":479,"abstract":480,"tags":481},[48],[48],[470],{"code":252,"score":483,"supported":44,"title":484,"abstract":485,"tags":486},11,[276],[276],[],{"code":51,"score":368,"supported":14,"title":488,"abstract":489,"tags":490},[],[],[469,26],{"code":58,"score":20,"supported":14,"title":492,"abstract":493,"tags":494},[],[],[25],[496,497],"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"]