[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1929":3,"related-1929":78},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":8,"published_at":11,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":24,"tags":26,"primary_topic":32,"secondary_topics":38,"classification":39,"search_phrases":73,"slug":76,"view_count":21,"doi":8,"paper":8,"created_at":77},1929,"中国农科院资划所举办\"天然草原智能放牧与草畜精准管控技术\"项目培训班","https:\u002F\u002Fiarrp.caas.cn\u002Fysdt\u002Fzhxw\u002F8a849f901c884b10b461fd8945988996.htm","中国农业科学院农业资源与农业区划研究所联合全国畜牧总站主办的\"天然草原智能放牧与草畜精准管控技术\"项目培训班（一期）8月20—23日在内蒙古呼伦贝尔市举行。来自中国农业科学院、中国科学院地理科学与资源研究所、中国农业大学、北京理工大学及中科星图股份有限公司的多位专家学者围绕天然草原植被参数遥感监测、机器视觉在畜牧养殖中的应用、机器人放牧应用展望、刈牧草地地力精准诊断系统等前沿议题作深入系统的报告；并前往谢尔塔拉天然草原现场观摩智能放牧演示。",null,"为加快推进“十四五”国家重点研发计划“天然草原智能放牧与草畜精准管控技术”项目相关技术、配套设备及成果的落地应用，全面提升农牧民和基层技术人员的现代牧场管理水平，由中国农业科学院农业资源与农业区划研究所联合全国畜牧总站主办的“天然草原智能放牧与草畜精准管控技术”项目培训班（一期）于2026年8月20-23日在内蒙古呼伦贝尔市举行。该培训班依托内蒙古呼伦贝尔国家野外科学观测研究站（呼伦贝尔站）举办，吸引了来自甘肃、内蒙古、黑龙江等地的多位行业专家、基层业务骨干及科研人员共同参与。\n\n8月21日，培训班举行了开班仪式与专题学术报告会。开班式上，呼伦贝尔站站长辛晓平研究员致欢迎辞，会议主持人资划所邵长亮研究员代表项目组对各位远道而来的领导、特邀专家以及基层学员表示了热烈的欢迎，并就本次培训班举办的战略背景、核心目标及培训纪律要求作了全面阐述。全国畜牧总站副处长齐晓、内蒙古自治区农牧业技术推广中心饲料饲草技术处处长王永杰以及呼伦贝尔市畜牧技术推广中心主任祁航分别作了开班讲话，对项目的战略意义及未来应用前景提出了殷切期望与明确要求。随后，来自中国农业科学院、中国科学院地理科学与资源研究所、中国农业大学、北京理工大学及中科星图股份有限公司的多位知名专家学者围绕天然草原植被参数遥感监测、大型野生食草动物与家畜调查方法、高时频植被冠层生产力监测仪研发、草原牧场“一张图”原型系统、机器视觉在畜牧养殖中的应用、机器人放牧应用展望、草地改良修复与合理利用技术、刈牧草地地力精准诊断系统以及天然草原放牧智能管控云平台等前沿议题作了深入系统的报告。\n\n![Image 1](https:\u002F\u002Fiarrp.caas.cn\u002Fimages\u002F2026-09\u002Fd7bc90fabd7e4429aec79fc0017c021d.jpeg)![Image 2](https:\u002F\u002Fiarrp.caas.cn\u002Fimages\u002F2026-09\u002Fff1876356c3444cdb547b314492e7add.jpeg)\n\n![Image 3](https:\u002F\u002Fiarrp.caas.cn\u002Fimages\u002F2026-09\u002F0d1fdd3cbfec4ab6a5d97dd1238f7c5a.jpeg)![Image 4](https:\u002F\u002Fiarrp.caas.cn\u002Fimages\u002F2026-09\u002F5434a9570d1d49e48a309498459ffc52.jpeg)\n\n![Image 5](https:\u002F\u002Fiarrp.caas.cn\u002Fimages\u002F2026-09\u002F54ee61c5e4cf4145af2d8bf048ac28f5.jpeg)![Image 6](https:\u002F\u002Fiarrp.caas.cn\u002Fimages\u002F2026-09\u002F6a3159c84ca14403a17d9d6d2c700dea.jpeg)\n\n在理论授课之外，培训班高度注重实践与示范。8月22日，与会人员前往谢尔塔拉天然草原现场观摩智能放牧演示，并实地考察了呼伦贝尔楚拉乳业示范基地与陈巴尔虎旗草原修复平台，通过现场演示与成果展示，直观感受到了智能放牧与草畜精准管控技术在现代草原生态保护及高效利用中的巨大潜力和显著成效。\n\n![Image 7](https:\u002F\u002Fiarrp.caas.cn\u002Fimages\u002F2026-09\u002F2bd853d3d5704fcc97b92519e8e43ed4.jpeg)![Image 8](https:\u002F\u002Fiarrp.caas.cn\u002Fimages\u002F2026-09\u002F9b8ebba8168549919e6b72ebc45d9bd5.jpeg)\n\n![Image 9](https:\u002F\u002Fiarrp.caas.cn\u002Fimages\u002F2026-09\u002Fe2a0272fa0bc4c2ba5d37c35265178d7.jpeg)![Image 10](https:\u002F\u002Fiarrp.caas.cn\u002Fimages\u002F2026-09\u002F2658923e49a84b3b967251e3df682c6a.jpeg)\n\n![Image 11](https:\u002F\u002Fiarrp.caas.cn\u002Fimages\u002F2026-09\u002F4787d987d6824fe6adb28f9fc52b6c7d.jpeg)![Image 12](https:\u002F\u002Fiarrp.caas.cn\u002Fimages\u002F2026-09\u002Ffc6516914f30418cabce1b7ce04ad730.jpeg)\n\n本次培训班紧密围绕国家生态文明建设与草原畜牧业高质量发展需求，内容涵盖理论前沿、核心技术攻关以及基层应用示范。与会代表纷纷表示，通过此次系统培训，不仅拓宽了视野、掌握了前沿智能技术，更为今后在基层的推广应用奠定了坚实基础，将为助力我国草原生态保护与草原畜牧业转型升级积极贡献力量。","中国农业科学院农业资源与农业区划研究所 2026-08-23","2026-08-23T00:00:00Z","报道",10,false,69,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},20,18,15,14,2,1,"国家级科研机构主办的智能放牧技术培训，内容详实，但时效性较低，仍具行业参考价值。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","技术培训","智能放牧","草原生态","草畜平衡",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"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":43},"classified","2026-10-22.2","rules",{"candidates":44,"agricultural_evidence":48},[45,53,58,63,68],{"code":46,"score":47,"supported":48,"title":49,"abstract":50,"tags":52},"C",3,true,[],[51],"畜牧",[],{"code":33,"score":47,"supported":48,"title":54,"abstract":55,"tags":57},[],[56],"遥感",[],{"code":59,"score":22,"supported":14,"title":60,"abstract":61,"tags":62},"G1",[],[],[28],{"code":64,"score":22,"supported":14,"title":65,"abstract":66,"tags":67},"H6",[],[],[30],{"code":69,"score":22,"supported":14,"title":70,"abstract":71,"tags":72},"I1",[],[],[27],[74,75],"技术培训 智慧农业 智能放牧 草原生态","技术培训 智慧农业","技术培训智慧农业智能放牧草原生态-1929","2026-09-09T00:03:54.176268Z",{"total":79,"page":22,"page_size":79,"items":80},6,[81,167,276,367,427,476],{"id":82,"title":83,"url":84,"summary":85,"summary_zh":86,"content":8,"source_name":87,"source_url":84,"published_at":88,"category":89,"cover_url":8,"hotness":13,"is_selected":14,"score":90,"score_detail":91,"sources":96,"tags":98,"primary_topic":102,"secondary_topics":103,"classification":104,"search_phrases":129,"slug":132,"view_count":21,"doi":133,"paper":134,"created_at":166},2536,"Mapping