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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. 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Feng","https:\u002F\u002Forcid.org\u002F0009-0000-1391-9591",{"name":662,"orcid":11},"吴彩霞",{"tldr":664,"method":665,"finding":666,"direction":667,"opportunity":668},"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","农业遥感与作物表型","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。","openalex","2026-10-22T11:20:39.622844Z",{"id":672,"title":673,"url":674,"summary":675,"summary_zh":11,"content":11,"source_name":676,"source_url":11,"published_at":579,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":677,"score_detail":678,"sources":680,"tags":682,"primary_topic":686,"secondary_topics":687,"classification":690,"search_phrases":710,"slug":713,"view_count":455,"doi":11,"paper":714,"created_at":722},5535,"基于无人机多光谱图像与VGG21模型的小麦渍害调控效果识别","https:\u002F\u002Ffinance.sina.cn\u002F2026-10-16\u002Fdetail-inivmprm8363597.d.html","江苏省农科院农业信息研究所联合中国农科院环发所与湖北省农科院粮作所，在小麦拔节至抽穗、抽穗至灌浆两个阶段设置对照、渍水胁迫、硅肥与氨基酸调控试验，用大疆精灵4采集冠层多光谱影像。VGG21模型对硅肥调控识别正确率超91%，精度96.42%、召回率89.53%。","智慧农业（中英文）2026-10-21",82,{"impact":361,"substance":321,"depth":361,"authority":345,"freshness":385,"relevant":455,"comment":679},"省级科研机构联合攻关，方法新颖、数据扎实，对渍害遥感识别与调控评估有实质参考价值，值得入选。",[681],{"name":676,"url":674},[589,591,683,684,685],"作物表型","无人机多光谱","小麦渍害",{"code":216,"slug":217,"name":218,"path":219,"parent_code":200},[688,689],{"code":32,"slug":33,"name":34,"path":35,"parent_code":7},{"code":184,"slug":185,"name":186,"path":187,"parent_code":128},{"status":599,"locked":581,"version":4,"method":600,"evidence":691},{"candidates":692,"agricultural_evidence":604},[693,697,701,706],{"code":216,"score":524,"supported":604,"title":694,"abstract":695,"tags":696},[218],[218],[],{"code":32,"score":524,"supported":604,"title":698,"abstract":699,"tags":700},[684],[],[683,591,684],{"code":184,"score":173,"supported":604,"title":702,"abstract":704,"tags":705},[703],"渍害",[],[],{"code":16,"score":455,"supported":581,"title":707,"abstract":708,"tags":709},[],[],[589],[711,712],"江苏省农科院 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硅肥调控","江苏省农科院小麦渍害多光谱-5535",{"doi":11,"openalex_id":11,"authors":715,"venue":11,"cited_by_count":424,"oa_url":11,"card":716,"direction":667,"ingested_from":721},[],{"tldr":717,"method":718,"finding":719,"direction":667,"opportunity":720},"用无人机多光谱图像和VGG21模型识别小麦渍害后硅肥与氨基酸调控效果。","大疆精灵4采集冠层多光谱影像，构建VGG21深度学习分类模型。","VGG21对硅肥调控识别正确率超91%，精度96.42%、召回率89.53%。","可扩展多生育期、多调控组合与轻量化模型，探索渍害胁迫等级与调控效果的同步识别。","agent","2026-10-22T00:07:10.070916Z",{"id":724,"title":725,"url":726,"summary":727,"summary_zh":11,"content":11,"source_name":728,"source_url":11,"published_at":729,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":730,"score_detail":731,"sources":735,"tags":737,"primary_topic":738,"secondary_topics":739,"classification":742,"search_phrases":761,"slug":764,"view_count":424,"doi":11,"paper":765,"created_at":772},5296,"雷添杰研究员团队《基于无人机多光谱图像和VGG21模型的小麦渍害调控效果识别》","https:\u002F\u002Ffinance.sina.com.cn\u002Froll\u002F2026-10-16\u002Fdoc-inivmprm8363597.shtml","《智慧农业(中英文)》2026年第8卷第4期刊载梁万杰、刘晓君、雷添杰等论文,在小麦拔节—抽穗和抽穗—灌浆两个生长阶段进行对照、渍水胁迫、硅肥调控、氨基酸调控控制试验;利用大疆精灵4多光谱无人机采集小麦冠层多光谱图像建立数据集,基于VGG21模型构建识别模型,发现硅肥调控样本识别性能最好,识别精度最高,具良好鲁棒性和可扩展性,可为小麦渍害综合防控提供理论与技术支持。","智慧农业(中英文)(2026,8(4))","2026-10-16T00:00:00Z",76,{"impact":584,"substance":732,"depth":733,"authority":345,"freshness":173,"relevant":455,"comment":734},21,17,"核心期刊论文，方法新颖、结论可靠，对小麦渍害防控有实用价值，值得进入每日精选。",[736],{"name":728,"url":726},[589,591,683,684,685],{"code":216,"slug":217,"name":218,"path":219,"parent_code":200},[740,741],{"code":184,"slug":185,"name":186,"path":187,"parent_code":128},{"code":32,"slug":33,"name":34,"path":35,"parent_code":7},{"status":599,"locked":581,"version":4,"method":600,"evidence":743},{"candidates":744,"agricultural_evidence":604},[745,749,753,757],{"code":216,"score":524,"supported":604,"title":746,"abstract":747,"tags":748},[218],[218],[],{"code":184,"score":524,"supported":604,"title":750,"abstract":751,"tags":752},[703],[703],[],{"code":32,"score":524,"supported":604,"title":754,"abstract":755,"tags":756},[684],[],[683,591,684],{"code":16,"score":400,"supported":604,"title":758,"abstract":759,"tags":760},[],[589],[589],[762,763],"雷添杰 小麦渍害 无人机多光谱","VGG21 小麦渍害 识别模型","雷添杰小麦渍害无人机多光谱-5296",{"doi":11,"openalex_id":11,"authors":766,"venue":11,"cited_by_count":424,"oa_url":11,"card":767,"direction":667,"ingested_from":721},[],{"tldr":768,"method":769,"finding":770,"direction":667,"opportunity":771},"用无人机多光谱图像和VGG21模型识别小麦渍害调控效果。","大疆精灵4采集冠层多光谱图像，构建VGG21识别模型。","硅肥调控样本识别性能最好、精度最高，模型鲁棒可扩展。","可探索多生育期多光谱特征与不同调控措施的耦合机制，并扩展到其他作物渍害监测。","2026-10-19T00:07:48.071055Z",{"id":774,"title":775,"url":776,"summary":777,"summary_zh":778,"content":11,"source_name":779,"source_url":776,"published_at":780,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":582,"score_detail":781,"sources":785,"tags":787,"primary_topic":790,"secondary_topics":791,"classification":793,"search_phrases":809,"slug":812,"view_count":424,"doi":813,"paper":814,"created_at":831},4802,"A Privacy-Preserving Federated Learning Framework for Paddy Leaf Disease Detection Using an Enhanced Pretrained Novel EfficientNet-Lite4 Model","https:\u002F\u002Fdoi.org\u002F10.15832\u002Fankutbd.1837705","The correct and prompt detection of leaf diseases in paddy crops remains essential for protecting agricultural output and yield stability and reducing major losses that occur during severe disease outbreaks. The widespread use of conventional centralized machine learning techniques becomes impractical for real-world agricultural applications, as their effectiveness diminishes under three main conditions: data privacy limitations, delayed transmission of information, along with reduced performance accuracy from non-independent and identically distributed (non-IID) data properties across different agro-climatic regions. This research develops a decentralized diagnostic system which combines Federated Learning with an Enhanced Pretrained Novel EfficientNet-Lite4 model designed for paddy leaf disease classification. The Paddy Doctor dataset served as training material for the system which contained 16,225 RGB images that classified 12 disease types alongside healthy categories. The proposed model reached 98.7% classification accuracy and 98.5% precision and 98.6% F1-score while maintaining a final loss of 0.21 after 100 communication rounds. The system underwent field testing which demonstrated its ability to generate accurate diagnostic results in real time in various working conditions. The study demonstrates how privacy preserving AI-based paddy disease management works in practice and recommends local agricultural institutions to build decentralized learning systems. The suggested system will give farmers direct access to field level decision support and facilitate the creation of sustainable digital pest management systems required to ensure food supply.","正确且及时地检测水稻作物叶片病害，对于保护农业产出和产量稳定、减少病害严重暴发期间发生的重大损失仍然至关重要。传统集中式机器学习技术的广泛使用在现实农业应用中变得不切实际，因为在三种主要条件下其有效性会下降：数据隐私限制、信息传输延迟，以及不同农业气候区域间非独立同分布（non-IID）数据特性导致的性能准确率降低。本研究开发了一种去中心化诊断系统，将联邦学习与一种增强的预训练新型高效网络模型EfficientNet-Lite4相结合，用于水稻叶片病害分类。Paddy Doctor数据集作为该系统的训练材料，包含16,225张RGB图像，涵盖12种病害类型以及健康类别。所提出的模型在100轮通信后达到了98.7%的分类准确率、98.5%的精确率和98.6%的F1分数，同时最终损失保持在0.21。该系统经过实地测试，证明其能够在各种工作条件下实时生成准确的诊断结果。该研究展示了基于隐私保护人工智能的水稻病害管理如何在实践中运作，并建议地方农业机构构建去中心化学习系统。所建议的系统将为农民提供直接的田间决策支持，并促进建立确保粮食供应所需的可持续数字病虫害管理系统。","Tarım Bilimleri Dergisi","2026-10-12T00:00:00Z",{"impact":361,"substance":321,"depth":361,"authority":782,"freshness":783,"relevant":455,"comment":784},13,9,"将联邦学习与轻量级EfficientNet-Lite4结合用于水稻叶病识别，兼顾隐私保护与98.7%高精度，对智慧农业落地有参考价值。",[786],{"name":779,"url":776},[589,590,210,788,789],"病虫害识别","联邦学习",{"code":208,"slug":209,"name":210,"path":211,"parent_code":200},[792],{"code":24,"slug":25,"name":26,"path":27,"parent_code":7},{"status":599,"locked":581,"version":4,"method":600,"evidence":794},{"candidates":795,"agricultural_evidence":604},[796,801,805],{"code":208,"score":612,"supported":604,"title":797,"abstract":799,"tags":800},[798],"paddy",[210,798],[210],{"code":24,"score":416,"supported":604,"title":802,"abstract":803,"tags":804},[],[628,629,631],[590,788,789],{"code":16,"score":455,"supported":581,"title":806,"abstract":807,"tags":808},[],[],[589],[810,811],"Paddy Doctor 数据集 水稻病害","EfficientNet-Lite4 水稻叶片病害","PaddyDoctor数据集水稻病害-4802","10.15832\u002Fankutbd.1837705",{"doi":813,"openalex_id":815,"authors":816,"venue":779,"cited_by_count":424,"oa_url":823,"card":824,"direction":830,"ingested_from":669},"W7221522859",[817,820],{"name":818,"orcid":819},"B Johnson","https:\u002F\u002Forcid.org\u002F0009-0009-8281-4051",{"name":821,"orcid":822},"Chandrakumar Thangavel","https:\u002F\u002Forcid.org\u002F0000-0002-1186-5988","https:\u002F\u002Fdergipark.org.tr\u002Fen\u002Fdownload\u002Farticle-file\u002F5486058",{"tldr":825,"method":826,"finding":827,"direction":828,"opportunity":829},"提出联邦学习结合改进EfficientNet-Lite4的水稻叶片病害检测框架，兼顾隐私与精度。","联邦学习+预训练EfficientNet-Lite4，用Paddy Doctor","12类病害分类准确率达98.7%，100轮通信后损失0.21，支持实时诊断。","农业人工智能与决策模型","可探索非IID数据下联邦聚合优化与轻量化模型在边缘设备的部署研究。","智慧农业 \u002F 农业物联网","2026-10-13T23:30:08.809781Z",{"id":833,"title":834,"url":835,"summary":836,"summary_zh":11,"content":11,"source_name":837,"source_url":835,"published_at":838,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":839,"score_detail":840,"sources":842,"tags":844,"primary_topic":848,"secondary_topics":849,"classification":851,"search_phrases":871,"slug":874,"view_count":69,"doi":875,"paper":876,"created_at":894},4650,"A process-oriented framework for daily dynamic monitoring of summer maize drought: From meteorological stress to agricultural impact","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.agsy.2026.105011","A process-oriented framework for daily dynamic monitoring of summer maize drought: From meteorological stress to agricultural impact。