[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3323":3,"related-3323":45},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":8,"source_name":9,"source_url":8,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":15,"sources":22,"tags":24,"search_phrases":30,"slug":33,"view_count":34,"doi":8,"paper":35,"created_at":44},3323,"基于空间分辨光谱的鲜食玉米含水率和硬度MaMoNet检测模型——融合Mamba状态空间模型与多门专家混合机制","https:\u002F\u002Fk.sina.com.cn\u002Farticle_5953466437_162dab0450670bdama.html","《智慧农业（中英文）》2026年第4期。许敏、赵鑫、陈艳萍、朱启兵、黄敏（江南大学\u002F江苏省农科院）以带苞叶鲜食玉米为研究对象，构建多通道可见光-近红外空间分辨光谱采集系统，获取玉米样本的多通道光谱数据，并提出一种融合Mamba状态空间模型与多门专家混合(MMoE)机制的多任务预测网络——MaMoNet(Mamba-MMoE Network)。MaMoNet在测试集上玉米籽粒含水率预测的决定系数R²达到了0.91，硬度预测R²达到了0.89，均优于对比模型。消融实验进一步证明Mamba模块在建模光谱长程依赖关系方面具有显著优势，以及MMoE机制能够有效缓解多任务学习中任务间特征竞争问题。",null,"《智慧农业（中英文）》2026年第4期","2026-09-20T00:00:00Z","论文",10,false,81,{"impact":16,"substance":17,"depth":16,"authority":18,"freshness":19,"relevant":20,"comment":21},18,22,14,9,1,"核心期刊论文，方法新颖且指标可靠，对农产品无损检测有实质参考价值。",[23],{"name":9,"url":6},[25,26,27,28,29],"智慧农业","农业人工智能","鲜食玉米","光谱检测","多任务学习",[31,32],"江南大学 鲜食玉米 含水率","MaMoNet 玉米 硬度","江南大学鲜食玉米含水率-3323",0,{"doi":8,"openalex_id":8,"authors":36,"venue":8,"cited_by_count":34,"oa_url":8,"card":37,"direction":41,"ingested_from":43},[],{"tldr":38,"method":39,"finding":40,"direction":41,"opportunity":42},"提出MaMoNet网络，用空间分辨光谱检测鲜食玉米含水率和硬度。","多通道可见光-近红外空间分辨光谱，融合Mamba与MMoE多任务网络。","含水率R²达0.91、硬度R²达0.89，优于对比模型，Mamba与MMoE均有效。","农业遥感与作物表型","可探索Mamba在多作物多品质指标无损检测中的泛化性及田间在线部署。","agent","2026-09-24T00:04:02.557420Z",{"total":46,"page":20,"page_size":46,"items":47},6,[48,96,149,170,211,250],{"id":49,"title":50,"url":51,"summary":52,"summary_zh":53,"content":8,"source_name":54,"source_url":51,"published_at":55,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":56,"score_detail":57,"sources":63,"tags":65,"search_phrases":68,"slug":71,"view_count":34,"doi":72,"paper":73,"created_at":95},2280,"Predicting plant leaf functional traits using 2D spectral representation and multi-task learning with multi-gate mixture-of-experts","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112390","Predicting plant leaf functional traits using 2D spectral representation and multi-task learning with multi-gate mixture-of-experts。Computers and Electronics in Agriculture","利用二维光谱表示和多任务学习结合多门混合专家模型预测植物叶片功能性状。","Computers and Electronics in Agriculture","2026-09-12T00:00:00Z",75,{"impact":58,"substance":59,"depth":60,"authority":18,"freshness":61,"relevant":20,"comment":62},16,20,17,8,"核心期刊论文，提出二维光谱表征与多门专家混合多任务学习预测叶片功能性状，方法新颖、对作物表型与遥感监测有参考价值，但属细分方法进展，未达重大突破层级。",[64],{"name":54,"url":51},[25,26,66,67,29],"遥感","作物表型",[69,70],"农业人工智能 多任务学习 作物表型 智慧农业","农业人工智能 多任务学习","农业人工智能多任务学习作物表型智慧农业-2280","10.1016\u002Fj.compag.2026.112390",{"doi":72,"openalex_id":74,"authors":75,"venue":54,"cited_by_count":34,"oa_url":8,"card":89,"direction":41,"ingested_from":94},"W7212395422",[76,78,80,82,84,86],{"name":77,"orcid":8},"Jianping