[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4418":3,"related-4418":114},{"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":23,"tags":25,"primary_topic":31,"secondary_topics":37,"classification":38,"search_phrases":84,"slug":87,"view_count":88,"doi":89,"paper":90,"created_at":113},4418,"Recent Developments in Smart Agriculture and Horticulture: A Critical Appraisal of Sensing, Robotics, Analytics and Controlled-Environment Production","https:\u002F\u002Fdoi.org\u002F10.9734\u002Facri\u002F2026\u002Fv26i102207","Digital and automated technologies have been promoted as a route to higher productivity, lower input use and improved resilience in both field agriculture and horticulture, yet the evidence underpinning these expectations remains uneven and, in several domains, contested. This review critically examines developments in smart agriculture and horticulture across six interdependent technological domains: proximal and remote sensing, machine-learning analytics, field and glasshouse robotics, controlled-environment production, postharvest quality assessment and chain integrity, and the integration architectures that connect them. Literature was identified through structured searching of open scholarly databases and indexes, supplemented by backward and forward citation tracking and by targeted retrieval from institutional sources, with critical appraisal focused on methodological adequacy, ecological validity and the correspondence between reported performance and operational conditions. Three findings dominate the synthesis. First, the technical performance of sensing and recognition systems is frequently established under conditions that differ materially from commercial practice, and the resulting laboratory-to-field generalisation deficit is the single most consistent methodological weakness across the literature. Second, horticultural systems impose constraints that arable-derived technologies do not resolve automatically, including three-dimensional canopy occlusion, delicate and heterogeneous produce, high crop value per unit area and dense, frequently repeated operations; the most convincing advances involve co-design of the crop system and the machine rather than the machine alone. Third, evidence on economic and environmental outcomes is considerably weaker than evidence on technical feasibility, with profitability strongly conditional on within-field variability, holding size, labour costs and energy prices, and with sustainability claims for controlled-environment production remaining sensitive to assumptions about electricity supply and system boundaries. Substantial gaps persist in multi-season field validation, transparent reporting of failure modes, distributional analysis of who benefits from digitalisation, and governance of agricultural data. Confidence in current conclusions is constrained by geographical concentration of evidence and by the rapid obsolescence of technology-specific findings.","数字与自动化技术已被推崇为提高大田农业和园艺作物生产力、降低投入品使用并增强韧性的路径，但支撑这些预期的证据仍不均衡，且在若干领域存在争议。本综述批判性地考察了智慧农业与园艺在六个相互依赖的技术领域中的进展：近端与遥感、机器学习分析、田间与温室机器人、受控环境生产、采后品质评估与供应链完整性，以及连接上述各环节的集成架构。文献通过结构化检索开放学术数据库与索引获得，并辅以引文回溯与追踪以及从机构来源进行定向检索，批判性评价聚焦于方法学充分性、生态效度以及所报告性能与实际运行条件之间的对应关系。综合结果中呈现三项主要发现。第一，传感与识别系统的技术性能往往在与商业实践存在实质性差异的条件下确立，由此产生的“实验室到田间”泛化差距是文献中最一致的方法学弱点。