Agricultural Systems","Agricultural Systems","2026-10-11T00:00:00Z",81,{"impact":361,"substance":732,"depth":361,"authority":345,"freshness":385,"relevant":455,"comment":841},"核心期刊提出的面向过程的夏玉米干旱逐日动态监测框架，方法新颖、面向农业信息化应用，时效性强，值得进入每日精选。",[843],{"name":837,"url":835},[589,591,845,846,847],"夏玉米","农业气象","干旱监测",{"code":224,"slug":225,"name":226,"path":227,"parent_code":200},[850],{"code":184,"slug":185,"name":186,"path":187,"parent_code":128},{"status":599,"locked":581,"version":4,"method":600,"evidence":852},{"candidates":853,"agricultural_evidence":604},[854,859,863,867],{"code":184,"score":782,"supported":604,"title":855,"abstract":857,"tags":858},[856],"drought",[856],[846,847],{"code":224,"score":416,"supported":604,"title":860,"abstract":861,"tags":862},[225],[225],[845],{"code":16,"score":455,"supported":581,"title":864,"abstract":865,"tags":866},[],[],[589],{"code":32,"score":455,"supported":581,"title":868,"abstract":869,"tags":870},[],[],[591],[872,873],"夏玉米 干旱 动态监测","气象胁迫 农业影响 监测框架","夏玉米干旱动态监测-4650","10.1016\u002Fj.agsy.2026.105011",{"doi":875,"openalex_id":877,"authors":878,"venue":837,"cited_by_count":424,"oa_url":835,"card":11,"direction":667,"ingested_from":669},"W7221308782",[879,881,883,885,888,890,892],{"name":880,"orcid":11},"Yuanda Zhang",{"name":882,"orcid":11},"Peijuan Wang",{"name":884,"orcid":11},"Jun Zhu",{"name":886,"orcid":887},"Yang Li","https:\u002F\u002Forcid.org\u002F0000-0001-9021-0201",{"name":889,"orcid":11},"Dianchen Han",{"name":891,"orcid":11},"Dingrong Wu",{"name":893,"orcid":11},"Jianping Guo","2026-10-11T23:30:03.734429Z",{"id":896,"title":897,"url":898,"summary":899,"summary_zh":900,"content":11,"source_name":901,"source_url":898,"published_at":902,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":839,"score_detail":903,"sources":905,"tags":907,"primary_topic":911,"secondary_topics":912,"classification":915,"search_phrases":939,"slug":942,"view_count":408,"doi":943,"paper":944,"created_at":961},4433,"PROSAIL-DNN-TL: A Hyperspectral Inversion Framework for Winter Wheat Leaf Chlorophyll Content Based on Fine-Tuned Transfer Learning","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fagronomy16191971","Accurate and efficient estimation of leaf chlorophyll content (LCC) is essential for crop physiological monitoring and precision agriculture. Current remote sensing approaches for LCC estimation mainly rely on machine learning and deep learning methods, which exhibit strong nonlinear modelling capabilities but require sufficient and representative samples. Radiative transfer models (RTMs) can simulate canopy spectral response based on physical mechanisms and generate large-scale datasets, thereby overcoming the limitations of purely data-driven approaches. However, their applications under diverse environmental conditions remain challenging due to uncertainties caused by model parameterization and discrepancies between simulated and observed data. This study proposed a hybrid framework, PROSAIL-DNN-TL, integrating the PROSAIL RTM with full-network fine-tuned transfer learning for winter wheat LCC estimation. First, 85,680 simulated samples generated by PROSAIL were used to pre-train a deep neural network (DNN), and field observations (n = 270) were subsequently used for full-network fine-tuning. A conventional DNN model trained only with field data and machine learning models based on different feature selection strategies were established for comparison. The results showed that: (1) UVE-selected bands were distributed across visible, red-edge, and near-infrared regions; CARS retained more bands in the visible region, whereas SPA mainly selected near-infrared bands; (2) RR and SVR achieved higher accuracy than KNN, with the full-spectrum and CARS feature sets showing superior performance and SPA showing the lowest accuracy; (3) PROSAIL-DNN-TL achieved reliable LCC estimation performance, with an average test R2 of 0.79 across 500 repeated experiments, and showed improved performance compared with conventional machine learning models; (4) PROSAIL-DNN-TL achieved reliable predictive performance when evaluated using datasets collected during the 2024 and 2025 growing seasons, with R2 values of 0.75 and 0.92, respectively. Overall, the proposed hybrid framework integrates physical knowledge from RTMs with data-driven modelling through transfer learning, providing a potential approach for crop chlorophyll monitoring. However, the SPAD-to-LCC conversion used in this study was not specifically calibrated for the instrument and crop, and therefore, the absolute LCC values should be interpreted with caution.","准确、高效地估算叶片叶绿素含量（leaf chlorophyll content，LCC）对作物生理监测和精准农业至关重要。当前遥感估算LCC的方法主要依赖机器学习和深度学习方法，这类方法具有较强的非线性建模能力，但需要充足且具有代表性的样本。辐射传输模型（radiative transfer models，RTMs）能够基于物理机制模拟冠层光谱响应并生成大规模数据集，从而克服纯数据驱动方法的局限性。然而，由于模型参数化带来的不确定性以及模拟数据与观测数据之间的差异，其在不同环境条件下的应用仍面临挑战。本研究提出了一种混合框架PROSAIL-DNN-TL，将PROSAIL辐射传输模型与全网络微调迁移学习相结合，用于冬小麦LCC估算。首先，利用PROSAIL生成的85 680个模拟样本预训练深度神经网络（deep neural network，DNN），随后利用田间观测数据（n = 270）进行全网络微调。同时建立了仅使用田间数据训练的传统DNN模型以及基于不同特征选择策略的机器学习模型作为对比。结果表明：（1）UVE选择的波段分布在可见光、红边和近红外区域；CARS在可见光区域保留了更多波段，而SPA主要选择近红外波段；（2）RR和SVR的精度高于KNN，全光谱和CARS特征集表现较优，SPA精度最低；（3）PROSAIL-DNN-TL取得了可靠的LCC估算性能，在500次重复实验中平均测试R²为0.79，且相比传统机器学习模型表现出更优的性能；（4）利用2024年和2025年生长季采集的数据集进行评估时，PROSAIL-DNN-TL均表现出可靠的预测性能，R²分别为0.75和0.92。总体而言，所提出的混合框架通过迁移学习将辐射传输模型的物理知识与数据驱动建模相结合，为作物叶绿素监测提供了一种潜在途径。然而，本研究中使用的SPAD到LCC的转换并未针对仪器和作物进行专门校准，因此绝对LCC值应谨慎解读。","Agronomy","2026-10-07T00:00:00Z",{"impact":361,"substance":321,"depth":361,"authority":345,"freshness":783,"relevant":455,"comment":904},"将PROSAIL辐射传输模型与深度神经网络迁移学习结合，用8万余模拟样本预训练、270个田间样本微调，冬小麦叶绿素反演精度可靠，方法新颖且数据规模扎实，对作物生理监测与精准农业有参考价值。",[906],{"name":901,"url":898},[589,591,908,909,910],"冬小麦","迁移学习","叶绿素监测",{"code":216,"slug":217,"name":218,"path":219,"parent_code":200},[913,914],{"code":32,"slug":33,"name":34,"path":35,"parent_code":7},{"code":24,"slug":25,"name":26,"path":27,"parent_code":7},{"status":599,"locked":581,"version":4,"method":600,"evidence":916},{"candidates":917,"agricultural_evidence":604},[918,924,928,932],{"code":216,"score":919,"supported":604,"title":920,"abstract":922,"tags":923},26,[921,217],"winter wheat",[218,921,217],[908],{"code":32,"score":612,"supported":604,"title":925,"abstract":926,"tags":927},[606],[608,609],[591],{"code":24,"score":385,"supported":604,"title":929,"abstract":930,"tags":931},[],[627,628,629,630],[909],{"code":16,"score":933,"supported":604,"title":934,"abstract":935,"tags":938},7,[],[936,937],"精准农业","precision agriculture",[589],[940,941],"PROSAIL 冬小麦 叶绿素","高光谱 叶绿素 反演","PROSAIL冬小麦叶绿素-4433","10.3390\u002Fagronomy16191971",{"doi":943,"openalex_id":945,"authors":946,"venue":901,"cited_by_count":424,"oa_url":898,"card":956,"direction":667,"ingested_from":669},"W7220844556",[947,949,951,954],{"name":948,"orcid":11},"Xueqing Zhu",{"name":950,"orcid":11},"Ying Nian",{"name":952,"orcid":953},"Jikai Liu","https:\u002F\u002Forcid.org\u002F0000-0003-2365-5221",{"name":955,"orcid":11},"Xinwei Li",{"tldr":957,"method":958,"finding":959,"direction":667,"opportunity":960},"提出PROSAIL-DNN-TL框架，用PROSAIL模拟数据预训练DNN并全网络微调，反演冬小麦叶","PROSAIL辐射传输模型生成85680样本预训练DNN，270个田间样本全网络","500次重复实验平均测试R²为0.79，2024和2025年生长季验证R²分别为0.75和0.92，","SPAD到LCC转换未针对仪器和作物标定，可研究跨传感器、跨作物、跨生长季的迁移学习泛化与绝对叶绿素标定。","2026-10-08T23:30:29.383957Z",{"id":963,"title":964,"url":965,"summary":966,"summary_zh":967,"content":11,"source_name":968,"source_url":965,"published_at":969,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":582,"score_detail":970,"sources":972,"tags":974,"primary_topic":977,"secondary_topics":978,"classification":979,"search_phrases":1014,"slug":1017,"view_count":424,"doi":1018,"paper":1019,"created_at":1038},4234,"Opportunity Cost of Precision Conservation","https:\u002F\u002Fdoi.org\u002F10.3368\u002Fle.103.2.110525-0095r1","Abstract Conservation practices provide public environmental benefits, but the associated private costs are chiefly borne by farmers. Precision agriculture technologies can help reduce farmer private costs by identifying low-yielding areas, thereby minimizing foregone revenue. This study estimates the opportunity cost of such precision conservation on 29 commercial fields in Michigan, USA, using fine-scale yield maps collected between 2020 and 2024. We employ spatial regression models and Monte Carlo simulations to account for spatial heterogeneity and price variability. Results show that under a corn-soybean crop rotation, precision conservation improves profitability in 22 of 29 fields, indicating potential for adoption without government subsidies.","摘要 保护性耕作实践能带来公共环境效益，但相关的私人成本主要由农民承担。精准农业技术有助于通过识别低产区域来降低农民的私人成本，从而最大限度地减少放弃的收入。本研究利用2020年至2024年间收集的精细尺度产量图，估算了美国密歇根州29块商业农田上此类精准保护性耕作的机会成本。我们采用空间回归模型和蒙特卡洛模拟来处理空间异质性和价格波动。