[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4288":3,"related-4288":84},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":8,"source_name":9,"source_url":6,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":15,"sources":23,"tags":25,"primary_topic":31,"secondary_topics":37,"classification":38,"search_phrases":58,"slug":61,"view_count":21,"doi":62,"paper":63,"created_at":83},4288,"Estimation of leaf nitrogen content of typical fruit trees in arid regions based on fusion of UAV multispectral information and texture features","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11119-026-10465-z","Estimation of leaf nitrogen content of typical fruit trees in arid regions based on fusion of UAV multispectral information and texture features。Precision Agriculture",null,"Precision Agriculture","2026-10-05T00:00:00Z","论文",10,false,78,{"impact":16,"substance":17,"depth":18,"authority":19,"freshness":20,"relevant":21,"comment":22},16,21,18,14,9,1,"核心期刊论文，将无人机多光谱与纹理特征融合用于干旱区果树叶片氮素估测，方法有创新、结论可靠，对果园精准施肥有参考价值，但属细分领域技术进展，影响范围有限。",[24],{"name":9,"url":6},[26,27,28,29,30],"智慧农业","农业遥感","无人机遥感","果树种植","氮素营养诊断",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",[],{"status":39,"locked":13,"version":40,"method":41,"evidence":42},"classified","2026-10-22.2","rules",{"candidates":43,"agricultural_evidence":46},[44,51],{"code":32,"score":45,"supported":46,"title":47,"abstract":49,"tags":50},13,true,[48],"multispectral",[48],[27,28],{"code":52,"score":53,"supported":46,"title":54,"abstract":55,"tags":57},"I1",4,[],[56],"precision agriculture",[26],[59,60],"无人机 多光谱 果树 氮含量","干旱区 果树 遥感 估测","无人机多光谱果树氮含量-4288","10.1007\u002Fs11119-026-10465-z",{"doi":62,"openalex_id":64,"authors":65,"venue":9,"cited_by_count":81,"oa_url":8,"card":8,"direction":8,"ingested_from":82},"W7220458084",[66,68,71,73,75,77,79],{"name":67,"orcid":8},"Ran Pang",{"name":69,"orcid":70},"Mamat Sawut","https:\u002F\u002Forcid.org\u002F0009-0004-9961-7381",{"name":72,"orcid":8},"Eniwar Sidik",{"name":74,"orcid":8},"Shanshan Liu",{"name":76,"orcid":8},"Weichen Tian",{"name":78,"orcid":8},"Reheman Rouzi",{"name":80,"orcid":8},"Xiaolong Zhang",0,"openalex","2026-10-06T23:30:02.240820Z",{"total":85,"page":21,"page_size":85,"items":86},6,[87,186,280,342,391,465],{"id":88,"title":89,"url":90,"summary":91,"summary_zh":92,"content":8,"source_name":93,"source_url":90,"published_at":94,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":95,"score_detail":96,"sources":101,"tags":103,"primary_topic":106,"secondary_topics":112,"classification":114,"search_phrases":147,"slug":150,"view_count":53,"doi":151,"paper":152,"created_at":185},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":18,"substance":97,"depth":98,"authority":99,"freshness":20,"relevant":21,"comment":100},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。",[102],{"name":93,"url":90},[26,27,104,105,28],"棉花","精准农业",{"code":107,"slug":108,"name":109,"path":110,"parent_code":111},"A6","cash-crops","棉麻糖与特色经作","\u002Ftopic\u002Ffield-crops\u002Fcash-crops","A",[113],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":13,"version":40,"method":41,"evidence":115},{"candidates":116,"agricultural_evidence":46},[117,122,127,133,140],{"code":107,"score":20,"supported":46,"title":118,"abstract":120,"tags":121},[119],"cotton",[],[104],{"code":52,"score":123,"supported":46,"title":124,"abstract":125,"tags":126},8,[],[105,56],[26,105],{"code":32,"score":123,"supported":46,"title":128,"abstract":129,"tags":132},[],[130,131],"遥感","remote