[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3049":3,"related-3049":46},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":8,"source_name":9,"source_url":8,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":15,"sources":23,"tags":25,"search_phrases":31,"slug":34,"view_count":35,"doi":8,"paper":36,"created_at":45},3049,"土壤压实与灌溉管理：对精准农业中土壤水力变化的启示","https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F18\u002F1853","意大利帕多瓦大学A.C.与L.B.评估土壤压实通过改变土壤水力特性对精准农业灌溉管理的综合影响。研究维护土壤结构作为维持土壤水力功能、提升灌溉效率与农业系统长期可持续性最有效途径，使用HYPROP水力特性分析仪测定田间持水量（FC）、永久萎蔫点（PWP）、饱和水力传导度（Ksat）等关键参数，结合无人机遥感（UAV）与决策支持系统（DSS）实现精准灌溉调度。研究获SOILWAT（BIRD 2026）项目资助，为精准农业管理决策提供可量化水力参数基础。",null,"MDPI Agronomy 16(18):1853","2026-09-20T00:00:00Z","论文",10,false,68,{"impact":16,"substance":17,"depth":18,"authority":19,"freshness":20,"relevant":21,"comment":22},12,18,16,13,9,1,"学术论文，方法结合HYPROP与无人机遥感，对精准灌溉有参考价值，但属细分领域研究，公共影响有限。",[24],{"name":9,"url":6},[26,27,28,29,30],"决策支持系统","精准农业","无人机遥感","智慧灌溉","土壤压实",[32,33],"帕多瓦大学 土壤压实 灌溉","HYPROP 水力特性 精准灌溉","帕多瓦大学土壤压实灌溉-3049",0,{"doi":8,"openalex_id":8,"authors":37,"venue":8,"cited_by_count":35,"oa_url":8,"card":38,"direction":42,"ingested_from":44},[],{"tldr":39,"method":40,"finding":41,"direction":42,"opportunity":43},"评估土壤压实改变水力特性对精准灌溉管理的影响，并提出维护土壤结构的对策。","用HYPROP测FC、PWP、Ksat，结合无人机遥感与决策支持系统调度灌溉。","维护土壤结构是保持水力功能、提升灌溉效率与长期可持续性的最有效途径。","智慧农业 \u002F 农业物联网","可探索压实-水力参数-遥感反演耦合模型，实现压实风险与灌溉调度的实时协同优化。","agent","2026-09-21T00:04:39.395594Z",{"total":47,"page":21,"page_size":47,"items":48},6,[49,109,146,190,224,251],{"id":50,"title":51,"url":52,"summary":53,"summary_zh":54,"content":8,"source_name":55,"source_url":52,"published_at":56,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":57,"score_detail":58,"sources":63,"tags":65,"search_phrases":69,"slug":72,"view_count":35,"doi":73,"paper":74,"created_at":108},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":17,"substance":59,"depth":60,"authority":61,"freshness":20,"relevant":21,"comment":62},23,19,15,"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。",[64],{"name":55,"url":52},[66,67,68,27,28],"智慧农业","农业遥感","棉花",[70,71],"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":73,"openalex_id":75,"authors":76,"venue":55,"cited_by_count":35,"oa_url":52,"card":101,"direction":105,"ingested_from":107},"W7213661690",[77,80,82,84,86,88,91,94,96,99],{"name":78,"orcid":79},"Qing Li","https:\u002F\u002Forcid.org\u002F0009-0004-4580-7761",{"name":81,"orcid":8},"Dalei Hao",{"name":83,"orcid":8},"Jan Pisek",{"name":85,"orcid":8},"Zicheng Ji",{"name":87,"orcid":8},"Yanan Wei",{"name":89,"orcid":90},"Youngryel Ryu","https:\u002F\u002Forcid.org\u002F0000-0001-6238-2479",{"name":92,"orcid":93},"Jiarui Xu","https:\u002F\u002Forcid.org\u002F0000-0003-4925-2770",{"name":95,"orcid":8},"Yangmin Feng",{"name":97,"orcid":98},"Shaozhong Kang","https:\u002F\u002Forcid.org\u002F0000-0002-8019-2537",{"name":100,"orcid":8},"Yelu