Native Grass Cover with Random Forest Models: Sentinel-2 Versus Sentinel-2 Combined with Sentinel-1 SAR-Derived GLCM Texture Metrics","https:\u002F\u002Fdoi.org\u002F10.3390\u002Frs18183150","Temperate native grasslands in southeastern Australia have been extensively cleared for agriculture, and the remaining patches are under growing pressure from further land use change, climate variability, and invasive species. Mapping and monitoring their distribution and the cover of native and exotic grasses are critical for their conservation and management. Field-based methods are not always scalable or time-effective, and this study aimed to develop a scalable method to map and monitor the fractional cover-class maps of native C3 and native C4 grass cover as a component of remnant native grasslands on the western outskirts of Melbourne, Victoria, Australia. Field-based reference data for training and validation of random forest machine learning models were collected across multiple sites in 2021. Sentinel-2 optical spectral bands and vegetation indices were used as the primary input data, and Sentinel-1 Synthetic Aperture Radar (SAR)-derived Grey Level Co-occurrence Matrix (GLCM) texture metrics were assessed for their capacity to improve the model. Results show that random forest models trained on Sentinel-2 data without GLCM texture information derived from Sentinel-1 SAR data provided a moderate overall accuracy (C3: 59.1%, C4: 78.1%). Class-specific metrics showed that reliability was highest for better represented lower-cover classes, particularly the 6–25% native C3 class and the 0–5% native C4 class, while higher-cover classes were less reliable because of the limited number of training and validation samples. Grass cover fractions were modelled well for sparse to moderate grass cover, but dense grass cover was not modelled accurately, probably due to limited high-cover samples in the training dataset. Model performance was not improved by the inclusion of Sentinel-1 SAR-derived GLCM texture metrics, indicating that C-band VH-polarised SAR is not sensitive to the fine-scale structural heterogeneity that characterises native grassland ecosystems. Sparse native C3 and C4 grasses could be mapped most reliably in the lower-cover classes as a component of grasslands with optical remote sensing, and the method developed here can now be applied to enable evidence-based management of grasslands, biodiversity conservation and the monitoring of grassland composition in the WGR and elsewhere. Higher-resolution structural datasets and more sophisticated machine learning approaches may be required to accurately predict native C3 and C4 grass cover fractions in denser grasslands.","澳大利亚东南部的温带原生草原已被大面积开垦用于农业，残余斑块正面临土地利用进一步变化、气候变率和入侵物种日益增大的压力。对其分布以及原生和外来草类覆盖度进行制图和监测，对于草原的保护和管理至关重要。基于实地调查的方法并非总是可扩展或省时的，本研究旨在开发一种可扩展的方法，以制图和监测作为残余原生草原组成部分的原生C3和原生C4草类覆盖度的分数覆盖等级图，研究区位于澳大利亚维多利亚州墨尔本西郊。用于训练和验证随机森林机器学习模型的实地参考数据于2021年在多个样点采集。研究以Sentinel-2光学光谱波段和植被指数作为主要输入数据，并评估了Sentinel-1合成孔径雷达（SAR）衍生的灰度共生矩阵（GLCM）纹理指标对提升模型性能的能力。结果表明，仅使用Sentinel-2数据（不含Sentinel-1 SAR衍生的GLCM纹理信息）训练的随机森林模型提供了中等的总体精度（C3：59.1%，C4：78.1%）。分类别指标显示，对于代表性较好的低覆盖度类别，可靠性最高，尤其是6–25%原生C3类别和0–5%原生C4类别，而较高覆盖度类别的可靠性较低，原因是训练和验证样本数量有限。草类覆盖度分数在稀疏至中等草类覆盖条件下建模效果良好，但茂密草类覆盖未能准确建模，可能是由于训练数据集中高覆盖度样本有限。纳入Sentinel-1 SAR衍生的GLCM纹理指标并未改善模型性能，表明C波段VH极化SAR对原生草原生态系统所特有的精细尺度结构异质性不敏感。作为草原的组成部分，稀疏的原生C3和C4草类在低覆盖度类别中利用光学遥感可最可靠地制图，本研究开发的方法现可应用于西维多利亚草原（WGR）及其他地区，以实现基于证据的草原管理、生物多样性保护和草原组成监测。