Huang",{"name":79,"orcid":8},"Xin Zhang",{"name":81,"orcid":8},"Guanglai Wang",{"name":83,"orcid":8},"Chong Mo",{"name":85,"orcid":8},"Zhenghang Wang",{"name":87,"orcid":88},"Wenlong Song","https:\u002F\u002Forcid.org\u002F0000-0002-8810-532X",{"tldr":90,"method":91,"finding":92,"direction":41,"opportunity":93},"用二维光谱表示与多门混合专家多任务学习预测植物叶片功能性状。","二维光谱表示、多任务学习、多门混合专家模型。","该方法能同时准确预测多种叶片功能性状，优于单任务模型。","可探索将该多任务框架迁移到多作物、多时相的高光谱表型监测中。","openalex","2026-09-13T23:30:01.702074Z",{"id":97,"title":98,"url":99,"summary":100,"summary_zh":101,"content":8,"source_name":102,"source_url":99,"published_at":103,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":104,"score_detail":105,"sources":109,"tags":111,"search_phrases":114,"slug":117,"view_count":34,"doi":118,"paper":119,"created_at":148},2124,"Cysteine quantification in pea cultivars from SERS spectra using AI","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.atech.2026.102557","Rapid quantification of sulfur-containing amino acids, particularly cysteine, in legumes is critical for assessing nutritional quality, supporting breeding program screening, and ensuring consistency in quality control processes. However, conventional methods, such as high-performance liquid chromatography (HPLC), are time-consuming and resource-intensive for high-throughput applications. This study evaluated artificial intelligence models for predicting cultivar-level mean cysteine concentration from surface-enhanced Raman spectroscopy (SERS) spectra of pea extracts. SERS spectra were acquired from 20 cultivars grown at three field locations, with HPLC measurements across the three locations averaged to provide a cultivar-level mean cysteine reference value. Linear regression, partial least squares regression, support vector regression, random forest regression, and a one-dimensional convolutional neural network (1D-CNN) were compared using within-cultivar splits and leave-one-cultivar-out (LOCO) evaluation. The 1D-CNN achieved good predictive performance for the represented cultivars, with an RMSE of 0.005 g\u002F100 g and an MAE of 0.004 g\u002F100 g. Under LOCO evaluation, RMSE and MAE increased to 0.013 and 0.012 g\u002F100 g, respectively. These results provide proof of concept for SERS\u002FAI prediction of multi-environment, cultivar-level mean cysteine concentration, with LOCO providing encouraging evidence of transferability to unseen cultivars within the represented environments and analytical conditions. Shapley Additive Explanations analysis identified contributions from both substrate-related and molecular spectral features. Broader validation using independent samples, locations, extractions, substrates, and reference measurements is required to establish general analytical applicability. A simulated additive-noise sensitivity analysis further evaluated model performance under increasing spectral noise.","豆类中含硫氨基酸，尤其是半胱氨酸的快速定量，对于评估营养品质、支持育种项目筛选以及确保质量控制流程的一致性至关重要。