第二，园艺系统施加的约束并不能由源自大田作物的技术自动解决，包括三维冠层遮挡、娇嫩且异质的农产品、单位面积作物价值高以及密集且频繁重复的作业；最具说服力的进展涉及作物系统与机器的协同设计，而非仅靠机器本身。第三，关于经济与环境结果的证据明显弱于关于技术可行性的证据，盈利能力强烈取决于田内变异性、经营规模、劳动力成本和能源价格，而受控环境生产的可持续性主张仍对电力供应和系统边界的假设高度敏感。在多季节田间验证、失败模式的透明报告、数字化受益者的分布分析以及农业数据治理方面仍存在显著空白。当前结论的可信度受到证据地理集中以及技术特定发现迅速过时的制约。",null,"Archives of Current Research International","2026-10-07T00:00:00Z","论文",10,false,78,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,22,12,8,1,"系统评述智慧农业与园艺六大技术域的综述论文，指出实验室到田间泛化不足、经济环境证据薄弱等关键缺口，学术增量扎实但属国际综述、非国内政策或产业事件。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","农业遥感","农业机器人","设施园艺","农业数据治理",{"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":14,"version":40,"method":41,"evidence":42},"classified","2026-10-22.2","rules",{"candidates":43,"agricultural_evidence":47},[44,52,59,66,73,79],{"code":45,"score":46,"supported":47,"title":48,"abstract":50,"tags":51},"I1",15,true,[49],"smart agriculture",[26,49],[26],{"code":32,"score":53,"supported":47,"title":54,"abstract":55,"tags":58},7,[],[56,57],"遥感","remote sensing",[27],{"code":60,"score":61,"supported":47,"title":62,"abstract":63,"tags":65},"B7",4,[],[64],"温室",[29],{"code":67,"score":68,"supported":47,"title":69,"abstract":70,"tags":72},"I2",3,[],[71],"机器学习",[],{"code":74,"score":68,"supported":47,"title":75,"abstract":76,"tags":78},"I6",[],[77],"agricultural data",[],{"code":80,"score":21,"supported":14,"title":81,"abstract":82,"tags":83},"F6",[],[],[28],[85,86],"农业数据治理 农业机器人 农业遥感 智慧农业","农业数据治理 农业机器人","农业数据治理农业机器人农业遥感智慧农业-4418",2,"10.9734\u002Facri\u002F2026\u002Fv26i102207",{"doi":89,"openalex_id":91,"authors":92,"venue":10,"cited_by_count":105,"oa_url":6,"card":106,"direction":110,"ingested_from":112},"W7220849440",[93,95,97,99,101,103],{"name":94,"orcid":9},"Devesh Arya",{"name":96,"orcid":9},"D. Swami",{"name":98,"orcid":9},"M. C. Vinodraj",{"name":100,"orcid":9},"Vivek Kumar Pathak",{"name":102,"orcid":9},"Punabati Heisnam",{"name":104,"orcid":9},"Abhinash Moirangthem",0,{"tldr":107,"method":108,"finding":109,"direction":110,"opportunity":111},"综述智慧农业与园艺在传感、机器人、分析及可控环境生产方面的进展，并批判性评估其证据质量。","结构化文献检索与引文追踪，重点评估方法学、生态效度及实际适用性。","技术性能多在非商业条件下验证，经济环境证据弱，园艺需作物-机器协同设计。","智慧农业 \u002F 农业物联网","需多季田间验证、失败模式透明报告、数字化受益分布分析及农业数据治理研究。","openalex","2026-10-08T23:30:09.539116Z",{"total":115,"page":21,"page_size":115,"items":116},6,[117,215,277,327,401,480],{"id":118,"title":119,"url":120,"summary":121,"summary_zh":122,"content":9,"source_name":123,"source_url":120,"published_at":124,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":125,"score_detail":126,"sources":130,"tags":132,"primary_topic":136,"secondary_topics":142,"classification":150,"search_phrases":182,"slug":185,"view_count":105,"doi":186,"paper":187,"created_at":214},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":127,"substance":18,"depth":17,"authority":128,"freshness":13,"relevant":21,"comment":129},16,14,"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。",[131],{"name":123,"url":120},[26,133,27,134,135],"农业人工智能","氮素监测","紫花苜蓿",{"code":137,"slug":138,"name":139,"path":140,"parent_code":141},"A7","forage-cropping","饲草与耕作制度","\u002Ftopic\u002Ffield-crops\u002Fforage-cropping","A",[143,144],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"code":145,"slug":146,"name":147,"path":148,"parent_code":149},"C4","feed","饲料与饲草","\u002Ftopic\u002Flivestock\u002Ffeed","C",{"status":39,"locked":14,"version":40,"method":41,"evidence":151},{"candidates":152,"agricultural_evidence":47},[153,158,164,170,178],{"code":32,"score":17,"supported":47,"title":154,"abstract":156,"tags":157},[155],"hyperspectral",[56,57,155],[27],{"code":137,"score":46,"supported":47,"title":159,"abstract":161,"tags":163},[160],"alfalfa",[162,160],"苜蓿",[135],{"code":145,"score":13,"supported":47,"title":165,"abstract":166,"tags":169},[],[167,162,168],"饲草","forage",[135],{"code":67,"score":13,"supported":47,"title":171,"abstract":172,"tags":177},[],[173,71,174,175,176],"深度学习","machine learning","deep learning","decision support",[133],{"code":45,"score":21,"supported":14,"title":179,"abstract":180,"tags":181},[],[],[26],[183,184],"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":186,"openalex_id":188,"authors":189,"venue":123,"cited_by_count":105,"oa_url":120,"card":208,"direction":212,"ingested_from":112},"W7223154576",[190,192,194,196,198,201,203,206],{"name":191,"orcid":9},"Shuai Fu",{"name":193,"orcid":9},"Yunhao Li",{"name":195,"orcid":9},"Chunli Miao",{"name":197,"orcid":9},"Jie Liu",{"name":199,"orcid":200},"Tiangang Liang","https:\u002F\u002Forcid.org\u002F0000-0001-5599-6587",{"name":202,"orcid":9},"Yongjia Hu",{"name":204,"orcid":205},"Qisheng Feng","https:\u002F\u002Forcid.org\u002F0009-0000-1391-9591",{"name":207,"orcid":9},"吴彩霞",{"tldr":209,"method":210,"finding":211,"direction":212,"opportunity":213},"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。","2026-10-22T11:20:39.622844Z",{"id":216,"title":217,"url":218,"summary":219,"summary_zh":9,"content":9,"source_name":220,"source_url":9,"published_at":124,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":221,"score_detail":222,"sources":224,"tags":226,"primary_topic":230,"secondary_topics":235,"classification":243,"search_phrases":264,"slug":267,"view_count":21,"doi":9,"paper":268,"created_at":276},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":17,"substance":18,"depth":17,"authority":128,"freshness":13,"relevant":21,"comment":223},"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。",[225],{"name":220,"url":218},[26,27,227,228,229],"作物表型","无人机多光谱","小麦渍害",{"code":231,"slug":232,"name":233,"path":234,"parent_code":141},"A2","wheat","小麦","\u002Ftopic\u002Ffield-crops\u002Fwheat",[236,237],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"code":238,"slug":239,"name":240,"path":241,"parent_code":242},"H7","climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate","H",{"status":39,"locked":14,"version":40,"method":41,"evidence":244},{"candidates":245,"agricultural_evidence":47},[246,251,255,260],{"code":231,"score":247,"supported":47,"title":248,"abstract":249,"tags":250},11,[233],[233],[],{"code":32,"score":247,"supported":47,"title":252,"abstract":253,"tags":254},[228],[],[227,27,228],{"code":238,"score":20,"supported":47,"title":256,"abstract":258,"tags":259},[257],"渍害",[],[],{"code":45,"score":21,"supported":14,"title":261,"abstract":262,"tags":263},[],[],[26],[265,266],"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":9,"openalex_id":9,"authors":269,"venue":9,"cited_by_count":105,"oa_url":9,"card":270,"direction":212,"ingested_from":275},[],{"tldr":271,"method":272,"finding":273,"direction":212,"opportunity":274},"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