sensing",[27,28],{"code":134,"score":85,"supported":46,"title":135,"abstract":136,"tags":139},"H3",[],[137,138],"灌溉","irrigation",[],{"code":141,"score":85,"supported":46,"title":142,"abstract":143,"tags":146},"I2",[],[144,145],"深度学习","deep learning",[],[148,149],"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":151,"openalex_id":153,"authors":154,"venue":93,"cited_by_count":81,"oa_url":90,"card":179,"direction":183,"ingested_from":82},"W7213661690",[155,158,160,162,164,166,169,172,174,177],{"name":156,"orcid":157},"Qing Li","https:\u002F\u002Forcid.org\u002F0009-0004-4580-7761",{"name":159,"orcid":8},"Dalei Hao",{"name":161,"orcid":8},"Jan Pisek",{"name":163,"orcid":8},"Zicheng Ji",{"name":165,"orcid":8},"Yanan Wei",{"name":167,"orcid":168},"Youngryel Ryu","https:\u002F\u002Forcid.org\u002F0000-0001-6238-2479",{"name":170,"orcid":171},"Jiarui Xu","https:\u002F\u002Forcid.org\u002F0000-0003-4925-2770",{"name":173,"orcid":8},"Yangmin Feng",{"name":175,"orcid":176},"Shaozhong Kang","https:\u002F\u002Forcid.org\u002F0000-0002-8019-2537",{"name":178,"orcid":8},"Yelu Zeng",{"tldr":180,"method":181,"finding":182,"direction":183,"opportunity":184},"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。","2026-09-20T23:30:21.262698Z",{"id":187,"title":188,"url":189,"summary":190,"summary_zh":191,"content":8,"source_name":192,"source_url":189,"published_at":193,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":194,"score_detail":195,"sources":198,"tags":200,"primary_topic":204,"secondary_topics":209,"classification":217,"search_phrases":248,"slug":251,"view_count":81,"doi":252,"paper":253,"created_at":279},5573,"Improving alfalfa nitrogen estimation with canopy structural priors from DSSAT–PROSAIL and UAV hyperspectral data","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.eja.2026.128375","Accurate estimation of nitrogen (N) concentration in alfalfa ( Medicago sativa L .) supports forage quality assessment and harvest scheduling. Empirical remote sensing models often overfit because of confounding effects of three-dimensional canopy structure. Incorporating structural parameters can stabilize biochemical inversions, but concurrent in situ morphological measurements are difficult to acquire at scale. We developed the Morphology-Aware Latent Embedding Network (MALENet), a hybrid framework integrating physiological and morphological mechanisms with hyperspectral data. The Decision Support System for Agrotechnology Transfer (DSSAT), coupled with the PROSAIL radiative transfer model, generated a structurally rich synthetic hyperspectral prior. A convolutional neural network (CNN) with a Squeeze-and-Excitation (SE) attention mechanism, was pre-trained on these data to distill ecophysiological knowledge into a dense latent representation. This mechanistically derived embedding was then fused with empirical hyperspectral measurements collected under diverse field conditions. Evaluation using multi-year field quadrats in the Hexi Corridor showed improved predictive performance ( R² = 0.71, RMSE = 0.30%) relative to conventional machine learning and deep learning models (LASSO-ANN: R² = 0.61; CNN: R² = 0.66). Feature attribution indicated that MALENet used near-infrared multiple scattering (58.3% attribution) as a structural proxy to calibrate nitrogen concentration retrieval against the confounding effects of canopy architecture. Despite constraints imposed by sample size and PROSAIL's 1D canopy assumption, these findings demonstrate the potential of physically guided deep learning to use structural information for agricultural trait retrieval in precision forage management.","准确估算紫花苜蓿（Medicago sativa L.）的氮浓度有助于饲草品质评估和收获期安排。经验性遥感模型常因三维冠层结构的混杂效应而出现过拟合。引入结构参数可以稳定生化反演，但同步开展原位形态学测量难以大规模获取。我们开发了形态感知潜在嵌入网络（Morphology-Aware Latent Embedding Network，MALENet），这是一个将生理与形态机制同高光谱数据相结合的混合框架。农业技术转移决策支持系统（Decision Support System for Agrotechnology Transfer，DSSAT）与PROSAIL辐射传输模型耦合，生成了结构信息丰富的合成高光谱先验数据。在此基础上，预训练了一个带有挤压—激励（Squeeze-and-Excitation，SE）注意力机制的卷积神经网络（convolutional neural network，CNN），将生态生理学知识提炼为稠密潜在表征。随后，将这一机制性导出的嵌入与在不同田间条件下采集的经验性高光谱测量数据相融合。利用河西走廊多年田间样方进行的评估显示，相较于传统机器学习和深度学习模型（LASSO-ANN：R² = 0.61；CNN：R² = 0.66），预测性能有所提升（R² = 0.71，RMSE = 0.30%）。特征归因表明，MALENet利用近红外多次散射（归因占比58.3%）作为结构代理，以校准氮浓度反演，抵御冠层结构的混杂效应。尽管受样本量和PROSAIL一维冠层假设的限制，这些发现表明，物理引导的深度学习有潜力利用结构信息，在精准饲草管理中进行农业性状反演。","European Journal of Agronomy","2026-10-21T00:00:00Z",80,{"impact":16,"substance":196,"depth":18,"authority":19,"freshness":12,"relevant":21,"comment":197},22,"该研究将作物模型与辐射传输模型耦合生成结构先验，并结合无人机高光谱数据反演苜蓿氮素，方法新颖、田间验证扎实，对精准饲草管理有参考价值，值得进入每日精选。",[199],{"name":192,"url":189},[26,201,27,202,203],"农业人工智能","氮素监测","紫花苜蓿",{"code":205,"slug":206,"name":207,"path":208,"parent_code":111},"A7","forage-cropping","饲草与耕作制度","\u002Ftopic\u002Ffield-crops\u002Fforage-cropping",[210,211],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"code":212,"slug":213,"name":214,"path":215,"parent_code":216},"C4","feed","饲料与饲草","\u002Ftopic\u002Flivestock\u002Ffeed","C",{"status":39,"locked":13,"version":40,"method":41,"evidence":218},{"candidates":219,"agricultural_evidence":46},[220,225,231,237,244],{"code":32,"score":18,"supported":46,"title":221,"abstract":223,"tags":224},[222],"hyperspectral",[130,131,222],[27],{"code":205,"score":99,"supported":46,"title":226,"abstract":228,"tags":230},[227],"alfalfa",[229,227],"苜蓿",[203],{"code":212,"score":12,"supported":46,"title":232,"abstract":233,"tags":236},[],[234,229,235],"饲草","forage",[203],{"code":141,"score":12,"supported":46,"title":238,"abstract":239,"tags":243},[],[144,240,241,145,242],"机器学习","machine learning","decision support",[201],{"code":52,"score":21,"supported":13,"title":245,"abstract":246,"tags":247},[],[],[26],[249,250],"DSSAT PROSAIL 苜蓿 氮素","无人机 高光谱 苜蓿","DSSATPROSAIL苜蓿氮素-5573","10.1016\u002Fj.eja.2026.128375",{"doi":252,"openalex_id":254,"authors":255,"venue":192,"cited_by_count":81,"oa_url":189,"card":274,"direction":183,"ingested_from":82},"W7223154576",[256,258,260,262,264,267,269,272],{"name":257,"orcid":8},"Shuai Fu",{"name":259,"orcid":8},"Yunhao Li",{"name":261,"orcid":8},"Chunli Miao",{"name":263,"orcid":8},"Jie Liu",{"name":265,"orcid":266},"Tiangang Liang","https:\u002F\u002Forcid.org\u002F0000-0001-5599-6587",{"name":268,"orcid":8},"Yongjia Hu",{"name":270,"orcid":271},"Qisheng