[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3255":3,"related-3255":90},{"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":64,"slug":67,"view_count":53,"doi":68,"paper":69,"created_at":89},3255,"Grape yield estimation using on-the-fly MIMO millimeter-wave radar at operational field speed: A variety-dependent proof of concept","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112395","Accurate pre-harvest grape yield estimation is a critical challenge in precision viticulture, yet current optical approaches (RGB cameras, LiDAR) are fundamentally limited by foliage occlusion and sensitivity to ambient lighting conditions. This paper presents a ground-based grape yield estimation system using a dual 77 GHz Multiple-Input Multiple-Output (MIMO) Frequency-Modulated Continuous-Wave (FM-CW) radar mounted on a rover operating at an average speed of 1.0 m\u002Fs (3.7 km\u002Fh), matching practical vineyard tractor speeds. Unlike prior radar-based studies conducted in static or slow-moving setups, the proposed pipeline achieves continuous 3D reconstruction of vinerows through digital beamforming in the elevation plane, combined with an IMU-fused Kalman filter for point cloud stabilization on irregular terrain. An experimental campaign was conducted on 136 km of scanned vinerows across seven grape varieties and three phenological growth stages (BBCH 75–89) in a commercial vineyard in Gascony, France. Statistical features extracted from 3D radar echo level images are used to train a Support Vector Regressor (SVR) evaluated in cross-validation. The primary out-of-sample result is a cross-validation Mean Absolute Error (CV MAE) of 31.2% on the full multi-variety dataset without any defoliation, achieved against certified wine-vat bulk weights as the ground truth reference. This figure should be contextualized against the ∼ 24–25% discrepancy observed between portable field weighing plates and vat-certified weights across the same dataset, and against manual pre-harvest estimation errors routinely exceeding 30% in viticultural practice. A sensitivity analysis over physical parameters reveals that grape variety is the dominant −and limiting- source of performance heterogeneity: CV MAE ranges from 7% for Tannat to 69% for Baco, demonstrating that a single global model is insufficient for multi-variety deployment and that variety-specific calibration is a necessary condition for operational use. These results are best understood as an architectural proof of concept: the primary contribution is the demonstration that MIMO digital beamforming removes the speed bottleneck of prior mechanically-scanned radar systems, enabling gapless 3D vinerow reconstruction at practical field speeds. The yield estimation results provide a first quantitative characterization of the system’s sensing capability and its variety-dependent limitations and establish a baseline for future variety-specific model development.","准确的采前葡萄产量估测是精准葡萄栽培中的一项关键挑战，然而当前的光学方法（RGB相机、LiDAR）从根本上受到冠层遮挡和对环境光照条件敏感性的限制。本文提出了一种地基葡萄产量估测系统，采用双77 GHz多输入多输出（MIMO）调频连续波（FM-CW）雷达，搭载于以平均速度1.0 m\u002Fs（3.7 km\u002Fh）行驶的移动平台上，与实际葡萄园拖拉机作业速度相匹配。与先前在静态或慢速移动装置中进行的雷达研究不同，所提出的处理流程通过仰角平面上的数字波束成形实现葡萄行连续三维重建，并结合IMU融合卡尔曼滤波器在不规则地形上实现点云稳定。在法国加斯科尼的一个商业葡萄园中，对七个葡萄品种和三个物候生长期（BBCH 75–89）的136 km扫描葡萄行进行了实验。