结果表明，在玉米-大豆轮作下，精准保护性耕作提高了29块农田中22块的盈利能力，表明无需政府补贴也具备采用潜力。","Land Economics","2026-10-02T00:00:00Z",{"impact":361,"substance":321,"depth":361,"authority":345,"freshness":173,"relevant":455,"comment":971},"基于2020-2024年29块商业田块精细产量图，量化精准保护性耕作的机会成本，结论显示多数田块无需补贴即可盈利，对智慧农业落地与农业绿色政策有参考价值。",[973],{"name":968,"url":965},[589,591,936,975,976],"保护性耕作","产量地图",{"code":200,"slug":201,"name":202,"path":203,"parent_code":11},[],{"status":980,"locked":581,"version":4,"method":600,"evidence":981},"partial",{"candidates":982,"agricultural_evidence":604,"reason":1013},[983,987,993,999,1005,1009],{"code":16,"score":173,"supported":604,"title":984,"abstract":985,"tags":986},[],[936,937],[589,936],{"code":224,"score":988,"supported":604,"title":989,"abstract":990,"tags":992},6,[],[226,991],"corn",[],{"code":232,"score":988,"supported":604,"title":994,"abstract":995,"tags":998},[],[996,997],"大豆","soybean",[],{"code":256,"score":988,"supported":604,"title":1000,"abstract":1001,"tags":1004},[],[1002,1003],"轮作","crop rotation",[],{"code":192,"score":400,"supported":604,"title":1006,"abstract":1007,"tags":1008},[],[975],[975],{"code":32,"score":455,"supported":581,"title":1010,"abstract":1011,"tags":1012},[],[],[591],"一级领域明确，二级方向需要细分",[1015,1016],"密歇根 精准保护性耕作 机会成本","精准农业 产量地图 玉米大豆轮作","密歇根精准保护性耕作机会成本-4234","10.3368\u002Fle.103.2.110525-0095r1",{"doi":1018,"openalex_id":1020,"authors":1021,"venue":968,"cited_by_count":424,"oa_url":1031,"card":1032,"direction":667,"ingested_from":669},"W7215722779",[1022,1025,1028],{"name":1023,"orcid":1024},"Seo Woo Lee","https:\u002F\u002Forcid.org\u002F0009-0000-4165-9520",{"name":1026,"orcid":1027},"Scott M. Swinton","https:\u002F\u002Forcid.org\u002F0000-0002-3751-1811",{"name":1029,"orcid":1030},"Bruno Basso","https:\u002F\u002Forcid.org\u002F0000-0003-2090-4616","https:\u002F\u002Fle.uwpress.org\u002Fcontent\u002Fwple\u002Fearly\u002F2026\u002F09\u002F24\u002Fle.103.2.110525-0095R1.full.pdf",{"tldr":1033,"method":1034,"finding":1035,"direction":1036,"opportunity":1037},"基于密歇根29块田的精细产量图，估算精准保护的机会成本并评估其经济可行性。","2020-2024年精细产量图，空间回归与蒙特卡洛模拟。","玉米-大豆轮作下，29块田中22块精准保护可提高利润，无需政府补贴。","农业绿色发展与碳","可拓展至其他作物与区域，探究精准保护对农户收益与环境效益的协同机制。","2026-10-05T23:30:26.394346Z",{"id":1040,"title":1041,"url":1042,"summary":1043,"summary_zh":1044,"content":11,"source_name":1045,"source_url":1042,"published_at":1046,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":1047,"score_detail":1048,"sources":1050,"tags":1052,"primary_topic":1054,"secondary_topics":1055,"classification":1056,"search_phrases":1071,"slug":1074,"view_count":1075,"doi":1076,"paper":1077,"created_at":1093},4020,"Robust QR Code Detection in Oblique Aerial Images for Agricultural Field Experiments","https:\u002F\u002Fdoi.org\u002F10.3390\u002Frs18193361","Field experiments in precision agriculture require reliable linkage of plant variants to experimental plots, which is difficult in oblique aerial imagery because the drone position does not identify the imaged ground location. We present and evaluate a reproducible workflow for detecting QR reference markers in high-resolution UAV images acquired over a wheat field before crop growth at a 45° viewing angle. The workflow combines manually defined regions of interest, documented preprocessing, a rotation sweep, and independent evaluation of OpenCV and pyzbar decoding backends. QR codes were selected for practical field deployment because their payload can directly encode a plot identifier and can be checked with an ordinary smartphone; the study does not benchmark QR codes against other fiducial-marker families. In 500 field-collected QR-code crops from 20 images, 491 codes (98.2%) were decoded; all nine failures had incomplete code visibility. The observed result is therefore a system-level outcome of both image acquisition and decoding under the tested configuration, rather than a growing-season operational accuracy. The mean processing time was approximately 0.41 s per region of interest on a standard PC. The work demonstrates QR-marker detection under the stated pre-growth conditions and provides a basis for future plot-label reference, calibration, and downstream Fusarium Head Blight phenotyping.","精准农业中的田间试验需要将植物变异可靠地关联到试验小区，而这在倾斜航空影像中较为困难，因为无人机位置并不能标识所成像的地面位置。我们提出并评估了一套可重复的工作流程，用于在作物生长前以45°视角获取的小麦田高分辨率无人机影像中检测QR参考标记。该工作流程结合了人工定义的感兴趣区域、有记录的前处理步骤、旋转扫描，以及对OpenCV和pyzbar解码后端的独立评估。选择QR码用于实际田间部署，是因为其有效载荷可直接编码小区标识符，并可用普通智能手机进行核验；本研究并未将QR码与其他基准标记类型进行基准比较。在来自20幅影像的500个田间采集QR码裁剪区域中，491个码（98.2%）被成功解码；全部9个失败案例均存在码可见性不完整的问题。因此，所观察到的结果是测试配置下图像采集与解码共同作用的系统级结果，而非整个生长季的运行精度。在标准PC上，每个感兴趣区域的平均处理时间约为0.41 s。该工作证明了在上述生长前条件下QR标记检测的可行性，并为未来的小区标签参考、校准以及下游赤霉病（Fusarium Head Blight）表型分析提供了基础。","Remote Sensing","2026-10-01T00:00:00Z",65,{"impact":173,"substance":361,"depth":584,"authority":345,"freshness":783,"relevant":455,"comment":1049},"该论文提出斜视无人机影像中QR码检测的可复现流程，在500个样本中解码成功率达98.2%，为精准农业田间试验的地块标识提供了实用方案，方法新颖且数据可靠，但属细分技术进展，影响范围有限。",[1051],{"name":1045,"url":1042},[589,1053,591,218,936],"无人机",{"code":216,"slug":217,"name":218,"path":219,"parent_code":200},[],{"status":599,"locked":581,"version":4,"method":600,"evidence":1057},{"candidates":1058,"agricultural_evidence":604},[1059,1063,1067],{"code":16,"score":173,"supported":604,"title":1060,"abstract":1061,"tags":1062},[],[936,937],[589,936],{"code":216,"score":933,"supported":604,"title":1064,"abstract":1065,"tags":1066},[],[218,217],[218],{"code":32,"score":455,"supported":581,"title":1068,"abstract":1069,"tags":1070},[],[],[591],[1072,1073],"无人机 斜视影像 QR码 检测","小麦 田间试验 地块标识","无人机斜视影像QR码检测-4020",3,"10.3390\u002Frs18193361",{"doi":1076,"openalex_id":1078,"authors":1079,"venue":1045,"cited_by_count":424,"oa_url":1042,"card":1088,"direction":667,"ingested_from":669},"W7215065180",[1080,1082,1085],{"name":1081,"orcid":11},"Gudrun Kinz",{"name":1083,"orcid":1084},"Hermann Buerstmayr","https:\u002F\u002Forcid.org\u002F0000-0002-0748-2351",{"name":1086,"orcid":1087},"Peter M. Roth","https:\u002F\u002Forcid.org\u002F0000-0001-9566-1298",{"tldr":1089,"method":1090,"finding":1091,"direction":667,"opportunity":1092},"提出并评估了在45°斜视无人机图像中检测田间二维码标记的可复现流程，解码成功率达98.2%。","结合手动ROI、预处理、旋转扫描及OpenCV与pyzbar双后端解码，使用50","491\u002F500个二维码成功解码，9个失败均因码可见性不完整，平均处理时间约0.41秒\u002FROI。","可探索斜视图像下多类基准标记的鲁棒检测与自动ROI定位，并接入生长季表型分析流程。","2026-10-02T23:30:19.815764Z",{"id":1095,"title":1096,"url":1097,"summary":1098,"summary_zh":1099,"content":11,"source_name":837,"source_url":1097,"published_at":1046,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":839,"score_detail":1100,"sources":1102,"tags":1104,"primary_topic":1109,"secondary_topics":1110,"classification":1111,"search_phrases":1132,"slug":1135,"view_count":1075,"doi":1136,"paper":1137,"created_at":1157},3987,"Spatio-temporal diagnosis of land fragmentation in the Yellow River Basin: providing a basis for comprehensive land improvement","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.agsy.2026.104971","CONTEXT Land fragmentation (LF) poses a major challenge to sustainable land management, land consolidation, and agricultural productivity, especially in ecologically sensitive and strategically important regions such as the Yellow River Basin (YRB) in China. This area faces prominent issues such as LF and land degradation during land-resource utilization, underscoring the need for spatially adaptive governance and targeted land consolidation strategies. OBJECTIVE This study aims to systematically examine the spatiotemporal evolution of LF in the YRB from 2000 to 2022, with the goal of identifying regional differentiation patterns and influencing factors. It further seeks to propose tailored land consolidation strategies based on fine-grained spatial assessment and functional zoning, in order to support sustainable land use and national food security. METHODS Landscape pattern indices and profile line analysis were used across five time periods to capture LF dynamics. A composite land fragmentation index (LFI) is developed from six landscape metrics, weighted by the entropy method, enabling high-resolution spatial evaluation at the grid scale. Additionally, a Four-Quadrant Model was applied to classify functional zones for land consolidation, incorporating factors such as elevation, temperature, and GDP. RESULTS AND CONCLUSIONS LF in the YRB exhibits significant regional variation, with lower fragmentation in the upper reaches and higher fragmentation in eastern regions—especially in Shanxi and Shandong provinces. Over 60% of the basin remained stable, and LF was strongly associated with elevation, temperature, and economic activity. Based on these patterns, four functional zones were delineated: dynamic reconstruction, optimization and enhancement, steady-state restoration, and collaborative demonstration areas. Comprehensive policy suggestions are provided for integrated land consolidation aligned with LF characteristics and zonal planning. SIGNIFICANCE This study innovatively developed a scientific and systematic LF evaluation framework from a raster-based perspective. Using this framework, we conducted a precise quantitative analysis of the dynamic LF process and explored the mechanisms and impacts of various factors such as policy direction, natural environmental conditions, and socioeconomic factors on LF. Based on the LF findings, this study proposes a comprehensive and feasible land consolidation plan, providing solid, reliable data support for the plan's rationale and scientific foundation. The results are of significant theoretical and practical value for optimizing land resource allocation, enhancing land use efficiency, and promoting regional sustainable development.","背景 土地细碎化（LF）是可持续土地管理、土地整治和农业生产率面临的重大挑战，尤其是在黄河 basin（YRB）等生态敏感且具有战略重要性的地区。