[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3473":3,"related-3473":97},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":24,"tags":26,"primary_topic":32,"secondary_topics":38,"classification":39,"search_phrases":63,"slug":66,"view_count":67,"doi":68,"paper":69,"created_at":96},3473,"TB-YOLOv8n: an improved YOLOv8n-based model for tip-burn detection in plant factory-grown pakchoi under LED lighting","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffpls.2026.1933084","Introduction Plant factory-grown pakchoi is prone to tip-burn under high-density cultivation, limited airflow, and rapid growth conditions, which reduces leaf integrity and commercial quality. Image-based detection of small or mild visible tip-burn symptoms remains challenging because the affected regions are usually small, irregularly shaped, low in color contrast, and easily disturbed by red-blue LED lighting, leaf overlap, curled leaf margins, and heart-leaf occlusion. Methods To address these challenges, this study constructed a pakchoi tip-burn image dataset under plant factory conditions and proposed TB-YOLOv8n, a lightweight detection model based on YOLOv8n. AFGCAttention was introduced at the end of the backbone to enhance informative channel responses related to subtle tip-burn regions and suppress redundant background information. GLSA was embedded between the backbone and neck to integrate local texture details with global spatial context under leaf overlap and low-contrast symptom conditions. In addition, a P2-enhanced BiFPN neck was constructed to strengthen cross-scale feature fusion and preserve fine spatial information for small visible tip-burn regions. Results Experimental results showed that TB-YOLOv8n achieved a precision of 89.4%, a recall of 87.6%, an mAP@0.5 of 93.2% ± 0.3%, and an mAP@0.5–0.95 of 59.0% ± 0.3%, which were 4.2, 3.9, 3.4, and 10.0 percentage points higher than those of the original YOLOv8n, respectively. The model contained only 2.2 M parameters, required 7.6 G FLOPs, had a model size of 4.8 MB, and achieved a model-forward inference speed of 138 FPS on the RTX 4090 and 33.0 FPS on the Jetson AGX Orin. Ablation experiments, heatmap visualization, and comparisons with mainstream detection models further confirmed the effectiveness of the proposed improvements. Discussion The results indicate that TB-YOLOv8n achieves a favorable balance among detection accuracy, lightweight structure, and real-time inference capability. The proposed model provides a basis for visible tip-burn monitoring and subsequent production decision support in plant factories.","引言 植物工厂栽培的小白菜在高密度种植、气流受限和快速生长条件下易发生焦边，导致叶片完整性和商品品质下降。基于图像的检测方法在识别微小或轻微可见的焦边症状时仍面临挑战，因为受害区域通常面积小、形状不规则、颜色对比度低，且易受红蓝LED光照、叶片重叠、叶缘卷曲和心叶遮挡的干扰。方法 为解决上述问题，本研究构建了植物工厂条件下的小白菜焦边图像数据集，并提出了TB-YOLOv8n，一种基于YOLOv8n的轻量化检测模型。在主干网络末端引入AFGCAttention，以增强与细微焦边区域相关的信息性通道响应并抑制冗余背景信息。在主干网络与颈部网络之间嵌入GLSA，以在叶片重叠和低对比度症状条件下整合局部纹理细节与全局空间上下文。此外，构建了P2增强的BiFPN颈部网络，以加强跨尺度特征融合并保留小面积可见焦边区域的精细空间信息。结果 实验结果表明，TB-YOLOv8n的精确率为89.4%，召回率为87.6%，mAP@0.5为93.2% ± 0.3%，mAP@0.5–0.95为59.0% ± 0.3%，分别比原始YOLOv8n提高了4.2、3.9、3.4和10.0个百分点。该模型仅含2.2 M参数，需要7.6 G FLOPs，模型大小为4.8 MB，在RTX 4090上的模型前向推理速度为138 FPS，在Jetson AGX Orin上为33.0 FPS。消融实验、热力图可视化以及与主流检测模型的比较进一步证实了所提改进的有效性。讨论 结果表明，TB-YOLOv8n在检测精度、轻量化结构和实时推理能力之间取得了良好平衡。