":278,"title":279,"url":280,"summary":281,"summary_zh":9,"content":9,"source_name":282,"source_url":9,"published_at":283,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":284,"score_detail":285,"sources":289,"tags":291,"primary_topic":292,"secondary_topics":293,"classification":296,"search_phrases":315,"slug":318,"view_count":105,"doi":9,"paper":319,"created_at":326},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":127,"substance":286,"depth":287,"authority":128,"freshness":20,"relevant":21,"comment":288},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。",[290],{"name":282,"url":280},[26,27,227,228,229],{"code":231,"slug":232,"name":233,"path":234,"parent_code":141},[294,295],{"code":238,"slug":239,"name":240,"path":241,"parent_code":242},{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":297},{"candidates":298,"agricultural_evidence":47},[299,303,307,311],{"code":231,"score":247,"supported":47,"title":300,"abstract":301,"tags":302},[233],[233],[],{"code":238,"score":247,"supported":47,"title":304,"abstract":305,"tags":306},[257],[257],[],{"code":32,"score":247,"supported":47,"title":308,"abstract":309,"tags":310},[228],[],[227,27,228],{"code":45,"score":61,"supported":47,"title":312,"abstract":313,"tags":314},[],[26],[26],[316,317],"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":9,"openalex_id":9,"authors":320,"venue":9,"cited_by_count":105,"oa_url":9,"card":321,"direction":212,"ingested_from":275},[],{"tldr":322,"method":323,"finding":324,"direction":212,"opportunity":325},"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":328,"title":329,"url":330,"summary":331,"summary_zh":332,"content":9,"source_name":333,"source_url":330,"published_at":283,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":334,"score_detail":335,"sources":339,"tags":341,"primary_topic":344,"secondary_topics":345,"classification":350,"search_phrases":373,"slug":376,"view_count":21,"doi":377,"paper":378,"created_at":400},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":19,"substance":336,"depth":127,"authority":19,"freshness":337,"relevant":21,"comment":338},20,9,"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。",[340],{"name":333,"url":330},[26,27,173,342,343],"油棕","病害诊断",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[346],{"code":67,"slug":347,"name":348,"path":349,"parent_code":36},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"status":39,"locked":14,"version":40,"method":41,"evidence":351},{"candidates":352,"agricultural_evidence":47},[353,357,362,369],{"code":32,"score":46,"supported":47,"title":354,"abstract":355,"tags":356},[57],[56,57],[27],{"code":67,"score":19,"supported":47,"title":358,"abstract":359,"tags":361},[175],[360],"YOLO",[173],{"code":363,"score":115,"supported":47,"title":364,"abstract":365,"tags":368},"B5",[],[366,367],"蘑菇","mushroom",[],{"code":45,"score":21,"supported":14,"title":370,"abstract":371,"tags":372},[],[],[26],[374,375],"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":377,"openalex_id":379,"authors":380,"venue":333,"cited_by_count":105,"oa_url":330,"card":395,"direction":212,"ingested_from":112},"W7222626135",[381,384,387,389,392],{"name":382,"orcid":383},"Wan Siu Hong","https:\u002F\u002Forcid.org\u002F0000-0002-8254-4962",{"name":385,"orcid":386},"Jonathan Yong Chung