[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2276":3,"related-2276":105},{"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":23,"tags":25,"primary_topic":31,"secondary_topics":37,"classification":44,"search_phrases":73,"slug":76,"view_count":77,"doi":78,"paper":79,"created_at":104},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。",null,"Computers and Electronics in Agriculture","2026-09-12T00:00:00Z","论文",10,false,81,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,22,14,9,1,"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。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","农业人工智能","黄瓜","病害检测","高光谱遥感",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"B1","vegetables","蔬菜","\u002Ftopic\u002Fhorticulture\u002Fvegetables","B",[38],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",{"status":45,"locked":14,"version":46,"method":47,"evidence":48},"classified","2026-10-22.2","rules",{"candidates":49,"agricultural_evidence":52},[50,57,63,68],{"code":32,"score":51,"supported":52,"title":53,"abstract":55,"tags":56},15,true,[54],"cucumber",[28,54],[28],{"code":39,"score":58,"supported":52,"title":59,"abstract":61,"tags":62},12,[60],"hyperspectral",[60],[30],{"code":64,"score":21,"supported":14,"title":65,"abstract":66,"tags":67},"I1",[],[],[26],{"code":69,"score":21,"supported":14,"title":70,"abstract":71,"tags":72},"I2",[],[],[27],[74,75],"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",4,"10.1016\u002Fj.compag.2026.112433",{"doi":78,"openalex_id":80,"authors":81,"venue":10,"cited_by_count":96,"oa_url":9,"card":97,"direction":101,"ingested_from":103},"W7212310174",[82,85,87,89,91,93],{"name":83,"orcid":84},"Yibin Li","https:\u002F\u002Forcid.org\u002F0000-0002-5906-5074",{"name":86,"orcid":9},"Zonghuan Han",{"name":88,"orcid":9},"Yong Wang",{"name":90,"orcid":9},"Wei Gao",{"name":92,"orcid":9},"Yiding Zhang",{"name":94,"orcid":95},"Lingxian Zhang","https:\u002F\u002Forcid.org\u002F0000-0002-8665-7075",0,{"tldr":98,"method":99,"finding":100,"direction":101,"opportunity":102},"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。","openalex","2026-09-13T23:30:01.409569Z",{"total":106,"page":21,"page_size":106,"items":107},6,[108,193,267,382,463,513],{"id":109,"title":110,"url":111,"summary":112,"summary_zh":113,"content":9,"source_name":114,"source_url":111,"published_at":115,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":116,"score_detail":117,"sources":121,"tags":123,"primary_topic":126,"secondary_topics":127,"classification":133,"search_phrases":166,"slug":168,"view_count":169,"doi":170,"paper":171,"created_at":192},2805,"Synergistic effects of spectral preprocessing and machine learning algorithms for nitrogen estimation in tomato using hyperspectral spectral data","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-71908-1","Abstract Accurate estimation of leaf nitrogen content is essential for optimizing fertilization management and improving crop productivity. This study proposes a nondestructive hyperspectral imaging framework combined with advanced machine learning algorithms to classify nitrogen levels in tomato leaves (Solanum lycopersicum L., Royal variety). A total of 300 hyperspectral samples were collected from plants subjected to three nitrogen treatments (N-30%, N-60%, and N-90%) under controlled conditions. Reflectance data (400–1000 nm) were calibrated and preprocessed using Standard Normal Variate (SNV), Multiplicative Scatter Correction (MSC), and Savitzky–Golay (SG) filtering. Both unsupervised and supervised algorithms were systematically evaluated. Clustering results demonstrated that