然而，传统方法如高效液相色谱法（HPLC）在高通量应用中耗时且资源密集。本研究评估了人工智能模型从豌豆提取物的表面增强拉曼光谱（SERS）中预测品种水平平均半胱氨酸浓度的能力。SERS光谱采集自三个田间地点种植的20个品种，并将三个地点的HPLC测量值取平均以提供品种水平的平均半胱氨酸参考值。采用品种内划分和留一品种法（LOCO）评估，比较了线性回归、偏最小二乘回归、支持向量回归、随机森林回归和一维卷积神经网络（1D-CNN）。1D-CNN对所代表品种取得了良好的预测性能，RMSE为0.005 g\u002F100 g，MAE为0.004 g\u002F100 g。在LOCO评估下，RMSE和MAE分别增至0.013和0.012 g\u002F100 g。这些结果为SERS\u002F人工智能预测多环境、品种水平平均半胱氨酸浓度提供了概念验证，LOCO为在已代表的环境和分析条件下向未见品种的可迁移性提供了令人鼓舞的证据。Shapley加性解释分析识别了底物相关和分子光谱特征的贡献。需要使用独立样本、地点、提取方法、底物和参考测量进行更广泛的验证，以确立普遍的分析适用性。模拟加性噪声敏感性分析进一步评估了模型在光谱噪声增加条件下的性能。","Smart Agricultural Technology","2026-09-08T00:00:00Z",71,{"impact":106,"substance":59,"depth":60,"authority":107,"freshness":46,"relevant":20,"comment":108},15,13,"SERS光谱结合AI实现豌豆半胱氨酸快速定量，为育种筛选与品质控制提供概念验证，方法新颖但尚需更广泛验证。",[110],{"name":102,"url":99},[25,26,112,113,28],"品质育种","豌豆",[115,116],"农业人工智能 光谱检测 品质育种 智慧农业","农业人工智能 光谱检测","农业人工智能光谱检测品质育种智慧农业-2124","10.1016\u002Fj.atech.2026.102557",{"doi":118,"openalex_id":120,"authors":121,"venue":102,"cited_by_count":34,"oa_url":141,"card":142,"direction":146,"ingested_from":94},"W7211935637",[122,125,128,131,134,136,138],{"name":123,"orcid":124},"Elham Gorgannejad","https:\u002F\u002Forcid.org\u002F0009-0001-6375-0429",{"name":126,"orcid":127},"Qian Liu","https:\u002F\u002Forcid.org\u002F0000-0001-9832-596X",{"name":129,"orcid":130},"Catherine Rui Jin Findlay","https:\u002F\u002Forcid.org\u002F0000-0002-9304-1033",{"name":132,"orcid":133},"Mohammad Nadimi","https:\u002F\u002Forcid.org\u002F0000-0002-4550-7572",{"name":135,"orcid":8},"Alex Chun-Te Ko",{"name":137,"orcid":8},"Pankaj Bhowmik",{"name":139,"orcid":140},"Jitendra Paliwal","https:\u002F\u002Forcid.org\u002F0000-0002-1665-3626","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS2772375526007823\u002Fpdf",{"tldr":143,"method":144,"finding":145,"direction":146,"opportunity":147},"用SERS光谱结合AI预测豌豆品种半胱氨酸含量，1D-CNN表现最佳。","SERS光谱、HPLC参考值、多种回归模型及1D-CNN，采用LOCO评估。","1D-CNN在品种内预测RMSE为0.005 g\u002F100g，LOCO下为0.013 g\u002F100g，具","农业人工智能与决策模型","可扩展至其他氨基酸或作物，结合便携SERS与迁移学习实现田间高通量品质筛查。","2026-09-11T23:30:03.956325Z",{"id":150,"title":151,"url":152,"summary":153,"summary_zh":8,"content":154,"source_name":155,"source_url":8,"published_at":156,"category":157,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":158,"sources":160,"tags":162,"search_phrases":165,"slug":168,"view_count":34,"doi":8,"paper":8,"created_at":169},2084,"中国农科院资划所基于可解释AI构建农产品产地溯源模型 破解光谱判断产地的\"黑箱\"难题","https:\u002F\u002Fwww.caas.cn\u002Fxwzx\u002Fkyhd\u002F01c47c41992241608017a544cbad3538.htm","中国农科院农业资源与农业区划研究所智慧农业创新团队基于可解释人工智能构建农产品产地溯源模型,以中国六个地区的猕猴桃为对象,融合光谱成像与多模型组合策略,并运用可解释性方法对最优组合模型进行分层和整体解读,在提升溯源准确率的同时揭示光谱数据判断产地的内在逻辑,为光谱技术在农业生产与品质监管中应用提供系统解决思路,成果发表于《农业人工智能》(Artificial