[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1599":3,"related-1599":112},{"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":30,"secondary_topics":36,"classification":48,"search_phrases":83,"slug":86,"view_count":87,"doi":88,"paper":89,"created_at":111},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。",null,"Smart Agricultural Technology","2026-09-01T00:00:00Z","论文",10,false,64,{"impact":17,"substance":18,"depth":18,"authority":17,"freshness":19,"relevant":20,"comment":21},12,18,4,1,"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。",[23],{"name":10,"url":6},[25,26,27,28,29],"智慧农业","农业人工智能","番茄","遥感","病害检测",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},"B1","vegetables","蔬菜","\u002Ftopic\u002Fhorticulture\u002Fvegetables","B",[37,42],{"code":38,"slug":39,"name":40,"path":41,"parent_code":35},"B7","protected-horticulture","设施园艺与植物工厂","\u002Ftopic\u002Fhorticulture\u002Fprotected-horticulture",{"code":43,"slug":44,"name":45,"path":46,"parent_code":47},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",{"status":49,"locked":14,"version":50,"method":51,"evidence":52},"classified","2026-10-22.2","rules",{"candidates":53,"agricultural_evidence":56},[54,61,68,73,78],{"code":31,"score":55,"supported":56,"title":57,"abstract":59,"tags":60},15,true,[58],"tomato",[27,58],[27],{"code":38,"score":62,"supported":56,"title":63,"abstract":65,"tags":67},14,[64],"greenhouse",[66,64],"温室",[],{"code":43,"score":17,"supported":56,"title":69,"abstract":71,"tags":72},[70],"hyperspectral",[70],[28],{"code":74,"score":20,"supported":14,"title":75,"abstract":76,"tags":77},"I1",[],[],[25],{"code":79,"score":20,"supported":14,"title":80,"abstract":81,"tags":82},"I2",[],[],[26],[84,85],"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":88,"openalex_id":90,"authors":91,"venue":10,"cited_by_count":103,"oa_url":6,"card":104,"direction":108,"ingested_from":110},"W7207729517",[92,94,96,99,101],{"name":93,"orcid":9},"Gaoshoutong Si",{"name":95,"orcid":9},"Peter Ling",{"name":97,"orcid":98},"Anna L. Testen","https:\u002F\u002Forcid.org\u002F0000-0002-6504-9599",{"name":100,"orcid":9},"Heping Zhu",{"name":102,"orcid":9},"Hongyoung Jeon",0,{"tldr":105,"method":106,"finding":107,"direction":108,"opportunity":109},"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。","智慧农业 \u002F 农业物联网","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。","openalex","2026-09-04T23:30:04.578607Z",{"total":113,"page":20,"page_size":113,"items":114},6,[115,185,305,396,442,485],{"id":116,"title":117,"url":118,"summary":119,"summary_zh":120,"content":9,"source_name":121,"source_url":118,"published_at":122,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":123,"score_detail":124,"sources":128,"tags":130,"primary_topic":133,"secondary_topics":134,"classification":136,"search_phrases":156,"slug":159,"view_count":19,"doi":160,"paper":161,"created_at":184},2276,"PT-GNN: A physiological topology-aware graph neural network for multimodal early detection of cucumber downy mildew using hyperspectral and chlorophyll fluorescence sensing","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112433","PT-GNN: A physiological topology-aware graph neural network for multimodal early detection of cucumber downy mildew using hyperspectral and chlorophyll fluorescence sensing。