[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3184":3,"related-3184":56},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":24,"tags":26,"search_phrases":32,"slug":35,"view_count":36,"doi":37,"paper":38,"created_at":55},3184,"Soil Compaction and Irrigation Management: Implications of Soil Hydraulic Changes for Precision Agriculture","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fagronomy16181853","Soil compaction is one of the main forms of physical soil degradation affecting agricultural productivity. Although its effects on soil structure and crop growth have been extensively investigated, their implications for irrigation management have received little attention. This review synthesises current knowledge on how soil compaction modifies the hydraulic functioning of agricultural soils and discusses the consequences for irrigation scheduling and precision irrigation. The available evidence shows that soil compaction reduces water infiltration and saturated hydraulic conductivity while altering soil water retention and plant-available water. Surface sealing and crust formation can further restrict infiltration, particularly when bare soil is exposed to high-energy rainfall or sprinkler irrigation. These changes modify water movement, redistribution and storage within the soil profile, affecting root development, plant physiological responses and crop productivity. The magnitude of these effects depends on soil texture, soil water status, crop species and environmental conditions. Conventional irrigation scheduling generally assumes stable soil hydraulic properties and may not adequately represent compacted soils. Recent advances in soil moisture sensing, crop modelling, remote sensing and decision support systems provide new opportunities to incorporate soil structural variability into irrigation management. Combining precision irrigation with preventive traffic management and practices that enhance soil structural and biological resilience may improve irrigation efficiency, optimise water use and enhance the long-term sustainability of irrigated agricultural systems.","土壤压实是影响农业生产力的土壤物理退化的主要形式之一。尽管其对土壤结构和作物生长的影响已得到广泛研究，但其对灌溉管理的影响却鲜受关注。本文综述了关于土壤压实如何改变农业土壤水力功能的现有知识，并探讨了其对灌溉制度和精准灌溉的影响。现有证据表明，土壤压实降低了水分入渗和饱和导水率，同时改变了土壤持水性和植物有效水量。地表封闭和结皮的形成会进一步限制入渗，尤其是当裸露土壤暴露于高能量降雨或喷灌时。这些变化改变了土壤剖面中水分的运动、再分布和储存，进而影响根系发育、植物生理响应和作物生产力。这些影响的程度取决于土壤质地、土壤水分状况、作物种类和环境条件。传统灌溉制度通常假设土壤水力性质稳定，可能无法充分反映压实土壤的情况。土壤水分传感、作物建模、遥感和决策支持系统方面的最新进展，为将土壤结构变异性纳入灌溉管理提供了新的机遇。将精准灌溉与预防性机械作业管理及增强土壤结构和生物韧性的措施相结合，有望提高灌溉效率、优化水资源利用，并增强灌溉农业系统的长期可持续性。",null,"Agronomy","2026-09-20T00:00:00Z","论文",10,false,78,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},18,21,17,14,8,1,"系统综述土壤压实对土壤水力特性与灌溉管理的影响，提出将土壤结构变异纳入精准灌溉决策，对智慧农业与节水灌溉有参考价值。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","农业遥感","精准灌溉","土壤墒情监测","土壤压实",[33,34],"土壤压实 精准灌溉","土壤水力特性 灌溉管理","土壤压实精准灌溉-3184",0,"10.3390\u002Fagronomy16181853",{"doi":37,"openalex_id":39,"authors":40,"venue":10,"cited_by_count":36,"oa_url":6,"card":47,"direction":53,"ingested_from":54},"W7213866178",[41,44],{"name":42,"orcid":43},"Alessia Cogato","https:\u002F\u002Forcid.org\u002F0000-0001-8354-7324",{"name":45,"orcid":46},"Lucia Bortolini","https:\u002F\u002Forcid.org\u002F0000-0001-6863-4542",{"tldr":48,"method":49,"finding":50,"direction":51,"opportunity":52},"综述土壤压实改变水力特性对灌溉管理的影响，提出将结构变异纳入精准灌溉。","文献综述，整合土壤水力、传感、作物模型与遥感技术。","压实降低入渗与导水率，传统灌溉调度假设不成立，需纳入结构变异。","智慧农业 \u002F 农业物联网","可开发融合土壤压实空间变异与实时传感的精准灌溉决策模型，填补结构退化下的调度空白。","农业遥感与作物表型","openalex","2026-09-22T23:30:25.358657Z",{"total":57,"page":22,"page_size":57,"items":58},6,[59,103,138,178,212,254],{"id":60,"title":61,"url":62,"summary":63,"summary_zh":64,"content":9,"source_name":65,"source_url":62,"published_at":66,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":67,"score_detail":68,"sources":73,"tags":75,"search_phrases":79,"slug":82,"view_count":36,"doi":83,"paper":84,"created_at":102},3160,"Determination of Water Stress in Okra Plants Using Canopy Temperature Measurement by Infrared Thermometer","https:\u002F\u002Fdoi.org\u002F10.22214\u002Fijraset.2026.84906","Water scarcity represents one of the most severe constraints on agricultural productivity in semi-arid and waterstressed cultivation zones. In traditional vegetable cultivation, farmers predominantly rely on visual symptoms of wilting to detect water deficit stress, which typically manifest only after substantial physiological damage, cellular dehydration, and irreversible yield loss have occurred. This study presents a non-destructive, precision agriculture framework for the early detection and classification of water stress in okra (Abelmoschus esculentus) using handheld infrared thermometry coupled with an ensemble Random Forest machine learning classifier. A primary field dataset comprising 100 observations was acquired under variable diurnal meteorological conditions in Jammu, India. The recorded and derived parameters encompassed canopy surface temperature (Tc), wet-bulb reference temperature (Twet), dry-bulb reference temperature (Tdry), ambient air temperature (Ta), relative humidity (RH), air saturation vapor pressure (SVPair), actual vapor pressure (AVP), leaf saturation vapor pressure (SVPleaf), leaf vapor pressure deficit (VPDleaf), canopy-air thermal differential (Tc – Ta), and the Crop Water Stress Index (CWSI). Using an empirical CWSI threshold of 0.30, samples were categorized into non-stressed (CWSI ≤ 0.30) and stressed (CWSI > 0.30) physiological states. The dataset was partitioned into an 80:20 training and testing split (80 training samples, 20 testing samples). The trained Random Forest classifier achieved an overall classification accuracy of 95.0% (19\u002F20 correct classifications) on the unseen test set. For the non-stressed class, the model demonstrated a precision of 1.00, recall of 0.92, and an F1-score of 0.96 (support = 12). For the water-stressed class, the model yielded a precision of 0.89, recall of 1.00, and an F1- score of 0.94 (support = 8), with zero false negatives (FN = 0), ensuring that no stressed crops were missed. Gini feature importance analysis revealed that dry-bulb reference temperature (Tdry, score = 0.187), leaf saturation vapor pressure (SVPleaf, score = 0.172), wet-bulb reference temperature (Twet, score = 0.160), leaf vapor pressure deficit (VPDleaf, score = 0.112), and Tc – Ta (score = 0.101) were the primary drivers governing classification. The findings confirm that coupling thermal radiometry with psychrometric feature engineering and ensemble learning provides a reliable, non-contact diagnostic mechanism for precision irrigation scheduling in smallholder horticulture.","水资源短缺是半干旱及水分胁迫耕作区农业生产力的最严重制约因素之一。