Feng","https:\u002F\u002Forcid.org\u002F0009-0000-1391-9591",{"name":273,"orcid":8},"吴彩霞",{"tldr":275,"method":276,"finding":277,"direction":183,"opportunity":278},"构建MALENet融合DSSAT-PROSAIL结构先验与无人机高光谱，提升苜蓿氮浓度估算精度。","DSSAT耦合PROSAIL生成合成高光谱先验，预训练CNN-SE网络，再融合田","MALENet精度优于传统机器学习和深度学习模型，R²达0.71，近红外多次散射作为结构代理贡献58","可探索将作物模型结构先验迁移至其他牧草或作物生化参数反演，并缓解PROSAIL一维冠层假设局限。","2026-10-22T11:20:39.622844Z",{"id":281,"title":282,"url":283,"summary":284,"summary_zh":8,"content":8,"source_name":285,"source_url":8,"published_at":193,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":286,"score_detail":287,"sources":289,"tags":291,"primary_topic":295,"secondary_topics":300,"classification":308,"search_phrases":329,"slug":332,"view_count":21,"doi":8,"paper":333,"created_at":341},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":18,"substance":196,"depth":18,"authority":19,"freshness":12,"relevant":21,"comment":288},"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。",[290],{"name":285,"url":283},[26,27,292,293,294],"作物表型","无人机多光谱","小麦渍害",{"code":296,"slug":297,"name":298,"path":299,"parent_code":111},"A2","wheat","小麦","\u002Ftopic\u002Ffield-crops\u002Fwheat",[301,302],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"code":303,"slug":304,"name":305,"path":306,"parent_code":307},"H7","climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate","H",{"status":39,"locked":13,"version":40,"method":41,"evidence":309},{"candidates":310,"agricultural_evidence":46},[311,316,320,325],{"code":296,"score":312,"supported":46,"title":313,"abstract":314,"tags":315},11,[298],[298],[],{"code":32,"score":312,"supported":46,"title":317,"abstract":318,"tags":319},[293],[],[292,27,293],{"code":303,"score":123,"supported":46,"title":321,"abstract":323,"tags":324},[322],"渍害",[],[],{"code":52,"score":21,"supported":13,"title":326,"abstract":327,"tags":328},[],[],[26],[330,331],"江苏省农科院 小麦渍害 多光谱","VGG21 小麦 硅肥调控","江苏省农科院小麦渍害多光谱-5535",{"doi":8,"openalex_id":8,"authors":334,"venue":8,"cited_by_count":81,"oa_url":8,"card":335,"direction":183,"ingested_from":340},[],{"tldr":336,"method":337,"finding":338,"direction":183,"opportunity":339},"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":343,"title":344,"url":345,"summary":346,"summary_zh":8,"content":8,"source_name":347,"source_url":8,"published_at":348,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":349,"score_detail":350,"sources":353,"tags":355,"primary_topic":356,"secondary_topics":357,"classification":360,"search_phrases":379,"slug":382,"view_count":81,"doi":8,"paper":383,"created_at":390},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":16,"substance":17,"depth":351,"authority":19,"freshness":123,"relevant":21,"comment":352},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。",[354],{"name":347,"url":345},[26,27,292,293,294],{"code":296,"slug":297,"name":298,"path":299,"parent_code":111},[358,359],{"code":303,"slug":304,"name":305,"path":306,"parent_code":307},{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":13,"version":40,"method":41,"evidence":361},{"candidates":362,"agricultural_evidence":46},[363,367,371,375],{"code":296,"score":312,"supported":46,"title":364,"abstract":365,"tags":366},[298],[298],[],{"code":303,"score":312,"supported":46,"title":368,"abstract":369,"tags":370},[322],[322],[],{"code":32,"score":312,"supported":46,"title":372,"abstract":373,"tags":374},[293],[],[292,27,293],{"code":52,"score":53,"supported":46,"title":376,"abstract":377,"tags":378