该区域在土地资源利用过程中面临土地细碎化和土地退化等突出问题，凸显了空间适应性治理和针对性土地整治策略的必要性。 目标 本研究旨在系统考察2000年至2022年黄河流域土地细碎化的时空演变，以识别区域分异格局及其影响因素。研究进一步寻求基于精细化空间评估和功能分区提出有针对性的土地整治策略，以支持可持续土地利用和国家粮食安全。 方法 采用景观格局指数和剖面线分析，涵盖五个时期，以刻画土地细碎化动态。基于六个景观指标构建综合土地细碎化指数（LFI），并采用熵权法赋权，从而实现网格尺度上的高分辨率空间评估。此外，应用四象限模型对土地整治功能区进行分类，纳入海拔、气温和GDP等因素。 结果与结论 黄河流域土地细碎化表现出显著的区域差异，上游细碎化程度较低，东部地区细碎化程度较高，尤其是山西和山东两省。超过60%的流域保持稳定，土地细碎化与海拔、气温和经济活动密切相关。基于这些格局，划定了四类功能区：动态重构区、优化提升区、稳态修复区和协同示范区。并提出了与土地细碎化特征和分区规划相一致的综合土地整治政策建议。 意义 本研究创新性地从栅格视角构建了科学、系统的土地细碎化评价框架。利用该框架，我们对土地细碎化动态过程进行了精确的定量分析，并探讨了政策导向、自然环境条件和社会经济因素等多种因素对土地细碎化的作用机制及影响。基于土地细碎化研究结果，本研究提出了综合且可行的土地整治方案，为",{"impact":361,"substance":321,"depth":361,"authority":345,"freshness":783,"relevant":455,"comment":1101},"基于栅格尺度构建土地细碎化指数并划分整治功能分区，方法新颖、数据扎实，对黄河流域耕地保护与土地整治有直接参考价值。",[1103],{"name":837,"url":1097},[591,1105,1106,1107,1108],"耕地保护","黄河流域","遥感监测","土地整治",{"code":263,"slug":264,"name":265,"path":266,"parent_code":200},[],{"status":599,"locked":581,"version":4,"method":600,"evidence":1112},{"candidates":1113,"agricultural_evidence":604},[1114,1120,1124,1128],{"code":263,"score":988,"supported":604,"title":1115,"abstract":1116,"tags":1119},[],[1117,1118],"粮食安全","food security",[],{"code":136,"score":69,"supported":581,"title":1121,"abstract":1122,"tags":1123},[],[],[1108,1105],{"code":32,"score":69,"supported":581,"title":1125,"abstract":1126,"tags":1127},[],[],[591,1107],{"code":496,"score":455,"supported":581,"title":1129,"abstract":1130,"tags":1131},[],[],[1108],[1133,1134],"黄河流域 土地细碎化","土地细碎化指数 土地整治","黄河流域土地细碎化-3987","10.1016\u002Fj.agsy.2026.104971",{"doi":1136,"openalex_id":1138,"authors":1139,"venue":837,"cited_by_count":424,"oa_url":1097,"card":1152,"direction":667,"ingested_from":669},"W7215038160",[1140,1142,1145,1147,1150],{"name":1141,"orcid":11},"Xiaoxu Wei",{"name":1143,"orcid":1144},"Qiaolin Shu","https:\u002F\u002Forcid.org\u002F0009-0009-9478-6625",{"name":1146,"orcid":11},"Wei Wei",{"name":1148,"orcid":1149},"Binbin Xie","https:\u002F\u002Forcid.org\u002F0000-0003-0224-5485",{"name":1151,"orcid":11},"Junju Zhou",{"tldr":1153,"method":1154,"finding":1155,"direction":667,"opportunity":1156},"构建栅格尺度土地破碎化指数，诊断黄河流域2000-2022年时空演变并划分整治功能区。","景观格局指数、剖面线分析、熵权法综合指数与四象限模型，结合高程、气温、GDP数据","上游破碎化低、东部山西山东高，超60%区域稳定，破碎化与高程、气温和经济活动强相关。","可将栅格破碎化指数与遥感作物分类、地块边界提取耦合，发展面向智慧农业的田块尺度整治与种植决策模型。","2026-10-02T23:30:06.999389Z",{"id":1159,"title":1160,"url":1161,"summary":1162,"summary_zh":1163,"content":11,"source_name":1164,"source_url":1161,"published_at":1165,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":582,"score_detail":1166,"sources":1168,"tags":1170,"primary_topic":1174,"secondary_topics":1175,"classification":1176,"search_phrases":1191,"slug":1194,"view_count":408,"doi":1195,"paper":1196,"created_at":1215},3529,"Cultivated land system resilience in China’s major rice-producing areas: spatiotemporal patterns and zonal regulation strategies","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1942721","Introduction Cultivated land systems face convergent pressures from land-use change, farmland marginalization, and dietary upgrading, and thus understanding their resilience is essential for sustaining agricultural functions and food security. Methods Focusing on China’s major rice-producing areas (MRPAs), we developed a spatially explicit resilience framework that integrates internal adaptive capacities and external socio-economic conditions, and examined the spatial and temporal patterns of cultivated land system resilience using three representative years (2000, 2010, and 2023). We quantified resilience, diagnosed dominant constraints via an obstacle model, and developed differentiated regulation pathways based on the identified constraints. Results Results show that: (1) overall resilience increased with fluctuations, with a growing role of socio-economic conditions alongside persistent environmental constraints. This improvement coincided with infrastructure development and policy interventions, although urbanization pressures may have constrained resilience in peri-urban areas. (2) resilience exhibited marked spatial differentiation. Large-scale agricultural regions maintained higher resilience, coinciding with favorable natural endowments and relatively well-developed infrastructure, whereas fragmented and highly urbanized areas remained more vulnerable amid intensified human–land conflicts. (3) dominant obstacles shifted from natural background constraints to socio-economic factors over time. Four regulation zones were identified: background-restricted low-resilience, external-stress resilience risk, resource-constrained lagging recovery, and market-sensitive hidden risk. Discussion These findings provide a diagnostic basis for differentiated management by tailoring strategies to region-specific resilience constraints.","引言 耕地系统面临土地利用变化、农田边际化和饮食升级等多重趋同压力，因此理解其韧性对于维持农业功能和粮食安全至关重要。方法 本文以中国水稻主产区为研究区，构建了一个整合内部适应能力与外部社会经济条件的空间显式韧性框架，并选取2000年、2010年和2023年三个代表性年份，考察耕地系统韧性的时空格局。我们量化了韧性水平，通过障碍度模型诊断了主导约束因子，并基于识别出的约束条件提出了差异化调控路径。结果 结果表明：(1) 总体韧性呈波动上升趋势，社会经济条件的作用不断增强，而环境约束持续存在。这一改善与基础设施建设和政策干预相吻合，尽管城市化压力可能制约了城郊地区的韧性提升。(2) 韧性表现出显著的空间分异。大规模农业区域维持了较高的韧性，这与有利的自然禀赋和相对完善的基础设施相吻合，而破碎化程度高和高度城市化的区域在人地矛盾加剧的背景下仍较为脆弱。(3) 主导障碍因子随时间推移从自然本底约束转向社会经济因素。研究识别出四类调控分区：本底限制型低韧性区、外部胁迫型韧性风险区、资源约束型滞后恢复区和市场敏感型隐性风险区。讨论 这些发现为因地制宜制定差异化策略、针对区域特定韧性约束实施差异化管理提供了诊断依据。","Frontiers in Sustainable Food Systems","2026-09-25T00:00:00Z",{"impact":361,"substance":321,"depth":361,"authority":782,"freshness":783,"relevant":455,"comment":1167},"基于2000—2023年三时相数据构建耕地系统韧性评估框架并划分四类调控区，方法新颖、结论具体，对主产区差异化治理有参考价值。",[1169],{"name":1164,"url":1161},[591,1117,1171,1172,1173],"耕地韧性","水稻主产区","分区调控",{"code":208,"slug":209,"name":210,"path":211,"parent_code":200},[],{"status":599,"locked":581,"version":4,"method":600,"evidence":1177},{"candidates":1178,"agricultural_evidence":604},[1179,1183,1187],{"code":208,"score":345,"supported":604,"title":1180,"abstract":1181,"tags":1182},[209],[210,209],[],{"code":263,"score":933,"supported":604,"title":1184,"abstract":1185,"tags":1186},[],[1117,1118],[1117],{"code":32,"score":455,"supported":581,"title":1188,"abstract":1189,"tags":1190},[],[],[591],[1192,1193],"中国水稻主产区 耕地韧性","耕地系统韧性 时空格局","中国水稻主产区耕地韧性-3529","10.3389\u002Ffsufs.2026.1942721",{"doi":1195,"openalex_id":1197,"authors":1198,"venue":1164,"cited_by_count":424,"oa_url":1161,"card":1210,"direction":1036,"ingested_from":669},"W7214283232",[1199,1202,1205,1208],{"name":1200,"orcid":1201},"Renjing Wang","https:\u002F\u002Forcid.org\u002F0009-0003-3257-5140",{"name":1203,"orcid":1204},"Xinrui Li","https:\u002F\u002Forcid.org\u002F0000-0002-8653-5828",{"name":1206,"orcid":1207},"Qi Wang","https:\u002F\u002Forcid.org\u002F0000-0002-5672-7095",{"name":1209,"orcid":11},"Jiayan Yang",{"tldr":1211,"method":1212,"finding":1213,"direction":1036,"opportunity":1214},"构建耕地系统韧性评估框架，分析中国水稻主产区2000-2023年韧性时空格局并提出分区调控策略。","构建空间显式韧性框架，结合内部适应能力与外部社会经济条件，用障碍度模型诊断主导约","韧性总体波动上升，主导障碍由自然背景转向社会经济因素，识别出四类调控分区。","可探究城市化边缘区韧性下降机制，并将韧性框架与碳汇\u002F生态服务耦合，支撑分区精准调控。","2026-09-26T23:30:07.427444Z",{"id":1217,"title":1218,"url":1219,"summary":1220,"summary_zh":1221,"content":11,"source_name":1222,"source_url":1219,"published_at":1223,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":1224,"score_detail":1225,"sources":1227,"tags":1229,"primary_topic":1231,"secondary_topics":1232,"classification":1235,"search_phrases":1255,"slug":1258,"view_count":173,"doi":1259,"paper":1260,"created_at":1301},3278,"Research on the optimal modeling path for inversion of Pb content in rice leaves based on hyperspectral data of ground objects and machine learning and cross-scale remote sensing monitoring","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs10661-026-15959-x","Research on the optimal modeling path for inversion of Pb content in rice leaves based on hyperspectral data of ground objects and machine learning and cross-scale remote sensing monitoring。Environmental Monitoring and Assessment","基于地物高光谱数据与机器学习的稻叶铅含量反演最优建模路径及跨尺度遥感监测研究。