Computers and Electronics in Agriculture","PT-GNN：一种生理拓扑感知图神经网络，利用高光谱和叶绿素荧光传感进行黄瓜霜霉病的多模态早期检测。","Computers and Electronics in Agriculture","2026-09-12T00:00:00Z",81,{"impact":18,"substance":125,"depth":18,"authority":62,"freshness":126,"relevant":20,"comment":127},22,9,"提出生理拓扑感知图神经网络融合高光谱与叶绿素荧光实现黄瓜霜霉病早期检测，方法新颖且发表于农业信息领域核心期刊，具备较强专业参考价值。",[129],{"name":121,"url":118},[25,26,131,29,132],"黄瓜","高光谱遥感",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[135],{"code":43,"slug":44,"name":45,"path":46,"parent_code":47},{"status":49,"locked":14,"version":50,"method":51,"evidence":137},{"candidates":138,"agricultural_evidence":56},[139,144,148,152],{"code":31,"score":55,"supported":56,"title":140,"abstract":142,"tags":143},[141],"cucumber",[131,141],[131],{"code":43,"score":17,"supported":56,"title":145,"abstract":146,"tags":147},[70],[70],[132],{"code":74,"score":20,"supported":14,"title":149,"abstract":150,"tags":151},[],[],[25],{"code":79,"score":20,"supported":14,"title":153,"abstract":154,"tags":155},[],[],[26],[157,158],"农业人工智能 高光谱遥感 智慧农业 病害检测","农业人工智能 高光谱遥感","农业人工智能高光谱遥感智慧农业病害检测-2276","10.1016\u002Fj.compag.2026.112433",{"doi":160,"openalex_id":162,"authors":163,"venue":121,"cited_by_count":103,"oa_url":9,"card":178,"direction":182,"ingested_from":110},"W7212310174",[164,167,169,171,173,175],{"name":165,"orcid":166},"Yibin Li","https:\u002F\u002Forcid.org\u002F0000-0002-5906-5074",{"name":168,"orcid":9},"Zonghuan Han",{"name":170,"orcid":9},"Yong Wang",{"name":172,"orcid":9},"Wei Gao",{"name":174,"orcid":9},"Yiding Zhang",{"name":176,"orcid":177},"Lingxian Zhang","https:\u002F\u002Forcid.org\u002F0000-0002-8665-7075",{"tldr":179,"method":180,"finding":181,"direction":182,"opportunity":183},"提出生理拓扑感知图神经网络PT-GNN，融合高光谱与叶绿素荧光实现黄瓜霜霉病早期检测。","构建生理拓扑图神经网络，融合高光谱与叶绿素荧光多模态传感数据。","PT-GNN能有效利用生理拓扑关系，提升黄瓜霜霉病早期检测精度。","农业遥感与作物表型","可探索生理拓扑图构建的通用性，迁移至其他作物病害及多模态传感器融合场景。","2026-09-13T23:30:01.409569Z",{"id":186,"title":187,"url":188,"summary":189,"summary_zh":190,"content":9,"source_name":191,"source_url":188,"published_at":192,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":193,"score_detail":194,"sources":196,"tags":198,"primary_topic":201,"secondary_topics":205,"classification":208,"search_phrases":286,"slug":288,"view_count":87,"doi":289,"paper":290,"created_at":304},1299,"Remote Sensing and Machine Learning for Futuristic Horticulture: A Comprehensive Review","https:\u002F\u002Fdoi.org\u002F10.25125\u002Fijoear-aug-2026-26","Horticultural crops—fruits, vegetables, plantation crops, flowers, spices and medicinal plants—form the nutritional and economic backbone of India's agricultural sector. However, they face persistent challenges of yield variability, water and nutrient inefficiency, pest and disease pressure and post-harvest losses. This review synthesises the transformative role of Remote Sensing (RS) and Machine Learning (ML) in enabling futuristic, sustainable and precision horticulture. The evolution of agricultural production systems from Agriculture 1.0 to Agriculture 5.0 is traced, followed by a comprehensive discussion of RS principles, elements, four fundamental resolutions (spatial, spectral, temporal, radiometric), and multi-platform architectures (satellite, aerial, UAV, ground-based). A parallel taxonomy of ML—supervised, semi-supervised, unsupervised, reinforcement and deep-learning frameworks—is presented, with emphasis on Random Forest, Support Vector Machine, XGBoost, and Convolutional Neural Network algorithms most widely applied to horticultural datasets. Five representative case studies are analysed in depth: (i) UAV multispectral + ensemble learning for leaf nitrogen monitoring in custard apple; (ii) hyperspectral + ANN \u002F RF \u002F DT models for mango and strawberry