[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2955":3,"related-2955":64},{"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":63},2955,"Precision agriculture applied to coffee cultivation in agroforestry systems","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs10457-026-01651-z","Abstract The search for more sustainable agricultural production systems is a goal of both agroforestry systems (AFS) and precision agriculture (PA). In this context, integrating PA technologies into coffee cultivation under AFS presents the potential to increase productive efficiency and sustainability. Thus, the objective of this study was to evaluate whether the variability of environmental conditions in coffee agroforestry systems can be estimated using technologies associated with PA. The studies were conducted on three coffee farms in Coimbra and Araponga, Minas Gerais, Brazil. For management zone delineation, combinations of apparent soil electrical conductivity (ECa), Normalized Difference Vegetation Index (NDVI), and Digital Terrain Model (DTM) were used. Microclimatic variability was evaluated using weather stations, considering different agroforestry management practices and altitude conditions in mountain coffee cultivation. Canopy cover was estimated by digital image processing techniques applied to aerial images obtained by an Unmanned Aerial Vehicle (UAV). The combination ECa + NDVI was efficient in delineating zones with distinct physical and chemical attributes. Agroforestry management practices and altitude influenced temperature and relative humidity. Tree canopy estimation proved viable to support management. It is concluded that PA is strategic for coffee cultivation in AFS, favoring site-specific and more sustainable management.","摘要 寻求更可持续的农业生产系统是农林业系统（AFS）和精准农业（PA）的共同目标。在此背景下，将精准农业技术整合到农林业系统下的咖啡种植中，具有提高生产效率和可持续性的潜力。因此，本研究旨在评估是否可以利用与精准农业相关的技术来估算咖啡农林业系统中环境条件的变异性。研究在巴西米纳斯吉拉斯州科英布拉和阿拉蓬加的三个咖啡农场进行。为划定管理区，采用了表观土壤电导率（ECa）、归一化差异植被指数（NDVI）和数字地形模型（DTM）的组合。利用气象站评估微气候变异性，考虑了山地咖啡种植中不同的农林业管理措施和海拔条件。通过将数字图像处理技术应用于无人机（UAV）获取的航空图像来估算冠层覆盖度。ECa + NDVI的组合能够有效划定具有不同物理和化学属性的区域。农林业管理措施和海拔影响了温度和相对湿度。树木冠层估算被证明可用于支持管理。结论是，精准农业对农林业系统下的咖啡种植具有战略意义，有利于因地制宜且更可持续的管理。",null,"Agroforestry Systems","2026-09-18T00:00:00Z","论文",10,false,71,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},12,21,17,13,8,1,"巴西咖啡农林复合系统中应用精准农业技术的研究，方法新颖、结论可靠，对智慧农业与遥感应用有参考价值，但属区域性研究，影响范围有限。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","精准农业","遥感监测","农林复合系统","咖啡种植",[33,34],"巴西 咖啡 农林复合系统 精准农业","无人机 冠层覆盖 NDVI 咖啡","巴西咖啡农林复合系统精准农业-2955",0,"10.1007\u002Fs10457-026-01651-z",{"doi":37,"openalex_id":39,"authors":40,"venue":10,"cited_by_count":36,"oa_url":6,"card":55,"direction":61,"ingested_from":62},"W7213541962",[41,43,46,49,52],{"name":42,"orcid":9},"Wagner Silva dos Santos",{"name":44,"orcid":45},"Francisco de Assis de Carvalho Pinto","https:\u002F\u002Forcid.org\u002F0000-0002-8279-9535",{"name":47,"orcid":48},"André Luiz de Freitas Coelho","https:\u002F\u002Forcid.org\u002F0000-0002-7595-9713",{"name":50,"orcid":51},"Daniel Marçal de Queiroz","https:\u002F\u002Forcid.org\u002F0000-0003-0987-3855",{"name":53,"orcid":54},"Bruno Nery Fernandes