preprocessing substantially influenced class separability. The MSC + GMM combination yielded the best unsupervised results, with the lowest Davies–Bouldin index (0.443) and the highest silhouette coefficient (0.975), indicating improved cluster compactness and separation. In supervised learning, Neural Networks (NN) consistently outperformed the other models, achieving 100% accuracy and an ROC–AUC of 1.00 under SNV, MSC, and SG preprocessing. Gradient Boosting (GB) and Support Vector Machine (SVM) also demonstrated strong predictive capability, whereas conventional models, including LDA, LR, KNN, and NB, showed moderate improvements following preprocessing. Statistical analysis confirmed the significant effect of spectral preprocessing on both clustering and classification outcomes ( p \u003C 0.05). Overall, integrating hyperspectral imaging with appropriate preprocessing and nonlinear machine learning models provided strong classification performance under the controlled experimental conditions. However, the exceptionally high performance observed for some models should be interpreted cautiously because of the relatively small dataset and experimentally controlled nitrogen treatments. Moreover, the absence of an independent external validation dataset limits the assessment of generalizability across growing conditions, cultivars, and field environments. Therefore, independent validation using larger and more diverse datasets is required before broader application of the proposed framework in practical precision agriculture.","摘要 准确估算叶片氮含量对于优化施肥管理和提高作物生产力至关重要。本研究提出了一种无损高光谱成像框架，结合先进的机器学习算法对番茄叶片（Solanum lycopersicum L.，Royal品种）的氮水平进行分类。在受控条件下，从接受三种氮处理（N-30%、N-60%和N-90%）的植株中共采集了300个高光谱样本。反射率数据（400–1000 nm）经标准正态变量变换（SNV）、多元散射校正（MSC）和Savitzky–Golay（SG）滤波进行校准和预处理。系统评估了无监督和有监督算法。聚类结果表明，预处理对类别可分性产生了显著影响。MSC + GMM组合取得了最佳无监督结果，具有最低的Davies–Bouldin指数（0.443）和最高的轮廓系数（0.975），表明聚类紧密度和分离度得到改善。在有监督学习中，神经网络（NN）始终优于其他模型，在SNV、MSC和SG预处理下均达到100%的准确率和1.00的ROC–AUC。梯度提升（GB）和支持向量机（SVM）也表现出较强的预测能力，而传统模型（包括LDA、LR、KNN和NB）在预处理后表现出中等程度的改善。统计分析证实了光谱预处理对聚类和分类结果均有显著影响（p \u003C 0.05）。总体而言，将高光谱成像与适当的预处理及非线性机器学习模型相结合，在受控实验条件下提供了较强的分类性能。然而，由于数据集相对较小且氮处理为实验受控条件，某些模型所表现出的极高性能应谨慎解读。此外，缺乏独立的外部验证数据集限制了对跨生长条件、品种和田间环境泛化能力的评估。因此，在将该框架广泛应用于实际精准农业之前，需要使用更大规模、更多样化的数据集进行独立验证。","Scientific Reports","2026-09-17T00:00:00Z",77,{"impact":51,"substance":118,"depth":119,"authority":19,"freshness":13,"relevant":21,"comment":120},21,17,"高光谱结合机器学习实现番茄叶片氮素无损估测，方法系统、结论明确，但样本量小且缺乏外部验证，属细分领域方法学进展。",[122],{"name":114,"url":111},[26,27,124,125,30],"精准施肥","番茄种植",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[128,132],{"code":69,"slug":129,"name":130,"path":131,"parent_code":43},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"status":45,"locked":14,"version":46,"method":47,"evidence":134},{"candidates":135,"agricultural_evidence":52},[136,142,148,152,160],{"code":32,"score":51,"supported":52,"title":137,"abstract":139,"tags":141},[138],"tomato",[140,138],"番茄",[125],{"code":69,"score":51,"supported":52,"title":143,"abstract":145,"tags":147},[144],"machine learning",[146,144],"机器学习",[27],{"code":39,"score":58,"supported":52,"title":149,"abstract":150,"tags":151},[60],[60],[30],{"code":153,"score":154,"supported":52,"title":155,"abstract":156,"tags":159},"F3",7,[],[157,158],"施肥","fertilization",[124],{"code":64,"score":154,"supported":52,"title":161,"abstract":162,"tags":165},[],[163,164],"精准农业","precision agriculture",[26],[167,75],"农业人工智能 高光谱遥感 智慧农业 番茄种植","农业人工智能高光谱遥感智慧农业番茄种植-2805",2,"10.1038\u002Fs41598-026-71908-1",{"doi":170,"openalex_id":172,"authors":173,"venue":114,"cited_by_count":96,"oa_url":111,"card":186,"direction":191,"ingested_from":103},"W7213472002",[174,177,180,183],{"name":175,"orcid":176},"Mohammad