在传统蔬菜种植中，农民主要依赖萎蔫的视觉症状来检测水分亏缺胁迫，而这类症状通常只有在发生大量生理损伤、细胞脱水及不可逆产量损失之后才会显现。本研究提出了一种非破坏性精准农业框架，利用手持式红外测温仪结合集成随机森林机器学习分类器，实现对秋葵（Abelmoschus esculentus）水分胁迫的早期检测与分类。在印度查谟地区多变的气象日变化条件下，采集了包含100个观测值的初始田间数据集。记录及衍生的参数包括冠层表面温度（Tc）、湿球参考温度（Twet）、干球参考温度（Tdry）、环境气温（Ta）、相对湿度（RH）、空气饱和水汽压（SVPair）、实际水汽压（AVP）、叶片饱和水汽压（SVPleaf）、叶片水汽压亏缺（VPDleaf）、冠层-空气温差（Tc – Ta）以及作物水分胁迫指数（CWSI）。采用经验性CWSI阈值0.30，将样本划分为非胁迫（CWSI ≤ 0.30）和胁迫（CWSI > 0.30）生理状态。数据集按80:20划分为训练集和测试集（80个训练样本，20个测试样本）。训练后的随机森林分类器在未见测试集上实现了95.0%的总体分类准确率（20个中正确分类19个）。对于非胁迫类别，模型精确率为1.00，召回率为0.92，F1分数为0.96（支持样本数=12）。对于水分胁迫类别，模型精确率为0.89，召回率为1.00，F1分数为0.94（支持样本数=8），假阴性为零（FN = 0），确保无胁迫作物被漏检。基尼特征重要性分析表明，干球参考温度（Tdry，得分=0.187）、叶片饱和水汽压（SVPleaf，得分=0.172）、湿球参考温度（Twet，得分=0.160）、叶片水汽压亏缺（VPDleaf，得分=0.112）以及Tc – Ta（得分=0.101）是主导分类的主要驱动因素。研究结果证实，将热辐射测量与湿度特征工程及集成学习相结合，可提供一种可靠的非接触式诊断机制","International Journal for Research in Applied Science and Engineering Technology","2026-09-21T00:00:00Z",63,{"impact":21,"substance":69,"depth":70,"authority":13,"freshness":71,"relevant":22,"comment":72},20,16,9,"印度查谟地区小样本田间研究，红外测温结合随机森林实现秋葵水分胁迫早期识别，方法可迁移至小农精准灌溉，但样本量仅100条、地域局限，产业影响有限。",[74],{"name":65,"url":62},[27,76,77,29,78],"机器学习","作物水分胁迫","红外测温",[80,81],"秋葵 冠层温度 水分胁迫","红外测温 作物水分胁迫指数","秋葵冠层温度水分胁迫-3160","10.22214\u002Fijraset.2026.84906",{"doi":83,"openalex_id":85,"authors":86,"venue":65,"cited_by_count":36,"oa_url":62,"card":97,"direction":51,"ingested_from":54},"W7213933715",[87,89,91,93,95],{"name":88,"orcid":9},"Muneeb Ajmer",{"name":90,"orcid":9},"Sayam Prajapati",{"name":92,"orcid":9},"Somil Narang",{"name":94,"orcid":9},"Rudraksh Sharma",{"name":96,"orcid":9},"Saksham Khajuria",{"tldr":98,"method":99,"finding":100,"direction":53,"opportunity":101},"用红外测温仪测秋葵冠层温度并结合随机森林，实现水分胁迫的早期无损分类。","手持红外测温获取冠层温度与气象参数，计算CWSI，用随机森林分类。","随机森林测试集准确率95%，无漏判胁迫样本，Tdry、SVPleaf等为关键特征。","可扩展到多作物、多生育期及无人机热红外尺度，验证CWSI阈值与模型迁移性。","2026-09-22T23:30:11.117810Z",{"id":104,"title":105,"url":106,"summary":107,"summary_zh":9,"content":9,"source_name":108,"source_url":106,"published_at":109,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":110,"score_detail":111,"sources":114,"tags":116,"search_phrases":119,"slug":122,"view_count":36,"doi":123,"paper":124,"created_at":137},3135,"Non-destructive nitrogen estimation in pastures from UAV multispectral imagery using a heterogeneity-driven machine-learning framework","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11119-026-10453-3","Non-destructive nitrogen estimation in pastures from UAV multispectral imagery using a heterogeneity-driven machine-learning framework。Precision Agriculture","Precision Agriculture","2026-09-22T00:00:00Z",82,{"impact":17,"substance":112,"depth":17,"authority":20,"freshness":13,"relevant":22,"comment":113},22,"方法新颖、数据可靠，对草地精准施肥有参考价值，但属细分领域研究，未达重大突破层级。",[115],{"name":108,"url":106},[27,117,28,76,118],"无人机","草地氮素",[120,121],"UAV 多光谱 草地 氮素","Precision Agriculture 氮素估算","UAV多光谱草地氮素-3135","10.1007\u002Fs11119-026-10453-3",{"doi":123,"openalex_id":125,"authors":126,"venue":108,"cited_by_count":36,"oa_url":9,"card":9,"direction":9,"ingested_from":54},"W7213942551",[127,129,132,135],{"name":128,"orcid":9},"Antônio de Oliveira Costa Neto",{"name":130,"orcid":131},"Yiannis Ampatzidis","https:\u002F\u002Forcid.org\u002F0000-0002-3660-3298",{"name":133,"orcid":134},"Andrea Lazzari","https:\u002F\u002Forcid.org\u002F0000-0002-1521-6942",{"name":136,"orcid":9},"Jim