准确预测原生C3和C4草类覆盖度分数可能需要更高分辨率的结构数据集和更先进的机器学习方法。","Remote Sensing","2026-09-13T00:00:00Z","论文",71,{"impact":92,"substance":17,"depth":93,"authority":20,"freshness":94,"relevant":22,"comment":95},12,17,8,"方法新颖、结论明确（SAR纹理未提升精度），对草地遥感监测有参考价值，但属细分领域研究，影响范围有限。",[97],{"name":87,"url":84},[27,99,100,30,101],"机器学习","遥感监测","植被覆盖",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":105},{"candidates":106,"agricultural_evidence":48},[107,112,119,125],{"code":33,"score":94,"supported":48,"title":108,"abstract":109,"tags":111},[],[56,110],"remote sensing",[101,100],{"code":64,"score":113,"supported":48,"title":114,"abstract":115,"tags":118},7,[],[116,117],"生物多样性","biodiversity",[30],{"code":120,"score":113,"supported":48,"title":121,"abstract":122,"tags":124},"I2",[],[99,123],"machine learning",[99],{"code":69,"score":22,"supported":14,"title":126,"abstract":127,"tags":128},[],[],[27],[130,131],"智慧农业 机器学习 植被覆盖 草原生态","智慧农业 机器学习","智慧农业机器学习植被覆盖草原生态-2536","10.3390\u002Frs18183150",{"doi":133,"openalex_id":135,"authors":136,"venue":87,"cited_by_count":158,"oa_url":84,"card":159,"direction":163,"ingested_from":165},"W7212561645",[137,140,143,146,148,150,153,155],{"name":138,"orcid":139},"Sabah Sabaghy","https:\u002F\u002Forcid.org\u002F0000-0002-9453-8922",{"name":141,"orcid":142},"M. Abuzar","https:\u002F\u002Forcid.org\u002F0000-0002-6101-1307",{"name":144,"orcid":145},"Steve J. Sinclair","https:\u002F\u002Forcid.org\u002F0000-0002-4282-1021",{"name":147,"orcid":8},"Tony Dugdale",{"name":149,"orcid":8},"Vanessa Hutchins",{"name":151,"orcid":152},"Yogendra K. Karna","https:\u002F\u002Forcid.org\u002F0000-0002-2120-4710",{"name":154,"orcid":8},"Jonathan Wilson",{"name":156,"orcid":157},"Kathryn Sheffield","https:\u002F\u002Forcid.org\u002F0000-0003-2624-9739",0,{"tldr":160,"method":161,"finding":162,"direction":163,"opportunity":164},"用随机森林结合Sentinel-2与Sentinel-1纹理特征，绘制澳洲原生草地C3\u002FC4草覆盖度","2021年野外样点训练随机森林，Sentinel-2光谱与植被指数为主，Sent","仅用Sentinel-2精度中等（C3 59.1%、C4 78.1%），加入SAR纹理未提升，高覆盖","农业遥感与作物表型","高覆盖度草地样本不足且C波段SAR不敏感，可探索高分辨率结构数据与深度模型提升密草覆盖反演。","openalex","2026-09-15T23:30:21.287053Z",{"id":168,"title":169,"url":170,"summary":171,"summary_zh":172,"content":8,"source_name":173,"source_url":170,"published_at":174,"category":89,"cover_url":8,"hotness":13,"is_selected":14,"score":175,"score_detail":176,"sources":180,"tags":182,"primary_topic":187,"secondary_topics":188,"classification":193,"search_phrases":248,"slug":251,"view_count":158,"doi":252,"paper":253,"created_at":275},5582,"AI-Based RGB Imaging and 14-Channel Multispectral Food Quality Scanner","https:\u002F\u002Fdoi.org\u002F10.22214\u002Fijraset.2026.85255","Food-quality assessment plays an important role in food safety, agricultural processing, storage, and supply-chain management. Conventional inspection methods depend on visual judgement or laboratory-based testing, which may require considerable time, specialised equipment, and manual effort. Recent developments in computer vision, multispectral sensing, and artificial intelligence (AI) provide opportunities for automated, non-destructive food-quality assessment. This paper reviews the application of RGB imaging, multispectral sensing, machine learning, and deep learning for fruit-ripeness estimation, surfacedefect detection, grain classification, and food-authenticity assessment. The reviewed literature demonstrates that RGB imaging can extract visible features such as colour, shape, and texture, whereas multispectral sensing provides wavelength-dependent information associated with food characteristics. Published studies have reported promising results in tomato-ripeness classification, apple-defect detection, rice-grain analysis, and honey-adulteration screening. Based on these findings, a compact food-quality scanner integrating an RGB camera, a 14-channel multispectral sensor, controlled illumination, and an embedded processing unit is proposed. The review discusses sensor integration, data preprocessing, feature fusion, classification methods, evaluation metrics, and implementation challenges. It also identifies the need for broader datasets, standardised illumination, independent validation, and resource-efficient AI models to improve the reliability of portable food-inspection systems.","食品质量评估在食品安全、农产品加工、储运和供应链管理中发挥着重要作用。传统检测方法依赖人工目视判断或实验室检测，往往需要较长时间、专用设备和人工投入。近年来，计算机视觉、多光谱传感和人工智能（AI）的发展为自动化、无损食品质量评估提供了新的机遇。本文综述了RGB成像、多光谱传感、机器学习和深度学习在果实成熟度估计、表面缺陷检测、谷物分类和食品真实性评估中的应用。所综述的文献表明，RGB成像能够提取颜色、形状和纹理等可见特征，而多光谱传感则可提供与食品特性相关的波长依赖信息。已发表研究在番茄成熟度分类、苹果缺陷检测、稻米籽粒分析和蜂蜜掺假筛查等方面取得了良好结果。基于上述研究结果，本文提出一种集成了RGB相机、14通道多光谱传感器、受控照明和嵌入式处理单元的紧凑型食品质量扫描仪。