},[],[26],[26],[380,381],"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":8,"openalex_id":8,"authors":384,"venue":8,"cited_by_count":81,"oa_url":8,"card":385,"direction":183,"ingested_from":340},[],{"tldr":386,"method":387,"finding":388,"direction":183,"opportunity":389},"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":392,"title":393,"url":394,"summary":395,"summary_zh":396,"content":8,"source_name":397,"source_url":394,"published_at":348,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":398,"score_detail":399,"sources":403,"tags":405,"primary_topic":408,"secondary_topics":409,"classification":414,"search_phrases":437,"slug":440,"view_count":21,"doi":441,"paper":442,"created_at":464},5165,"Proximal Remote Sensing of Oil Palm Basal Stem Rot Using Deep Learning Vision Algorithms and Ground-based Images","https:\u002F\u002Fdoi.org\u002F10.22266\u002Fijies2026.1130.31","Basal Stem Rot (BSR), caused by Ganoderma boninense, threatens oil palm productivity in Southeast Asia.This study develops and benchmarks a low-cost remote sensing framework for BSR severity diagnosis from smartphone-captured RGB trunk images, evaluating two pathways for future field deployment.The first pairs YOLO detection with a Random Forest classifier: YOLO26n gave the best accuracy-to-cost trade-off among three nanovariants for localizing BSR symptoms (mushroom, rot, white-button), and the end-to-end YOLO26n+RF pipeline achieved 0.83 accuracy and macro-F1 of 0.79 across Healthy, Mild, Moderate, and Severe classes.Rot was consistently the hardest symptom to localize, owing to its diffuse, boundary-ambiguous appearance.The second pathway trained four image classifiers (EfficientNet-B0, MobileNetV2, Swin-Tiny, and ConvNeXt-V2-Tiny) directly on severity labels, with ConvNeXt-V2-Tiny performing best (0.83 accuracy, 0.83 macro-F1).Both pathways performed well at severity extremes but struggled with Moderate, reflecting visual overlap between stages.SHAP analysis identified rot-area ratio and fruiting-body count as top predictors.These offline results show accurate BSR diagnosis is potentially achievable from smartphone imagery, laying groundwork for future on-device deployment.","由灵芝（Ganoderma boninense）引起的基茎腐病（Basal Stem Rot, BSR）威胁着东南亚油棕的生产力。本研究开发并基准测试了一个低成本遥感框架，用于从智能手机拍摄的RGB树干图像中诊断基茎腐病的严重程度，并评估了未来田间部署的两条技术路径。第一条路径将YOLO检测与随机森林分类器相结合：在三种纳米变体中，YOLO26n在定位基茎腐病症状（蘑菇、腐烂、白钮）方面实现了最佳的精度-成本权衡，端到端的YOLO26n+RF流程在健康、轻度、中度和重度四个类别上达到了0.83的准确率和0.79的宏F1值。腐烂始终是最难定位的症状，原因在于其弥散且边界模糊的外观。第二条路径直接在严重程度标签上训练了四个图像分类器（EfficientNet-B0、MobileNetV2、Swin-Tiny和ConvNeXt-V2-Tiny），其中ConvNeXt-V2-Tiny表现最佳（准确率0.83，宏F1值0.83）。两条路径在严重程度极端类别上表现良好，但在中度类别上表现欠佳，反映出各阶段之间存在视觉重叠。SHAP分析确定腐烂面积比和子实体数量为最重要的预测因子。这些离线结果表明，利用智能手机图像有望实现准确的基茎腐病诊断，为未来设备端部署奠定了基础。","International journal of intelligent engineering and systems",69,{"impact":400,"substance":401,"depth":16,"authority":400,"freshness":20,"relevant":21,"comment":402},12,20,"该研究利用智能手机图像与深度学习实现油棕基腐病严重度诊断，方法新颖、结论可靠，但属细分作物研究，产业影响有限，适合作为技术类精选。",[404],{"name":397,"url":394},[26,27,144,406,407],"油棕","病害诊断",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[410],{"code":141,"slug":411,"name":412,"path":413,"parent_code":36},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"status":39,"locked":13,"version":40,"method":41,"evidence":415},{"candidates":416,"agricultural_evidence":46},[417,421,426,433],{"code":32,"score":99,"supported":46,"title":418,"abstract":419,"tags":420},[131],[130,131],[27],{"code":141,"score":400,"supported":46,"title":422,"abstract":423,"tags":425},[145],[424],"YOLO",[144],{"code":427,"score":85,"supported":46,"title":428,"abstract":429,"tags":432},"B5",[],[430,431],"蘑菇","mushroom",[],{"code":52,"score":21,"supported":13,"title":434,"abstract":435,"tags":436},[],[],[26],[438,439],"油棕 