Zeng",{"tldr":102,"method":103,"finding":104,"direction":105,"opportunity":106},"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。","农业遥感与作物表型","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。","openalex","2026-09-20T23:30:21.262698Z",{"id":110,"title":111,"url":112,"summary":113,"summary_zh":114,"content":8,"source_name":115,"source_url":112,"published_at":116,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":117,"score_detail":118,"sources":121,"tags":123,"search_phrases":126,"slug":129,"view_count":35,"doi":130,"paper":131,"created_at":145},2039,"An Adaptive AI-DSS Framework for Real-Time Image Analysis and Decision-Making in Precision Agriculture","https:\u002F\u002Fdoi.org\u002F10.14445\u002F22315381\u002Fijett-v74i8p136","Environmental variability, pest infestation, and resource inefficiency have become increasingly problematic to agriculture, and on-line decision making using intelligent, adaptive technologies has been called for. This research is suggesting a novel Integrated Artificial Intelligence Decision Support System (AI-DSS) in precision agriculture so as to attain proper context-aware analysis and recommendation across dynamic field conditions. The framework is based on lightweight deep learning frameworks (MobileNetV2, Efficient Net-lite) and a newly designed Adaptive Feature Optimization (AFO) engine, which dynamically reweights convolutional features based on the temporal stability and environment consistency. Mathematically, the AFO mechanism is obtained via a weighted pooling of the instantaneous features of CNN with the temporal averaged prototypes using adaptive attention weights, which filter out transient noises due to illumination variations, occlusion, and sensor noises. The optimized features are fused with data from environmental and soil sensors in a hybrid Decision Support System (DSS) based on rule-based reasoning, Bayesian inference, and temporal tracking to construct explainable and region-specific recommendations to farmers. Experimental evaluations using the experimental data PlantVillage, DeepWeeds, and Fieldstream Sim demonstrate the superiority of the proposed AFO enhanced framework over the baseline CNN classifier in terms of classification accuracy (.95), macro F1 score (.95), and robustness to distortions (.15% improvement). Additionally, when deployed on edge devices like Raspberry Pi 4 and Nvidia Jetson Nano, the system achieves real-time inference latency (\u003C200 ms) and low energy consumption (~520 mJ\u002Fframe), which validates the scalability of the system in low-resource settings. In addition, the expert agreement was enhanced with the DSS module integration to 91.4% with 57% less false alarms. The obtained results validate the proposed AI-DSS framework with AFO as a promising solution to cover the distance between accuracy in a controlled lab environment and reliability in the field, providing a strong, explainable, and resource-efficient solution to the digital sustainable agriculture problem. This solution is further being expanded into proactive farm intelligence and climate resilience through multimodal sensing, satellite assisted crops monitoring, and adaptive decision-making-led solutions.","环境变异、病虫害侵袭和资源低效问题对农业的影响日益严重，亟需利用智能自适应技术进行在线决策。本研究提出了一种用于精准农业的新型集成人工智能决策支持系统（AI-DSS），旨在实现动态田间条件下的情境感知分析与推荐。