Vasconcelos","https:\u002F\u002Forcid.org\u002F0000-0001-6298-9748",{"tldr":56,"method":57,"finding":58,"direction":59,"opportunity":60},"评估精准农业技术能否估算农林复合咖啡系统的环境变异，以支持可持续管理。","用ECa、NDVI、DTM划分管理区，气象站测微气候，无人机图像估树冠覆盖。","ECa+NDVI可有效划分土壤属性差异区，农林管理和海拔影响温湿度，树冠估算可行。","智慧农业 \u002F 农业物联网","可探索多源遥感与物联网融合的实时管理区动态划分，并验证其对咖啡产量与碳汇的长期效应。","数字乡村与农业信息化","openalex","2026-09-19T23:30:40.775897Z",{"total":65,"page":22,"page_size":65,"items":66},6,[67,122,177,215,251,289],{"id":68,"title":69,"url":70,"summary":71,"summary_zh":72,"content":9,"source_name":73,"source_url":70,"published_at":74,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":75,"score_detail":76,"sources":81,"tags":83,"search_phrases":86,"slug":89,"view_count":36,"doi":90,"paper":91,"created_at":121},2801,"A comprehensive survey on machine vision applications in precision agriculture: current trends and future perspectives","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs41060-026-01278-4","A comprehensive survey on machine vision applications in precision agriculture: current trends and future perspectives。International Journal of Data Science and Analytics","精准农业中机器视觉应用的综合综述：当前趋势与未来展望。《国际数据科学与分析杂志》","International Journal of Data Science and Analytics","2026-09-17T00:00:00Z",77,{"impact":77,"substance":78,"depth":19,"authority":20,"freshness":79,"relevant":22,"comment":80},18,20,9,"核心期刊发表的机器视觉精准农业综述，方法梳理与趋势判断具参考价值，但属综述类论文，产业影响有限。",[82],{"name":73,"url":70},[27,84,28,29,85],"农业人工智能","机器视觉",[87,88],"农业人工智能 智慧农业 机器视觉 精准农业","农业人工智能 智慧农业","农业人工智能智慧农业机器视觉精准农业-2801","10.1007\u002Fs41060-026-01278-4",{"doi":90,"openalex_id":92,"authors":93,"venue":73,"cited_by_count":36,"oa_url":9,"card":115,"direction":119,"ingested_from":62},"W7213471057",[94,96,98,100,103,105,107,110,113],{"name":95,"orcid":9},"Shirun Gu",{"name":97,"orcid":9},"Xinyuan Fan",{"name":99,"orcid":9},"Lihui Zhu",{"name":101,"orcid":102},"Caixia Song","https:\u002F\u002Forcid.org\u002F0000-0003-3897-7629",{"name":104,"orcid":9},"Lei Mu",{"name":106,"orcid":9},"Zichen Zhang",{"name":108,"orcid":109},"Rui Zhang","https:\u002F\u002Forcid.org\u002F0000-0002-8634-3519",{"name":111,"orcid":112},"Tong Xu","https:\u002F\u002Forcid.org\u002F0000-0001-5564-192X",{"name":114,"orcid":9},"Zhiyuan Zhang",{"tldr":116,"method":117,"finding":118,"direction":119,"opportunity":120},"综述机器视觉在精准农业中的应用现状与未来趋势。","文献综述，梳理机器视觉在精准农业中的技术路线。","机器视觉已广泛用于作物监测、病虫害识别等，但落地仍受数据与算力限制。","农业人工智能与决策模型","可聚焦轻量化模型与边缘部署，解决田间实时性与数据稀缺问题。","2026-09-17T23:30:54.103781Z",{"id":123,"title":124,"url":125,"summary":126,"summary_zh":127,"content":9,"source_name":128,"source_url":125,"published_at":129,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":130,"score_detail":131,"sources":135,"tags":137,"search_phrases":140,"slug":143,"view_count":36,"doi":144,"paper":145,"created_at":176},2664,"Soil Salinity Mapping from UAV-Borne Hyperspectral Imagery with Soil Moisture Correction","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fagronomy16181812","Soil salinization poses a significant threat to sustainable agricultural development and ecological security worldwide, resulting in considerable crop losses annually. The advent of drone-based hyperspectral remote sensing offers promising solutions for monitoring soil salinity, due to its high spatial resolution and versatile data acquisition capabilities. However, soil moisture alters both the scattering and absorption