环境监测与评估","Environmental Monitoring and Assessment","2026-09-22T00:00:00Z",64,{"impact":173,"substance":361,"depth":584,"authority":782,"freshness":783,"relevant":455,"comment":1226},"基于地物高光谱与机器学习的水稻叶片铅含量反演建模研究，方法有创新但属细分领域学术进展，公共影响有限。",[1228],{"name":1222,"url":1219},[589,591,210,628,1230],"高光谱遥感",{"code":208,"slug":209,"name":210,"path":211,"parent_code":200},[1233,1234],{"code":32,"slug":33,"name":34,"path":35,"parent_code":7},{"code":24,"slug":25,"name":26,"path":27,"parent_code":7},{"status":599,"locked":581,"version":4,"method":600,"evidence":1236},{"candidates":1237,"agricultural_evidence":604},[1238,1243,1247,1251],{"code":32,"score":1239,"supported":604,"title":1240,"abstract":1241,"tags":1242},27,[609,606],[608,609,606],[591,1230],{"code":24,"score":612,"supported":604,"title":1244,"abstract":1245,"tags":1246},[629],[628,629],[628],{"code":208,"score":416,"supported":604,"title":1248,"abstract":1249,"tags":1250},[209],[209],[210],{"code":16,"score":455,"supported":581,"title":1252,"abstract":1253,"tags":1254},[],[],[589],[1256,1257],"水稻叶片 铅含量 高光谱 反演","稻米 重金属 遥感 监测","水稻叶片铅含量高光谱反演-3278","10.1007\u002Fs10661-026-15959-x",{"doi":1259,"openalex_id":1261,"authors":1262,"venue":1222,"cited_by_count":424,"oa_url":11,"card":1296,"direction":667,"ingested_from":669},"W7214027889",[1263,1266,1269,1272,1275,1278,1280,1282,1285,1288,1290,1292,1294],{"name":1264,"orcid":1265},"Zhenlong Zhang","https:\u002F\u002Forcid.org\u002F0009-0008-2354-9123",{"name":1267,"orcid":1268},"Zhe Wang","https:\u002F\u002Forcid.org\u002F0000-0003-1266-7251",{"name":1270,"orcid":1271},"Chengxia Wang","https:\u002F\u002Forcid.org\u002F0009-0001-0820-2462",{"name":1273,"orcid":1274},"Wenxue Lin","https:\u002F\u002Forcid.org\u002F0000-0002-8245-9063",{"name":1276,"orcid":1277},"Jingyan Zhang","https:\u002F\u002Forcid.org\u002F0009-0004-4567-5316",{"name":1279,"orcid":11},"Ying Luo",{"name":1281,"orcid":11},"Jiaqian Zhang",{"name":1283,"orcid":1284},"Kai Ye","https:\u002F\u002Forcid.org\u002F0000-0002-2851-6741",{"name":1286,"orcid":1287},"Yiming Chen","https:\u002F\u002Forcid.org\u002F0000-0002-8121-3109",{"name":1289,"orcid":11},"Chaoliang Peng",{"name":1291,"orcid":11},"Duan Tian",{"name":1293,"orcid":11},"Weihao Wang",{"name":1295,"orcid":11},"Jiaxin Liu",{"tldr":1297,"method":1298,"finding":1299,"direction":667,"opportunity":1300},"研究基于地面高光谱与机器学习反演水稻叶片铅含量，并探索跨尺度遥感监测的最优建模路径。","地面高光谱数据结合机器学习建模，开展跨尺度遥感监测。","明确了水稻叶片铅含量反演的最优建模路径，实现跨尺度遥感监测。","可探索多尺度遥感数据融合与迁移学习，提升重金属胁迫反演的普适性与精度。","2026-09-23T23:30:19.291361Z",{"id":1303,"title":1304,"url":1305,"summary":1306,"summary_zh":11,"content":11,"source_name":1307,"source_url":11,"published_at":1308,"category":580,"cover_url":11,"hotness":385,"is_selected":604,"score":1309,"score_detail":1310,"sources":1315,"tags":1317,"primary_topic":1321,"secondary_topics":1322,"classification":1323,"search_phrases":1351,"slug":1354,"view_count":400,"doi":11,"paper":1355,"created_at":1362},3125,"Full-Season Agentic Farm System FAIRY: Event-Driven Multi-Agent Orchestration for Soybean Production（FAIRY 全季节智能体农场系统：大豆生产的事件驱动多智能体编排）","https:\u002F\u002Faiagentstore.ai\u002Fai-agent-news\u002Ftopic\u002Fagriculture-food\u002F2026-08-11","哈尔滨工业大学研究人员发布并部署全栈、事件驱动的智能体引擎 FAIRY 于中国运行中的大豆研究农场。FAIRY 集成传感器、无人机、卫星植被产品、机械 API、作物过程模型与多智能体编排层，执行起垄→播种→灌溉→病虫害防治→收获→干燥→存储工作流，并在 64 垄研究场上跨 100 个全季节场景评估 9 个智能体控制器。这是智能体系统能够在大农业时间尺度和延迟结果下进行推理的最清晰演示之一，将农业中的智能体工作从实验室演示推进到全过程评估。同期 arXiv 推出 HarvestBench 基准将 LLM 驱动智能体置于农场网格世界（拖拉机面临动物选择绕行或碾压），结果显示模型差异巨大、对道德简报高度敏感、避免意愿具有可衡量的价格弹性。","Harbin Institute of Technology \u002F arXiv","2026-09-18T00:00:00Z",89,{"impact":1311,"substance":1312,"depth":1313,"authority":345,"freshness":783,"relevant":455,"comment":1314},24,23,19,"哈工大在真实大豆农场部署全季节事件驱动多智能体系统并配套 HarvestBench 基准，是农业智能体从演示走向全过程评估的标志性进展，专业深度与信息增量俱佳。",[1316],{"name":1307,"url":1305},[589,1318,590,591,1319,1320],"无人农场","多智能体","大豆生产",{"code":232,"slug":233,"name":234,"path":235,"parent_code":200},[],{"status":599,"locked":581,"version":4,"method":600,"evidence":1324},{"candidates":1325,"agricultural_evidence":604},[1326,1330,1335,1339,1343,1347],{"code":232,"score":1313,"supported":604,"title":1327,"abstract":1328,"tags":1329},[996,997],[996],[],{"code":152,"score":1075,"supported":604,"title":1331,"abstract":1332,"tags":1334},[],[1333],"灌溉",[],{"code":24,"score":69,"supported":581,"title":1336,"abstract":1337,"tags":1338},[],[],[590,1319],{"code":120,"score":455,"supported":581,"title":1340,"abstract":1341,"tags":1342},[],[],[1318],{"code":16,"score":455,"supported":581,"title":1344,"abstract":1345,"tags":1346},[],[],[589],{"code":32,"score":455,"supported":581,"title":1348,"abstract":1349,"tags":1350},[],[],[591],[1352,1353],"哈工大 FAIRY 大豆","HarvestBench 智能体 农场","哈工大FAIRY大豆-3125",{"doi":11,"openalex_id":11,"authors":1356,"venue":11,"cited_by_count":424,"oa_url":11,"card":1357,"direction":828,"ingested_from":721},[],{"tldr":1358,"method":1359,"finding":1360,"direction":828,"opportunity":1361},"部署全季节事件驱动多智能体系统FAIRY，在大豆农场完成从起垄到存储的全流程评估。","集成传感器、无人机、卫星、作物模型与机械API，用9个智能体控制器在64垄100","智能体系统能在大农业时间尺度下推理，模型差异大且对道德简报敏感。","可研究多智能体在长周期、延迟反馈农业任务中的鲁棒性与伦理约束机制。","2026-09-22T00:05:38.611717Z",{"id":1364,"title":1365,"url":1366,"summary":1367,"summary_zh":1368,"content":11,"source_name":1369,"source_url":1366,"published_at":1370,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":1371,"score_detail":1372,"sources":1374,"tags":1376,"primary_topic":1379,"secondary_topics":1380,"classification":1382,"search_phrases":1406,"slug":1409,"view_count":400,"doi":1410,"paper":1411,"created_at":1443},3014,"Detecting diurnal dynamics of cotton leaf inclination angle under water-salt stress","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.rse.2026.115674","Leaf inclination angle (LIA) dynamics act as a rapid response mechanism to abiotic stress, regulating canopy energy balance and water use efficiency. While the adaptive value of diurnal LIA plasticity (e.g., paraheliotropism) is well-recognized in ecology, most current remote sensing algorithms and ecosystem models still treat canopy architecture as static and neglect stress-induced geometric adjustments. Furthermore, the diurnal dynamics of LIA under combined abiotic stresses, such as concurrent water deficit and salinity, still remain poorly understood. Recent advances in unmanned aerial vehicle (UAV) photogrammetry offer a promising approach for capturing LIA dynamics at high spatial and temporal resolution. However, accurately resolving fine scale, dynamic leaf movements in real environments using UAVs remains challenging. To address these gaps, we developed the Constraint-Assisted Point cloud fusion for Leaf scale Analysis (CAPLA), an integrated UAV analytical workflow that combines deep learning with Structure from Motion (SfM). CAPLA employs 2D semantic masks to strictly constrain 3D mesh reconstruction, effectively mitigating motion-induced artifacts. Independent validation against 19 plot level mean leaf angle (MLA) observations collected at 9:30 am and 12:00 pm yielded an R 2 of 0.89 and an RMSE of 0.9°, supporting plot level MLA estimation under the validated acquisition conditions. CAPLA was subsequently applied across five observation times to characterize diurnal canopy structural dynamics. Importantly, repeated measures analysis of the high frequency observations revealed significant effects of irrigation, salinity, and observation time on MLA, together with a significant irrigation × time interaction ( P = 0.0109), indicating that diurnal MLA trajectories differed among irrigation levels. In contrast, neither the irrigation × salinity interaction ( P = 0.8800) nor the irrigation × salinity × time interaction ( P = 0.9086) was significant. Descriptive differences in within-day variability were nevertheless observed among individual treatment combinations, highlighting the value of time-resolved structural monitoring for characterizing canopy responses to combined water and salinity stresses. These findings highlight the complex structural plasticity of canopies under interacting stresses, emphasizing the critical need to transition from static canopy assumptions to dynamic structural monitoring for improving ecosystem models and precision agriculture.","叶片倾角（LIA）动态变化是植物对非生物胁迫的快速响应机制，调控冠层能量平衡与水分利用效率。尽管昼夜LIA可塑性（如避日运动）的适应价值在生态学中已得到广泛认可，但当前大多数遥感算法和生态系统模型仍将冠层结构视为静态，忽略了胁迫诱导的几何调整。此外，在水分亏缺与盐分胁迫等复合非生物胁迫条件下，LIA的昼夜动态变化仍知之甚少。近年来无人机（UAV）摄影测量技术的进展为在高时空分辨率下捕捉LIA动态提供了有前景的方法。然而，利用无人机在真实环境中精确解析精细尺度的动态叶片运动仍具挑战性。为弥补上述不足，我们开发了约束辅助点云融合叶片尺度分析流程（CAPLA），这是一种集成了深度学习与运动恢复结构（SfM）的无人机综合分析工作流。CAPLA利用二维语义掩膜严格约束三维网格重建，有效减轻了运动诱导的伪影。基于上午9：30和中午12：00采集的19个样地水平平均叶倾角（MLA）观测值进行独立验证，结果R²为0.89，RMSE为0.9°，支持在验证采集条件下进行样地水平MLA估算。随后将CAPLA应用于五个观测时段以表征冠层结构的昼夜动态变化。重要的是，对高频观测的重复测量分析揭示了灌溉、盐分和观测时间对MLA的显著影响，以及显著的灌溉×时间交互效应（P = 0.0109），表明不同灌溉水平下MLA的昼夜变化轨迹存在差异。相比之下，灌溉×盐分交互效应（P = 0.8800）和灌溉×盐分×时间交互效应（P = 0.9086）均不显著。尽管如此，在各处理组合之间仍观察到日内变异性的描述性差异，凸显了时间分辨结构监测在表征冠层对水分与盐分复合胁迫响应方面的价值。这些发现揭示了冠层在交互胁迫下的复杂结构可塑性，强调亟需从静态冠层假设转向动态结构监测，以改进生态系统模型和精准农业。","Remote Sensing of Environment","2026-09-19T00:00:00Z",84,{"impact":361,"substance":1312,"depth":1313,"authority":612,"freshness":783,"relevant":455,"comment":1373},"该研究提出CAPLA无人机点云融合方法，实现水盐胁迫下棉花叶倾角昼夜动态的高精度监测，方法新颖、数据可靠，对作物表型与精准农业有实质参考价值。",[1375],{"name":1369,"url":1366},[589,591,1377,936,1378],"棉花","无人机遥感",{"code":248,"slug":249,"name":250,"path":251,"parent_code":200},[1381],{"code":32,"slug":33,"name":34,"path":35,"parent_code":7},{"status":599,"locked":581,"version":4,"method":600,"evidence":1383},{"candidates":1384,"agricultural_evidence":604},[1385,1390,1394,1398,1402],{"code":248,"score":783,"supported":604,"title":1386,"abstract":1388,"tags":1389},[1387],"cotton",[],[1377],{"code":16,"score":173,"supported":604,"title":1391,"abstract":1392,"tags":1393},[],[936,937],[589,936],{"code":32,"score":173,"supported":604,"title":1395,"abstract":1396,"tags":1397},[],[608,609],[591,1378],{"code":152,"score":988,"supported":604,"title":1399,"abstract":1400,"tags":1401},[],[1333,153],[],{"code":24,"score":988,"supported":604,"title":1403,"abstract":1404,"tags":1405},[],[627,630],[],[1407,1408],"无人机 棉花 叶倾角 水盐胁迫","CAPLA 冠层结构 动态监测","无人机棉花叶倾角水盐胁迫-3014","10.1016\u002Fj.rse.2026.115674",{"doi":1410,"openalex_id":1412,"authors":1413,"venue":1369,"cited_by_count":424,"oa_url":1366,"card":1438,"direction":667,"ingested_from":669},"W7213661690",[1414,1417,1419,1421,1423,1425,1428,1431,1433,1436],{"name":1415,"orcid":1416},"Qing Li","https:\u002F\u002Forcid.org\u002F0009-0004-4580-7761",{"name":1418,"orcid":11},"Dalei Hao",{"name":1420,"orcid":11},"Jan Pisek",{"name":1422,"orcid":11},"Zicheng Ji",{"name":1424,"orcid":11},"Yanan Wei",{"name":1426,"orcid":1427},"Youngryel Ryu","https:\u002F\u002Forcid.org\u002F0000-0001-6238-2479",{"name":1429,"orcid":1430},"Jiarui Xu","https:\u002F\u002Forcid.org\u002F0000-0003-4925-2770",{"name":1432,"orcid":11},"Yangmin Feng",{"name":1434,"orcid":1435},"Shaozhong Kang","https:\u002F\u002Forcid.org\u002F0000-0002-8019-2537",{"name":1437,"orcid":11},"Yelu Zeng",{"tldr":1439,"method":1440,"finding":1441,"direction":667,"opportunity":1442},"提出CAPLA无人机点云融合方法，监测水盐胁迫下棉花叶倾角昼夜动态。","结合深度学习与SfM，用2D语义掩膜约束3D网格重建，无人机高频观测。","灌溉、盐分和时间显著影响叶倾角，灌溉×时间交互显著，昼夜轨迹因灌溉而异。","可将动态叶倾角参数化嵌入作物模型，提升水盐胁迫下冠层结构与蒸散模拟精度。","2026-09-20T23:30:21.262698Z",{"id":1445,"title":1446,"url":1447,"summary":1448,"summary_zh":11,"content":11,"source_name":1449,"source_url":11,"published_at":1450,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":1451,"score_detail":1452,"sources":1454,"tags":1456,"primary_topic":1458,"secondary_topics":1459,"classification":1460,"search_phrases":1492,"slug":1495,"view_count":69,"doi":11,"paper":1496,"created_at":1503},2858,"农业视觉感知综述:基于4D混沌和Mamba网络的新颖隐私保护框架——Frontiers in Plant Science 9月17日","https:\u002F\u002Fwww.ebiotrade.com\u002Fnewsf\u002F2026-9\u002F20260917000338095.htm","基于冬小麦-夏玉米双季种植系统的长期定位田间试验,氮肥后效对土壤-作物协调及微生物功能的调控机制尚不明确。