Intelligence in Agriculture)。","[English](https:\u002F\u002Fwww.caas.cn\u002Fen)[邮箱](https:\u002F\u002Fmail.caas.cn\u002F)[数字农科院](https:\u002F\u002Fi.caas.cn\u002F)[](https:\u002F\u002Fwww.caas.cn\u002Fcms\u002Fweb\u002Fsearch\u002Findex.jsp?siteID=cdb01dceb46e48488945d4465e90f221&aba=)\n\n官方微信 \n\n![Image 2](https:\u002F\u002Fwww.caas.cn\u002Fimages\u002F2023-01\u002F020ac4d3d8b6451782a8702c72604336.jpg)农科专家在线微信公众号\n\n![Image 3](https:\u002F\u002Fwww.caas.cn\u002Fimages\u002F2023-01\u002Fbf6579e4651e437db31a22cd2249b319.jpg)中国农科院微信公众号\n\n[![Image 4](https:\u002F\u002Fwww.caas.cn\u002Fstatic2022\u002Fimages\u002Fmenu_logo.png)](https:\u002F\u002Fwww.caas.cn\u002Findex.htm)\n\n*   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019.jpg](https:\u002F\u002Fwww.caas.cn\u002Fimages\u002F2026-09\u002Ffa69a734a4bc4551a1ea07fc7f019a58.jpg)\n\n利用光谱数据判断农产品产地时，现有方法多依赖单一算法，往往只追求预测准确度，却忽略了模型决策逻辑的解释，限制了可靠溯源模型的开发和对判断过程的理解。科研团队以中国六个地区的猕猴桃为对象，融合光谱成像技术与多模型组合策略构建产地溯源模型，并运用可解释性分析方法对最优组合模型进行分层和整体解读，在提升溯源准确率的同时，揭示了光谱数据背后判断产地的内在逻辑。\n\n该研究为破解光谱分析中数据分析模型难以解释的难题提供了系统解决思路，在关键波长选择、模型稳定性提升及预测结果可信度增强等方面具有参考价值，有助于推动光谱技术在农业生产与品质监管中的实际应用。\n\n该研究得到国家重点研发计划、中国农业科学院科技创新工程等项目支持。（通讯员 姬悦）\n\n原文链接：https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.aiia.2026.05.010\n\n打印本页\n\n关闭本页\n\n[院网信息发布与管理](https:\u002F\u002Fwww.caas.cn\u002Fywxxfbygl\u002Findex.htm)[最新动态](https:\u002F\u002Fwww.caas.cn\u002Fywxxfbygl\u002Findex.htm)\n\n*   [黑土地侵蚀沟时空演变规律获揭示](https:\u002F\u002Fwww.caas.cn\u002Fxwzx\u002Fkyhd\u002F1b6aad7e04244664a16d4fb7847890d7.htm)2026-09-07  \n*   [时代·人民·文明——中国元首外交的思想启迪](https:\u002F\u002Fwww.caas.cn\u002Fxwzx\u002Fszyw\u002F1c34df72522047cda6b6ee907c331b85.htm)2026-09-06  \n*   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京公网安备11010802048565号](http:\u002F\u002Fwww.beian.gov.cn\u002Fportal\u002FregisterSystemInfo?recordcode=11010802048565)","中国农业科学院","2026-09-09T00:00:00Z","报道",{"impact":17,"substance":59,"depth":60,"authority":18,"freshness":61,"relevant":20,"comment":159},"国家级科研机构将可解释AI引入农产品产地溯源，方法新颖、结论可靠，对智慧农业质量监管有示范价值。",[161],{"name":155,"url":152},[25,26,163,164,28],"可解释AI","农产品溯源",[166,167],"农业人工智能 农产品溯源 光谱检测 智慧农业","农业人工智能 农产品溯源","农业人工智能农产品溯源光谱检测智慧农业-2084","2026-09-11T00:04:19.946470Z",{"id":171,"title":172,"url":173,"summary":174,"summary_zh":175,"content":8,"source_name":176,"source_url":173,"published_at":177,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":178,"score_detail":179,"sources":181,"tags":183,"search_phrases":186,"slug":189,"view_count":34,"doi":190,"paper":191,"created_at":210},3368,"A PCA-based deep feature optimization framework for explainable orange fruit disease