综述讨论了传感器集成、数据预处理、特征融合、分类方法、评价指标和实现挑战，并指出需要更广泛的数据集、标准化照明、独立验证和资源高效的AI模型，以提高便携式食品检测系统的可靠性。","International Journal for Research in Applied Science and Engineering Technology","2026-10-21T00:00:00Z",65,{"impact":92,"substance":18,"depth":177,"authority":178,"freshness":13,"relevant":22,"comment":179},16,9,"综述性论文，提出集成RGB与14通道多光谱的便携食品质量扫描仪方案，方法框架清晰但缺乏自有实验数据，属细分领域技术进展，可作参考但非重大突破。",[181],{"name":173,"url":170},[27,183,184,185,186],"农业人工智能","无损检测","多光谱成像","食品质量检测",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[189],{"code":120,"slug":190,"name":191,"path":192,"parent_code":37},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"status":40,"locked":14,"version":41,"method":42,"evidence":194},{"candidates":195,"agricultural_evidence":48},[196,201,210,217,224,231,238,244],{"code":33,"score":92,"supported":48,"title":197,"abstract":199,"tags":200},[198],"multispectral",[198],[185],{"code":120,"score":13,"supported":48,"title":202,"abstract":203,"tags":209},[],[204,99,205,123,206,207,208],"深度学习","计算机视觉","deep learning","artificial intelligence","computer vision",[183],{"code":211,"score":113,"supported":48,"title":212,"abstract":213,"tags":216},"E4",[],[214,215],"食品安全","food safety",[184],{"code":218,"score":79,"supported":48,"title":219,"abstract":220,"tags":223},"A1",[],[221,222],"稻米","rice",[],{"code":225,"score":79,"supported":48,"title":226,"abstract":227,"tags":230},"B1",[],[228,229],"番茄","tomato",[],{"code":232,"score":79,"supported":48,"title":233,"abstract":234,"tags":237},"B2",[],[235,236],"苹果","apple",[],{"code":239,"score":47,"supported":48,"title":240,"abstract":241,"tags":243},"E1",[],[242],"农产品加工",[],{"code":69,"score":22,"supported":14,"title":245,"abstract":246,"tags":247},[],[],[27],[249,250],"RGB成像 多光谱 食品质量","14通道 多光谱 扫描仪","RGB成像多光谱食品质量-5582","10.22214\u002Fijraset.2026.85255",{"doi":252,"openalex_id":254,"authors":255,"venue":173,"cited_by_count":158,"oa_url":170,"card":268,"direction":274,"ingested_from":165},"W7223139905",[256,258,260,262,264,266],{"name":257,"orcid":8},"Eshwari Take",{"name":259,"orcid":8},"Avanish Raut",{"name":261,"orcid":8},"Vedant Joshi",{"name":263,"orcid":8},"Shravani Akolkar",{"name":265,"orcid":8},"R. M. Gharat",{"name":267,"orcid":8},"Shruti Dive",{"tldr":269,"method":270,"finding":271,"direction":272,"opportunity":273},"综述RGB与多光谱成像结合AI的食品品质无损检测，并提出一款集成14通道多光谱传感器的紧凑型扫描仪方","综述RGB成像、多光谱传感与机器学习\u002F深度学习在食品检测中的应用，并提出传感器集","RGB与多光谱融合在番茄成熟度、苹果缺陷、稻米分类和蜂蜜掺假检测中表现良好。","农业人工智能与决策模型","可研究多源光谱特征融合与轻量化模型，构建标准化数据集和便携设备独立验证体系。","数字乡村与农业信息化","2026-10-22T11:20:48.684398Z",{"id":277,"title":278,"url":279,"summary":280,"summary_zh":281,"content":8,"source_name":282,"source_url":279,"published_at":174,"category":89,"cover_url":8,"hotness":13,"is_selected":14,"score":283,"score_detail":284,"sources":287,"tags":289,"primary_topic":293,"secondary_topics":299,"classification":306,"search_phrases":335,"slug":338,"view_count":158,"doi":339,"paper":340,"created_at":366},5573,"Improving alfalfa nitrogen estimation with canopy structural priors from DSSAT–PROSAIL and UAV hyperspectral data","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.eja.2026.128375","Accurate estimation of nitrogen (N) concentration in alfalfa ( Medicago sativa L .) supports forage quality assessment and harvest scheduling. Empirical remote sensing models often overfit because of confounding effects of three-dimensional canopy structure. Incorporating structural parameters can stabilize biochemical inversions, but concurrent in situ morphological measurements are difficult to acquire at scale. We developed the Morphology-Aware Latent Embedding Network (MALENet), a hybrid framework integrating physiological and morphological mechanisms with hyperspectral data. The Decision Support System for Agrotechnology Transfer (DSSAT), coupled with the PROSAIL radiative transfer model, generated a structurally rich synthetic hyperspectral prior. A convolutional neural network (CNN) with a Squeeze-and-Excitation (SE) attention mechanism, was pre-trained on these data to distill ecophysiological knowledge into a dense latent representation. This mechanistically derived embedding was then fused with empirical hyperspectral measurements collected under diverse field conditions. Evaluation using multi-year field quadrats in the Hexi Corridor showed improved predictive performance ( R² = 0.71, RMSE = 0.30%) relative to conventional machine learning and deep learning models (LASSO-ANN: R² = 0.61; CNN: R² = 0.66). Feature attribution indicated that MALENet used near-infrared multiple scattering (58.3% attribution) as a structural proxy to calibrate nitrogen concentration retrieval against the confounding effects of canopy architecture. Despite constraints imposed by sample size and PROSAIL's 1D canopy assumption, these findings demonstrate the potential of physically guided deep learning to use structural information for agricultural trait retrieval in precision forage management.","准确估算紫花苜蓿（Medicago sativa L.）