所提模型为植物工厂中可见焦边监测及后续生产决策支持提供了依据。",null,"Frontiers in Plant Science","2026-09-23T00:00:00Z","论文",10,false,78,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},15,22,18,14,9,1,"面向植物工厂小白菜干烧心的轻量化检测模型，方法新颖、指标扎实且可实时部署，对设施蔬菜智能监测有实用参考价值。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","农业人工智能","植物工厂","病害检测","小白菜",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"B7","protected-horticulture","设施园艺与植物工厂","\u002Ftopic\u002Fhorticulture\u002Fprotected-horticulture","B",[],{"status":40,"locked":14,"version":41,"method":42,"evidence":43},"classified","2026-10-22.2","rules",{"candidates":44,"agricultural_evidence":46},[45,51,58],{"code":33,"score":17,"supported":46,"title":47,"abstract":49,"tags":50},true,[48],"plant factory",[29,48],[29],{"code":52,"score":53,"supported":46,"title":54,"abstract":55,"tags":57},"I2",4,[],[56],"decision support",[28],{"code":59,"score":22,"supported":14,"title":60,"abstract":61,"tags":62},"I1",[],[],[27],[64,65],"植物工厂 小白菜 干烧心 检测","TB-YOLOv8n 干烧心 识别","植物工厂小白菜干烧心检测-3473",3,"10.3389\u002Ffpls.2026.1933084",{"doi":68,"openalex_id":70,"authors":71,"venue":10,"cited_by_count":86,"oa_url":87,"card":88,"direction":94,"ingested_from":95},"W7214074852",[72,75,77,79,81,83],{"name":73,"orcid":74},"Peng Wang","https:\u002F\u002Forcid.org\u002F0000-0002-4589-0137",{"name":76,"orcid":9},"Wenwen Hu",{"name":78,"orcid":9},"Xiang Ma",{"name":80,"orcid":9},"Shipu Xu",{"name":82,"orcid":9},"Zhiwen Zhou",{"name":84,"orcid":85},"Yunsheng Wang","https:\u002F\u002Forcid.org\u002F0000-0002-0348-4608",0,"https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fplant-science\u002Farticles\u002F10.3389\u002Ffpls.2026.1933084\u002Fpdf",{"tldr":89,"method":90,"finding":91,"direction":92,"opportunity":93},"提出轻量模型TB-YOLOv8n，在植物工厂LED光下检测小白菜干烧心。","构建植物工厂小白菜干烧心图像数据集，改进YOLOv8n加入AFGCAttenti","mAP@0.5达93.2%，较YOLOv8n提升3.4个百分点，仅2.2M参数、138FPS。","农业人工智能与决策模型","可延伸至多作物苗期胁迫早期检测，结合时序图像与决策模型实现预警。","智慧农业 \u002F 农业物联网","openalex","2026-09-25T23:30:11.141664Z",{"total":98,"page":22,"page_size":98,"items":99},6,[100,183,281,361,407,490],{"id":101,"title":102,"url":103,"summary":104,"summary_zh":105,"content":9,"source_name":106,"source_url":103,"published_at":107,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":108,"score_detail":109,"sources":112,"tags":114,"primary_topic":117,"secondary_topics":122,"classification":130,"search_phrases":157,"slug":160,"view_count":161,"doi":162,"paper":163,"created_at":182},1599,"Continual Learning Model Evaluation for Hyperspectral-Based Tomato Bacterial Spot Detection in Greenhouse","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.atech.2026.102535","Detection of tomato bacterial spot (TBS) at canopy level is critical in greenhouse seedling production, yet most previous hyperspectral studies have been conducted at leaf scale under tightly controlled conditions. This study evaluated a domain-incremental continual-learning framework for automated canopy-scale TBS detection using hyperspectral sensing across five greenhouse experiments. Three spectral feature sets were evaluated, and Full-memory and Bounded-memory Gradient Episodic Memory multilayer perceptrons (GEM-MLP) were compared with Sequential and Joint-training MLP models under two conditions: a future unseen-experiment generalization test (Scenario 1) and a same-distribution benchmark (Scenario 2). Genetic-algorithm feature selection reduced input dimensionality but did not identify a single feature subset that was stable across all folds