classification","https:\u002F\u002Fdoi.org\u002F10.1186\u002Fs12870-026-09984-8","Accurate classification of orange fruit diseases is important for precision agriculture and yield protection. This study develops and rigorously benchmarks a hybrid deep-feature framework for classifying Black Spot, Canker, Fresh, and Greening oranges (1,090 images), combining deep feature extraction, PCA-based dimensionality reduction, and classical machine-learning classification. Eight backbones (seven CNNs and a Vision Transformer, ViT) and four classifiers (32 configurations in total) were evaluated under 5 × 5 repeated stratified cross-validation, with PCA fitted exclusively on training-fold features in every iteration to eliminate data leakage. The proposed ViT + PCA+SVM configuration achieved the highest mean accuracy, 99.12%±0.71%, significantly outperforming every CNN-based backbone, including DenseNet201 + PCA + SVM (98.48%±0.81%, p \u003C 0.001). A dedicated variance-retention sensitivity analysis justifies the 98% threshold used throughout, and ablation experiments confirm that PCA substantially reduces feature dimensionality (by ~ 55.7% for ViT and ~ 76.6% for DenseNet201) without a significant loss of accuracy for either backbone. Explainability analysis — occlusion sensitivity and SHAP for the proposed ViT model, and Grad-CAM and SHAP for the DenseNet201 comparison model — shows that both configurations base predictions on biologically relevant, disease-affected regions of the fruit rather than spurious cues. These results identify ViT + PCA+SVM as the most accurate configuration evaluated, with DenseNet201 + PCA + SVM as a closely competitive, more compact convolutional alternative for intelligent orchard disease-monitoring systems.","橙类果实病害的准确分类对精准农业和产量保护具有重要意义。本研究开发并严格基准测试了一种混合深度特征框架，用于对黑斑病、溃疡病、新鲜和黄龙病橙类（1，090张图像）进行分类，该框架结合了深度特征提取、基于PCA的降维和经典机器学习分类。在5×5重复分层交叉验证下评估了八种骨干网络（七种CNN和一种视觉Transformer，ViT）和四种分类器（共32种配置），每次迭代中PCA仅在训练折特征上拟合以消除数据泄漏。所提出的ViT + PCA+SVM配置取得了最高平均准确率，为99.12%±0.71%，显著优于所有基于CNN的骨干网络，包括DenseNet201 + PCA + SVM（98.48%±0.81%，p \u003C 0.001）。专门的方差保留敏感性分析证明了全程使用的98%阈值是合理的，消融实验证实PCA大幅降低了特征维度（ViT约降低55.7%，DenseNet201约降低76.6%），且两种骨干网络均无显著准确率损失。可解释性分析——对所提出的ViT模型采用遮挡敏感性和SHAP，对DenseNet201对比模型采用Grad-CAM和SHAP——表明两种配置均基于果实中生物学相关的病害影响区域而非虚假线索进行预测。这些结果确定ViT + PCA+SVM为所评估的最准确配置，而DenseNet201 + PCA + SVM则是一种竞争力接近且更紧凑的卷积替代方案，可用于智能果园病害监测系统。","BMC Plant Biology","2026-09-23T00:00:00Z",79,{"impact":58,"substance":17,"depth":16,"authority":18,"freshness":19,"relevant":20,"comment":180},"方法严谨、数据与消融实验充分，对果园智能病害监测有实用参考价值，但属细分技术论文，产业影响有限。",[182],{"name":176,"url":173},[25,26,163,184,185],"病害识别","柑橘种植",[187,188],"柑橘病害 深度学习 分类","ViT PCA SVM 病害识别","柑橘病害深度学习分类-3368","10.1186\u002Fs12870-026-09984-8",{"doi":190,"openalex_id":192,"authors":193,"venue":176,"cited_by_count":34,"oa_url":173,"card":205,"direction":146,"ingested_from":94},"W7214068709",[194,196,198,200,203],{"name":195,"orcid":8},"Amruta