characteristics of electromagnetic radiation, thereby modifying soil spectral reflectance and masking salinity-related diagnostic spectral features, which can reduce the accuracy of conventional salinity estimation models. This study evaluates six spectral transformation methods—raw reflectance data (Ref), first derivative (FDR), Piecewise Direct Standardization (PDS), Orthogonal Signal Correction (OSC), FDR + PDS, and FDR + OSC—in conjunction with three machine learning algorithms: K-Nearest Neighbors (KNN), Support Vector Regression (SVR), and Multi-Layer Perceptron (MLP). A Stacking ensemble model integrating these base learners was further developed to improve soil salinity inversion under moisture interference. The results demonstrated that the Stacking model achieved the highest accuracy and stability among the evaluated models. Additional comparisons with XGBoost and Random Forest (RF) further confirmed the competitive performance of the proposed Stacking framework. The FDR + OSC–Stacking combination achieved the best validation performance, with Rp2 = 0.87, RMSEP = 0.67 mS·cm−1, and RPD = 2.93. Compared with the Ref–Stacking model, Rp2 increased by 0.32 (from 0.55 to 0.87), while RMSEP decreased by 0.58 mS·cm−1 (from 1.25 to 0.67 mS·cm−1). The results showed that PDS had limited effectiveness in correcting moisture-related spectral variation, whereas OSC more effectively mitigated moisture interference while preserving spectral information relevant to salinity estimation. Among the machine learning models evaluated, the Stacking ensemble model achieved better predictive performance than MLP, SVR, and KNN. Furthermore, the FDR + OSC–Stacking combination provided the best performance among the evaluated modeling frameworks and was successfully applied to UAV hyperspectral imagery for spatial mapping of EC1:5. These findings demonstrate the potential of combining appropriate spectral correction with Stacking for UAV-based soil salinity assessment and provide useful technical support for site-specific salinity management in precision agriculture.","土壤盐渍化对全球农业可持续发展和生态安全构成重大威胁，每年造成大量作物损失。基于无人机的高光谱遥感凭借其高空间分辨率和灵活的数据获取能力，为土壤盐分监测提供了有前景的解决方案。然而，土壤水分会改变电磁辐射的散射和吸收特性，从而改变土壤光谱反射率并掩盖与盐分相关的诊断性光谱特征，这可能降低传统盐分估算模型的精度。本研究评估了六种光谱变换方法——原始反射率数据（Ref）、一阶导数（FDR）、分段直接标准化（PDS）、正交信号校正（OSC）、FDR + PDS和FDR + OSC——并结合三种机器学习算法：K近邻（KNN）、支持向量回归（SVR）和多层感知机（MLP）。进一步构建了集成这些基学习器的Stacking集成模型，以提高水分干扰下的土壤盐分反演精度。结果表明，Stacking模型在所评估的模型中达到了最高的精度和稳定性。与XGBoost和随机森林（RF）的额外比较进一步证实了所提出的Stacking框架的竞争性性能。FDR + OSC–Stacking组合取得了最佳的验证表现，Rp2 = 0.87，RMSEP = 0.67 mS·cm−1，RPD = 2.93。与Ref–Stacking模型相比，Rp2提高了0.32（从0.55增至0.87），而RMSEP降低了0.58 mS·cm−1（从1.25降至0.67 mS·cm−1）。结果表明，PDS在校正水分相关光谱变异方面效果有限，而OSC在保留与盐分估算相关的光谱信息的同时，更有效地减轻了水分干扰。在所评估的机器学习模型中，Stacking集成模型取得了优于MLP、SVR和KNN的预测性能。此外，FDR + OSC–Stacking组合在所评估的建模框架中表现最佳，并成功应用于无人机高光谱影像进行EC1:5的空间制图。