and scenarios. Under Scenario 1, Full-memory GEM-MLP using the fixed 18-VI feature set achieved 58.1% asymptomatic recall and 81.2% symptomatic recall. Its clearest advantage was knowledge retention: backward transfer (BWT) was -1.4%, compared with -7.2% for Sequential MLP, while forward transfer was similar (7.4% versus 7.0%). Under Scenario 2, Full-memory GEM-MLP achieved 46.5% asymptomatic recall and 86.2% symptomatic recall, while Joint-training MLP was highly competitive and reached the highest mean asymptomatic recall (53.4%). Full-memory GEM-MLP again showed better retention, with BWT of +1.8% compared with -3.2% for Sequential MLP. These results indicate that GEM did not provide universally superior classification performance, but it consistently reduced forgetting during sequential learning. The persistent difficulty of asymptomatic detection, even when all experimental domains were represented during model development, further suggests that early canopy-scale disease detection is limited by factors beyond cross-experiment distribution shift.","在温室育苗生产中，冠层尺度上的番茄细菌性斑点病（TBS）检测至关重要，然而以往大多数高光谱研究都是在严格控制条件下于叶片尺度进行的。本研究评估了一种基于域增量持续学习框架的自动化冠层尺度TBS检测方法，该方法利用跨五个温室实验的高光谱传感数据。研究评估了三组光谱特征集，并在两种条件下将全记忆与有界记忆梯度情景记忆多层感知器（GEM-MLP）与顺序训练和联合训练MLP模型进行了比较：一种是对未来未见实验的泛化测试（情景1），另一种是同分布基准测试（情景2）。遗传算法特征选择降低了输入维度，但未能识别出在所有折和情景下均稳定的单一特征子集。在情景1下，使用固定18-VI特征集的全记忆GEM-MLP实现了58.1%的无症状召回率和81.2%的有症状召回率。其最显著的优势在于知识保持能力：后向迁移（BWT）为-1.4%，而顺序MLP为-7.2%，同时前向迁移相近（7.4%对7.0%）。在情景2下，全记忆GEM-MLP实现了46.5%的无症状召回率和86.2%的有症状召回率，而联合训练MLP表现极具竞争力，达到了最高的平均无症状召回率（53.4%）。全记忆GEM-MLP再次展现出更好的保持能力，其BWT为+1.8%，而顺序MLP为-3.2%。这些结果表明，GEM并未提供普遍优越的分类性能，但在顺序学习过程中始终减少了遗忘。即使在模型开发阶段涵盖了所有实验域，无症状检测仍持续存在困难，这进一步表明早期冠层尺度病害检测受到跨实验分布偏移之外因素的限制。","Smart Agricultural Technology","2026-09-01T00:00:00Z",64,{"impact":110,"substance":19,"depth":19,"authority":110,"freshness":53,"relevant":22,"comment":111},12,"研究评估持续学习模型在温室番茄细菌性斑点病高光谱检测中的应用，虽为细分领域进展，但方法新颖且数据详实，对农业AI落地有参考价值。",[113],{"name":106,"url":103},[27,28,115,116,30],"番茄","遥感",{"code":118,"slug":119,"name":120,"path":121,"parent_code":37},"B1","vegetables","蔬菜","\u002Ftopic\u002Fhorticulture\u002Fvegetables",[123,124],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"code":125,"slug":126,"name":127,"path":128,"parent_code":129},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",{"status":40,"locked":14,"version":41,"method":42,"evidence":131},{"candidates":132,"agricultural_evidence":46},[133,138,144,149,153],{"code":118,"score":17,"supported":46,"title":134,"abstract":136,"tags":137},[135],"tomato",[115,135],[115],{"code":33,"score":20,"supported":46,"title":139,"abstract":141,"tags":143},[140],"greenhouse",[142,140],"温室",[],{"code":125,"score":110,"supported":46,"title":145,"abstract":147,"tags":148},[146],"hyperspectral",[146],[116],{"code":59,"score":22,"supported":14,"title":150,"abstract":151,"tags":152},[],[],[27],{"code":52,"score":22,"supported":14,"title":154,"abstract":155,"tags":156},[],[],[28],[158,159],"农业人工智能 智慧农业 病害检测 番茄","农业人工智能 智慧农业","农业人工智能智慧农业病害检测番茄-1599",2,"10.1016\u002Fj.atech.2026.102535",{"doi":162,"openalex_id":164,"authors":165,"venue":106,"cited_by_count":86,"oa_url":103,"card":177,"direction":94,"ingested_from":95},"W7207729517",[166,168,170,173,175],{"name":167,"orcid":9},"Gaoshoutong Si",{"name":169,"orcid":9},"Peter Ling",{"name":171,"orcid":172},"Anna L. Testen","https:\u002F\u002Forcid.org\u002F0000-0002-6504-9599",{"name":174,"orcid":9},"Heping