试验设置四个处理:CK(小麦季和玉米季均不施氮)、W1M0(仅在冬小麦季施氮)、W0M1(仅在夏玉米季施氮)、W1M1(小麦季和玉米季均施氮)。与不施氮对照(CK)相比,W1M0和W0M1分别使平均年产量提高45.7%和42.2%,而W1M1提高58.6%。PLS-SEM揭示,前茬小麦季土壤氮含量解释了后茬玉米土壤理化性质97.5%的方差。","Frontiers in Plant Science \u002F 生物通","2026-09-17T00:00:00Z",78,{"impact":361,"substance":508,"depth":733,"authority":782,"freshness":385,"relevant":455,"comment":1453},"该文提出基于4D混沌与Mamba网络的农业视觉感知隐私保护框架，方法新颖且属农业人工智能前沿交叉方向，信源为核心期刊，时效性强，具备进入每日精选的价值。",[1455],{"name":1449,"url":1447},[589,590,591,683,1457],"隐私保护",{"code":200,"slug":201,"name":202,"path":203,"parent_code":11},[],{"status":980,"locked":581,"version":4,"method":600,"evidence":1461},{"candidates":1462,"agricultural_evidence":604,"reason":1013},[1463,1467,1471,1476,1480,1484,1488],{"code":216,"score":1075,"supported":604,"title":1464,"abstract":1465,"tags":1466},[],[218],[],{"code":224,"score":1075,"supported":604,"title":1468,"abstract":1469,"tags":1470},[],[226],[],{"code":144,"score":1075,"supported":604,"title":1472,"abstract":1473,"tags":1475},[],[1474],"土壤",[],{"code":32,"score":69,"supported":581,"title":1477,"abstract":1478,"tags":1479},[],[],[683,591],{"code":16,"score":455,"supported":581,"title":1481,"abstract":1482,"tags":1483},[],[],[589],{"code":24,"score":455,"supported":581,"title":1485,"abstract":1486,"tags":1487},[],[],[590],{"code":56,"score":455,"supported":581,"title":1489,"abstract":1490,"tags":1491},[],[],[1457],[1493,1494],"农业人工智能 作物表型 农业遥感 智慧农业","农业人工智能 作物表型","农业人工智能作物表型农业遥感智慧农业-2858",{"doi":11,"openalex_id":11,"authors":1497,"venue":11,"cited_by_count":424,"oa_url":11,"card":1498,"direction":1036,"ingested_from":721},[],{"tldr":1499,"method":1500,"finding":1501,"direction":1036,"opportunity":1502},"通过长期定位试验研究氮肥后效对冬小麦-夏玉米轮作系统土壤-作物协调及微生物功能的调控机制。","长期定位田间试验，设置四种施氮处理，结合PLS-SEM分析。","双季施氮年产量提高58.6%，前茬小麦季土壤氮解释后茬玉米土壤理化性质97.5%的方差。","可探究氮肥后效对土壤微生物功能及碳氮循环的长期影响，优化轮作施氮策略。","2026-09-18T00:03:31.115969Z",{"id":1505,"title":1506,"url":1507,"summary":1508,"summary_zh":1509,"content":11,"source_name":1510,"source_url":1507,"published_at":1511,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":1451,"score_detail":1512,"sources":1514,"tags":1516,"primary_topic":1517,"secondary_topics":1518,"classification":1520,"search_phrases":1537,"slug":1540,"view_count":69,"doi":1541,"paper":1542,"created_at":1565},2663,"Research on an automated mapping method for rice aboveground biomass based on low-altitude remote sensing","https:\u002F\u002Fdoi.org\u002F10.4081\u002Fjae.2026.2061","Accurate monitoring of rice aboveground biomass (AGB) is crucial for guiding agricultural production management. This study focuses on high-precision estimation and automated mapping of rice AGB. Field experiments were conducted in Nanxun District, Huzhou City, Zhejiang Province. We collected UAV RGB and multispectral images, rice AGB, and plant height data. By integrating vegetation indices, texture features, and plant height information, the AGB estimation model was established using algorithms such as Stacking. A framework combining \"SAM + MobileNetV3-Small classification\" was proposed to achieve automated paddy field extraction and phenology recognition. The results demonstrate that the rice AGB prediction model based on the Stacking ensemble algorithm performed excellently. The introduction of plant height significantly improved model accuracy. For example, during the heading stage, R2 increased from 0.421 to 0.739, and RPIQ rose from 1.995 to 3.015. The automated paddy field extraction and phenology recognition framework developed in this study achieved a segmentation accuracy of 0.968 and a classification accuracy of 0.993 on the dataset used in this study, without requiring manual annotation. This research provides a technical reference for automated and high-precision mapping of rice AGB.","准确监测水稻地上生物量(AGB)对指导农业生产管理至关重要。本研究聚焦水稻AGB的高精度估算与自动化制图。田间试验在浙江省湖州市南浔区开展，采集了无人机RGB和多光谱影像、水稻AGB及株高数据。通过融合植被指数、纹理特征和株高信息，利用Stacking等算法构建AGB估算模型。提出了一种“SAM+MobileNetV3-Small分类”框架，以实现稻田自动化提取和物候识别。结果表明，基于Stacking集成算法的水稻AGB预测模型表现优异，株高的引入显著提高了模型精度。例如，在抽穗期，R2从0.421提升至0.739，RPIQ从1.995提升至3.015。本研究开发的稻田自动化提取与物候识别框架在本研究数据集上实现了0.968的分割精度和0.993的分类精度，且无需人工标注。本研究为水稻AGB的自动化高精度制图提供了技术参考。","Journal of Agricultural Engineering","2026-09-15T00:00:00Z",{"impact":584,"substance":321,"depth":361,"authority":345,"freshness":173,"relevant":455,"comment":1513},"基于无人机遥感与Stacking集成模型实现水稻地上生物量自动化制图，方法新颖、数据扎实，对精准农业管理有参考价值。",[1515],{"name":1510,"url":1507},[589,1053,591,210,683],{"code":208,"slug":209,"name":210,"path":211,"parent_code":200},[1519],{"code":32,"slug":33,"name":34,"path":35,"parent_code":7},{"status":599,"locked":581,"version":4,"method":600,"evidence":1521},{"candidates":1522,"agricultural_evidence":604},[1523,1528,1533],{"code":208,"score":361,"supported":604,"title":1524,"abstract":1525,"tags":1527},[209],[210,1526,209,798],"稻田",[210],{"code":32,"score":782,"supported":604,"title":1529,"abstract":1530,"tags":1532},[609],[1531],"multispectral",[683,591],{"code":16,"score":455,"supported":581,"title":1534,"abstract":1535,"tags":1536},[],[],[589],[1538,1539],"作物表型 农业遥感 智慧农业 无人机","作物表型 农业遥感","作物表型农业遥感智慧农业无人机-2663","10.4081\u002Fjae.2026.2061",{"doi":1541,"openalex_id":1543,"authors":1544,"venue":1510,"cited_by_count":424,"oa_url":1507,"card":1560,"direction":667,"ingested_from":669},"W7213229220",[1545,1547,1549,1551,1553,1556,1558],{"name":1546,"orcid":11},"Honggang Xu",{"name":1548,"orcid":11},"Xuehan Li",{"name":1550,"orcid":11},"Jia Shen",{"name":1552,"orcid":11},"Ziyi Li",{"name":1554,"orcid":1555},"Zhe Li","https:\u002F\u002Forcid.org\u002F0009-0008-6496-3697",{"name":1557,"orcid":11},"Yiming Li",{"name":1559,"orcid":11},"Pengcheng Nie",{"tldr":1561,"method":1562,"finding":1563,"direction":667,"opportunity":1564},"基于无人机RGB与多光谱影像，结合株高与Stacking集成算法，实现水稻地上生物量高精度自动制图。","无人机RGB\u002F多光谱影像、植被指数、纹理与株高，Stacking集成及SAM+M","引入株高显著提升精度，抽穗期R²从0.421升至0.739；自动稻田提取与物候识别精度达0.968和","可探索多生育期、多品种下株高与纹理特征的迁移性，并耦合深度学习实现全自动生物量时空制图。","2026-09-16T23:30:28.929376Z",{"id":1567,"title":1568,"url":1569,"summary":1570,"summary_zh":1571,"content":11,"source_name":1572,"source_url":1569,"published_at":1573,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":1574,"score_detail":1575,"sources":1577,"tags":1579,"primary_topic":1583,"secondary_topics":1584,"classification":1587,"search_phrases":1625,"slug":1628,"view_count":455,"doi":1629,"paper":1630,"created_at":1658},2658,"A remote sensing–based approach to the regionalization of socioeconomic indicators in an agricultural headwater catchment in Northern Benin","https:\u002F\u002Fdoi.org\u002F10.5194\u002Fpiahs-389-45-2026","The agroecosystems of the rural continuum of the Sudano-Sahelian region of the Volta River Basin are undergoing severe degradation, resulting in serious declines in ecosystem service capacities and weakening community livelihoods, as agriculture remains the primary source of income in the region. This degradation is mainly driven by poor management of hydroclimatic risks, non-adapted agricultural practices characterized by intensive use of chemical fertilizers and pesticides, and insufficient consideration of spatial heterogeneity in decision-making processes. The objectives of this study are (i) to identify a Network of 30 m resolution Grid Cells (NGC) representative of the spatial heterogeneity of the studied agroecosystem – the Dassari headwater catchment (550 km 2 ), a tributary of the Volta River in northern Benin – and (ii) to regionalize plot-scale socioeconomic data to support improved decision-making. The NGC was derived by combining iterative principal component analysis (IPCA) with a Conditioned Latin Hypercube Sampling (CLHS) approach using 37 satellite-derived variables (e.g. Normalized Difference Vegetation Index, saturation index, coloration index), resulting in the selection of 150 grid cells. Field and laboratory investigations provided soil properties (e.g. texture, carbon, field capacity, nitrogen content) and socioeconomic data (e.g. harvest quantity, input costs, total production cost, and gross income), which were standardized and analyzed for major crops (millet, sorghum, maize, and cotton). Results show that natural spatial disparities among NGC cells translate into additional labor and input costs that may be unsustainable for farmers. Multiple linear regression models were developed to relate socioeconomic indicators to soil and remote sensing variables for each crop type, and the resulting regional models proved robust, with predicted and observed values closely aligned within the 95 % confidence interval, coefficients of determination exceeding 70 %, and p -values below 0.01.","沃尔特河流域苏丹-萨赫勒地区农村连续体的农业生态系统正在经历严重退化，导致生态系统服务能力严重下降，社区生计日益脆弱，而农业仍是该地区的主要收入来源。