这些发现证明了将适当的光谱校正与Stacking相结合用于基于无人机的土壤盐分评估的潜力，并为精准农业中的针对性盐分管理提供了有用的技术支持。","Agronomy","2026-09-15T00:00:00Z",78,{"impact":132,"substance":133,"depth":77,"authority":20,"freshness":79,"relevant":22,"comment":134},16,22,"该研究提出FDR+OSC光谱校正与Stacking集成模型相结合的无人机高光谱土壤盐分反演方法，验证精度显著提升（Rp²=0.87），为盐渍化农田精准管理提供了可落地的技术方案，专业深度与信息增量均较突出。",[136],{"name":128,"url":125},[27,138,28,29,139],"无人机","土壤盐渍化",[141,142],"土壤盐渍化 智慧农业 精准农业 遥感监测","土壤盐渍化 智慧农业","土壤盐渍化智慧农业精准农业遥感监测-2664","10.3390\u002Fagronomy16181812",{"doi":144,"openalex_id":146,"authors":147,"venue":128,"cited_by_count":36,"oa_url":125,"card":170,"direction":174,"ingested_from":62},"W7213244904",[148,150,152,154,156,158,160,162,165,167],{"name":149,"orcid":9},"Haiye Yu",{"name":151,"orcid":9},"Muyan Yu",{"name":153,"orcid":9},"Ranzhe Jiang",{"name":155,"orcid":9},"Xin Zhang",{"name":157,"orcid":9},"Zhu Guo",{"name":159,"orcid":9},"Yaohui Fu",{"name":161,"orcid":9},"Xingbang Liu",{"name":163,"orcid":164},"Xingyu Sun","https:\u002F\u002Forcid.org\u002F0009-0008-8684-498X",{"name":166,"orcid":9},"Bingze Li",{"name":168,"orcid":169},"Yuanyuan Sui","https:\u002F\u002Forcid.org\u002F0000-0003-4849-9077",{"tldr":171,"method":172,"finding":173,"direction":174,"opportunity":175},"用无人机高光谱结合水分校正与Stacking集成模型反演土壤盐分。","六种光谱变换与KNN\u002FSVR\u002FMLP及Stacking，对比XGBoost、RF","FDR+OSC-Stacking最优，Rp²=0.87，OSC比PDS更能消除水分干扰。","农业遥感与作物表型","可探索水分校正与深度学习结合、多时相无人机盐分动态监测及跨区域迁移。","2026-09-16T23:30:29.016721Z",{"id":178,"title":179,"url":180,"summary":181,"summary_zh":182,"content":9,"source_name":128,"source_url":180,"published_at":183,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":184,"score_detail":185,"sources":188,"tags":190,"search_phrases":192,"slug":195,"view_count":36,"doi":196,"paper":197,"created_at":214},2538,"Multitemporal UAV-Based Estimation of Kenaf (Hibiscus cannabinus L.) Plant Height Under Nitrogen and Compost Treatments","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fagronomy16181805","This study aimed to evaluate the growth responses of kenaf (Hibiscus cannabinus L.) under nitrogen and compost treatments and to develop a UAV-based plant height estimation model. Ground-measured plant height differences were not significant at harvest (110 days after sowing, DAS), highlighting the need for multitemporal monitoring. Multispectral drone imagery was acquired at five growth stages (20–110 DAS). Object-based image segmentation was applied to extract pure vegetation areas, and digital surface model differencing (ΔDSMt) was used to reduce micro-topographic effects. A UAV-based multiple linear regression (UAV-MLR) model was developed using ΔDSMt, NDVI, GNDVI, and NGRDI to integrate complementary structural and spectral information. Evaluated on the calibration dataset, the UAV-MLR model demonstrated high fitting performance (adjusted R2 = 0.9858, RMSE = 14.95 cm, MAE = 11.31 cm), outperforming the ground-based simple linear regression (G-SLR) model based on stem diameter (adjusted R2 = 0.9615, RMSE = 39.68 cm, MAE = 29.16 cm). By integrating structural and spectral information, the proposed approach reduced RMSE by 62.3%, offering a highly accurate, non-destructive tool for crop monitoring and precision agriculture.","本研究旨在评估在氮肥和堆肥处理下洋麻（Hibiscus cannabinus L.）的生长响应，并构建基于无人机（UAV）的株高估测模型。在收获期（播种后110天，DAS），地面实测株高差异不显著，凸显了多时相监测的必要性。在5个生长阶段（20–110 DAS）获取了多光谱无人机影像。采用基于对象的图像分割提取纯植被区域，并利用数字表面模型差值（ΔDSMt）以降低微地形效应。利用ΔDSMt、NDVI、GNDVI和NGRDI构建了基于无人机的多元线性回归（UAV-MLR）模型，以整合互补的结构与光谱信息。在标定数据集上评估，UAV-MLR模型表现出较高的拟合性能（调整R² = 0.9858，RMSE = 14.95 cm，MAE = 11.31 cm），优于基于茎粗的地面简单线性回归（G-SLR）模型（调整R² = 0.9615，RMSE = 39.68 cm，MAE = 29.16 cm）。