的氮浓度有助于饲草品质评估和收获期安排。经验性遥感模型常因三维冠层结构的混杂效应而出现过拟合。引入结构参数可以稳定生化反演，但同步开展原位形态学测量难以大规模获取。我们开发了形态感知潜在嵌入网络（Morphology-Aware Latent Embedding Network，MALENet），这是一个将生理与形态机制同高光谱数据相结合的混合框架。农业技术转移决策支持系统（Decision Support System for Agrotechnology Transfer，DSSAT）与PROSAIL辐射传输模型耦合，生成了结构信息丰富的合成高光谱先验数据。在此基础上，预训练了一个带有挤压—激励（Squeeze-and-Excitation，SE）注意力机制的卷积神经网络（convolutional neural network，CNN），将生态生理学知识提炼为稠密潜在表征。随后，将这一机制性导出的嵌入与在不同田间条件下采集的经验性高光谱测量数据相融合。利用河西走廊多年田间样方进行的评估显示，相较于传统机器学习和深度学习模型（LASSO-ANN：R² = 0.61；CNN：R² = 0.66），预测性能有所提升（R² = 0.71，RMSE = 0.30%）。特征归因表明，MALENet利用近红外多次散射（归因占比58.3%）作为结构代理，以校准氮浓度反演，抵御冠层结构的混杂效应。尽管受样本量和PROSAIL一维冠层假设的限制，这些发现表明，物理引导的深度学习有潜力利用结构信息，在精准饲草管理中进行农业性状反演。","European Journal of Agronomy",80,{"impact":177,"substance":285,"depth":18,"authority":20,"freshness":13,"relevant":22,"comment":286},22,"该研究将作物模型与辐射传输模型耦合生成结构先验，并结合无人机高光谱数据反演苜蓿氮素，方法新颖、田间验证扎实，对精准饲草管理有参考价值，值得进入每日精选。",[288],{"name":282,"url":279},[27,183,290,291,292],"农业遥感","氮素监测","紫花苜蓿",{"code":294,"slug":295,"name":296,"path":297,"parent_code":298},"A7","forage-cropping","饲草与耕作制度","\u002Ftopic\u002Ffield-crops\u002Fforage-cropping","A",[300,301],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"code":302,"slug":303,"name":304,"path":305,"parent_code":46},"C4","feed","饲料与饲草","\u002Ftopic\u002Flivestock\u002Ffeed",{"status":40,"locked":14,"version":41,"method":42,"evidence":307},{"candidates":308,"agricultural_evidence":48},[309,314,320,326,331],{"code":33,"score":18,"supported":48,"title":310,"abstract":312,"tags":313},[311],"hyperspectral",[56,110,311],[290],{"code":294,"score":19,"supported":48,"title":315,"abstract":317,"tags":319},[316],"alfalfa",[318,316],"苜蓿",[292],{"code":302,"score":13,"supported":48,"title":321,"abstract":322,"tags":325},[],[323,318,324],"饲草","forage",[292],{"code":120,"score":13,"supported":48,"title":327,"abstract":328,"tags":330},[],[204,99,123,206,329],"decision support",[183],{"code":69,"score":22,"supported":14,"title":332,"abstract":333,"tags":334},[],[],[27],[336,337],"DSSAT PROSAIL 苜蓿 氮素","无人机 高光谱 苜蓿","DSSATPROSAIL苜蓿氮素-5573","10.1016\u002Fj.eja.2026.128375",{"doi":339,"openalex_id":341,"authors":342,"venue":282,"cited_by_count":158,"oa_url":279,"card":361,"direction":163,"ingested_from":165},"W7223154576",[343,345,347,349,351,354,356,359],{"name":344,"orcid":8},"Shuai Fu",{"name":346,"orcid":8},"Yunhao Li",{"name":348,"orcid":8},"Chunli Miao",{"name":350,"orcid":8},"Jie Liu",{"name":352,"orcid":353},"Tiangang Liang","https:\u002F\u002Forcid.org\u002F0000-0001-5599-6587",{"name":355,"orcid":8},"Yongjia Hu",{"name":357,"orcid":358},"Qisheng Feng","https:\u002F\u002Forcid.org\u002F0009-0000-1391-9591",{"name":360,"orcid":8},"吴彩霞",{"tldr":362,"method":363,"finding":364,"direction":163,"opportunity":365},"构建MALENet融合DSSAT-PROSAIL结构先验与无人机高光谱，提升苜蓿氮浓度估算精度。","DSSAT耦合PROSAIL生成合成高光谱先验，预训练CNN-SE网络，再融合田","MALENet精度优于传统机器学习和深度学习模型，R²达0.71，近红外多次散射作为结构代理贡献58","可探索将作物模型结构先验迁移至其他牧草或作物生化参数反演，并缓解PROSAIL一维冠层假设局限。","2026-10-22T11:20:39.622844Z",{"id":368,"title":369,"url":370,"summary":371,"summary_zh":372,"content":8,"source_name":373,"source_url":370,"published_at":174,"category":89,"cover_url":8,"hotness":13,"is_selected":14,"score":374,"score_detail":375,"sources":377,"tags":379,"primary_topic":382,"secondary_topics":383,"classification":384,"search_phrases":402,"slug":405,"view_count":158,"doi":406,"paper":407,"created_at":426},5571,"Temporal Multi-Scale Spatio-Temporal Network (TMST): Polarization–Temporal Feature Selection and Multi-Scale Spatiotemporal Fusion for Crop Mapping","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fagronomy16202068","Accurate and timely crop mapping supports agricultural resource management, yield prediction, and food security decision-making. In this study, we propose the Temporal Multi-scale Spatio-Temporal Network (TMST), a crop mapping method that integrates multi-source remote sensing time series through polarization–temporal feature selection and multi-scale spatiotemporal fusion. TMST combines Sentinel-1 