Ee","https:\u002F\u002Forcid.org\u002F0000-0003-4079-8005",{"name":388,"orcid":9},"Jin Yuan Chan",{"name":390,"orcid":391},"Mohamad Izzuddin Anuar","https:\u002F\u002Forcid.org\u002F0000-0002-4929-5175",{"name":393,"orcid":394},"Mahmud Iwan Solihin","https:\u002F\u002Forcid.org\u002F0000-0002-5293-7466",{"tldr":396,"method":397,"finding":398,"direction":212,"opportunity":399},"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":402,"title":403,"url":404,"summary":405,"summary_zh":406,"content":9,"source_name":407,"source_url":404,"published_at":408,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":409,"score_detail":410,"sources":413,"tags":415,"primary_topic":418,"secondary_topics":419,"classification":426,"search_phrases":451,"slug":454,"view_count":105,"doi":455,"paper":456,"created_at":479},4898,"An Interpretable Deep Learning Framework for Spatial Prediction of Soil Salinity Using Multi-Source Remote Sensing and Environmental Data: A Case Study of Cangzhou, Hebei Province, China","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fs26206465","Soil salinity poses a significant challenge to agricultural productivity and sustainable land management in coastal and semi-arid regions. Soil salinity in coastal agricultural regions is controlled by complex interactions among surface conditions, groundwater processes, soil properties, and terrain characteristics. Accurate spatial prediction requires integrating remote sensing observations with environmental information that represents these processes. This study presents a multi-source deep learning framework that integrates Sentinel-2-derived spectral indices with environmental covariates for regional soil salinity spatial prediction under the environmental conditions of the 2024 sampling period. SHapley Additive exPlanations (SHAP) is employed to quantify the contribution of environmental variables within the prediction framework and improve model interpretability. A convolutional neural network (CNN) incorporating a squeeze-and-excitation (SE) attention mechanism is developed as a nonlinear feature representation framework for integrating heterogeneous predictors, while the Crested Porcupine Optimizer (CPO) is used to optimize model hyperparameters. The proposed framework achieved the highest predictive performance among the benchmark models, with an R2 of 0.785, an RMSE of 0.928, and an MAE of 0.720. Integrating environmental covariates with spectral features further improves the prediction performance, increasing the R2 from 0.701 to 0.785. The results indicate the potential of integrating multi-source environmental information with interpretable deep learning for spatial prediction of near-surface soil salinity under similar coastal agricultural conditions.","土壤盐渍化对沿海和半干旱地区的农业生产力和可持续土地管理构成了重大挑战。沿海农业区域的土壤盐渍化受地表条件、地下水过程、土壤属性和地形特征之间复杂相互作用的控制。准确的空间预测需要将遥感观测与表征这些过程的环境信息相结合。本研究提出了一种多源深度学习框架，该框架将Sentinel-2衍生的光谱指数与环境协变量相结合，用于2024年采样期环境条件下的区域土壤盐渍化空间预测。采用SHapley加法解释（SHAP）方法量化预测框架中环境变量的贡献并提高模型可解释性。开发了一种融合压缩-激励（SE）注意力机制的卷积神经网络（CNN）作为非线性特征表示框架，用于整合异质性预测因子，同时使用冠豪猪优化器（CPO）优化模型超参数。所提出的框架在基准模型中取得了最高的预测性能，R²为0.785，RMSE为0.928，MAE为0.720。将环境协变量与光谱特征相结合进一步提高了预测性能，R²从0.701提升至0.785。