Vahedi Torshizi","https:\u002F\u002Forcid.org\u002F0000-0003-3648-1515",{"name":178,"orcid":179},"Sajad Sabzi","https:\u002F\u002Forcid.org\u002F0000-0003-2439-5329",{"name":181,"orcid":182},"Mohsen Azadbakht","https:\u002F\u002Forcid.org\u002F0000-0002-5726-9321",{"name":184,"orcid":185},"Razieh Pourdarbani","https:\u002F\u002Forcid.org\u002F0000-0003-0766-8305",{"tldr":187,"method":188,"finding":189,"direction":101,"opportunity":190},"用高光谱成像结合预处理与机器学习，对番茄叶片氮素水平进行分类。","300个高光谱样本，SNV、MSC、SG预处理，聚类与多种监督分类算法对比。","MSC+GMM聚类最优，神经网络在三种预处理下均达100%准确率。","样本少且无外部验证，可扩展多品种、多环境田间数据并做独立验证以提升泛化性。","农业人工智能与决策模型","2026-09-17T23:30:59.361085Z",{"id":194,"title":195,"url":196,"summary":197,"summary_zh":198,"content":9,"source_name":199,"source_url":196,"published_at":200,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":201,"score_detail":202,"sources":204,"tags":206,"primary_topic":208,"secondary_topics":209,"classification":216,"search_phrases":241,"slug":244,"view_count":169,"doi":245,"paper":246,"created_at":266},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":58,"substance":17,"depth":17,"authority":58,"freshness":77,"relevant":21,"comment":203},"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。",[205],{"name":199,"url":196},[26,27,140,207,29],"遥感",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[210,215],{"code":211,"slug":212,"name":213,"path":214,"parent_code":36},"B7","protected-horticulture","设施园艺与植物工厂","\u002Ftopic\u002Fhorticulture\u002Fprotected-horticulture",{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"status":45,"locked":14,"version":46,"method":47,"evidence":217},{"candidates":218,"agricultural_evidence":52},[219,223,229,233,237],{"code":32,"score":51,"supported":52,"title":220,"abstract":221,"tags":222},[138],[140,138],[140],{"code":211,"score":19,"supported":52,"title":224,"abstract":226,"tags":228},[225],"greenhouse",[227,225],"温室",[],{"code":39,"score":58,"supported":52,"title":230,"abstract":231,"tags":232},[60],[60],[207],{"code":64,"score":21,"supported":14,"title":234,"abstract":235,"tags":236},[],[],[26],{"code":69,"score":21,"supported":14,"title":238,"abstract":239,"tags":240},[],[],[27],[242,243],"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","10.1016\u002Fj.atech.2026.102535",{"doi":245,"openalex_id":247,"authors":248,"venue":199,"cited_by_count":96,"oa_url":196,"card":260,"direction":264,"ingested_from":103},"W7207729517",[249,251,253,256,258],{"name":250,"orcid":9},"Gaoshoutong Si",{"name":252,"orcid":9},"Peter Ling",{"name":254,"orcid":255},"Anna L. Testen","https:\u002F\u002Forcid.org\u002F0000-0002-6504-9599",{"name":257,"orcid":9},"Heping Zhu",{"name":259,"orcid":9},"Hongyoung Jeon",{"tldr":261,"method":262,"finding":263,"direction":264,"opportunity":265},"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。","2026-09-04T23:30:04.578607Z",{"id":268,"title":269,"url":270,"summary":271,"summary_zh":272,"content":9,"source_name":273,"source_url":270,"published_at":274,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":275,"score_detail":276,"sources":278,"tags":280,"primary_topic":282,"secondary_topics":283,"classification":286,"search_phrases":363,"slug":365,"view_count":169,"doi":366,"paper":367,"created_at":381},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":17,"substance":18,"depth":17,"authority":58,"freshness":77,"relevant":21,"comment":277},"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。",[279],{"name":273,"url":270},[26,27,146,207,281],"园艺",{"code":69,"slug":129,"name":130,"path":131,"parent_code":43},[284,285],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":45,"locked":14,"version":46,"method":47,"evidence":287},{"candidates":288,"agricultural_evidence":52},[289,295,302,308,319,326,333,340,347,353,359],{"code":69,"score":290,"supported":52,"title":291,"abstract":292,"tags":294},19,[144],[293,146,144],"深度学习",[27,146],{"code":39,"score":17,"supported":52,"title":296,"abstract":298,"tags":301},[297],"remote 