这种退化主要由水文气候风险管理不善、以大量使用化肥和农药为特征的非适应性农业实践，以及决策过程中对空间异质性考虑不足所驱动。本研究的目标是：(i) 识别一个30米分辨率网格单元网络（Network of 30 m resolution Grid Cells, NGC），以代表所研究农业生态系统的空间异质性——达萨里源头集水区（550平方公里），位于贝宁北部沃尔特河的一条支流；以及 (ii) 将地块尺度的社会经济数据进行区域化，以支持改进决策。NGC通过将迭代主成分分析（Iterative Principal Component Analysis, IPCA）与条件拉丁超立方采样（Conditioned Latin Hypercube Sampling, CLHS）方法相结合，利用37个卫星衍生变量（如归一化植被指数、饱和度指数、着色指数）得出，最终选取了150个网格单元。实地和实验室调查提供了土壤属性（如质地、碳、田间持水量、氮含量）和社会经济数据（如收获量、投入成本、总生产成本和总收入），这些数据经过标准化处理，并针对主要作物（小米、高粱、玉米和棉花）进行了分析。结果表明，NGC网格单元之间的自然空间差异转化为额外的劳动力和投入成本，对农民而言可能难以承受。研究针对每种作物类型建立了多元线性回归模型，将社会经济指标与土壤和遥感变量相关联，所得区域模型表现稳健，预测值与观测值在95%置信区间内高度吻合，决定系数超过70%，p值低于0.01。","Proceedings of the International Association of Hydrological Sciences","2026-09-16T00:00:00Z",68,{"impact":173,"substance":508,"depth":733,"authority":782,"freshness":385,"relevant":455,"comment":1576},"该研究以30米分辨率遥感变量结合IPCA与CLHS抽样构建代表性网格，将地块尺度社会经济指标区域化，模型R²超70%，方法新颖、数据扎实，对农业信息化与精准决策有参考价值，但研究对象为贝宁流域，国内落地相关性有限，属细分领域学术进展。",[1578],{"name":1572,"url":1569},[591,1580,608,1581,1582],"空间异质性","西非农业","小农户生计",{"code":32,"slug":33,"name":34,"path":35,"parent_code":7},[1585,1586],{"code":240,"slug":241,"name":242,"path":243,"parent_code":200},{"code":176,"slug":177,"name":178,"path":179,"parent_code":128},{"status":599,"locked":581,"version":4,"method":600,"evidence":1588},{"candidates":1589,"agricultural_evidence":604},[1590,1594,1601,1608,1612,1616,1620],{"code":32,"score":584,"supported":604,"title":1591,"abstract":1592,"tags":1593},[609],[608,609],[591,608],{"code":240,"score":783,"supported":604,"title":1595,"abstract":1596,"tags":1600},[],[1597,1598,1599],"高粱","sorghum","millet",[],{"code":176,"score":783,"supported":604,"title":1602,"abstract":1603,"tags":1607},[],[1604,1605,1606],"农业生态","生态系统服务","ecosystem service",[],{"code":224,"score":988,"supported":604,"title":1609,"abstract":1610,"tags":1611},[],[226,225],[],{"code":248,"score":988,"supported":604,"title":1613,"abstract":1614,"tags":1615},[],[1377,1387],[],{"code":144,"score":988,"supported":604,"title":1617,"abstract":1618,"tags":1619},[],[1474,145],[],{"code":104,"score":1075,"supported":604,"title":1621,"abstract":1622,"tags":1624},[],[1623],"农药",[],[1626,1627],"小农户生计 空间异质性 农业遥感 西非农业","小农户生计 空间异质性","小农户生计空间异质性农业遥感西非农业-2658","10.5194\u002Fpiahs-389-45-2026",{"doi":1629,"openalex_id":1631,"authors":1632,"venue":1572,"cited_by_count":424,"oa_url":1569,"card":1653,"direction":667,"ingested_from":669},"W7213239844",[1633,1635,1637,1640,1643,1645,1648,1650],{"name":1634,"orcid":11},"Yaovi Aymar Bossa",{"name":1636,"orcid":11},"Adjo Brigitte Bossa",{"name":1638,"orcid":1639},"Yacouba Yira","https:\u002F\u002Forcid.org\u002F0000-0003-3879-8153",{"name":1641,"orcid":1642},"Ozias Hounkpatin","https:\u002F\u002Forcid.org\u002F0000-0003-1105-8649",{"name":1644,"orcid":11},"Octave Djangni",{"name":1646,"orcid":1647},"Jean Hounkpè","https:\u002F\u002Forcid.org\u002F0000-0002-5521-9339",{"name":1649,"orcid":11},"Hélyette Arielle Odoumbourou",{"name":1651,"orcid":1652},"Ernest Amoussou","https:\u002F\u002Forcid.org\u002F0000-0002-9402-4446",{"tldr":1654,"method":1655,"finding":1656,"direction":667,"opportunity":1657},"结合遥感与采样方法，将地块级社会经济指标区域化到贝宁北部农业流域。","迭代主成分分析与条件拉丁超立方采样，结合37个遥感变量和多元线性回归。","区域模型稳健，决定系数超70%，p值小于0.01，可预测作物社会经济指标。","可探索将遥感区域化社会经济指标方法迁移至其他流域，并耦合农户决策行为模型。","2026-09-16T23:30:28.573293Z",{"id":1660,"title":1661,"url":1662,"summary":1663,"summary_zh":1664,"content":11,"source_name":1665,"source_url":1662,"published_at":1573,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":1666,"score_detail":1667,"sources":1669,"tags":1671,"primary_topic":1674,"secondary_topics":1675,"classification":1676,"search_phrases":1695,"slug":1698,"view_count":408,"doi":1699,"paper":1700,"created_at":1735},2615,"A low-cost RGB image and point cloud integrated scanning system for wheat phenotyping","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112441","A low-cost RGB image and point cloud integrated scanning system for wheat phenotyping。Computers and Electronics in Agriculture","一种用于小麦表型分析的低成本RGB图像与点云集成扫描系统。","Computers and Electronics in Agriculture",79,{"impact":361,"substance":508,"depth":733,"authority":345,"freshness":385,"relevant":455,"comment":1668},"低成本RGB与点云融合的小麦表型扫描系统，方法新颖且面向育种实践，具备细分领域参考价值。",[1670],{"name":1665,"url":1662},[589,591,218,1672,1673],"表型组学","三维点云",{"code":216,"slug":217,"name":218,"path":219,"parent_code":200},[],{"status":599,"locked":581,"version":4,"method":600,"evidence":1677},{"candidates":1678,"agricultural_evidence":604},[1679,1683,1687,1691],{"code":216,"score":612,"supported":604,"title":1680,"abstract":1681,"tags":1682},[217],[218,217],[218],{"code":16,"score":455,"supported":581,"title":1684,"abstract":1685,"tags":1686},[],[],[589],{"code":24,"score":455,"supported":581,"title":1688,"abstract":1689,"tags":1690},[],[],[1673],{"code":32,"score":455,"supported":581,"title":1692,"abstract":1693,"tags":1694},[],[],[591],[1696,1697],"三维点云 农业遥感 智慧农业 表型组学","三维点云 农业遥感","三维点云农业遥感智慧农业表型组学-2615","10.1016\u002Fj.compag.2026.112441",{"doi":1699,"openalex_id":1701,"authors":1702,"venue":1665,"cited_by_count":424,"oa_url":11,"card":1730,"direction":667,"ingested_from":669},"W7213304555",[1703,1705,1707,1709,1711,1713,1715,1717,1719,1721,1723,1725,1727],{"name":1704,"orcid":11},"Yan Li",{"name":1706,"orcid":11},"Suyi Liu",{"name":1708,"orcid":11},"Xuerui Qi",{"name":1710,"orcid":11},"Chuanjing Jin",{"name":1712,"orcid":11},"Xiangxin Zhuang",{"name":1714,"orcid":11},"Zetao Wang",{"name":1716,"orcid":11},"Jinyong Li",{"name":1718,"orcid":11},"Chengming Zhao",{"name":1720,"orcid":11},"Xia Yao",{"name":1722,"orcid":11},"Yongchao Tian",{"name":1724,"orcid":11},"Yan Zhu",{"name":1726,"orcid":11},"Weixing Cao",{"name":1728,"orcid":1729},"Xiaohu Zhang","https:\u002F\u002Forcid.org\u002F0000-0001-5654-1445",{"tldr":1731,"method":1732,"finding":1733,"direction":667,"opportunity":1734},"开发低成本RGB与点云融合扫描系统，用于小麦表型测量。","低成本RGB相机与点云融合扫描系统，采集小麦三维表型数据。","该系统能低成本获取小麦RGB与点云数据，实现表型分析。","可探索低成本多模态表型系统在田间高通量育种和抗逆监测中的应用。","2026-09-16T23:30:01.907231Z",{"id":1737,"title":1738,"url":1739,"summary":1740,"summary_zh":1741,"content":11,"source_name":1665,"source_url":1739,"published_at":1742,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":45,"score_detail":1743,"sources":1745,"tags":1747,"primary_topic":1751,"secondary_topics":1752,"classification":1755,"search_phrases":1784,"slug":1787,"view_count":400,"doi":1788,"paper":1789,"created_at":1812},2501,"Numerical analysis of droplet deposition and drift patterns for multi-rotor UAVs in soybean-maize strip intercropping system","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112407","Numerical analysis of droplet deposition and drift patterns for multi-rotor UAVs in soybean-maize strip intercropping system。Computers and Electronics in Agriculture","多旋翼无人机在大豆-玉米带状间作系统中的雾滴沉积与飘移模式数值分析。","2026-09-14T00:00:00Z",{"impact":416,"substance":361,"depth":733,"authority":345,"freshness":173,"relevant":455,"comment":1744},"核心期刊论文，用数值模拟揭示多旋翼无人机在大豆玉米带状复合种植中的雾滴沉积与飘移规律，方法新颖、结论对精准植保有参考价值，但属细分技术进展，公共影响有限。",[1746],{"name":1665,"url":1739},[589,591,1748,1749,1750],"精准施药","大豆玉米带状复合种植","植保无人机",{"code":224,"slug":225,"name":226,"path":227,"parent_code":200},[1753,1754],{"code":232,"slug":233,"name":234,"path":235,"parent_code":200},{"code":256,"slug":257,"name":258,"path":259,"parent_code":200},{"status":599,"locked":581,"version":4,"method":600,"evidence":1756},{"candidates":1757,"agricultural_evidence":604},[1758,1762,1766,1772,1776,1780],{"code":224,"score":612,"supported":604,"title":1759,"abstract":1760,"tags":1761},[225],[226,225],[1749],{"code":232,"score":345,"supported":604,"title":1763,"abstract":1764,"tags":1765},[997],[996,997],[],{"code":256,"score":345,"supported":604,"title":1767,"abstract":1769,"tags":1771},[1768],"intercropping",[1770,1768],"间作",[],{"code":104,"score":69,"supported":581,"title":1773,"abstract":1774,"tags":1775},[],[],[1750,1748],{"code":16,"score":455,"supported":581,"title":1777,"abstract":1778,"tags":1779},[],[],[589],{"code":32,"score":455,"supported":581,"title":1781,"abstract":1782,"tags":1783},[],[],[591],[1785,1786],"大豆玉米带状复合种植 植保无人机 农业遥感 智慧农业","大豆玉米带状复合种植 植保无人机","大豆玉米带状复合种植植保无人机农业遥感智慧农业-2501","10.1016\u002Fj.compag.2026.112407",{"doi":1788,"openalex_id":1790,"authors":1791,"venue":1665,"cited_by_count":424,"oa_url":11,"card":1807,"direction":830,"ingested_from":669},"W7212969118",[1792,1794,1796,1798,1799,1802,1805],{"name":1793,"orcid":11},"Xinghua Liu",{"name":1795,"orcid":11},"Lei Yang",{"name":1797,"orcid":11},"Zhi Li",{"name":886,"orcid":11},{"name":1800,"orcid":1801},"Qiaolin Chen","https:\u002F\u002Forcid.org\u002F0000-0002-3878-0001",{"name":1803,"orcid":1804},"Yanping Tian","https:\u002F\u002Forcid.org\u002F0000-0003-4303-6524",{"name":1806,"orcid":11},"Xiang-Dong Li",{"tldr":1808,"method":1809,"finding":1810,"direction":830,"opportunity":1811},"用数值模拟分析多旋翼无人机在大豆-玉米带状间作系统中的雾滴沉积与飘移规律。","基于CFD数值模拟，结合多旋翼无人机下洗气流与作物冠层结构建模。","揭示了间作系统下无人机喷雾沉积分布与飘移特征，为精准施药提供依据。","可结合田间实测与机器学习，构建间作系统无人机变量喷雾决策模型。","2026-09-15T23:30:01.587453Z",{"id":1814,"title":1815,"url":1816,"summary":1817,"summary_zh":11,"content":11,"source_name":1818,"source_url":11,"published_at":1819,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":677,"score_detail":1820,"sources":1822,"tags":1824,"primary_topic":1826,"secondary_topics":1827,"classification":1828,"search_phrases":1847,"slug":1850,"view_count":1075,"doi":11,"paper":1851,"created_at":1858},2218,"《基于现役作物多模态无人机图像的大豆种子成分基因型感知预测》","https:\u002F\u002Fwww.mdpi.com\u002F2072-4292\u002F18\u002F18\u002F3121","Vasit Sagan等(圣路易斯大学、密苏里大学)开发端到端卷积神经网络框架,从基于无人机的多传感器图像及相关基因型和物候元数据估算大豆八种种子性状(蛋白质、油、蔗糖、纤维、淀粉、灰分、复合和简单碳水化合物)。