Fletcher","2026-09-22T23:30:03.351447Z",{"id":139,"title":140,"url":141,"summary":142,"summary_zh":143,"content":9,"source_name":144,"source_url":141,"published_at":66,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":145,"score_detail":146,"sources":148,"tags":150,"search_phrases":154,"slug":157,"view_count":36,"doi":158,"paper":159,"created_at":177},3131,"Water stress detection from plant electrophysiology: A machine learning framework for irrigation management","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112434","Fast detection of plant stress is key to plant phenotyping, precision agriculture, and automated crop management. In particular, efficient irrigation management requires early identification of water stress to optimize resource use while maintaining crop performance. Direct physiological sensing offers the potential to detect stress responses before visible symptoms appear. In this study, we recorded electrophysiological signals from greenhouse-grown tomato plants subjected to water stress and developed a framework based on machine learning for online stress detection. The resulting time-series data were segmented into tumbling windows of 1 min, 5 min, 30 min, 1 h and 6 h, then passed through a processing pipeline that includes statistical feature extraction and selection, automated machine learning or alternatively deep learning, and probability calibration. Across multiple input time horizons, we found that a 30-minute look-back horizon strikes the best balance between rapid decision-making and classification performance. Using automated machine learning, the framework achieved classification accuracies of up to 92%, outperforming deep learning approaches. Sequential backward selection reduced the feature set while maintaining performance. Out-of-sample testing on withheld plant individuals provides methodological validation, confirming that the framework detects healthy-to-stress transitions despite inter-individual electrophysiological variability.Overall, we develop and provide a decision-support tool for agricultural practitioners and researchers and establish a foundation for biofeedback-driven irrigation control to improve resource efficiency in (semi-)autonomous crop production systems.","植物胁迫的快速检测是植物表型分析、精准农业和自动化作物管理的关键。特别是，高效的灌溉管理需要早期识别水分胁迫，以优化资源利用并维持作物表现。直接生理传感有望在可见症状出现之前检测到胁迫响应。在本研究中，我们记录了温室种植的番茄植株在水分胁迫下的电生理信号，并开发了一个基于机器学习的在线胁迫检测框架。所得时间序列数据被分割为1分钟、5分钟、30分钟、1小时和6小时的滑动窗口，然后通过一个处理流程，包括统计特征提取与选择、自动化机器学习或深度学习，以及概率校准。在多个输入时间跨度中，我们发现30分钟的回溯窗口在快速决策与分类性能之间取得了最佳平衡。使用自动化机器学习，该框架达到了高达92%的分类准确率，优于深度学习方法。序列后向选择在保持性能的同时减少了特征集。对保留的植株个体进行样本外测试提供了方法学验证，确认该框架能够在个体间电生理变异性存在的情况下检测从健康到胁迫的转变。总体而言，我们为农业从业者和研究人员开发并提供了一种决策支持工具，并为生物反馈驱动的灌溉控制奠定了基础，以提高（半）自主作物生产系统中的资源效率。","Computers and Electronics in Agriculture",81,{"impact":17,"substance":112,"depth":17,"authority":20,"freshness":71,"relevant":22,"comment":147},"该研究提出基于植物电生理信号与机器学习的在线水分胁迫检测框架，30分钟窗口下准确率达92%，为精准灌溉与生物反馈控制提供决策支持，方法新颖、结论可靠，值得进入每日精选。",[149],{"name":144,"url":141},[27,151,29,152,153],"农业人工智能","水分胁迫","植物电生理",[155,156],"植物电生理 水分胁迫 机器学习","番茄 水分胁迫 灌溉管理","植物电生理水分胁迫机器学习-3131","10.1016\u002Fj.compag.2026.112434",{"doi":158,"openalex_id":160,"authors":161,"venue":144,"cited_by_count":36,"oa_url":141,"card":171,"direction":53,"ingested_from":54},"W7204541435",[162,165,168],{"name":163,"orcid":164},"Eduard