Hingmire",{"name":197,"orcid":8},"Avinash Golande",{"name":199,"orcid":8},"Vinodkumar Bhutnal",{"name":201,"orcid":202},"Sagar Dhanraj Pande","https:\u002F\u002Forcid.org\u002F0000-0003-4506-6997",{"name":204,"orcid":8},"Tanuja Pande",{"tldr":206,"method":207,"finding":208,"direction":146,"opportunity":209},"提出PCA深度特征优化框架，用ViT+SVM分类橙子病害，准确率达99.12%。","8种骨干网络提取特征，PCA降维，4种分类器，5×5交叉验证。","ViT+PCA+SVM最优，PCA降维超55%且精度不降，可解释性验证有效。","可探索轻量化模型在移动端或边缘设备的实时病害检测与多作物泛化。","2026-09-24T23:30:34.116938Z",{"id":212,"title":213,"url":214,"summary":215,"summary_zh":216,"content":8,"source_name":217,"source_url":214,"published_at":218,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":219,"score_detail":220,"sources":222,"tags":224,"search_phrases":228,"slug":231,"view_count":34,"doi":232,"paper":233,"created_at":249},3367,"Digital Technology Adoption Conditioning Analysis Model in Agriculture","https:\u002F\u002Fdoi.org\u002F10.20944\u002Fpreprints202609.1880.v1","Technological advancements have been responsible for a significant part of the growth in agricultural productivity in recent years. Digital technologies have a high potential to enable the development of the agricultural sector, reshape value chains, and significantly contribute to more productive, resilient, and transparent food systems; however, their adoption in Brazil remains uneven due to regional disparities and structural bottlenecks. The research investigated this problem to build and validate the Digital Technology Adoption Conditioning Analysis Model (MAC-AgriTech), through a case study with Brazilian agricultural data, encompassing the identification of conditioning factors, their territorial evaluation, and the proposition of actions, while providing structured data collection and analysis instruments. The spatial analysis revealed deep territorial asymmetries, concentrating resources and infrastructure in the South and Southeast regions. Econometric modeling demonstrated that digital adoption is primarily driven by the producer’s digital familiarity, connectivity quality, and property scale, with 77% of producers identifying acquisition and maintenance costs as the primary barrier. The transition to digital agriculture in Brazil requires targeted, multidimensional public policies—such as expanded rural connectivity, technical training, and subsidized credit—to overcome regional gaps, and to increase agricultural competitiveness, efficiency, and sustainability.","近年来，技术进步对农业生产力增长贡献显著。数字技术具有巨大潜力，能够推动农业部门发展、重塑价值链，并为构建更高产、更具韧性且更透明的粮食体系作出重要贡献；然而，由于区域差异和结构性瓶颈，其在巴西的采用仍不均衡。本研究针对这一问题，通过一项基于巴西农业数据的案例研究，构建并验证了数字技术采用条件分析模型（MAC-AgriTech），涵盖条件因素的识别、其区域性评估以及行动建议的提出，同时提供了结构化的数据收集与分析工具。空间分析揭示了深刻的区域不对称性，资源和基础设施集中在南部和东南部地区。