2020-2021年密苏里州两个农场共采集372份大豆样本,在四个时间点捕获多光谱、热成像和LiDAR数据。蔗糖预测精度最高(R²=0.80),其次为简单碳水化合物(R²=0.70)和淀粉(R²=0.55)。多光谱图像在所有模态中提供最稳健的估计。","Remote Sensing 2026年9月11日","2026-09-11T00:00:00Z",{"impact":361,"substance":321,"depth":361,"authority":345,"freshness":385,"relevant":455,"comment":1821},"多模态无人机遥感结合深度学习预测大豆种子成分，方法新颖、数据扎实，对智慧育种与精准农业有参考价值。",[1823],{"name":1818,"url":1816},[589,1053,590,591,1825],"大豆育种",{"code":232,"slug":233,"name":234,"path":235,"parent_code":200},[],{"status":599,"locked":581,"version":4,"method":600,"evidence":1829},{"candidates":1830,"agricultural_evidence":604},[1831,1835,1839,1843],{"code":232,"score":416,"supported":604,"title":1832,"abstract":1833,"tags":1834},[996],[996],[1825],{"code":16,"score":455,"supported":581,"title":1836,"abstract":1837,"tags":1838},[],[],[589],{"code":24,"score":455,"supported":581,"title":1840,"abstract":1841,"tags":1842},[],[],[590],{"code":32,"score":455,"supported":581,"title":1844,"abstract":1845,"tags":1846},[],[],[591],[1848,1849],"农业人工智能 农业遥感 大豆育种 智慧农业","农业人工智能 农业遥感","农业人工智能农业遥感大豆育种智慧农业-2218",{"doi":11,"openalex_id":11,"authors":1852,"venue":11,"cited_by_count":424,"oa_url":11,"card":1853,"direction":667,"ingested_from":721},[],{"tldr":1854,"method":1855,"finding":1856,"direction":667,"opportunity":1857},"用无人机多模态图像和CNN预测大豆八种种子成分，蔗糖精度最高。","端到端CNN，融合多光谱、热成像、LiDAR及基因型物候元数据，372份样本。","多光谱最稳健，蔗糖R²=0.80，简单碳水化合物0.70，淀粉0.55。","可探索多模态融合与基因型-表型关联，提升低精度性状预测及跨年份泛化。","2026-09-12T00:06:40.739496Z",{"id":1860,"title":1861,"url":1862,"summary":1863,"summary_zh":1864,"content":11,"source_name":1865,"source_url":1862,"published_at":1866,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":1867,"score_detail":1868,"sources":1870,"tags":1872,"primary_topic":1874,"secondary_topics":1875,"classification":1877,"search_phrases":1907,"slug":1910,"view_count":988,"doi":1911,"paper":1912,"created_at":1940},1898,"Connecting earth observation anomalies to farmer surveys for monitoring impacts of agricultural drought on rainfed rice yields in Nigeria","https:\u002F\u002Fdoi.org\u002F10.5194\u002Feo-1-77-2026","Abstract. Agricultural drought threatens rainfed rice production in Nigeria, where smallholder farmers depend on rainfall and have limited capacity to buffer climate shocks. While meteorological drought indices such as the SPI and the SPEI are widely used in national early warning systems, their ability to capture the impacts of droughts on rainfed rice yields at the smallholder field-level remains uncertain. This study evaluates the added value of earth observation (EO)-derived vegetation and soil moisture anomalies for monitoring and predicting drought impacts on rainfed rice yields in Nigeria. Satellite-based Normalized Difference Vegetation Index anomalies (NDVIA) and Soil Water Index anomalies (SWIA) were derived using a zonal clustering and thresholding approach and combined with farmer survey and yield data collected from 146 rainfed rice farmers across four major rice-growing states between 2019 and 2024. Multivariate regression models were used to assess the relationships between EO indicator anomalies and annual yield changes, and the effects of different zonal clustering and anomaly thresholds on anomaly calculation were evaluated. Results show that SPI and SPEI explain a substantial share of yield variability in some years, particularly when droughts coincide with sensitive phenological stages. However, EO-based anomaly indicators, especially SWIA (maximum improved R2 = 0.25), provide complementary information and significantly improve yield predictions in years when meteorological indices alone perform poorly. The timing of anomalies relative to rice phenology was critical, with droughts during panicle initiation having the largest yield impacts. Integrating EO-based vegetation and soil moisture anomaly indicators with existing meteorological indices can contribute to the monitoring of agricultural droughts and improve the operational relevance of early warning systems for rainfed rice farmers in Nigeria.","摘要：农业干旱威胁着尼日利亚依赖雨养的水稻生产，当地小农户依靠降雨为生，缓冲气候冲击的能力有限。尽管诸如标准化降水指数（SPI）和标准化降水蒸散指数（SPEI）等气象干旱指数被广泛应用于国家早期预警系统，但它们在捕捉小农田间尺度上干旱对雨养水稻产量影响的能力仍不确定。本研究评估了基于地球观测（EO）的植被和土壤湿度异常在监测和预测尼日利亚干旱对雨养水稻产量影响方面的附加价值。采用分区聚类和阈值方法，推导出基于卫星的归一化差异植被指数异常（NDVIA）和土壤水分指数异常（SWIA），并结合2019年至2024年间从四个主要水稻种植州的146位雨养水稻农户收集的农户调查和产量数据。使用多元回归模型评估EO指标异常与年度产量变化之间的关系，并评估不同分区聚类和异常阈值对异常计算的影响。结果表明，在某些年份，SPI和SPEI解释了产量变异的很大一部分，特别是当干旱与敏感物候阶段重合时。然而，基于EO的异常指标，尤其是SWIA（最大改进R²=0.25），提供了补充信息，并在气象指数单独表现不佳的年份显著改善了产量预测。异常相对于水稻物候的时机至关重要，穗分化期发生的干旱对产量影响最大。将基于EO的植被和土壤湿度异常指标与现有气象指数相结合，有助于监测农业干旱，并提高尼日利亚雨养水稻农户早期预警系统的业务相关性。","Earth Observation","2026-09-07T00:00:00Z",74,{"impact":612,"substance":321,"depth":361,"authority":416,"freshness":933,"relevant":455,"comment":1869},"研究结合遥感与农户调查，提升雨养水稻干旱监测精度，对非洲小农预警有参考价值。",[1871],{"name":1865,"url":1862},[589,591,210,847,1873],"尼日利亚",{"code":208,"slug":209,"name":210,"path":211,"parent_code":200},[1876],{"code":184,"slug":185,"name":186,"path":187,"parent_code":128},{"status":599,"locked":581,"version":4,"method":600,"evidence":1878},{"candidates":1879,"agricultural_evidence":604},[1880,1884,1889,1895,1899,1903],{"code":208,"score":612,"supported":604,"title":1881,"abstract":1882,"tags":1883},[209],[210,209],[210],{"code":184,"score":612,"supported":604,"title":1885,"abstract":1886,"tags":1888},[856],[1887,856],"干旱",[847],{"code":496,"score":988,"supported":604,"title":1890,"abstract":1891,"tags":1894},[],[1892,1893],"小农户","smallholder",[],{"code":144,"score":988,"supported":604,"title":1896,"abstract":1897,"tags":1898},[],[1474,145],[],{"code":16,"score":455,"supported":581,"title":1900,"abstract":1901,"tags":1902},[],[],[589],{"code":32,"score":455,"supported":581,"title":1904,"abstract":1905,"tags":1906},[],[],[591],[1908,1909],"农业遥感 尼日利亚 干旱监测 智慧农业","农业遥感 尼日利亚","农业遥感尼日利亚干旱监测智慧农业-1898","10.5194\u002Feo-1-77-2026",{"doi":1911,"openalex_id":1913,"authors":1914,"venue":1865,"cited_by_count":424,"oa_url":1862,"card":1935,"direction":667,"ingested_from":669},"W7162756810",[1915,1918,1920,1923,1925,1928,1931,1933],{"name":1916,"orcid":1917},"Nick Gutkin","https:\u002F\u002Forcid.org\u002F0000-0003-4708-3483",{"name":1919,"orcid":11},"Chiamaka I. Ehiemere",{"name":1921,"orcid":1922},"Koen De Vos","https:\u002F\u002Forcid.org\u002F0000-0002-4607-7877",{"name":1924,"orcid":11},"Nnamdi Ehiemere",{"name":1926,"orcid":1927},"Jeroen Degerickx","https:\u002F\u002Forcid.org\u002F0000-0001-9022-9623",{"name":1929,"orcid":1930},"Sarah Gebruers","https:\u002F\u002Forcid.org\u002F0000-0001-8426-4158",{"name":1932,"orcid":11},"Uchechukwu Nwafor",{"name":1934,"orcid":11},"Anne Gobin",{"tldr":1936,"method":1937,"finding":1938,"direction":667,"opportunity":1939},"评估EO异常指标监测尼日利亚雨养水稻干旱影响，补充气象指数，提高产量预测。","结合NDVI和土壤水分指数异常、聚类阈值法、农户调查与回归模型。","EO指标（尤其SWIA）补充气象指数，显著提高干旱年份产量预测，关键物候期干旱影响最大。","可探索将EO异常与物候模型结合，开发针对小农的干旱预警系统，并验证其他作物和区域。","2026-09-08T23:30:18.761711Z",{"technologies":1942,"keywords":1995},[1943,1944,1946,1948,1949,1950,1951,1952,1953,1954,1956,1958,1960,1962,1964,1966,1968,1970,1972,1974,1976,1977,1979,1981,1983,1985,1987,1989,1991,1993,1994],{"name":590,"count":539},{"name":608,"count":1945},71,{"name":936,"count":1947},34,{"name":1107,"count":919},{"name":591,"count":1312},{"name":628,"count":361},{"name":683,"count":416},{"name":627,"count":416},{"name":1378,"count":385},{"name":1955,"count":933},"边缘计算",{"name":1957,"count":400},"物联网",{"name":1959,"count":400},"目标检测",{"name":1961,"count":400},"计算机视觉",{"name":1963,"count":1075},"农业机器人",{"name":1965,"count":1075},"分子育种",{"name":1967,"count":1075},"多光谱成像",{"name":1969,"count":1075},"数字孪生",{"name":1971,"count":69},"GIS",{"name":1973,"count":69},"图像分割",{"name":1975,"count":69},"基因编辑",{"name":684,"count":69},{"name":1978,"count":69},"高光谱",{"name":1980,"count":455},"AI",{"name":1982,"count":455},"农业大模型",{"name":1984,"count":455},"农业物联网",{"name":1986,"count":455},"区块链",{"name":1988,"count":455},"图像识别",{"name":1990,"count":455},"多模态",{"name":1992,"count":455},"多源遥感",{"name":789,"count":455},{"name":1230,"count":455},[1996,1998,1999,2000,2002,2004,2006,2008,2009,2010,2012,2014],{"name":589,"count":1997},243,{"name":1117,"count":45},{"name":210,"count":353},{"name":226,"count":2001},42,{"name":218,"count":2003},31,{"name":2005,"count":732},"产量预测",{"name":2007,"count":732},"气候智慧农业",{"name":1892,"count":1313},{"name":996,"count":782},{"name":2011,"count":782},"种业振兴",{"name":2013,"count":416},"作物模型",{"name":908,"count":416}]