基腐病 遥感","Ganoderma boninense 智能手机 检测","油棕基腐病遥感-5165","10.22266\u002Fijies2026.1130.31",{"doi":441,"openalex_id":443,"authors":444,"venue":397,"cited_by_count":81,"oa_url":394,"card":459,"direction":183,"ingested_from":82},"W7222626135",[445,448,451,453,456],{"name":446,"orcid":447},"Wan Siu Hong","https:\u002F\u002Forcid.org\u002F0000-0002-8254-4962",{"name":449,"orcid":450},"Jonathan Yong Chung Ee","https:\u002F\u002Forcid.org\u002F0000-0003-4079-8005",{"name":452,"orcid":8},"Jin Yuan Chan",{"name":454,"orcid":455},"Mohamad Izzuddin Anuar","https:\u002F\u002Forcid.org\u002F0000-0002-4929-5175",{"name":457,"orcid":458},"Mahmud Iwan Solihin","https:\u002F\u002Forcid.org\u002F0000-0002-5293-7466",{"tldr":460,"method":461,"finding":462,"direction":183,"opportunity":463},"用手机拍摄油棕树干图像，结合深度学习检测与分类，实现基腐病严重度诊断。","YOLO26n+随机森林与四种图像分类器，基于手机RGB图像，SHAP解释。","两条路径准确率均约0.83，中度病级最难区分，腐区比例与子实体数为关键预测因子。","可探索轻量化模型在手机端离线部署，并扩充多地区、多品种数据以提升中度病级辨识。","2026-10-17T23:30:28.307953Z",{"id":466,"title":467,"url":468,"summary":469,"summary_zh":470,"content":8,"source_name":471,"source_url":468,"published_at":472,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":473,"score_detail":474,"sources":476,"tags":478,"primary_topic":482,"secondary_topics":487,"classification":489,"search_phrases":512,"slug":515,"view_count":53,"doi":516,"paper":517,"created_at":540},4998,"Unmanned aerial vehicle time‐series phenotyping illuminates genotype‐specific responses to off‐target dicamba exposure in soybean","https:\u002F\u002Fdoi.org\u002F10.1002\u002Fppj2.70118","Abstract Dicamba, a synthetic auxin herbicide, is widely used for postemergence weed control. However, the high potential for volatility and spray drift raises concerns about damage to nontarget dicot crops lacking the dicamba monooxygenase transgene, which provides bioengineered resistance. Our objective was to evaluate the practical application of unmanned aerial vehicle (UAV)‐based multispectral imaging for quantifying off‐target dicamba injury, using non‐genetically modified (GM) soybean ( Glycine max (L.) Merr.) genotypes with known tolerant (T) and sensitive (S) responses to provide a reference framework. We conducted field experiments in 2021 and 2022 using varying dicamba rates. Temporal visual injury estimates (0–5 scale) were recorded approximately every 5 days for 6–8 weeks. Significant differences were detected between T and S lines, especially with 1\u002F500 single and weekly repeated treatments, which provided the largest separation. To augment the precision of visual injury assessment, UAV‐based multispectral imagery from each of multiple flights was used to calculate four vegetation indices (VIs): green leaf index (GLI), normalized excess green index (ExG2), normalized green red difference index (NGRDI), and red edge normalized difference vegetation index (RENDVI). These VIs captured differences