Zhu",{"name":176,"orcid":9},"Hongyoung Jeon",{"tldr":178,"method":179,"finding":180,"direction":94,"opportunity":181},"评估持续学习框架用于温室高光谱番茄细菌性斑点病冠层检测。","域增量持续学习，GEM-MLP与顺序\u002F联合训练MLP对比，遗传算法特征选择。","GEM-MLP减少遗忘但分类性能非最优，无症状检测仍困难。","可探索结合多模态数据或更先进持续学习方法，提升早期无症状病害检测精度。","2026-09-04T23:30:04.578607Z",{"id":184,"title":185,"url":186,"summary":187,"summary_zh":188,"content":9,"source_name":189,"source_url":186,"published_at":190,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":191,"score_detail":192,"sources":197,"tags":199,"primary_topic":203,"secondary_topics":204,"classification":211,"search_phrases":258,"slug":261,"view_count":86,"doi":9,"paper":262,"created_at":280},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":110,"substance":193,"depth":194,"authority":21,"freshness":195,"relevant":22,"comment":196},21,17,8,"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。",[198],{"name":189,"url":186},[27,28,200,201,202],"农业物联网","温室环境","小气候预测",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[205,210],{"code":206,"slug":207,"name":208,"path":209,"parent_code":129},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",{"code":118,"slug":119,"name":120,"path":121,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":212},{"candidates":213,"agricultural_evidence":46},[214,220,224,231,238,244,251],{"code":206,"score":17,"supported":46,"title":215,"abstract":217,"tags":219},[216],"IoT",[218,216],"物联网",[200],{"code":33,"score":20,"supported":46,"title":221,"abstract":222,"tags":223},[140],[142,140],[],{"code":118,"score":21,"supported":46,"title":225,"abstract":226,"tags":230},[],[115,227,228,135,229],"辣椒","黄瓜","cucumber",[],{"code":59,"score":232,"supported":46,"title":233,"abstract":234,"tags":237},7,[],[235,236],"精准农业","precision agriculture",[27],{"code":52,"score":232,"supported":46,"title":239,"abstract":240,"tags":243},[],[241,242],"深度学习","deep learning",[28],{"code":245,"score":98,"supported":46,"title":246,"abstract":247,"tags":250},"H2",[],[248,249],"土壤","soil",[],{"code":252,"score":98,"supported":46,"title":253,"abstract":254,"tags":257},"H3",[],[255,256],"灌溉","irrigation",[],[259,260],"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":263,"authors":264,"venue":189,"cited_by_count":86,"oa_url":9,"card":275,"direction":94,"ingested_from":95},"W7222909837",[265,267,269,271,273],{"name":266,"orcid":9},"Siman Emmanuel*",{"name":268,"orcid":9},"Edward Onyebueke Agu",{"name":270,"orcid":9},"Musa Gani",{"name":272,"orcid":9},"Oladunjoye John Abiodun",{"name":274,"orcid":9},"Anagu Emmanuel John",{"tldr":276,"method":277,"finding":278,"direction":94,"opportunity":279},"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":282,"title":283,"url":284,"summary":285,"summary_zh":286,"content":9,"source_name":287,"source_url":284,"published_at":288,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":289,"score_detail":290,"sources":294,"tags":296,"primary_topic":299,"secondary_topics":300,"classification":306,"search_phrases":335,"slug":338,"view_count":53,"doi":339,"paper":340,"created_at":360},4569,"SMART GREENHOUSE MEASUREMENT AND MONITORING SYSTEM BASED ON IOT AND COMPUTER VISION","https:\u002F\u002Fdoi.org\u002F10.46242\u002Fjstiuh.v83i05.6189","This paper presents a smart greenhouse measurement and monitoring system integrating IoT and computer vision for non-invasive