dual-polarization synthetic aperture radar (SAR), Sentinel-2 multispectral data, and vegetation indices. Its Temporal Feature Importance Explainer (TFIExplainer) selects informative polarization–time features, the Multi-Scale Attention Aggregation (MSAA) module enhances spatial representations, and the Spatio-Temporal Feature Encoder (STFE) models long-range spatial dependencies and phenological dynamics; a composite loss improves minority-class and difficult-pixel recognition. On the Xinxiang dataset, TMST achieved an overall accuracy (OA) of 0.8625 and a mean F1-score (mF1) of 0.8329. Module and data-source ablations suggested benefits of the combined modules and multi-source fusion, while a cropland mask reduced non-cropland misclassification. In direct cross-dataset evaluation, models trained on a self-constructed Austrian dataset and tested on the radar-extended Panoptic Agricultural Satellite Time Series (PASTIS-R) dataset showed that TMST outperformed the evaluated baselines under the specified source–target setting. Annual mapping of Xinxiang for 2019–2023, using year-specific samples and independently trained models, provided map-based estimates of regional cropping structures and spatial patterns. Under the evaluated settings, TMST provides an effective approach to crop classification and annually updated crop mapping.","准确的作物制图有助于农业资源管理、产量预测和粮食安全决策。本研究提出了一种时间多尺度时空网络（Temporal Multi-scale Spatio-Temporal Network，TMST），这是一种通过极化—时间特征选择和多尺度时空融合来整合多源遥感时间序列的作物制图方法。TMST结合了Sentinel-1双极化合成孔径雷达（SAR）、Sentinel-2多光谱数据和植被指数。其时间特征重要性解释器（Temporal Feature Importance Explainer，TFIExplainer）用于选择信息量丰富的极化—时间特征，多尺度注意力聚合（Multi-Scale Attention Aggregation，MSAA）模块增强空间表征，时空特征编码器（Spatio-Temporal Feature Encoder，STFE）建模长程空间依赖关系和物候动态；复合损失函数改善了少数类和困难像元的识别。在新乡数据集上，TMST取得了0.8625的总体精度（OA）和0.8329的平均F1分数（mF1）。模块和数据源消融实验表明，组合模块和多源融合具有优势，而耕地掩膜减少了非耕地误分类。在直接的跨数据集评估中，在自行构建的奥地利数据集上训练并在雷达扩展的Panoptic农业卫星时间序列（PASTIS-R）数据集上测试的模型表明，在指定的源—目标设置下，TMST优于所评估的基线方法。使用逐年样本和独立训练的模型对新乡2019—2023年进行年度制图，提供了基于制图的区域种植结构和空间格局估计。在所评估的设置下，TMST为作物分类和年度更新的作物制图提供了一种有效方法。","Agronomy",73,{"impact":92,"substance":17,"depth":93,"authority":20,"freshness":13,"relevant":22,"comment":376},"提出多源遥感融合的作物制图新方法，方法新颖、实验充分，但属细分技术进展，公共影响有限。",[378],{"name":373,"url":370},[27,380,56,381],"作物制图","SAR",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":385},{"candidates":386,"agricultural_evidence":48},[387,391,398],{"code":33,"score":13,"supported":48,"title":388,"abstract":389,"tags":390},[],[56,110,198],[56],{"code":392,"score":79,"supported":48,"title":393,"abstract":394,"tags":397},"A8",[],[395,396],"粮食安全","food security",[],{"code":69,"score":22,"supported":14,"title":399,"abstract":400,"tags":401},[],[],[27],[403,404],"Sentinel-1 Sentinel-2 作物分类","TMST 多尺度时空网络","Sentinel-1Sentinel-2作物分类-5571","10.3390\u002Fagronomy16202068",{"doi":406,"openalex_id":408,"authors":409,"venue":373,"cited_by_count":158,"oa_url":370,"card":421,"direction":163,"ingested_from":165},"W7223117203",[410,413,415,418],{"name":411,"orcid":412},"Xi Kan","https:\u002F\u002Forcid.org\u002F0000-0002-4629-9357",{"name":414,"orcid":8},"Wentao Lv",{"name":416,"orcid":417},"Lei Gong","https:\u002F\u002Forcid.org\u002F0000-0001-6429-267X",{"name":419,"orcid":420},"Linglong Zhu","https:\u002F\u002Forcid.org\u002F0000-0002-8863-9911",{"tldr":422,"method":423,"finding":424,"direction":163,"opportunity":425},"提出TMST网络，融合SAR与多光谱时序数据实现作物制图。","Sentinel-1\u002F2时序、极化-时序特征选择、多尺度注意力与时空编码器。","新乡OA达0.8625，多源融合与模块组合有效，跨数据集优于基线。","可探索极化-时序特征选择在跨区域迁移与少样本作物制图中的泛化能力。","2026-10-22T11:20:39.359881Z",{"id":428,"title":429,"url":430,"summary":431,"summary_zh":432,"content":8,"source_name":433,"source_url":430,"published_at":174,"category":89,"cover_url":8,"hotness":13,"is_selected":14,"score":434,"score_detail":435,"sources":437,"tags":439,"primary_topic":442,"secondary_topics":443,"classification":445,"search_phrases":460,"slug":463,"view_count":158,"doi":464,"paper":465,"created_at":475},5570,"Evaluation of Counterfactual Explanation Robustness for Computer Vision Agriculture Yield-Forecast Models Under Satellite Cloud-Mask Gaps: An Interpretability Reliability Bound for Cooperative Storage Decisions","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23545079","Counterfactual explanations are increasingly proposed as the interface through which cooperative storage