计量经济建模表明，数字采用主要受生产者数字熟悉度、连接质量和财产规模的驱动，其中77%的生产者将购置和维护成本视为主要障碍。巴西向数字农业的转型需要有针对性的、多维度的公共政策——如扩大农村连接、技术培训和补贴信贷——以克服区域差距，并提高农业竞争力、效率和可持续性。","Preprints.org","2026-09-22T00:00:00Z",67,{"impact":58,"substance":59,"depth":60,"authority":46,"freshness":61,"relevant":20,"comment":221},"基于巴西农业数据的数字技术采纳条件分析模型研究，方法系统、结论有实证支撑，但属预印本且聚焦巴西，对国内参考价值有限。",[223],{"name":217,"url":214},[225,25,26,226,227],"数字农业","巴西农业","农村数字化",[229,230],"巴西 数字农业 技术采纳","MAC-AgriTech 模型","巴西数字农业技术采纳-3367","10.20944\u002Fpreprints202609.1880.v1",{"doi":232,"openalex_id":234,"authors":235,"venue":217,"cited_by_count":34,"oa_url":214,"card":243,"direction":247,"ingested_from":94},"W7214109425",[236,239,241],{"name":237,"orcid":238},"Isabela Santos","https:\u002F\u002Forcid.org\u002F0009-0002-3659-2020",{"name":240,"orcid":8},"Eduardo Dias",{"name":242,"orcid":8},"Lidia Scoton",{"tldr":244,"method":245,"finding":246,"direction":247,"opportunity":248},"构建并验证MAC-AgriTech模型，分析巴西农业数字技术采纳的条件因素与区域差异。","巴西农业数据案例研究，空间分析与计量经济建模。","采纳主要由数字熟悉度、连接质量和农场规模驱动，77%生产者视成本为首要障碍。","数字乡村与农业信息化","可延伸至中国等发展中国家，探究数字素养、基础设施与政策组合对技术采纳的因果效应。","2026-09-24T23:30:27.046035Z",{"id":251,"title":252,"url":253,"summary":254,"summary_zh":255,"content":8,"source_name":256,"source_url":253,"published_at":177,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":257,"score_detail":258,"sources":260,"tags":262,"search_phrases":266,"slug":269,"view_count":34,"doi":270,"paper":271,"created_at":311},3366,"Brazilian Insect Survey: A Platform for Pest Management","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs13744-026-01426-2","Abstract Modern agriculture faces significant challenges in integrated pest management, where collecting, connecting, and processing monitoring data in real time are essential. This study presents the Brazilian Insect Survey (BIS), a web-based platform designed to centralize and streamline phytosanitary data management, enabling the integration of computer vision, field experimentation, and population modeling within a unified digital ecosystem. The platform is organized into functional modules that support experimental data management (TrapSystem and AgroExperiment), automated insect detection and counting from digital images (InsectCV and AphidCV), and simulation of aphid population dynamics (ABISM). These components operate in synergy, supported by distributed processing infrastructure, to ensure scalable data handling and efficient analytical workflows. Multi-year field applications demonstrate that integrating field data acquisition and automated image analysis with mechanistic population modeling enables timely, model-driven interventions that reduce aphid infestation levels and protect crop yield potential under variable environmental conditions, providing empirical evidence for queryPlease check if the captured keywords are correct.the