该框架基于轻量级深度学习框架（MobileNetV2、EfficientNet-lite）和新设计的自适应特征优化（AFO）引擎，后者根据时间稳定性和环境一致性对卷积特征进行动态重加权。在数学上，AFO机制通过自适应注意力权重将CNN的瞬时特征与时间平均原型进行加权池化，从而滤除由光照变化、遮挡和传感器噪声引起的瞬态噪声。优化后的特征与环境和土壤传感器数据在混合决策支持系统（DSS）中融合，该系统基于规则推理、贝叶斯推断和时间追踪，为农民构建可解释的、区域特定的推荐方案。使用PlantVillage、DeepWeeds和Fieldstream Sim实验数据的评估表明，所提出的AFO增强框架在分类准确率（.95）、宏F1分数（.95）和抗失真鲁棒性（提升.15%）方面均优于基线CNN分类器。此外，在Raspberry Pi 4和Nvidia Jetson Nano等边缘设备上部署时，系统实现了实时推理延迟（\u003C200 ms）和低能耗（约520 mJ\u002F帧），验证了该系统在低资源环境下的可扩展性。同时，集成DSS模块后专家一致率提升至91.4%，误报率降低57%。所得结果验证了所提出的带有AFO的AI-DSS框架是一种有前景的解决方案，能够弥合受控实验室环境中的准确性与田间可靠性之间的差距，为数字可持续农业问题提供了稳健、可解释且资源高效的解决方案。该方案正进一步通过多模态传感、卫星辅助作物监测和自适应决策驱动的解决方案，扩展至主动式农场智能和气候韧性领域。","International Journal of Engineering Trends and Technology","2026-09-09T00:00:00Z",79,{"impact":17,"substance":119,"depth":17,"authority":16,"freshness":20,"relevant":21,"comment":120},22,"提出融合自适应特征优化与多源传感器推理的轻量级AI决策支持框架，在多个公开数据集与边缘设备上验证了精度与实时性，方法新颖、结论可靠，对智慧农业落地有参考价值。",[122],{"name":115,"url":112},[66,124,125,26,27],"农业人工智能","边缘计算",[127,128],"农业人工智能 决策支持系统 智慧农业 精准农业","农业人工智能 决策支持系统","农业人工智能决策支持系统智慧农业精准农业-2039","10.14445\u002F22315381\u002Fijett-v74i8p136",{"doi":130,"openalex_id":132,"authors":133,"venue":115,"cited_by_count":35,"oa_url":138,"card":139,"direction":42,"ingested_from":107},"W7212021859",[134,136],{"name":135,"orcid":8},"Yebhushi Prashanth",{"name":137,"orcid":8},"Dr.Manna SheelaRani Chetty","https:\u002F\u002Fijettjournal.org\u002FVolume-74\u002FIssue-8\u002FIJETT-V74I8P136.pdf",{"tldr":140,"method":141,"finding":142,"direction":143,"opportunity":144},"提出自适应AI决策支持系统，用轻量网络与特征优化实现精准农业实时图像分析与决策。","MobileNetV2\u002FEfficientNet-lite加自适应特征优化引擎，","分类准确率与宏F1达0.95，边缘设备延迟低于200毫秒，专家一致率91.4%。","农业人工智能与决策模型","可探索多模态传感与卫星遥感融合，提升田间跨区域泛化与气候韧性决策能力。","2026-09-10T23:30:09.239423Z",{"id":147,"title":148,"url":149,"summary":150,"summary_zh":151,"content":8,"source_name":152,"source_url":149,"published_at":56,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":153,"score_detail":154,"sources":158,"tags":160,"search_phrases":165,"slug":168,"view_count":35,"doi":169,"paper":170,"created_at":189},3020,"Spatial variability of soil fertility indicators using GIS for sustainable land management in Banaskantha district western India","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs44378-026-00320-y","Soil fertility assessment is fundamental for sustainable land management and precision agriculture, particularly in semi-arid regions where soil properties exhibit considerable spatial variability. This study investigated the spatial distribution of major soil fertility indicators across Banaskantha district, Gujarat, India, using a systematic 15 × 15 km grid-based sampling approach integrated with Geographic Information System (GIS) analysis and multivariate statistics. A total of 46 geo-referenced composite soil samples were collected from agricultural fields representing all fourteen talukas and analysed for soil moisture, pH, electrical conductivity (EC), organic carbon (OC), available nitrogen (AvN), available phosphorus (AvP), and available potassium (AvK). GIS-based Inverse Distance Weighting (IDW) interpolation was employed to visualize the regional