通过整合结构与光谱信息，所提方法将RMSE降低了62.3%，为作物监测和精准农业提供了一种高精度、非破坏性工具。","2026-09-14T00:00:00Z",76,{"impact":186,"substance":133,"depth":77,"authority":20,"freshness":21,"relevant":22,"comment":187},15,"该研究提出融合结构与光谱信息的无人机多时相株高估测模型，精度显著优于地面模型，对作物无损监测与精准农业具有实质参考价值。",[189],{"name":128,"url":180},[27,138,28,29,191],"作物长势",[193,194],"作物长势 智慧农业 精准农业 遥感监测","作物长势 智慧农业","作物长势智慧农业精准农业遥感监测-2538","10.3390\u002Fagronomy16181805",{"doi":196,"openalex_id":198,"authors":199,"venue":128,"cited_by_count":36,"oa_url":180,"card":209,"direction":61,"ingested_from":62},"W7213116925",[200,203,206],{"name":201,"orcid":202},"TaekJin Yoon","https:\u002F\u002Forcid.org\u002F0009-0003-6507-8415",{"name":204,"orcid":205},"Tae Wan Kim","https:\u002F\u002Forcid.org\u002F0000-0002-1742-1982",{"name":207,"orcid":208},"Sung Yung Yoo","https:\u002F\u002Forcid.org\u002F0000-0002-7889-3924",{"tldr":210,"method":211,"finding":212,"direction":174,"opportunity":213},"用多时相无人机影像估算氮肥与堆肥处理下红麻株高，构建高精度回归模型。","五期多光谱无人机影像，对象分割提取植被，ΔDSMt结合NDVI等建多元线性回归。","UAV-MLR模型拟合优度达0.9858，RMSE比地面模型降低62.3%。","可推广至其他纤维\u002F能源作物，并融合机器学习与多源遥感提升跨生育期泛化能力。","2026-09-15T23:30:26.678242Z",{"id":216,"title":217,"url":218,"summary":219,"summary_zh":220,"content":9,"source_name":221,"source_url":218,"published_at":222,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":184,"score_detail":223,"sources":226,"tags":228,"search_phrases":230,"slug":233,"view_count":36,"doi":234,"paper":235,"created_at":250},2151,"An adaptive deep learning framework for multi-temporal crop and drought stress monitoring in precision agriculture","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-70304-z","Precision agriculture increasingly relies on data-driven methods to address the challenges of crop stress and drought monitoring under changing climatic conditions. Hence, a deep learning (DL) based innovative framework is required for assessing crop stress dynamics, aimed at improving agricultural productivity and ensuring regional food security, because conventional models often struggle to capture subtle spatial and spectral variations, resulting in limited accuracy and generalizability. To overcome these challenges, an innovative and adaptive DL-based approach has been proposed to utilize multi-temporal Sentinel-2 satellite images collected over Haldharmau village, Gonda District, Uttar Pradesh. The model effectively integrates local spectral and spatial details with broader contextual features, enabling crop classification and stress detection to support precision agriculture. The crop stress and drought maps have been generated with the help of a CNN+ViT model (i.e., A hybrid DL-based classification model) based classified images in conjunction with NDVI and NDWI images to evaluate drought-induced crop variability across 2023, 2024, and 2025. The monthly (January to June) stress maps derived from this process provide valuable insights into temporal patterns of crop exposure. The proposed framework offers quantitative insights into temporal and spatial patterns of crop vulnerability and resilience across semi‐arid agricultural landscapes. Therefore, the proposed framework advances precision agriculture modeling by integrating satellite images, deep learning, and phenological analysis, and is readily transferable to other drought-prone regions with analogous crop systems.","精准农业日益依赖数据驱动方法来应对气候变化条件下作物胁迫与干旱监测的挑战。