managers interrogate computer-vision yield-forecast models, yet their reliability under the satellite cloud-mask gaps that routinely afflict agricultural remote sensing has not been bounded. The bound is derived from a Lipschitz continuity assumption on the forecast model and a masking-operator perturbation analysis, and it is expressed as a function of the cloud-mask gap fraction, the model's local Lipschitz constant, and the counterfactual search tolerance. A replication-based evaluation of the before-after-control-impact estimator was conducted to characterise the operating characteristics of the design under a declared variance structure (between-cluster SD = 6.0, residual SD = 4.5, intraclass correlation = 0.64), with power estimated across 2000 design cells. The reliability bound degrades non-linearly as the gap fraction rises, and that the intraclass correlation of 0.64 implies that cooperative-level aggregation of counterfactual recourse is necessary to preserve explanation stability. The contribution is a decision-relevant reliability bound that storage cooperatives can use to set a maximum tolerable cloud-mask gap before counterfactual recourse is suspended.","反事实解释日益被提出作为合作式仓储管理者质询计算机视觉产量预测模型的接口，然而在农业遥感中经常出现的卫星云掩膜缺口条件下，其可靠性尚未得到界定。该界基于预测模型上的Lipschitz连续性假设和掩膜算子扰动分析推导得出，并表示为云掩膜缺口比例、模型局部Lipschitz常数和反事实搜索容差的函数。对前后对照影响估计量进行了基于重复的评估，以表征该设计在给定方差结构（组间标准差=6.0，残差标准差=4.5，组内相关系数=0.64）下的运行特征，并在2000个设计单元中估计了检验效能。可靠性界随缺口比例上升呈非线性退化，且组内相关系数0.64意味着需要对反事实救济进行合作社层面的聚合，以保持解释的稳定性。本文的贡献在于提供了一个与决策相关的可靠性界，仓储合作社可据此设定在暂停反事实救济之前可容忍的最大云掩膜缺口。","Zenodo (CERN European Organization for Nuclear Research)",67,{"impact":94,"substance":17,"depth":93,"authority":92,"freshness":13,"relevant":22,"comment":436},"该论文提出反事实解释在卫星云掩膜缺口下的可靠性边界，方法新颖且结论可用于合作社决策，但属细分方法学研究，产业影响有限。",[438],{"name":433,"url":430},[27,183,440,56,441],"产量预测","可解释性",{"code":120,"slug":190,"name":191,"path":192,"parent_code":37},[444],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":446},{"candidates":447,"agricultural_evidence":48},[448,452,456],{"code":120,"score":92,"supported":48,"title":449,"abstract":450,"tags":451},[208],[205],[183],{"code":33,"score":94,"supported":48,"title":453,"abstract":454,"tags":455},[],[56,110],[440,56],{"code":69,"score":22,"supported":14,"title":457,"abstract":458,"tags":459},[],[],[27],[461,462],"农业人工智能 产量预测 可解释性 智慧农业","农业人工智能 产量预测","农业人工智能产量预测可解释性智慧农业-5570","10.5281\u002Fzenodo.23545079",{"doi":464,"openalex_id":466,"authors":467,"venue":433,"cited_by_count":158,"oa_url":430,"card":470,"direction":163,"ingested_from":165},"W7223114002",[468],{"name":469,"orcid":8},"Kadiatou Barry",{"tldr":471,"method":472,"finding":473,"direction":163,"opportunity":474},"推导卫星云掩膜缺口下反事实解释可靠性的理论界，并评估其对合作社储粮决策的影响。","基于Lipschitz连续性假设与掩膜扰动分析推导可靠性界，并用2000个设计单","缺口比例上升使可靠性非线性下降；ICC=0.64表明需合作社层级聚合以保持解释稳定。","可研究不同作物区云掩膜统计特性下的自适应解释暂停阈值与聚合策略。","2026-10-22T11:20:39.240791Z",{"id":477,"title":478,"url":479,"summary":480,"summary_zh":481,"content":8,"source_name":482,"source_url":479,"published_at":483,"category":89,"cover_url":8,"hotness":13,"is_selected":14,"score":484,"score_detail":485,"sources":487,"tags":489,"primary_topic":492,"secondary_topics":493,"classification":495,"search_phrases":517,"slug":520,"view_count":158,"doi":521,"paper":522,"created_at":564},5567,"Capturing Spatial Heterogeneity Using UAV-Derived Multispectral Phenotypes and Random Regression Models Improves Genetic Evaluation in Alfalfa Breeding Trials","https:\u002F\u002Fdoi.org\u002F10.64898\u002F2026.10.14.759249","Spatial heterogeneity is a major source of environmental noise in field trials, reducing genetic evaluation accuracy. Conventional spatial correction methods use field layout and plot proximity but often fail to capture in-season dynamics or leverage temporal data from high-throughput phenotyping. We hypothesized that longitudinal UAV-derived multispectral measurements could characterize field-scale environmental heterogeneity and improve genetic evaluation in breeding trials. Multispectral imagery was collected throughout the growing season across eight alfalfa trials in New York and New Mexico (20222023). Vegetation indices were evaluated for heritability and association with biomass yield using univariate and multi-trait models. Longitudinal measurements were modeled with random regression (RR) to estimate