distribution of soil properties, while Pearson correlation analysis and Principal Component Analysis (PCA) were used to examine relationships among soil fertility indicators and identify the major factors influencing soil variability. The results revealed substantial spatial heterogeneity across the district, with alkaline and saline soils predominantly occurring in the western region, whereas comparatively higher soil moisture and organic carbon were observed in the eastern and northern areas. Available phosphorus was generally deficient throughout the district, while available nitrogen and potassium exhibited pronounced spatial variation associated with parent material, land use, and agricultural management practices. PCA identified salinity-related factors and organic matter–nutrient interactions as the principal contributors to soil fertility variability. The generated spatial distribution maps provide valuable baseline information for identifying nutrient-deficient zones and understanding regional patterns of soil fertility. Although the proposed nutrient management strategies require further validation through field-based agronomic studies, the integration of laboratory soil analysis, GIS, and multivariate statistical techniques provides an effective framework for regional soil fertility assessment and supports informed decision-making for sustainable land management in semi-arid environments.","土壤肥力评估是可持续土地管理和精准农业的基础，尤其是在土壤属性表现出显著空间变异性的半干旱地区。本研究采用基于15 × 15 km网格的系统采样方法，结合地理信息系统（GIS）分析和多元统计方法，调查了印度古吉拉特邦巴纳斯坎塔县主要土壤肥力指标的空间分布。共从代表全部十四个乡的农田中采集了46个地理参照复合土壤样品，并分析了土壤水分、pH、电导率（EC）、有机碳（OC）、有效氮（AvN）、有效磷（AvP）和有效钾（AvK）。采用基于GIS的反距离加权（IDW）插值法可视化土壤属性的区域分布，同时运用Pearson相关分析和主成分分析（PCA）考察土壤肥力指标之间的关系，并识别影响土壤变异性的主要因素。结果表明，该县土壤存在显著的空间异质性，碱性和盐渍化土壤主要分布在西部地区，而东部和北部地区土壤水分和有机碳相对较高。有效磷在整个县域普遍缺乏，而有效氮和有效钾则表现出与母质、土地利用和农业管理措施相关的显著空间变异。PCA识别出盐分相关因子和有机质—养分相互作用是土壤肥力变异性的主要贡献因素。所生成的空间分布图为识别养分缺乏区和理解土壤肥力区域格局提供了有价值的基线信息。尽管所提出的养分管理策略需要通过田间农艺研究进一步验证，但实验室土壤分析、GIS和多元统计技术的整合为区域土壤肥力评估提供了有效框架，并为半干旱环境下可持续土地管理的科学决策提供了支持。","Discover Soil.",65,{"impact":155,"substance":156,"depth":18,"authority":16,"freshness":20,"relevant":21,"comment":157},8,20,"基于GIS与多元统计的区域土壤肥力空间变异研究，方法规范、数据翔实，对精准农业与可持续土地管理有参考价值，但属区域性案例，公共影响有限。",[159],{"name":152,"url":149},[27,161,162,163,164],"可持续农业","遥感","GIS","土壤肥力",[166,167],"Banaskantha 土壤肥力 GIS","印度 半干旱区 土壤养分","Banaskantha土壤肥力GIS-3020","10.1007\u002Fs44378-026-00320-y",{"doi":169,"openalex_id":171,"authors":172,"venue":152,"cited_by_count":35,"oa_url":149,"card":184,"direction":143,"ingested_from":107},"W7213587271",[173,176,178,180,182],{"name":174,"orcid":175},"Mukesh P. Chaudhari","https:\u002F\u002Forcid.org\u002F0009-0000-2187-2375",{"name":177,"orcid":8},"Ruchi Nair",{"name":179,"orcid":8},"Pratik Chavda",{"name":181,"orcid":8},"Dharmik Patel",{"name":183,"orcid":8},"Divya Mishra",{"tldr":185,"method":186,"finding":187,"direction":105,"opportunity":188},"用GIS与多元统计评估印度半干旱区Banaskantha土壤肥力指标的空间变异。","15×15 