between T and S lines, indicating dicamba injury can be quantified through remote sensing across contrasting soybean genotypes. Strong negative correlations were observed between visual injury scores and VI scores across treatments at each flight date, with overall correlations for GLI, ExG2, NGRDI, and RENDVI being −0.43, −0.43, −0.44, and −0.39 in 2021 and −0.54, −0.54, −0.64, and −0.57 in 2022. These results support the utility of UAV‐derived VIs for detecting and quantifying off‐target herbicide injury in soybean, facilitating scientific research efforts to develop non‐GM‐tolerant genotypes.","摘要 麦草畏（dicamba）是一种合成生长素类除草剂，广泛用于苗后杂草防除。然而，其高挥发性和喷雾飘移潜力引发了人们对非靶标双子叶作物的担忧，这些作物缺乏提供生物工程抗性的麦草畏单加氧酶（dicamba monooxygenase）转基因。本研究旨在评估基于无人机（UAV）的多光谱成像在量化非靶标麦草畏药害中的实际应用，利用对麦草畏具有已知耐受（T）和敏感（S）响应的非转基因（GM）大豆（Glycine max (L.) Merr.）基因型作为参考框架。我们于2021年和2022年开展了田间试验，使用不同的麦草畏剂量。在约6–8周内，每约5天记录一次时序视觉药害评估（0–5级）。在T和S品系之间检测到显著差异，尤其是在1\u002F500单次和每周重复处理下，差异最为明显。为提高视觉药害评估的精度，利用多次飞行获取的无人机多光谱影像计算了4种植被指数（VIs）：绿叶指数（GLI）、归一化超绿指数（ExG2）、归一化绿红差异指数（NGRDI）和红边归一化差异植被指数（RENDVI）。这些植被指数捕捉到了T和S品系之间的差异，表明麦草畏药害可通过遥感在不同大豆基因型间进行量化。在各飞行日期的所有处理中，视觉药害评分与植被指数评分之间均观察到强负相关，2021年GLI、ExG2、NGRDI和RENDVI的总体相关系数分别为−0.43、−0.43、−0.44和−0.39，2022年分别为−0.54、−0.54、−0.64和−0.57。这些结果支持无人机衍生植被指数在大豆非靶标除草剂药害检测和量化中的实用性，有助于推动开发非转基因耐受基因型的科学研究工作。","The Plant Phenome Journal","2026-10-14T00:00:00Z",77,{"impact":16,"substance":196,"depth":18,"authority":45,"freshness":123,"relevant":21,"comment":475},"该研究利用无人机多光谱时序表型定量评估大豆非靶标除草剂药害，方法新颖、数据扎实，对智慧农业遥感监测具有参考价值。",[477],{"name":471,"url":468},[26,479,28,480,481],"大豆","植被指数","除草剂药害",{"code":483,"slug":484,"name":485,"path":486,"parent_code":111},"A4","soybean-oilseeds","大豆与油料","\u002Ftopic\u002Ffield-crops\u002Fsoybean-oilseeds",[488],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":13,"version":40,"method":41,"evidence":490},{"candidates":491,"agricultural_evidence":46},[492,497,501,508],{"code":483,"score":99,"supported":46,"title":493,"abstract":495,"tags":496},[494],"soybean",[479,494],[479],{"code":32,"score":312,"supported":46,"title":498,"abstract":499,"tags":500},[],[130,293,131,48],[28,480],{"code":502,"score":85,"supported":46,"title":503,"abstract":504,"tags":507},"F4",[],[505,506],"除草","weed control",[],{"code":52,"score":21,"supported":13,"title":509,"abstract":510,"tags":511},[],[],[26],[513,514],"无人机 多光谱 大豆 药害","麦草畏 飘移 大豆 遥感","无人机多光谱大豆药害-4998","10.1002\u002Fppj2.70118",{"doi":516,"openalex_id":518,"authors":519,"venue":471,"cited_by_count":81,"oa_url":468,"card":535,"direction":183,"ingested_from":82},"W7221759072",[520,523,526,529,532],{"name":521,"orcid":522},"Seda Ozer","https:\u002F\u002Forcid.org\u002F0000-0002-1644-2394",{"name":524,"orcid":525},"Brian W. Diers","https:\u002F\u002Forcid.org\u002F0000-0003-3584-5495",{"name":527,"orcid":528},"Dean E. Riechers","https:\u002F\u002Forcid.org\u002F0000-0002-6081-5629",{"name":530,"orcid":531},"Caio Canella Vieira","https:\u002F\u002Forcid.org\u002F0000-0001-5428-3753",{"name":533,"orcid":534},"Alper Adak","https:\u002F\u002Forcid.org\u002F0000-0002-2737-8041",{"tldr":536,"method":537,"finding":538,"direction":183,"opportunity":539},"用无人机多光谱时序表型量化非转基因大豆受除草剂飘移药害的基因型差异。","2021-2022田间试验，无人机多光谱影像计算GLI、ExG2、NGRDI、R","植被指数与目测药害评分显著负相关，能区分耐性与敏感基因型，2022年相关性更强。","可拓展到多作物、多除草剂飘移的无人机早期诊断，并融合机器学习构建药害分级预警模型。","2026-10-15T23:30:20.537887Z"]