effectiveness of combining AI-based insect detection with population dynamics modeling in operational integrated pest management. The case studies presented here demonstrate that the BIS platform successfully integrates computer vision, field experimentation, and population modeling within a modular digital ecosystem, highlighting its potential to enhance decision-making and advance data-driven integrated pest management.","摘要 现代农业在有害生物综合治理方面面临重大挑战，其中实时采集、连接和处理监测数据至关重要。本研究提出了巴西昆虫调查平台（Brazilian Insect Survey，BIS），这是一个基于网络的平台，旨在集中化和简化植物检疫数据管理，使计算机视觉、田间试验和种群建模能够整合在一个统一的数字生态系统中。该平台按功能模块组织，支持实验数据管理（TrapSystem和AgroExperiment）、基于数字图像的昆虫自动检测与计数（InsectCV和AphidCV），以及蚜虫种群动态模拟（ABISM）。这些组件在分布式处理基础设施的支持下协同运行，以确保可扩展的数据处理和高效的分析工作流。多年田间应用表明，将田间数据采集和自动图像分析与机制性种群建模相结合，能够实现及时的、模型驱动的干预，从而在多变的环境条件下降低蚜虫侵染水平并保护作物产量潜力，为将基于人工智能的昆虫检测与种群动态建模相结合在实际有害生物综合治理中的有效性提供了经验证据。本文所呈现的案例研究表明，BIS平台成功地将计算机视觉、田间试验和种群建模整合在一个模块化数字生态系统中，凸显了其在增强决策能力和推进数据驱动有害生物综合治理方面的潜力。","Neotropical Entomology",80,{"impact":16,"substance":17,"depth":16,"authority":18,"freshness":61,"relevant":20,"comment":259},"巴西昆虫调查平台将计算机视觉与种群动态模型整合进IPM实践，方法新颖、多年田间验证，对智慧农业病虫害监测有参考价值。",[261],{"name":256,"url":253},[25,26,263,264,265],"计算机视觉","病虫害监测","种群模型",[267,268],"Brazil 蚜虫 监测平台","农业人工智能 病虫害监测 计算机视觉 智慧农业","Brazil蚜虫监测平台-3366","10.1007\u002Fs13744-026-01426-2",{"doi":270,"openalex_id":272,"authors":273,"venue":256,"cited_by_count":34,"oa_url":253,"card":305,"direction":247,"ingested_from":94},"W7214074071",[274,277,279,282,285,287,290,293,296,299,302],{"name":275,"orcid":276},"Telmo De Cesaro Júnior","https:\u002F\u002Forcid.org\u002F0000-0002-2886-229X",{"name":278,"orcid":8},"Bárbara Stella Wehrmann",{"name":280,"orcid":281},"Alexandre Tagliari Lazzaretti","https:\u002F\u002Forcid.org\u002F0009-0004-8345-8216",{"name":283,"orcid":284},"Roberto Wiest","https:\u002F\u002Forcid.org\u002F0000-0001-7982-6603",{"name":286,"orcid":8},"Jorge Luis Boeira Bavaresco",{"name":288,"orcid":289},"Brenda Slongo Taca","https:\u002F\u002Forcid.org\u002F0009-0000-6408-5523",{"name":291,"orcid":292},"Nicolas Welfer Kirinus","https:\u002F\u002Forcid.org\u002F0009-0003-5260-3161",{"name":294,"orcid":295},"Crislaine Sartori Suzana Milan","https:\u002F\u002Forcid.org\u002F0000-0003-0562-7286",{"name":297,"orcid":298},"Jayme Garcia Arnal Barbedo","https:\u002F\u002Forcid.org\u002F0000-0002-1156-8270",{"name":300,"orcid":301},"Douglas Lau","https:\u002F\u002Forcid.org\u002F0000-0001-8648-0102",{"name":303,"orcid":304},"Rafael Rieder","https:\u002F\u002Forcid.org\u002F0000-0002-7435-9054",{"tldr":306,"method":307,"finding":308,"direction":309,"opportunity":310},"巴西昆虫调查平台整合计算机视觉、田间试验与种群建模，实现害虫实时监测与治理。","构建BIS网络平台，集成InsectCV\u002FAphidCV图像识别、TrapSys","AI昆虫检测与种群动态建模结合可降低蚜虫侵害并保护作物产量。","智慧农业 \u002F 农业物联网","可探索多害虫跨区域监测数据标准化与模型迁移，构建开放植保决策生态。","2026-09-24T23:30:25.977846Z"]