km网格采样46个样点，结合IDW插值、Pearson相关与PCA分","西部土壤偏碱盐化，东部北部有机碳较高，全区分区有效磷普遍缺乏。","可结合遥感植被指数与机器学习提升半干旱区土壤肥力制图精度并验证养分管理策略。","2026-09-20T23:30:43.716518Z",{"id":191,"title":192,"url":193,"summary":194,"summary_zh":195,"content":8,"source_name":196,"source_url":193,"published_at":56,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":197,"score_detail":198,"sources":201,"tags":203,"search_phrases":206,"slug":209,"view_count":35,"doi":210,"paper":211,"created_at":223},3019,"PSPE-UNet: Projection-based Similarity Prototype Embedding UNet for Apple Leaf Disease Segmentation","https:\u002F\u002Fdoi.org\u002F10.22266\u002Fijies2026.1031.18","Apple leaf disease segmentation plays a significant role in precision agriculture by enabling the accurate identification and localization of infected regions at the pixel level.However, diverse apple leaf diseases exhibit similar symptoms, such as overlapping lesions makes it challenging to distinguish between various disease classes.In this research, a Projection-based Similarity Prototype Embedding UNet (PSPE-UNet) is proposed to segment apple leaf diseases.Employing a projection head with a similarity prototype embedding in UNet enhances feature discrimination by mapping pixel-level representations into a normalized embedding space.This ensures better separation between healthy and disease regions, even when the regions exhibit similar texture and chromatic characteristics.Three learnable prototypes corresponding to healthy, disease, and boundary regions are used.The boundary prototype act as learnable auxiliary feature prototype within the auxiliary boundary branch to compute boundary probability map during training while disease prediction is based on healthy and disease prototypes.In addition, this method enhances the boundary delineation for irregular and small lesions by refining the feature alignment.Hence, the proposed PSPE-UNet achieves a high Pixel Accuracy (PA) of 98.96%, which is compared to existing methods such as the AS-DeepLabV3+ on the Apple Tree Leaf Disease Segmentation Dataset (ATLDSD).Moreover, proposed PSPE-UNet obtains an inference time of 0.0217s per batch (8 images), corresponding to 0.0027s per image on ATLDSD dataset compared to traditional methods like UNet.","苹果叶片病害分割在精准农业中具有重要意义，能够在像素级别上准确识别和定位感染区域。然而，不同苹果叶片病害表现出相似的症状，例如病灶重叠使得区分不同病害类别具有挑战性。本研究提出了一种基于投影的相似性原型嵌入UNet（PSPE-UNet）用于苹果叶片病害分割。在UNet中采用带有相似性原型嵌入的投影头，通过将像素级表示映射到归一化嵌入空间来增强特征判别能力。这确保了健康和病害区域之间更好的分离，即使这些区域表现出相似的纹理和色彩特征。使用三个可学习原型分别对应健康、病害和边界区域。边界原型在辅助边界分支中作为可学习辅助特征原型，在训练期间计算边界概率图，而病害预测则基于健康和病害原型。此外，该方法通过细化特征对齐增强了对不规则和小病灶的边界描绘。因此，所提出的PSPE-UNet在苹果树叶病害分割数据集（ATLDSD）上达到了98.96%的高像素精度（PA），并与现有方法如AS-DeepLabV3+进行了比较。此外，所提出的PSPE-UNet在ATLDSD数据集上获得了每批次（8张图像）0.0217秒的推理时间，相当于每张图像0.0027秒，与UNet等传统方法相比具有优势。","International journal of intelligent engineering and systems",70,{"impact":16,"substance":156,"depth":199,"authority":16,"freshness":20,"relevant":21,"comment":200},17,"提出基于相似度原型嵌入的UNet分割方法，在苹果叶病害数据集上取得98.96%像素精度，方法新颖、数据明确，但属细分算法研究，产业影响有限。",[202],{"name":196,"url":193},[66,124,27,204,205],"图像分割","苹果病害",[207,208],"PSPE-UNet 苹果叶病害 分割","苹果叶病害 数据集 ATLDSD","PSPE-UNet苹果叶病害分割-3019","10.22266\u002Fijies2026.1031.18",{"doi":210,"openalex_id":212,"authors":213,"venue":196,"cited_by_count":35,"oa_url":193,"card":218,"direction":143,"ingested_from":107},"W7213634285",[214,216],{"name":215,"orcid":8},"Vedamurthy