结果表明，在类似的沿海农业条件下，将多源环境信息与可解释深度学习相结合用于近地表土壤盐渍化空间预测具有潜力。","Sensors","2026-10-13T00:00:00Z",75,{"impact":127,"substance":286,"depth":287,"authority":411,"freshness":20,"relevant":21,"comment":412},13,"方法新颖、指标可靠，将多源遥感与可解释深度学习结合用于盐渍化空间预测，对沿海农业区土壤治理有参考价值。",[414],{"name":407,"url":404},[26,27,416,173,417],"可解释AI","土壤盐渍化",{"code":67,"slug":347,"name":348,"path":349,"parent_code":36},[420,421],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"code":422,"slug":423,"name":424,"path":425,"parent_code":242},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil",{"status":39,"locked":14,"version":40,"method":41,"evidence":427},{"candidates":428,"agricultural_evidence":47},[429,433,437,442,447],{"code":67,"score":127,"supported":47,"title":430,"abstract":431,"tags":432},[175],[173,175],[416,173],{"code":32,"score":46,"supported":47,"title":434,"abstract":435,"tags":436},[57],[56,57],[27],{"code":422,"score":128,"supported":47,"title":438,"abstract":439,"tags":441},[423],[440,423],"土壤",[],{"code":238,"score":68,"supported":47,"title":443,"abstract":444,"tags":446},[],[445],"干旱",[],{"code":45,"score":21,"supported":14,"title":448,"abstract":449,"tags":450},[],[],[26],[452,453],"沧州 土壤盐渍化 遥感","Sentinel-2 土壤盐分 预测","沧州土壤盐渍化遥感-4898","10.3390\u002Fs26206465",{"doi":455,"openalex_id":457,"authors":458,"venue":407,"cited_by_count":105,"oa_url":404,"card":474,"direction":212,"ingested_from":112},"W7221715654",[459,462,465,467,470,472],{"name":460,"orcid":461},"Mingyuan Ye","https:\u002F\u002Forcid.org\u002F0009-0001-6434-5505",{"name":463,"orcid":464},"Peng Chen","https:\u002F\u002Forcid.org\u002F0000-0002-2549-6765",{"name":466,"orcid":9},"Junchi Ma",{"name":468,"orcid":469},"Bingxia Liu","https:\u002F\u002Forcid.org\u002F0000-0001-7958-7112",{"name":471,"orcid":9},"Jiahao Qi",{"name":473,"orcid":9},"Yuan Jing",{"tldr":475,"method":476,"finding":477,"direction":212,"opportunity":478},"构建融合多源遥感与环境变量的可解释深度学习框架，预测沧州土壤盐分空间分布。","Sentinel-2光谱指数+环境协变量，CNN-SE注意力，CPO优化超参数，","环境协变量与光谱特征结合将R²从0.701提升至0.785，模型性能最优。","可迁移至其他滨海盐渍区，并探索时序动态预测与多深度土壤盐分协同反演。","2026-10-14T23:30:27.313838Z",{"id":481,"title":482,"url":483,"summary":484,"summary_zh":485,"content":9,"source_name":486,"source_url":483,"published_at":487,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":125,"score_detail":488,"sources":491,"tags":493,"primary_topic":496,"secondary_topics":497,"classification":498,"search_phrases":529,"slug":532,"view_count":21,"doi":533,"paper":534,"created_at":551},4563,"Seeing Crops in 3D: Remote Sensing for Smarter Precision Farming","https:\u002F\u002Fdoi.org\u002F10.12688\u002Fopenreseurope.25171.1","The growing adoption of digital technologies in agriculture is opening new opportunities for improving productivity, sustainability and resource efficiency through data-driven decision making. This paper presents the integrated precision-agriculture framework developed by RFSAT Limited within the H2020 AgriBIT project and currently being further enhanced through the ENACT project. The framework combines Internet-of-Things (IoT) sensing, centimetre-accuracy Global Navigation Satellite System (GNSS) positioning, high-resolution multispectral remote sensing and Artificial Intelligence (AI) analytics into a single smart-farming solution. A key enabler is a low-cost, centimetre-accurate GNSS receiver developed by RFSAT, which allows precise georeferencing of aerial and ground observations and, in turn, the generation of high-resolution multispectral 