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divide",[],{"code":341,"score":342,"supported":52,"title":343,"abstract":344,"tags":346},"B4",3,[],[345],"花卉",[],{"code":348,"score":342,"supported":52,"title":349,"abstract":350,"tags":352},"B6",[],[351],"药用植物",[],{"code":354,"score":342,"supported":52,"title":355,"abstract":356,"tags":358},"H3",[],[357],"水资源",[],{"code":64,"score":21,"supported":14,"title":360,"abstract":361,"tags":362},[],[],[26],[364,243],"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":366,"openalex_id":368,"authors":369,"venue":273,"cited_by_count":96,"oa_url":375,"card":376,"direction":264,"ingested_from":103},"W7204829467",[370,373],{"name":371,"orcid":372},"Jadala Shankaraswamy","https:\u002F\u002Forcid.org\u002F0000-0002-5623-6384",{"name":374,"orcid":9},"Md Abdul Gaffor","https:\u002F\u002Fijoear.com\u002Fassets\u002Farticles_menuscripts\u002Ffile\u002FIJOEAR-AUG-2026-26.pdf",{"tldr":377,"method":378,"finding":379,"direction":101,"opportunity":380},"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":383,"title":384,"url":385,"summary":386,"summary_zh":9,"content":9,"source_name":387,"source_url":385,"published_at":388,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":275,"score_detail":389,"sources":393,"tags":395,"primary_topic":397,"secondary_topics":402,"classification":405,"search_phrases":425,"slug":428,"view_count":21,"doi":429,"paper":430,"created_at":462},4500,"Integrating UAV-Based Remote Sensing and Deep Learning for Early Detection of Bacterial Leaf Blight in Rice","https:\u002F\u002Fdoi.org\u002F10.21203\u002Frs.3.rs-11163942\u002Fv1","Integrating UAV-Based Remote Sensing and Deep Learning for Early Detection of Bacterial Leaf Blight in Rice。Research Square","Research Square","2026-10-08T00:00:00Z",{"impact":17,"substance":390,"depth":17,"authority":13,"freshness":391,"relevant":21,"comment":392},20,8,"将无人机遥感与深度学习结合用于水稻白叶枯病早期检测，方法有创新、结论具应用价值，但属预印本且为细分技术进展，适合进入主题聚合而非头条精选。",[394],{"name":387,"url":385},[26,27,396,207,29],"水稻",{"code":398,"slug":399,"name":396,"path":400,"parent_code":401},"A1","rice","\u002Ftopic\u002Ffield-crops\u002Frice","A",[403,404],{"code":69,"slug":129,"name":130,"path":131,"parent_code":43},{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"status":45,"locked":14,"version":46,"method":47,"evidence":406},{"candidates":407,"agricultural_evidence":52},[408,412,417,421],{"code":398,"score":58,"supported":52,"title":409,"abstract":410,"tags":411},[399],[399],[396],{"code":69,"score":58,"supported":52,"title":413,"abstract":415,"tags":416},[414],"deep learning",[414],[27],{"code":39,"score":58,"supported":52,"title":418,"abstract":419,"tags":420},[297],[297],[207],{"code":64,"score":21,"supported":14,"title":422,"abstract":423,"tags":424},[],[],[26],[426,427],"无人机遥感 水稻 白叶枯病","深度学习 水稻病害 早期检测","无人机遥感水稻白叶枯病-4500","10.21203\u002Frs.3.rs-11163942\u002Fv1",{"doi":429,"openalex_id":431,"authors":432,"venue":387,"cited_by_count":96,"oa_url":385,"card":9,"direction":101,"ingested_from":103},"W7221055917",[433,436,439,441,443,446,448,450,453,455,457,459],{"name":434,"orcid":435},"Carlito B. Balingbing","https:\u002F\u002Forcid.org\u002F0000-0002-3672-9253",{"name":437,"orcid":438},"Dmytro