plant growth observation and remote supervision. The proposed architecture combines environmental sensors, STM32-based data acquisition and control modules, a Raspberry Pi 4 with a USB camera, a lightweight deep learning-based stem segmentation module, Firebase Realtime Database, and a web application for monitoring and control. In the computer vision module, tomato plant images are processed directly on the Raspberry Pi to segment stem regions and estimate plant height from calibrated image measurements, enabling edge-based phenotypic monitoring without destructive sampling or manual measurement. Experimental results on a tomato cultivation prototype show that the system can collect temperature, air humidity, soil moisture, and light intensity data in real time while providing automated plant height estimation from RGB images. During the reported period, temperature ranged from 27.8°C to 38.8°C, air humidity from 40.2% to 77.0%, soil moisture from 52.0% to 82.4%, and plant height increased from 65.8 cm to 77.0 cm. The segmentation results demonstrate the feasibility of extracting stem-level structural information under practical greenhouse conditions, including complex background, leaf occlusion, and non-uniform illumination. The prototype confirms the potential of a low-cost smart greenhouse monitoring system and provides a practical basis for future expansion toward richer agronomic indicators, such as stem width, leaf area, canopy coverage, growth rate estimation, and automated environmental control.","本文提出了一种集成物联网与计算机视觉的智能温室测量与监测系统，用于非侵入式植物生长观测与远程监管。所提出的架构结合了环境传感器、基于STM32的数据采集与控制模块、配备USB摄像头的Raspberry Pi 4、基于轻量级深度学习的茎秆分割模块、Firebase实时数据库以及用于监测与控制的Web应用程序。在计算机视觉模块中，番茄植株图像直接在Raspberry Pi上处理，以分割茎秆区域并根据标定后的图像测量值估算株高，从而实现基于边缘的表型监测，无需破坏性采样或人工测量。在番茄栽培原型上的实验结果表明，该系统能够实时采集温度、空气湿度、土壤湿度和光照强度数据，同时基于RGB图像提供自动株高估算。在报告期内，温度范围为27.8°C至38.8°C，空气湿度为40.2%至77.0%，土壤湿度为52.0%至82.4%，株高从65.8 cm增长至77.0 cm。分割结果证明了在实际温室条件下（包括复杂背景、叶片遮挡和非均匀光照）提取茎秆级结构信息的可行性。该原型验证了低成本智能温室监测系统的潜力，并为未来扩展至更丰富的农艺指标（如茎宽、叶面积、冠层覆盖度、生长速率估算和自动化环境控制）提供了实践基础。","Journal of Science and Technology - IUH","2026-10-08T00:00:00Z",66,{"impact":110,"substance":291,"depth":292,"authority":13,"freshness":195,"relevant":22,"comment":293},20,16,"低成本IoT与边缘视觉融合的温室监测原型，方法可行、数据翔实，但属细分技术验证，产业影响有限。",[295],{"name":287,"url":284},[27,28,297,200,298],"计算机视觉","温室种植",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[301,305],{"code":52,"slug":302,"name":303,"path":304,"parent_code":129},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"code":206,"slug":207,"name":208,"path":209,"parent_code":129},{"status":40,"locked":14,"version":41,"method":42,"evidence":307},{"candidates":308,"agricultural_evidence":46},[309,315,319,323,327,331],{"code":52,"score":310,"supported":46,"title":311,"abstract":313,"tags":314},19,[312],"computer vision",[241,297,242,312],[28,297],{"code":33,"score":17,"supported":46,"title":316,"abstract":317,"tags":318},[140],[142,140],[298],{"code":206,"score":17,"supported":46,"title":320,"abstract":321,"tags":322},[216],[218,216],[200],{"code":118,"score":98,"supported":46,"title":324,"abstract":325,"tags":326},[],[115,135],[],{"code":245,"score":98,"supported":46,"title":328,"abstract":329,"tags":330},[],[248,249],[],{"code":59,"score":22,"supported":14,"title":332,"abstract":333,"tags":334},[],[],[27],[336,337],"Raspberry Pi 温室监测","番茄 株高 