Hadavanahalli Kumaraiah",{"name":217,"orcid":8},"Shrinivasacharya Purohit",{"tldr":219,"method":220,"finding":221,"direction":143,"opportunity":222},"提出PSPE-UNet，用投影相似原型嵌入分割苹果叶片病害区域。","UNet+投影头+可学习原型（健康\u002F病害\u002F边界），ATLDSD数据集。","像素精度98.96%，单图推理0.0027秒，优于AS-DeepLabV3+和UNet。","可探索原型嵌入在相似症状多病害区分及轻量化边缘部署中的泛化能力。","2026-09-20T23:30:34.933307Z",{"id":225,"title":226,"url":227,"summary":228,"summary_zh":8,"content":8,"source_name":229,"source_url":8,"published_at":230,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":231,"score_detail":232,"sources":234,"tags":236,"search_phrases":239,"slug":242,"view_count":35,"doi":8,"paper":243,"created_at":250},3001,"UAV多光谱不同空间分辨率匹配春小麦多性状监测","https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F18\u002F1811","天津师范大学张程程等联合天津市农科院农业资源与环境研究所，从原生0.07 m四波段UAV多光谱影像通过像素聚合重采样生成14种空间分辨率（0.07-3.03 m），耦合PROSAIL辐射传输模型与随机森林评估尺度依赖反演性能。研究揭示了叶面积指数（LAI）、叶绿素含量（Cab）和冠层水分含量（Cw）反演精度对空间分辨率的非单调响应，提出物候阶段自适应分辨率策略并开发Heterogeneity-Scale Game Model（HSGM）刻画最优聚合尺度形成机制。","MDPI Agronomy 16(18):1811","2026-09-15T00:00:00Z",74,{"impact":61,"substance":119,"depth":17,"authority":19,"freshness":47,"relevant":21,"comment":233},"方法新颖、数据扎实的作物遥感反演研究，对精准农业变量施药与无人机监测有参考价值，但属细分领域学术进展，公共影响有限。",[235],{"name":229,"url":227},[66,27,162,237,238],"作物表型","春小麦",[240,241],"天津师范大学 春小麦 多光谱","UAV 多光谱 空间分辨率","天津师范大学春小麦多光谱-3001",{"doi":8,"openalex_id":8,"authors":244,"venue":8,"cited_by_count":35,"oa_url":8,"card":245,"direction":105,"ingested_from":44},[],{"tldr":246,"method":247,"finding":248,"direction":105,"opportunity":249},"用无人机多光谱重采样14种分辨率，结合PROSAIL与随机森林，研究春小麦多性状反演的空间尺度效应。","UAV四波段多光谱像素聚合重采样，耦合PROSAIL模型与随机森林反演LAI、C","反演精度对空间分辨率呈非单调响应，提出物候自适应分辨率策略与HSGM模型。","可探索不同作物与物候下最优分辨率普适规律，并将尺度自适应策略嵌入实时无人机监测系统。","2026-09-20T00:03:08.168753Z",{"id":252,"title":253,"url":254,"summary":255,"summary_zh":8,"content":8,"source_name":256,"source_url":8,"published_at":257,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":258,"score_detail":259,"sources":262,"tags":264,"search_phrases":267,"slug":270,"view_count":35,"doi":8,"paper":271,"created_at":278},2997,"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方法","https:\u002F\u002Fwww.toutiao.com\u002Farticle\u002F7685741381196825088","江苏省农业科学院农业信息研究所梁万杰等联合中国农科院农业环境与可持续发展研究所、湖北粮作所、扬州大学等团队，针对小麦渍害防控提出基于无人机多光谱图像和VGG21模型的快速无损识别方法。在小麦拔节-抽穗和抽穗-灌浆两个阶段开展对照、渍水胁迫、硅肥调控和氨基酸调控4个类别数据集，大疆精灵4多光谱无人机采集小麦冠层多光谱图像，测产评估调控效果。","智慧农业(中英文)2026,8(4):60-69","2026-09-15T12:42:00Z",78,{"impact":17,"substance":119,"depth":17,"authority":260,"freshness":47,"relevant":21,"comment":261},14,"多机构协作提出无人机多光谱结合VGG21的小麦渍害无损识别方法，方法新颖、数据扎实，对智慧农业植保监测有参考价值。",[263],{"name":256,"url":254},[66,124,28,265,266],"小麦渍害","多光谱成像",[268,269],"江苏省农科院 小麦渍害 无人机多光谱","VGG21 小麦 渍害识别","江苏省农科院小麦渍害无人机多光谱-2997",{"doi":8,"openalex_id":8,"authors":272,"venue":8,"cited_by_count":35,"oa_url":8,"card":273,"direction":105,"ingested_from":44},[],{"tldr":274,"method":275,"finding":276,"direction":105,"opportunity":277},"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多光谱无人机采集冠层图像，构建VGG21分类模型。","该方法可快速无损识别渍害及硅肥、氨基酸调控效果。","可探索多光谱与深度学习结合评估其他逆境调控措施，并迁移至多作物场景。","2026-09-20T00:03:07.858899Z"]