Chebotarov","https:\u002F\u002Forcid.org\u002F0000-0003-1351-9453",{"name":440,"orcid":9},"Ian  Carlo Perilla",{"name":442,"orcid":9},"Stephen Klassen",{"name":444,"orcid":445},"Nancy Castilla","https:\u002F\u002Forcid.org\u002F0000-0002-9143-4933",{"name":447,"orcid":9},"Raphael  Gabriel Carvajal",{"name":449,"orcid":9},"Kendrix Ortega",{"name":451,"orcid":452},"Joseph M. Sandro","https:\u002F\u002Forcid.org\u002F0000-0002-8311-2299",{"name":454,"orcid":9},"Nurmi Pangesti",{"name":456,"orcid":9},"Caesar Arloo R. Centeno",{"name":458,"orcid":9},"Arnel Rala",{"name":460,"orcid":461},"Li-Yu  Daisy Liu","https:\u002F\u002Forcid.org\u002F0000-0001-6997-8101","2026-10-09T23:30:18.390549Z",{"id":464,"title":465,"url":466,"summary":467,"summary_zh":9,"content":9,"source_name":468,"source_url":9,"published_at":469,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":470,"score_detail":471,"sources":473,"tags":475,"primary_topic":478,"secondary_topics":479,"classification":481,"search_phrases":501,"slug":503,"view_count":21,"doi":9,"paper":504,"created_at":512},2215,"中国农业大学马韫韬教授团队:攻克温室黄瓜冠层点云分割难题 助力设施数字化精准管理","https:\u002F\u002Fh.mffb.com.cn\u002Fa\u002F569056.html","马韫韬教授团队构建Cuc-HiSeg分割框架,通过尺度变换、角度旋转、边缘扰动等策略扩充数据集,显著缓解三维标注样本稀缺难题,使深度学习模型分割F1值提升5.6%-6.4%;研发结合形态学优化与K-Means++二次分割的改进区域生长算法,有效克服叶片重叠与点云缺失干扰,叶片实例分割F1值最高达95.0%,在结果期复杂场景下仍保持93.9%的高精度。该方法在苗期、开花初期、结果期均表现出优异的稳定性与适应性,可高效完成日光温室黄瓜冠层点云的高精度解析。","农业信息网 2026年9月8日","2026-09-08T00:00:00Z",78,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":106,"relevant":21,"comment":472},"中国农大团队提出黄瓜冠层点云分割新框架，F1值提升显著且多生育期稳定，属设施农业数字化领域有实质增量的技术突破。",[474],{"name":468,"url":466},[26,27,476,28,477],"设施农业","点云分割",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[480],{"code":211,"slug":212,"name":213,"path":214,"parent_code":36},{"status":45,"locked":14,"version":46,"method":47,"evidence":482},{"candidates":483,"agricultural_evidence":52},[484,488,493,497],{"code":32,"score":58,"supported":52,"title":485,"abstract":486,"tags":487},[28],[28],[28],{"code":211,"score":489,"supported":52,"title":490,"abstract":491,"tags":492},11,[227],[227],[],{"code":69,"score":77,"supported":52,"title":494,"abstract":495,"tags":496},[],[293],[27],{"code":64,"score":21,"supported":14,"title":498,"abstract":499,"tags":500},[],[],[26],[502,243],"农业人工智能 智慧农业 点云分割 设施农业","农业人工智能智慧农业点云分割设施农业-2215",{"doi":9,"openalex_id":9,"authors":505,"venue":9,"cited_by_count":96,"oa_url":9,"card":506,"direction":101,"ingested_from":511},[],{"tldr":507,"method":508,"finding":509,"direction":101,"opportunity":510},"构建Cuc-HiSeg框架，实现温室黄瓜冠层点云高精度叶片实例分割。","数据增强扩充点云样本，结合改进区域生长与K-Means++二次分割。","叶片分割F1最高95.0%，结果期复杂场景仍达93.9%，各生育期稳定。","可迁移至其他设施作物冠层解析，并探索点云分割与生长量、产量性状的自动关联。","agent","2026-09-12T00:06:40.512918Z",{"id":514,"title":515,"url":516,"summary":517,"summary_zh":518,"content":9,"source_name":519,"source_url":516,"published_at":520,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":116,"score_detail":521,"sources":523,"tags":525,"primary_topic":527,"secondary_topics":531,"classification":533,"search_phrases":559,"slug":561,"view_count":342,"doi":562,"paper":563,"created_at":575},1430,"Hyperspectral Imaging for Disease Detection in Wheat Crops: A Comprehensive Survey","https:\u002F\u002Fdoi.org\u002F10.18805\u002Fijare.a-6533","Hyperspectral imaging (HSI) has emerged as a game-changing technology for precision agriculture because it captures detailed spectral signatures over hundreds of finely-resolved, continuous wavelength bands. This feature makes possible a detailed description of species-specific plant physiological states to enable early, precise and