因此，需要一种基于深度学习（DL）的创新框架来评估作物胁迫动态，以提高农业生产力并保障区域粮食安全，因为传统模型往往难以捕捉细微的空间和光谱变化，导致精度和泛化能力有限。为克服这些挑战，提出了一种创新且自适应的基于深度学习的方法，利用在北方邦戈达县哈尔达毛村采集的多时相Sentinel-2卫星影像。该模型有效整合了局部光谱与空间细节以及更广泛的上下文特征，实现了作物分类和胁迫检测，以支持精准农业。作物胁迫和干旱地图借助CNN+ViT模型（即基于混合深度学习的分类模型）生成的分类影像，结合NDVI和NDWI影像，评估了2023年、2024年和2025年干旱引起的作物变异性。通过该过程得到的逐月（1月至6月）胁迫地图为作物暴露的时间格局提供了有价值的见解。所提出的框架为半干旱农业景观中作物脆弱性和恢复力的时空格局提供了定量认识。因此，该框架通过整合卫星影像、深度学习和物候分析，推进了精准农业建模，并可随时推广至具有类似作物系统的其他干旱易发地区。","Scientific Reports","2026-09-09T00:00:00Z",{"impact":132,"substance":18,"depth":19,"authority":224,"freshness":21,"relevant":22,"comment":225},14,"基于CNN+ViT混合模型与Sentinel-2多时相影像的作物分类与干旱胁迫监测框架，方法新颖、数据跨三年，对旱区精准农业有参考价值。",[227],{"name":221,"url":218},[27,84,28,29,229],"作物干旱胁迫",[231,232],"作物干旱胁迫 农业人工智能 智慧农业 精准农业","作物干旱胁迫 农业人工智能","作物干旱胁迫农业人工智能智慧农业精准农业-2151","10.1038\u002Fs41598-026-70304-z",{"doi":234,"openalex_id":236,"authors":237,"venue":221,"cited_by_count":36,"oa_url":244,"card":245,"direction":59,"ingested_from":62},"W7212034680",[238,241],{"name":239,"orcid":240},"Gausiya Yasmeen","https:\u002F\u002Forcid.org\u002F0000-0002-7853-1376",{"name":242,"orcid":243},"Tasneem Ahmed","https:\u002F\u002Forcid.org\u002F0000-0003-2702-3168","https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41598-026-70304-z_reference.pdf",{"tldr":246,"method":247,"finding":248,"direction":174,"opportunity":249},"提出CNN+ViT混合深度学习框架，利用多时相Sentinel-2影像监测作物分类与干旱胁迫。","CNN+ViT混合模型结合NDVI、NDWI，基于Sentinel-2多时相影像","框架可量化半干旱区作物时空脆弱性与恢复力，生成月度胁迫图。","可探索多源遥感与时序模型融合，提升跨区域干旱胁迫预警的泛化能力。","2026-09-11T23:30:13.461760Z",{"id":252,"title":253,"url":254,"summary":255,"summary_zh":256,"content":9,"source_name":257,"source_url":254,"published_at":258,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":259,"score_detail":260,"sources":262,"tags":264,"search_phrases":267,"slug":270,"view_count":36,"doi":271,"paper":272,"created_at":288},2122,"Heterogeneous behaviours towards precision agriculture adoption among Italian winegrowers: insights from latent class analysis","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11119-026-10448-0","Abstract Purpose Precision Agriculture technologies including satellite data, drones, field robots and automatic guidance, are increasingly promoted as tools to improve sustainability, competitiveness, and environmental efficiency in viticulture. However, the adoption of these practices among Italian winegrowers exhibits significant heterogeneity, influenced by a combination of structural and behavioural factors. This study aims to identify distinct groups of Italian winegrowers based on their intentions to adopt PATs, examining how these intentions relate to four behavioural constructs that capture cognitive, social, and risk-related influences. Method The present study integrates a profile-based perspective by estimating a latent-class generalised structural equation model on 272 Italian winegrowers. The model of class membership is predicated upon a multifaceted conceptualisation encompassing risk perception, risk tolerance, perceived ease of use, and subjective norms and the technology-specific five-year intentions are incorporated as class-specific probit intercepts. Results Three qualitatively distinct profiles emerge: risk-sensitive sceptics, selective pragmatists and usability-oriented high adopters with sharply different adoption patterns