3D models of crop canopies with spatial resolution exceeding 1 cm\u002Fpixel. These digital representations support detailed monitoring of crop growth, canopy development, biomass distribution and plant health throughout the season. Fused with an IoT monitoring infrastructure of environmental and microclimatic sensors, the georeferenced remote-sensing products form a comprehensive digital twin of the agricultural environment, over which machine-learning techniques automatically identify and localise crop anomalies including water stress, nutrient deficiencies, disease outbreaks and pest infestations. The technologies have been validated at operational pilot sites across Greece, Portugal and Italy, spanning orchards, vineyards and other high-value crops, where the combination of centimetre positioning, multispectral 3D modelling, IoT monitoring and AI analytics substantially improved monitoring accuracy, reduced input consumption and enhanced management decisions.","数字技术在农业中的日益普及，正通过数据驱动的决策为提升生产力、可持续性和资源效率开辟新的机遇。本文介绍了RFSAT有限公司在H2020 AgriBIT项目中开发的综合精准农业框架，该框架目前正通过ENACT项目进一步完善。该框架将物联网（IoT）传感、厘米级精度的全球导航卫星系统（GNSS）定位、高分辨率多光谱遥感以及人工智能（AI）分析整合为一个智慧农业解决方案。一个关键的使能因素是由RFSAT开发的低成本、厘米级精度GNSS接收器，它能够对航空和地面观测进行精确的地理参照，进而生成空间分辨率超过1厘米\u002F像素的作物冠层高分辨率多光谱三维模型。这些数字表示支持在整个生长季对作物生长、冠层发育、生物量分布和植物健康进行详细监测。通过与由环境和小气候传感器组成的物联网监测基础设施相融合，地理参照的遥感产品形成了农业环境的综合数字孪生，机器学习技术在此基础上自动识别和定位作物异常，包括水分胁迫、养分缺乏、病害暴发和虫害侵扰。这些技术已在希腊、葡萄牙和意大利的运营试点基地进行了验证，涵盖果园、葡萄园和其他高价值作物，其中厘米级定位、多光谱三维建模、物联网监测和AI分析的结合大幅提高了监测精度、降低了投入品消耗并改善了管理决策。","Open Research Europe","2026-10-09T00:00:00Z",{"impact":17,"substance":18,"depth":489,"authority":411,"freshness":20,"relevant":21,"comment":490},19,"欧盟H2020项目论文，融合厘米级GNSS、多光谱3D建模与AI分析，方法新颖且多国验证，对智慧农业技术路线有参考价值。",[492],{"name":486,"url":483},[26,133,27,494,495],"数字孪生","精准农业",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":499},{"candidates":500,"agricultural_evidence":47},[501,506,511,516,523],{"code":32,"score":17,"supported":47,"title":502,"abstract":503,"tags":505},[57],[56,57,504],"multispectral",[27],{"code":45,"score":19,"supported":47,"title":507,"abstract":508,"tags":510},[],[26,495,494,509],"digital twin",[494,26,495],{"code":67,"score":53,"supported":47,"title":512,"abstract":513,"tags":515},[],[71,514],"artificial intelligence",[133],{"code":517,"score":115,"supported":47,"title":518,"abstract":519,"tags":522},"I5",[],[520,521],"物联网","IoT",[],{"code":524,"score":68,"supported":47,"title":525,"abstract":526,"tags":528},"B2",[],[527],"葡萄",[],[530,531],"AgriBIT 精准农业 遥感","GNSS 多光谱 3D 作物","AgriBIT精准农业遥感-4563","10.12688\u002Fopenreseurope.25171.1",{"doi":533,"openalex_id":535,"authors":536,"venue":486,"cited_by_count":105,"oa_url":483,"card":546,"direction":110,"ingested_from":112},"W7221178461",[537,540,543],{"name":538,"orcid":539},"Artur Krukowski","https:\u002F\u002Forcid.org\u002F0000-0002-6635-7585",{"name":541,"orcid":542},"Emmanouela Vogiatzaki","https:\u002F\u002Forcid.org\u002F0000-0001-9239-2045",{"name":544,"orcid":545},"İzzet Kale","https:\u002F\u002Forcid.org\u002F0000-0001-5562-6885",{"tldr":547,"method":548,"finding":549,"direction":212,"opportunity":550},"提出融合物联网、厘米级GNSS、多光谱遥感和AI的精准农业框架，构建作物数字孪生。","低成本厘米级GNSS、多光谱3D建模、IoT传感器与机器学习，在希腊、葡萄牙、意","厘米定位与多光谱3D建模结合AI可提升监测精度、减少投入并改善管理决策。","可探索低成本厘米级GNSS与多光谱3D数字孪生在更多作物和区域的可扩展性及实时决策优化。","2026-10-10T23:30:07.913557Z"]