fruit-quality parameters; (iii) UAV-hyperspectral detection of citrus canker; (iv) machine-learning-based crop recommendation under NPK, pH and climatic variables; and (v) RS + GIS soil-site suitability for six major fruit crops in the Ganjigatti sub-watershed, Karnataka. National platforms such as CHAMAN, Bhoonidhi, Bhuvan and PMFBY are reviewed as operational demonstrations of the RS + ML value chain. Finally, technical, economic and ethical challenges—ground-truth scarcity, sensor cost, model interpretability, digital divide and data privacy—are discussed, and a way forward for Indian horticulture in the Agriculture 5.0 era is proposed.","园艺作物——包括水果、蔬菜、种植园作物、花卉、香料及药用植物——构成了印度农业部门营养与经济的基础。然而，它们持续面临产量波动、水资源与养分利用效率低下、病虫害压力以及采后损失等挑战。本综述综合探讨了遥感（Remote Sensing, RS）与机器学习（Machine Learning, ML）在推动未来化、可持续及精准园艺中的变革性作用。文章追溯了农业生产系统从农业1.0到农业5.0的演进历程，随后全面讨论了遥感的基本原理、要素、四种基本分辨率（空间、光谱、时间、辐射）以及多平台架构（卫星、航空、无人机、地面）。同时，提出了机器学习的并行分类体系——监督学习、半监督学习、无监督学习、强化学习及深度学习框架，并重点介绍了最广泛应用于园艺数据集的随机森林、支持向量机、XGBoost和卷积神经网络算法。文章深入分析了五个代表性案例研究：（i）基于无人机多光谱与集成学习对番荔枝叶片氮素监测；（ii）基于高光谱与人工神经网络\u002F随机森林\u002F决策树模型对芒果和草莓果实品质参数的评估；（iii）基于无人机高光谱的柑橘溃疡病检测；（iv）基于NPK、pH及气候变量的机器学习作物推荐系统；以及（v）基于遥感和地理信息系统（GIS）的卡纳塔克邦甘吉加蒂子流域六种主要果树土壤-立地适宜性评价。文章还回顾了CHAMAN、Bhoonidhi、Bhuvan和PMFBY等国家平台，作为遥感与机器学习价值链的运营示范。最后，讨论了技术、经济和伦理方面的挑战——包括地面真值稀缺、传感器成本、模型可解释性、数字鸿沟和数据隐私问题，并提出了印度园艺业在农业5.0时代的发展路径。","International Journal of Environmental and Agriculture Research","2026-08-31T00:00:00Z",74,{"impact":18,"substance":125,"depth":18,"authority":17,"freshness":19,"relevant":20,"comment":195},"综述全面，案例详实，对印度园艺精准农业有参考价值，但时效性一般。",[197],{"name":191,"url":188},[25,26,199,28,200],"机器学习","园艺",{"code":79,"slug":202,"name":203,"path":204,"parent_code":47},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",[206,207],{"code":43,"slug":44,"name":45,"path":46,"parent_code":47},{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},{"status":49,"locked":14,"version":50,"method":51,"evidence":209},{"candidates":210,"agricultural_evidence":56},[211,218,225,231,242,249,256,263,270,276,282],{"code":79,"score":212,"supported":56,"title":213,"abstract":215,"tags":217},19,[214],"machine learning",[216,199,214],"深度学习",[26,199],{"code":43,"score":18,"supported":56,"title":219,"abstract":221,"tags":224},[220],"remote sensing",[28,222,220,223,70],"无人机多光谱","multispectral",[28],{"code":31,"score":126,"supported":56,"title":226,"abstract":227,"tags":230},[],[33,228,229],"草莓","strawberry",[],{"code":232,"score":126,"supported":56,"title":233,"abstract":234,"tags":241},"B2",[],[235,236,237,238,239,240],"果树","柑橘","芒果","荔枝","apple","citrus",[],{"code":243,"score":113,"supported":56,"title":244,"abstract":245,"tags":248},"H2",[],[246,247],"土壤","soil",[],{"code":250,"score":113,"supported":56,"title":251,"abstract":252,"tags":255},"I4",[],[253,254],"地理信息","GIS",[],{"code":257,"score":113,"supported":56,"title":258,"abstract":259,"tags":262},"I6",[],[260,261],"数字鸿沟","digital divide",[],{"code":264,"score":265,"supported":56,"title":266,"abstract":267,"tags":269},"B4",3,[],[268],"花卉",[],{"code":271,"score":265,"supported":56,"title":272,"abstract":273,"tags":275},"B6",[],[274],"药用植物",[],{"code":277,"score":265,"supported":56,"title":278,"abstract":279,"tags":281},"H3",[],[280],"水资源",[],{"code":74,"score":20,"supported":14,"title":283,"abstract":284,"tags":285},[],[],[25],[287,85],"农业人工智能 智慧农业 机器学习 园艺","农业人工智能智慧农业机器学习园艺-1299","10.25125\u002Fijoear-aug-2026-26",{"doi":289,"openalex_id":291,"authors":292,"venue":191,"cited_by_count":103,"oa_url":298,"card":299,"direction":108,"ingested_from":110},"W7204829467",[293,296],{"name":294,"orcid":295},"Jadala