across technologies. Participation in rural development schemes is the strongest predictor of membership in the higher-propensity latent classes, namely the selective-pragmatist and usability-oriented high-adopter profiles. In addition, familiarity with PATs, male gender and participation in consortium are positively associated with the higher-propensity class, while age shows a weak negative correlation. Conclusion These results inform the design of extension and policy for specific segments, prioritising risk-mitigation trials for cautious growers, targeted information services for select profiles, and integrated PATs bundles for those with a high propensity.","摘要 目的 精准农业技术（Precision Agriculture Technologies，PATs），包括卫星数据、无人机、田间机器人和自动导航，日益被视为提升葡萄栽培可持续性、竞争力和环境效率的工具。然而，意大利葡萄种植者对上述技术的采纳呈现出显著异质性，受到结构性因素与行为因素的共同影响。本研究旨在基于意大利葡萄种植者采纳PATs的意愿识别不同群体，并考察这些意愿如何与四个行为构念相关联，这四个构念分别捕捉认知、社会和风险相关的影响。方法 本研究整合了基于剖面的视角，对272名意大利葡萄种植者估计了潜在类别广义结构方程模型。类别归属模型建立在涵盖风险感知、风险容忍度、感知易用性和主观规范的多维概念化基础之上，并将技术特定的五年意愿作为类别特定的probit截距纳入模型。结果 研究识别出三种性质不同的剖面：风险敏感型怀疑者、选择性实用主义者和以易用性为导向的高采纳者，其在不同技术上的采纳模式差异显著。参与农村发展计划是归属于较高倾向潜在类别（即选择性实用主义者和以易用性为导向的高采纳者剖面）的最强预测因素。此外，对PATs的熟悉程度、男性性别和参与合作社与较高倾向类别呈正相关，而年龄则表现出较弱的负相关。结论 上述结果为针对特定群体的推广和政策设计提供了依据，应优先为谨慎型种植者开展风险缓解试验，为特定剖面提供有针对性的信息服务，并为高倾向群体提供整合的PATs技术组合。","Precision Agriculture","2026-09-10T00:00:00Z",79,{"impact":132,"substance":133,"depth":77,"authority":224,"freshness":79,"relevant":22,"comment":261},"基于272户意大利葡萄种植者的潜类别分析，揭示精准农业采纳的异质性行为分群，对农户分类推广与政策设计有实质参考价值。",[263],{"name":257,"url":254},[27,265,28,29,266],"农户采纳","葡萄种植",[268,269],"农户采纳 智慧农业 精准农业 葡萄种植","农户采纳 智慧农业","农户采纳智慧农业精准农业葡萄种植-2122","10.1007\u002Fs11119-026-10448-0",{"doi":271,"openalex_id":273,"authors":274,"venue":257,"cited_by_count":36,"oa_url":254,"card":283,"direction":61,"ingested_from":62},"W7212151394",[275,277,280],{"name":276,"orcid":9},"Adriano Biondo",{"name":278,"orcid":279},"Antonino Galati","https:\u002F\u002Forcid.org\u002F0000-0003-0753-2934",{"name":281,"orcid":282},"Francesco Caracciolo","https:\u002F\u002Forcid.org\u002F0000-0001-9430-7529",{"tldr":284,"method":285,"finding":286,"direction":61,"opportunity":287},"基于272名意大利葡萄种植者，用潜类别分析识别精准农业技术采纳意向的异质性群体。","潜类别广义结构方程模型，纳入风险感知、风险容忍、易用性和主观规范。","分出风险敏感怀疑者、选择性实用主义者和易用性高采纳者三类，参与农村发展计划是最强预测因素。","可针对不同农户群体设计差异化推广策略，并研究政策参与如何通过行为路径影响技术采纳。","2026-09-11T23:30:02.943184Z",{"id":290,"title":291,"url":292,"summary":293,"summary_zh":9,"content":9,"source_name":294,"source_url":9,"published_at":129,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":295,"score_detail":296,"sources":298,"tags":300,"search_phrases":304,"slug":307,"view_count":36,"doi":9,"paper":308,"created_at":316},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",74,{"impact":186,"substance":133,"depth":77,"authority":20,"freshness":65,"relevant":22,"comment":297},"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。",[299],{"name":294,"url":292},[27,28,301,302,303],"遥感","作物表型","春小麦",[305,306],"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":9,"openalex_id":9,"authors":309,"venue":9,"cited_by_count":36,"oa_url":9,"card":310,"direction":174,"ingested_from":315},[],{"tldr":311,"method":312,"finding":313,"direction":174,"opportunity":314},"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。","agent","2026-09-20T00:03:08.168753Z"]