图像识别","RaspberryPi温室监测-4569","10.46242\u002Fjstiuh.v83i05.6189",{"doi":339,"openalex_id":341,"authors":342,"venue":287,"cited_by_count":86,"oa_url":354,"card":355,"direction":94,"ingested_from":95},"W7221046739",[343,345,347,349,351],{"name":344,"orcid":9},"HOANG THI TU UYEN",{"name":346,"orcid":9},"NGUYEN HOANH",{"name":348,"orcid":9},"NGO THANH QUYEN",{"name":350,"orcid":9},"Lam Binh Minh",{"name":352,"orcid":353},"Nguyễn Trung Nhân","https:\u002F\u002Forcid.org\u002F0000-0001-9328-7693","https:\u002F\u002Fjst.iuh.edu.vn\u002Findex.php\u002Fjst-iuh\u002Farticle\u002Fdownload\u002F6189\u002F1131",{"tldr":356,"method":357,"finding":358,"direction":94,"opportunity":359},"集成物联网与计算机视觉的智能温室系统，实现非破坏性植物生长监测与远程管控。","环境传感器、STM32、树莓派4、USB摄像头、轻量茎分割模型、Firebase","原型可实时采集温湿度、土壤水分和光照，并从RGB图像估计株高，验证了边缘表型监测可行性。","可扩展至茎宽、叶面积、冠层覆盖度等更丰富农艺指标，并实现自动化环境控制闭环。","2026-10-10T23:30:11.420750Z",{"id":362,"title":363,"url":364,"summary":365,"summary_zh":9,"content":366,"source_name":367,"source_url":9,"published_at":368,"category":369,"cover_url":9,"hotness":13,"is_selected":14,"score":370,"score_detail":371,"sources":373,"tags":375,"primary_topic":379,"secondary_topics":380,"classification":382,"search_phrases":402,"slug":405,"view_count":161,"doi":9,"paper":9,"created_at":406},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) | 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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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[更多>>](https:\u002F\u002Fblog.sciencenet.cn\u002Fblog.php?mod=recommend)\n\n[关于我们](http:\u002F\u002Fwww.sciencenet.cn\u002Faboutus.aspx) | [网站声明](http:\u002F\u002Fwww.sciencenet.cn\u002Fshengming.aspx) | [服务条款](http:\u002F\u002Fwww.sciencenet.cn\u002Ftiaokuang.aspx) | [联系方式](http:\u002F\u002Fwww.sciencenet.cn\u002Fcontact.aspx) | [举报](https:\u002F\u002Fwww.sciencenet.cn\u002Fjubao.aspx) | [中国科学报社](https:\u002F\u002Fstimes.sciencenet.cn\u002F)  \n[京ICP备07017567号-12](http:\u002F\u002Fwww.beian.miit.gov.cn\u002F)互联网新闻信息服务许可证10120230008 京公网安备 11010802032783  \nCopyright @ 2007-2026 中国科学报社 All Rights Reserved  \n地址：北京市海淀区中关村南一条乙三号 电话：010-62580783","科学网 中国农业科学院 2026-10-04","2026-10-04T00:00:00Z","报道",80,{"impact":18,"substance":193,"depth":194,"authority":20,"freshness":98,"relevant":22,"comment":372},"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。",[374],{"name":367,"url":364},[27,28,376,377,378],"作物生长模型","设施番茄","温室环控",{"code":118,"slug":119,"name":120,"path":121,"parent_code":37},[381],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":383},{"candidates":384,"agricultural_evidence":46},[385,389,394,398],{"code":118,"score":110,"supported":46,"title":386,"abstract":387,"tags":388},[115],[115],[377],{"code":33,"score":390,"supported":46,"title":391,"abstract":392,"tags":393},11,[142],[142],[],{"code":52,"score":161,"supported":14,"title":395,"abstract":396,"tags":397},[],[],[376,28],{"code":59,"score":22,"supported":14,"title":399,"abstract":400,"tags":401},[],[],[27],[403,404],"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",{"id":408,"title":409,"url":410,"summary":411,"summary_zh":412,"content":9,"source_name":413,"source_url":410,"published_at":414,"category":12,"cover_url":9,"hotness":13,"is_selected":46,"score":415,"score_detail":416,"sources":418,"tags":420,"primary_topic":422,"secondary_topics":423,"classification":428,"search_phrases":462,"slug":465,"view_count":22,"doi":466,"paper":467,"created_at":489},4371,"Intelligent