Shankaraswamy","https:\u002F\u002Forcid.org\u002F0000-0002-5623-6384",{"name":297,"orcid":9},"Md Abdul Gaffor","https:\u002F\u002Fijoear.com\u002Fassets\u002Farticles_menuscripts\u002Ffile\u002FIJOEAR-AUG-2026-26.pdf",{"tldr":300,"method":301,"finding":302,"direction":182,"opportunity":303},"综述遥感与机器学习在印度园艺作物精准管理中的应用，涵盖原理、方法、案例与挑战。","系统综述遥感平台与机器学习算法，分析五个典型案例及国家平台。","遥感与机器学习结合可提升园艺作物养分、病虫害、适种性等管理精度，但面临成本与数据挑战。","可探索低成本、可解释的遥感-机器学习模型，并集成多源数据以解决小农园艺场景的适用性问题。","2026-09-01T23:30:09.105185Z",{"id":306,"title":307,"url":308,"summary":309,"summary_zh":310,"content":9,"source_name":311,"source_url":308,"published_at":312,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":313,"score_detail":314,"sources":319,"tags":321,"primary_topic":325,"secondary_topics":326,"classification":333,"search_phrases":373,"slug":376,"view_count":103,"doi":9,"paper":377,"created_at":395},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":17,"substance":315,"depth":316,"authority":126,"freshness":317,"relevant":20,"comment":318},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。",[320],{"name":311,"url":308},[25,26,322,323,324],"农业物联网","温室环境","小气候预测",{"code":38,"slug":39,"name":40,"path":41,"parent_code":35},[327,332],{"code":328,"slug":329,"name":330,"path":331,"parent_code":47},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},{"status":49,"locked":14,"version":50,"method":51,"evidence":334},{"candidates":335,"agricultural_evidence":56},[336,342,346,351,358,363,367],{"code":328,"score":55,"supported":56,"title":337,"abstract":339,"tags":341},[338],"IoT",[340,338],"物联网",[322],{"code":38,"score":62,"supported":56,"title":343,"abstract":344,"tags":345},[64],[66,64],[],{"code":31,"score":126,"supported":56,"title":347,"abstract":348,"tags":350},[],[27,349,131,58,141],"辣椒",[],{"code":74,"score":352,"supported":56,"title":353,"abstract":354,"tags":357},7,[],[355,356],"精准农业","precision agriculture",[25],{"code":79,"score":352,"supported":56,"title":359,"abstract":360,"tags":362},[],[216,361],"deep learning",[26],{"code":243,"score":113,"supported":56,"title":364,"abstract":365,"tags":366},[],[246,247],[],{"code":277,"score":113,"supported":56,"title":368,"abstract":369,"tags":372},[],[370,371],"灌溉","irrigation",[],[374,375],"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":378,"authors":379,"venue":311,"cited_by_count":103,"oa_url":9,"card":390,"direction":108,"ingested_from":110},"W7222909837",[380,382,384,386,388],{"name":381,"orcid":9},"Siman Emmanuel*",{"name":383,"orcid":9},"Edward Onyebueke Agu",{"name":385,"orcid":9},"Musa Gani",{"name":387,"orcid":9},"Oladunjoye John Abiodun",{"name":389,"orcid":9},"Anagu Emmanuel John",{"tldr":391,"method":392,"finding":393,"direction":108,"opportunity":394},"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":397,"title":398,"url":399,"summary":400,"summary_zh":9,"content":401,"source_name":402,"source_url":9,"published_at":403,"category":404,"cover_url":9,"hotness":13,"is_selected":14,"score":405,"score_detail":406,"sources":408,"tags":410,"primary_topic":414,"secondary_topics":415,"classification":417,"search_phrases":437,"slug":440,"view_count":87,"doi":9,"paper":9,"created_at":441},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*   [江西组织开展2027年度自然科学基金项目申报工作](https:\u002F\u002Fnews.sciencenet.cn\u002Fhtmlnews\u002F2026\u002F10\u002F572484.shtm)\n\n*   5\n*   