Buss","https:\u002F\u002Forcid.org\u002F0000-0001-6993-5873",{"name":166,"orcid":167},"Till Aust","https:\u002F\u002Forcid.org\u002F0000-0003-2863-1341",{"name":169,"orcid":170},"Heiko Hamann","https:\u002F\u002Forcid.org\u002F0000-0002-2458-8289",{"tldr":172,"method":173,"finding":174,"direction":175,"opportunity":176},"利用番茄电生理信号与机器学习实现水分胁迫早期在线检测。","温室番茄电生理时序，滑窗分段、特征选择、自动机器学习与概率校准。","30分钟回溯窗口最优，自动机器学习分类准确率达92%，优于深度学习。","其他","可探索多作物电生理泛化、田间部署及闭环灌溉控制，提升资源效率。","2026-09-22T23:30:02.024960Z",{"id":179,"title":180,"url":181,"summary":182,"summary_zh":9,"content":9,"source_name":183,"source_url":9,"published_at":184,"category":12,"cover_url":9,"hotness":13,"is_selected":185,"score":186,"score_detail":187,"sources":192,"tags":194,"search_phrases":198,"slug":201,"view_count":36,"doi":9,"paper":202,"created_at":211},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",true,89,{"impact":188,"substance":189,"depth":190,"authority":20,"freshness":71,"relevant":22,"comment":191},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。",[193],{"name":183,"url":181},[27,195,151,28,196,197],"无人农场","多智能体","大豆生产",[199,200],"哈工大 FAIRY 大豆","HarvestBench 智能体 农场","哈工大FAIRY大豆-3125",{"doi":9,"openalex_id":9,"authors":203,"venue":9,"cited_by_count":36,"oa_url":9,"card":204,"direction":208,"ingested_from":210},[],{"tldr":205,"method":206,"finding":207,"direction":208,"opportunity":209},"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。","农业人工智能与决策模型","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。","agent","2026-09-22T00:05:38.611717Z",{"id":213,"title":214,"url":215,"summary":216,"summary_zh":217,"content":9,"source_name":218,"source_url":215,"published_at":219,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":220,"score_detail":221,"sources":225,"tags":227,"search_phrases":231,"slug":234,"view_count":36,"doi":235,"paper":236,"created_at":253},3060,"Geospatial Intelligence for Peri-Urban Land-Use Conflicts: Evaluating Agricultural Suitability against Rapid Urbanisation using the Analytical Hierarchy Process and Cloud Computing","https:\u002F\u002Fdoi.org\u002F10.59543\u002F6mpcwr41","This paper presents a geospatial multi-criteria evaluation of agricultural potential in the suburban region of Bapatla using eight physical and land-use characteristics: elevation, slope, road accessibility, proximity to water bodies, Land Surface Temperature (LST), Normalised Difference Vegetation Index (NDVI), Land Use\u002FLand Cover (LULC), and soil texture, processed using Google Earth Engine. The Analytic Hierarchy Process (AHP) was used to determine the relative weights of each criterion. NDVI received the highest weight (26.33%), followed by LST, slope, and proximity to water bodies (14.96% each), while elevation received the lowest weight (4.6%) due to the region's flat terrain. Weighted overlay analysis classified the 142.25 km² study area into Suitable (108.90 km²; 76.55%), Not Suitable (32.60 km²; 22.92%), and Highly Suitable (0.75 km²; 0.53%) categories. Suitable areas are mainly distributed across the southern and peripheral agricultural zones, whereas unsuitable areas are concentrated within Bapatla Urban and its surroundings. The limited extent of highly suitable land highlights the scarcity of optimal agricultural sites. The results reveal land-use conflicts driven primarily by urbanisation rather than environmental constraints. The AHP-weighted suitability map provides an evidence-based tool for agricultural land conservation, water-resource management, and sustainable urban expansion.","本文基于八项自然与土地利用特征，对巴帕特拉（Bapatla）郊区农业潜力进行了地理空间多准则评价，这些特征包括：海拔、坡度、道路可达性、距水体远近、地表温度（LST）、归一化植被指数（NDVI）、土地利用\u002F土地覆盖（LULC）以及土壤质地，并利用Google Earth Engine进行处理。