non-destructive identification of diseases in crops like wheat. With the rising demands worldwide for food security and healthy cultivation, the application of HSI in agro-diagnostics has expanded considerably. This paper provides a comprehensive and state-of-the-art review of hyperspectral imaging strategies devoted to wheat disease discrimination. It essentially introduces the theoretical backgrounds of HSI, such as data acquisition, preprocessing and spectral-spatial feature extraction. The survey further details a plethora of analytical approaches from classical statistical learning methods to deep learning and their utility in modelling HSI data. In particular, a comparative study of these methods is carried out and discussed for distinguishing healthy and diseased wheat, as well as different types of diseases under diverse environmental conditions. The paper also considers the benchmark datasets, sensors and platforms (field\u002Flaboratory-based) for which there are practical limitations. Key challenges are addressed, such as data dimensionality, variability as a consequence of atmospheric conditions and the requirement for online implementation in open-field scenarios. A set of suggestions for applying HSI in other domains has been proposed, such as the fusion of HSI with additional remote sensing information, introduction of compact models to edge devices and construction of a scalable and economical solution of HSI in commercial agriculture.","高光谱成像（HSI）已成为精准农业领域的一项变革性技术，因为它能够在数百个精细分辨、连续分布的波段上捕获详细的光谱特征。这一特性使得对物种特异性植物生理状态进行细致描述成为可能，从而能够对小麦等作物病害实现早期、精准且非破坏性的识别。随着全球对粮食安全和健康栽培需求的日益增长，HSI在农业诊断中的应用已显著扩展。本文对用于小麦病害区分的高光谱成像策略进行了全面且前沿的综述。文章首先介绍了HSI的理论基础，包括数据采集、预处理及光谱-空间特征提取。随后，综述详细探讨了从经典统计学习方法到深度学习的众多分析方法及其在HSI数据建模中的适用性。特别地，文中对这些方法进行了比较研究，并讨论了其在区分健康与患病小麦，以及在不同环境条件下识别不同类型病害方面的表现。本文还考虑了基准数据集、传感器及平台（田间\u002F实验室）在实际应用中存在的局限性。针对数据维度、大气条件引起的变异性以及开放田间场景下在线实施的需求等关键挑战进行了阐述。最后，提出了将HSI应用于其他领域的一系列建议，例如将HSI与额外遥感信息融合、将紧凑模型引入边缘设备，以及构建在商业农业中可扩展且经济可行的HSI解决方案。","Indian Journal of Agricultural Research","2026-09-02T00:00:00Z",{"impact":17,"substance":18,"depth":17,"authority":58,"freshness":154,"relevant":21,"comment":522},"高光谱成像用于小麦病害检测的综述，系统性强，方法比较全面，对智慧农业病害诊断有参考价值。",[524],{"name":519,"url":516},[26,27,526,207,29],"小麦",{"code":528,"slug":529,"name":526,"path":530,"parent_code":401},"A2","wheat","\u002Ftopic\u002Ffield-crops\u002Fwheat",[532],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"status":45,"locked":14,"version":46,"method":47,"evidence":534},{"candidates":535,"agricultural_evidence":52},[536,540,544,548,552],{"code":39,"score":17,"supported":52,"title":537,"abstract":538,"tags":539},[60],[207,297,60],[207],{"code":528,"score":51,"supported":52,"title":541,"abstract":542,"tags":543},[529],[526,529],[526],{"code":64,"score":154,"supported":52,"title":545,"abstract":546,"tags":547},[],[163,164],[26],{"code":69,"score":154,"supported":52,"title":549,"abstract":550,"tags":551},[],[293,414],[27],{"code":553,"score":106,"supported":52,"title":554,"abstract":555,"tags":558},"A8",[],[556,557],"粮食安全","food security",[],[560,243],"农业人工智能 智慧农业 病害检测 小麦","农业人工智能智慧农业病害检测小麦-1430","10.18805\u002Fijare.a-6533",{"doi":562,"openalex_id":564,"authors":565,"venue":519,"cited_by_count":96,"oa_url":516,"card":570,"direction":101,"ingested_from":103},"W7205005591",[566,568],{"name":567,"orcid":9},"M. Deepak",{"name":569,"orcid":9},"Gopal K. Shyam",{"tldr":571,"method":572,"finding":573,"direction":101,"opportunity":574},"综述高光谱成像在小麦病害检测中的应用，涵盖方法、挑战与未来方向。","综述数据采集、预处理、特征提取及统计学习与深度学习方法。","高光谱成像可早期、无损识别小麦病害，但面临维度高、环境变化和实时性挑战。","可探索高光谱与多源遥感融合、轻量化模型部署于边缘设备，以及低成本商业化方案。","2026-09-02T23:30:51.057495Z"]