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2026-10-04","2026-10-04T00:00:00Z","报道",80,{"impact":125,"substance":315,"depth":316,"authority":62,"freshness":113,"relevant":20,"comment":407},"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。",[409],{"name":402,"url":399},[25,26,411,412,413],"作物生长模型","设施番茄","温室环控",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[416],{"code":38,"slug":39,"name":40,"path":41,"parent_code":35},{"status":49,"locked":14,"version":50,"method":51,"evidence":418},{"candidates":419,"agricultural_evidence":56},[420,424,429,433],{"code":31,"score":17,"supported":56,"title":421,"abstract":422,"tags":423},[27],[27],[412],{"code":38,"score":425,"supported":56,"title":426,"abstract":427,"tags":428},11,[66],[66],[],{"code":79,"score":87,"supported":14,"title":430,"abstract":431,"tags":432},[],[],[411,26],{"code":74,"score":20,"supported":14,"title":434,"abstract":435,"tags":436},[],[],[25],[438,439],"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 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2026-10-04",82,{"impact":125,"substance":315,"depth":316,"authority":62,"freshness":317,"relevant":20,"comment":451},"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。",[453],{"name":448,"url":445},[25,26,455,456,457],"温室番茄","知识数据双驱动","生长预测",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[460],{"code":38,"slug":39,"name":40,"path":41,"parent_code":35},{"status":49,"locked":14,"version":50,"method":51,"evidence":462},{"candidates":463,"agricultural_evidence":56},[464,468,472,476],{"code":31,"score":425,"supported":56,"title":465,"abstract":466,"tags":467},[27],[27],[],{"code":38,"score":126,"supported":56,"title":469,"abstract":470,"tags":471},[66],[],[455],{"code":74,"score":20,"supported":14,"title":473,"abstract":474,"tags":475},[],[],[25],{"code":79,"score":20,"supported":14,"title":477,"abstract":478,"tags":479},[],[],[26],[481,482],"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",{"id":486,"title":487,"url":488,"summary":489,"summary_zh":9,"content":9,"source_name":490,"source_url":9,"published_at":491,"category":404,"cover_url":9,"hotness":13,"is_selected":14,"score":449,"score_detail":492,"sources":494,"tags":496,"primary_topic":498,"secondary_topics":499,"classification":501,"search_phrases":520,"slug":522,"view_count":265,"doi":9,"paper":9,"created_at":523},3935,"知识与数据双驱动赋能温室番茄生长精准预测——中国农科院环发所研究成果发表","https:\u002F\u002Fwww.caas.cn\u002Fxwzx\u002Fkyhd\u002F613bca00d9e84cfeb977251a606af346.htm","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，研究成果发表在《农业计算机与电子学（Computers and Electronics in Agriculture）》上。科研团队提出一种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。","中国农业科学院·2026年10月","2026-10-01T00:00:00Z",{"impact":18,"substance":125,"depth":18,"authority":55,"freshness":126,"relevant":20,"comment":493},"国家级科研机构在核心期刊发表的知识与数据双驱动温室番茄生长预测模型，方法新颖、结论可靠，对智慧农业与设施种植有实质参考价值。",[495],{"name":490,"url":488},[25,26,497,455,457],"设施农业",{"code":38,"slug":39,"name":40,"path":41,"parent_code":35},[500],{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},{"status":49,"locked":14,"version":50,"method":51,"evidence":502},{"candidates":503,"agricultural_evidence":56},[504,508,512,516],{"code":38,"score":17,"supported":56,"title":505,"abstract":506,"tags":507},[66],[66],[455],{"code":31,"score":425,"supported":56,"title":509,"abstract":510,"tags":511},[27],[27],[],{"code":74,"score":20,"supported":14,"title":513,"abstract":514,"tags":515},[],[],[25],{"code":79,"score":20,"supported":14,"title":517,"abstract":518,"tags":519},[],[],[26],[438,521],"知识与数据双驱动 番茄 模型","中国农科院环发所温室番茄生长预测-3935","2026-10-02T11:53:11.319506Z"]