采用层次分析法（AHP）确定各准则的相对权重。NDVI权重最高（26.33%），其次为LST、坡度和距水体远近（均为14.96%），而海拔因该地区地形平坦权重最低（4.6%）。加权叠加分析将142.25 km²的研究区划分为适宜（108.90 km²；76.55%）、不适宜（32.60 km²；22.92%）和高适宜（0.75 km²；0.53%）三类。适宜区主要分布于南部及外围农业区，而不适宜区集中于巴帕特拉城区及其周边。高适宜土地面积有限，凸显了优质农业用地的稀缺性。结果表明，土地利用冲突主要由城市化驱动，而非环境限制。基于AHP的适宜性地图为农业用地保护、水资源管理和可持续城市扩张提供了循证工具。","Journal of Urban Intelligence and Smart Systems","2026-09-19T00:00:00Z",70,{"impact":222,"substance":69,"depth":19,"authority":223,"freshness":21,"relevant":22,"comment":224},12,13,"该论文利用遥感与AHP方法评估城郊农业用地冲突，方法新颖、数据详实，对农业土地保护有参考价值，但属细分领域研究，影响范围有限。",[226],{"name":218,"url":215},[27,28,228,229,230],"农业信息化","遥感","土地利用",[232,233],"Bapatla 农业用地 城市化","Google Earth Engine 农业适宜性","Bapatla农业用地城市化-3060","10.59543\u002F6mpcwr41",{"doi":235,"openalex_id":237,"authors":238,"venue":218,"cited_by_count":36,"oa_url":247,"card":248,"direction":51,"ingested_from":54},"W7213644047",[239,241,243,245],{"name":240,"orcid":9},"Sreerama Naik Naik S R",{"name":242,"orcid":9},"T K Prasad",{"name":244,"orcid":9},"Feba Jose Jasmine",{"name":246,"orcid":9},"Jayapal G","https:\u002F\u002Fjuiss.org\u002Findex.php\u002Fjuiss\u002Farticle\u002Fdownload\u002F366\u002F231",{"tldr":249,"method":250,"finding":251,"direction":53,"opportunity":252},"用AHP与云平台评估印度Bapatla城郊农业适宜性，揭示城市化引发的土地利用冲突。","Google Earth Engine处理8个因子，AHP加权叠加分析142.2","76.55%区域适宜农业，但高度适宜仅0.53%，冲突主因是城市化而非环境限制。","可引入时序遥感与动态城市扩张模拟，构建城郊农业保护与城市增长协同优化模型。","2026-09-21T23:30:09.448414Z",{"id":255,"title":256,"url":257,"summary":258,"summary_zh":259,"content":9,"source_name":260,"source_url":257,"published_at":219,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":261,"score_detail":262,"sources":265,"tags":267,"search_phrases":271,"slug":274,"view_count":36,"doi":275,"paper":276,"created_at":308},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",84,{"impact":17,"substance":189,"depth":190,"authority":263,"freshness":71,"relevant":22,"comment":264},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。",[266],{"name":260,"url":257},[27,28,268,269,270],"棉花","精准农业","无人机遥感",[272,273],"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":275,"openalex_id":277,"authors":278,"venue":260,"cited_by_count":36,"oa_url":257,"card":303,"direction":53,"ingested_from":54},"W7213661690",[279,282,284,286,288,290,293,296,298,301],{"name":280,"orcid":281},"Qing Li","https:\u002F\u002Forcid.org\u002F0009-0004-4580-7761",{"name":283,"orcid":9},"Dalei Hao",{"name":285,"orcid":9},"Jan Pisek",{"name":287,"orcid":9},"Zicheng Ji",{"name":289,"orcid":9},"Yanan Wei",{"name":291,"orcid":292},"Youngryel Ryu","https:\u002F\u002Forcid.org\u002F0000-0001-6238-2479",{"name":294,"orcid":295},"Jiarui Xu","https:\u002F\u002Forcid.org\u002F0000-0003-4925-2770",{"name":297,"orcid":9},"Yangmin Feng",{"name":299,"orcid":300},"Shaozhong Kang","https:\u002F\u002Forcid.org\u002F0000-0002-8019-2537",{"name":302,"orcid":9},"Yelu Zeng",{"tldr":304,"method":305,"finding":306,"direction":53,"opportunity":307},"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"]