plot-specific permanent environmental effects (PEE), incorporated into genomic mixed models within a two-stage framework. Model performance was compared with conventional autoregressive (AR) and baseline (no correction) models using metrics of consistency and accuracy of estimated genetic effects across replicates. Vegetation indices showed moderate to high heritability, with GNDVI consistently outperforming NDVI and NDRE. Both univariate and multi-trait analyses revealed substantial spatial variability, particularly in New Mexico trials. RR-derived PEE captured field-scale spatial patterns and improved genetic signal recovery relative to baseline and AR models, with the largest gains under strong spatial heterogeneity and comparable performance where variability was limited. The RR framework improved reproducibility of genetic effect estimates across independent field subsets without compromising agreement with full-trial evaluations. These findings show that integrating UAV data via random regression enhances genetic signal recovery and selection accuracy, with broader potential for precision agriculture and biosensor-driven crop management.","空间异质性是田间试验中环境噪声的主要来源，会降低遗传评估的准确性。传统空间校正方法利用田间布局和小区邻近性，但往往无法捕捉生育期内的动态变化，也未能充分利用高通量表型分析产生的时间序列数据。我们假设，基于无人机（UAV）的纵向多光谱测量能够表征田块尺度的环境异质性，并改善育种试验中的遗传评估。在2022—2023年纽约州和新墨西哥州的8个紫花苜蓿试验中，于整个生长季收集了多光谱影像。采用单性状和多性状模型评估了植被指数的遗传力及其与生物量产量的关联。纵向测量数据采用随机回归（RR）建模，以估计小区特异性永久环境效应（PEE），并在两阶段框架中将其纳入基因组混合模型。利用重复间估计遗传效应的一致性和准确性指标，将模型性能与传统的自回归（AR）模型和基线（无校正）模型进行比较。植被指数表现出中到高的遗传力，其中GNDVI始终优于NDVI和NDRE。单性状和多性状分析均揭示了显著的空间变异，尤其在新墨西哥州的试验中更为明显。RR衍生的PEE捕捉到了田块尺度的空间格局，相对于基线模型和AR模型改善了遗传信号的恢复，在强空间异质性条件下增益最大，而在变异有限时性能相当。RR框架提高了遗传效应估计在独立田间子集间的可重复性，同时未损害与全试验评估的一致性。这些结果表明，通过随机回归整合UAV数据可增强遗传信号恢复和选择准确性，在精准农业和生物传感器驱动的作物管理方面具有更广泛的潜力。","bioRxiv (Cold Spring Harbor Laboratory)","2026-10-20T00:00:00Z",79,{"impact":18,"substance":285,"depth":18,"authority":92,"freshness":178,"relevant":22,"comment":486},"该研究利用无人机多光谱表型与随机回归模型校正田间空间异质性，显著提升苜蓿育种遗传评估精度，方法新颖、数据覆盖两州八组试验，对智慧育种有实质参考价值。",[488],{"name":482,"url":479},[27,490,491,56,292],"无人机","育种",{"code":294,"slug":295,"name":296,"path":297,"parent_code":298},[494],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":496},{"candidates":497,"agricultural_evidence":48},[498,502,506,512],{"code":294,"score":19,"supported":48,"title":499,"abstract":500,"tags":501},[316],[318,316],[292],{"code":33,"score":92,"supported":48,"title":503,"abstract":504,"tags":505},[198],[198],[56],{"code":69,"score":113,"supported":48,"title":507,"abstract":508,"tags":511},[],[509,510],"精准农业","precision agriculture",[27],{"code":302,"score":513,"supported":48,"title":514,"abstract":515,"tags":516},4,[],[318],[292],[518,519],"无人机 多光谱 苜蓿 育种","随机回归 空间异质性 遗传评估","无人机多光谱苜蓿育种-5567","10.64898\u002F2026.10.14.759249",{"doi":521,"openalex_id":523,"authors":524,"venue":482,"cited_by_count":158,"oa_url":479,"card":559,"direction":163,"ingested_from":165},"W7223083966",[525,528,531,534,537,539,541,544,547,550,553,556],{"name":526,"orcid":527},"Dinesh Ghimire","https:\u002F\u002Forcid.org\u002F0009-0008-2281-1396",{"name":529,"orcid":530},"Ranjita Thapa","https:\u002F\u002Forcid.org\u002F0000-0001-6116-7611",{"name":532,"orcid":533},"Karl H. Kunze","https:\u002F\u002Forcid.org\u002F0000-0003-4548-1808",{"name":535,"orcid":536},"Felipe Sabadin","https:\u002F\u002Forcid.org\u002F0000-0003-2937-1465",{"name":538,"orcid":8},"Julie Hansen",{"name":540,"orcid":8},"Christopher Pierce",{"name":542,"orcid":543},"Liam Wickes-Do","https:\u002F\u002Forcid.org\u002F0009-0003-2927-4590",{"name":545,"orcid":546},"Nicholas Santantonio","https:\u002F\u002Forcid.org\u002F0000-0002-4351-4023",{"name":548,"orcid":549},"Ian M. Ray","https:\u002F\u002Forcid.org\u002F0000-0003-2056-536X",{"name":551,"orcid":552},"Michael A. Gore","https:\u002F\u002Forcid.org\u002F0000-0001-6896-8024",{"name":554,"orcid":555},"Virginia Mae Moore","https:\u002F\u002Forcid.org\u002F0000-0001-7888-3366",{"name":557,"orcid":558},"Kelly R. Robbins","https:\u002F\u002Forcid.org\u002F0000-0001-9522-9585",{"tldr":560,"method":561,"finding":562,"direction":163,"opportunity":563},"用无人机多光谱时序表型和随机回归模型校正田间空间异质性，提升苜蓿育种遗传评估精度。","八项苜蓿试验的无人机多光谱植被指数，随机回归估计小区永久环境效应并嵌入基因组混合","随机回归提取的环境效应能捕捉田块尺度空间格局，在强异质性下显著提升遗传信号恢复与选择准确性。","可将无人机时序表型与随机回归框架推广到其他作物育种试验，并探索多源遥感与基因组选择的联合建模。","2026-10-22T11:20:32.655394Z"]