greenhouse irrigation control via multilayer perceptron neural network climate prediction integrated with fuzzy logic control","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compeleceng.2026.111587","Conventional irrigation methods rely on indoor sensor feedback, which fails to anticipate rapid microclimate fluctuations and often leads to over-irrigation or water stress. Additionally, traditional controllers used in the watering process lack the ability to adjust water flow rate dynamically, resulting in inefficient water usage and energy waste. Intelligent Greenhouse Irrigation Management (IGIM) remains a critical challenge for achieving sustainable, high-yield greenhouse agriculture. Precise irrigation management is especially important for crops like tomatoes, where both timing and quantity directly influence productivity and energy consumption. To address these challenges, this paper proposes a hybrid intelligent system that integrates a Multi-Layer Perceptron Artificial Neural Network (MLP-ANN) with an Adaptive Fuzzy Logic Controller (AFLC). The MLP-ANN forecasts key indoor variables such as temperature, humidity, and soil moisture, achieving R² values of 0.999. The Root Mean Square Errors of 0.266 °C, 0.254%, and 0.808%, respectively. These predictions directly feed into the AFLC, which employs an adaptive rule base to dynamically adjust irrigation flow rates in real time (0–18 LPH), significantly enhancing water-use efficiency and reducing waste to save energy consumption in the watering process. The key novelty of this work is the direct integration of MLP-predicted microclimate variables into the antecedent of adaptive fuzzy rules, enabling the controller to anticipate and respond to climate variations before they affect crop conditions. Unlike conventional reactive or static rule-based systems, the proposed MLP-AFLC framework creates a closed-loop coupling between forecasting and actuation, allowing predictive, rather than threshold-based, irrigation decisions. By integrating predictive intelligence with adaptive control, the proposed MLP-AFLC framework provides a robust, autonomous, and sustainable solution for greenhouse management, advancing precision agriculture in highly dynamic environments.","传统灌溉方法依赖室内传感器反馈，无法预判微气候的快速波动，常导致过度灌溉或水分胁迫。此外，灌溉过程中使用的传统控制器缺乏动态调节流速的能力，造成水资源利用效率低下和能源浪费。智能温室灌溉管理（IGIM）仍是实现可持续、高产温室农业的关键挑战。对于番茄等作物而言，精准灌溉管理尤为重要，因为灌溉时机和水量直接影响产量和能耗。为解决这些挑战，本文提出了一种将多层感知器人工神经网络（MLP-ANN）与自适应模糊逻辑控制器（AFLC）相集成的混合智能系统。MLP-ANN预测温度、湿度和土壤湿度等关键室内变量，R²值达到0.999，均方根误差分别为0.266 °C、0.254%和0.808%。这些预测结果直接输入AFLC，后者采用自适应规则库实时动态调节灌溉流速（0–18 LPH），显著提高水资源利用效率并减少浪费，从而在灌溉过程中节约能耗。本工作的关键创新在于将MLP预测的微气候变量直接集成到自适应模糊规则的前件中，使控制器能够在气候变化影响作物状况之前进行预判和响应。与传统反应式或静态规则系统不同，所提出的MLP-AFLC框架在预测与执行之间建立了闭环耦合，实现了基于预测而非基于阈值的灌溉决策。通过将预测智能与自适应控制相结合，所提出的MLP-AFLC框架为温室管理提供了一种稳健、自主且可持续的解决方案，推动了高度动态环境下的精准农业发展。","Computers & Electrical Engineering","2026-10-06T00:00:00Z",81,{"impact":19,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":417},"提出MLP-ANN与自适应模糊控制融合的温室预测性灌溉框架，R²达0.999，方法新颖且数据可靠，对精准农业节水节能有实质参考价值。",[419],{"name":413,"url":410},[27,28,421,235,298],"智能灌溉",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[424],{"code":252,"slug":256,"name":425,"path":426,"parent_code":427},"水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",{"status":40,"locked":14,"version":41,"method":42,"evidence":429},{"candidates":430,"agricultural_evidence":46},[431,436,440,444,448,452,458],{"code":252,"score":19,"supported":46,"title":432,"abstract":433,"tags":435},[256],[255,434,256],"水资源",[421],{"code":33,"score":17,"supported":46,"title":437,"abstract":438,"tags":439},[140],[142,140],[298],{"code":59,"score":195,"supported":46,"title":441,"abstract":442,"tags":443},[],[235,236],[27,235],{"code":245,"score":98,"supported":46,"title":445,"abstract":446,"tags":447},[],[248,249],[],{"code":118,"score":67,"supported":46,"title":449,"abstract":450,"tags":451},[],[115],[],{"code":453,"score":67,"supported":46,"title":454,"abstract":455,"tags":457},"H7",[],[456],"气候变化",[],{"code":52,"score":22,"supported":14,"title":459,"abstract":460,"tags":461},[],[],[28],[463,464],"MLP-AFLC 温室灌溉","神经网络 模糊控制 灌溉","MLP-AFLC温室灌溉-4371","10.1016\u002Fj.compeleceng.2026.111587",{"doi":466,"openalex_id":468,"authors":469,"venue":413,"cited_by_count":86,"oa_url":410,"card":484,"direction":92,"ingested_from":95},"W7220746994",[470,473,476,478,481],{"name":471,"orcid":472},"Jamel Riahi","https:\u002F\u002Forcid.org\u002F0009-0006-1434-4367",{"name":474,"orcid":475},"Hamza Nasri","https:\u002F\u002Forcid.org\u002F0009-0008-7343-4199",{"name":477,"orcid":9},"Abdelkader Mami",{"name":479,"orcid":480},"Mario Carpentieri","https:\u002F\u002Forcid.org\u002F0000-0001-5165-5873",{"name":482,"orcid":483},"Silvano Vergura","https:\u002F\u002Forcid.org\u002F0000-0002-0826-9237",{"tldr":485,"method":486,"finding":487,"direction":94,"opportunity":488},"提出MLP-ANN预测温室微气候并驱动自适应模糊控制器，实现预测性智能灌溉。","MLP神经网络预测温湿度与土壤水分，结合自适应模糊逻辑控制流量。","预测R²达0.999，模糊控制器动态调节0-18 LPH流量，显著节水节能。","可探索多作物多环境迁移学习与边缘部署，降低预测模型算力需求。","2026-10-07T23:30:43.848308Z",{"id":491,"title":492,"url":493,"summary":494,"summary_zh":9,"content":495,"source_name":496,"source_url":9,"published_at":368,"category":369,"cover_url":9,"hotness":13,"is_selected":14,"score":497,"score_detail":498,"sources":500,"tags":502,"primary_topic":506,"secondary_topics":507,"classification":509,"search_phrases":528,"slug":531,"view_count":22,"doi":9,"paper":9,"created_at":532},4259,"中国农科院构建知识与数据双驱动模型精准预测温室番茄生长","https:\u002F\u002Fwap.sciencenet.cn\u002Fmobile.php?cat=T&id=572492&mobile=1&type=detail","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提出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。成果发表在《Computers and Electronics in Agriculture》。","գйũҵѧԺũҵɳչоʩֲﻷ̴Ŷӹ֪ʶ˫ģͣʵҷĸ߾Ԥ⣬оɹڡũҵѧComputers and Electronics in Agricultureϡ\n\n׼ܿƵĺǰᡣȻͳģͲȡѡޣģͳȱԼԤʧ档ˣںϻʱɣʵָ߾Ԥǹؼս","科学网 2026-10-04",82,{"impact":18,"substance":193,"depth":194,"authority":20,"freshness":195,"relevant":22,"comment":499},"国家级科研机构在智慧种植领域提出知识数据双驱动模型，方法新颖、结论可靠，对温室作物精准管理有参考价值。",[501],{"name":496,"url":493},[27,28,503,504,505],"温室番茄","知识数据双驱动","生长预测",{"code":118,"slug":119,"name":120,"path":121,"parent_code":37},[508],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":510},{"candidates":511,"agricultural_evidence":46},[512,516,520,524],{"code":118,"score":390,"supported":46,"title":513,"abstract":514,"tags":515},[115],[115],[],{"code":33,"score":21,"supported":46,"title":517,"abstract":518,"tags":519},[142],[],[503],{"code":59,"score":22,"supported":14,"title":521,"abstract":522,"tags":523},[],[],[27],{"code":52,"score":22,"supported":14,"title":525,"abstract":526,"tags":527},[],[],[28],[529,530],"中国农科院 温室番茄 生长预测","知识数据双驱动 模型 番茄","中国农科院温室番茄生长预测-4259","2026-10-06T00:05:01.795653Z"]