从三维雷达回波强度图像中提取的统计特征用于训练支持向量回归器（SVR），并通过交叉验证进行评估。主要样本外结果为：在完整多品种数据集上、未进行任何去叶处理的情况下，交叉验证平均绝对误差（CV MAE）为31.2%，以认证酒罐散装重量作为地面真值参考。该数值应结合以下背景加以理解：在同一数据集上，便携式田间称重板与酒罐认证重量之间观察到约24–25%的差异，而葡萄栽培实践中人工采前估测误差通常超过30%。对物理参数的敏感性分析表明，葡萄品种是性能异质性的主要——也是限制性——来源：CV MAE从Tannat的7%到Baco的69%不等，表明单一全局模型不足以支持多品种部署，品种特异性校准是实际应用的必要条件。这些结果最好被理解为一种架构层面的概念验证：主要贡献在于证明了MIMO数字波束成形消除了先前机械扫描雷达系统的速度瓶颈，从而能够在实际田间速度下实现无间隙三维葡萄行重建。产量估测结果首次定量表征了该系统的感知能力及其品种依赖性局限，并为未来",null,"Computers and Electronics in Agriculture","2026-09-23T00:00:00Z","论文",10,false,84,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":13,"relevant":21,"comment":22},18,23,19,14,1,"首次在实用田间车速下用MIMO毫米波雷达实现葡萄产量预估，136公里商业园数据与品种依赖性结论具参考价值。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","精准农业","葡萄","产量预估","毫米波雷达",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"B2","fruit","果树","\u002Ftopic\u002Fhorticulture\u002Ffruit","B",[],{"status":39,"locked":14,"version":40,"method":41,"evidence":42},"classified","2026-10-22.2","rules",{"candidates":43,"agricultural_evidence":46},[44,51,58],{"code":32,"score":45,"supported":46,"title":47,"abstract":49,"tags":50},15,true,[48],"grape",[28,48],[28],{"code":52,"score":53,"supported":46,"title":54,"abstract":55,"tags":57},"F5",3,[],[56],"tractor",[],{"code":59,"score":60,"supported":14,"title":61,"abstract":62,"tags":63},"I1",2,[],[],[26,27],[65,66],"MIMO 毫米波雷达 葡萄 产量预估","葡萄园 雷达 三维重建","MIMO毫米波雷达葡萄产量预估-3255","10.1016\u002Fj.compag.2026.112395",{"doi":68,"openalex_id":70,"authors":71,"venue":10,"cited_by_count":81,"oa_url":6,"card":82,"direction":86,"ingested_from":88},"W7214043753",[72,75,78],{"name":73,"orcid":74},"Etienne Dedic","https:\u002F\u002Forcid.org\u002F0000-0002-1414-019X",{"name":76,"orcid":77},"Dominique Henry","https:\u002F\u002Forcid.org\u002F0000-0002-6016-140X",{"name":79,"orcid":80},"H. Aubert","https:\u002F\u002Forcid.org\u002F0000-0002-6113-0648",0,{"tldr":83,"method":84,"finding":85,"direction":86,"opportunity":87},"用双77GHz MIMO雷达在1m\u002Fs车速下重建葡萄藤行并估算产量，验证品种依赖性。","双77GHz MIMO FMCW雷达、数字波束成形、IMU卡尔曼滤波、SVR，1","全品种交叉验证MAE为31.2%，但品种间差异大（Tannat 7%至Baco 69%），需按品种校","农业遥感与作物表型","可研究品种自适应建模与迁移学习，解决多品种部署时单一模型失效问题，并融合光学与雷达数据。","openalex","2026-09-23T23:30:01.516727Z",{"total":91,"page":21,"page_size":91,"items":92},6,[93,138,207,258,333,402],{"id":94,"title":95,"url":96,"summary":97,"summary_zh":9,"content":98,"source_name":99,"source_url":9,"published_at":100,"category":101,"cover_url":9,"hotness":13,"is_selected":14,"score":102,"score_detail":103,"sources":108,"tags":110,"primary_topic":114,"secondary_topics":115,"classification":116,"search_phrases":133,"slug":136,"view_count":81,"doi":9,"paper":9,"created_at":137},5516,"上海农科院葡萄团队赴银川参加第47届世界葡萄与葡萄酒大会","https:\u002F\u002Fwww.saas.sh.cn\u002Fhzjl\u002Fgjhz\u002Fcontent_57128","10月12日至16日第47届世界葡萄与葡萄酒大会在银川举办，智慧农业研究中心葡萄团队负责人奚晓军研究员作专题报告，阐明疏穗与环剥处理对果实糖、花青素及脱落酸积累的源库调控机制。团队还观摩酿酒葡萄AI综合作业机器人成果展示，与研发单位围绕葡萄智慧装备与果园智能化管理深入交流。","2026年10月12-16日，第47届世界葡萄与葡萄酒大会在银川举办，智慧农业研究中心葡萄团队负责人奚晓军研究员一行赴会交流。团队围绕“提升葡萄种植生产韧性与可持续发展能力”主题，向全球同行展示最新科研成果。\n\n会上，奚晓军作“Changes in sugar, anthocyanin and ABA accumulation in response to cluster thinning and girdling in ‘Jumeigui’grape”专题报告。报告围绕葡萄疏穗与环剥处理开展研究，阐明源库比对果实糖、花青素及脱落酸积累的调控机制，为葡萄优质栽培技术提供理论支撑，报告内容引发国内外园艺专家广泛研讨。参会期间，团队前往银川国际会展中心观摩酿酒葡萄 AI 综合作业机器人成果展示，详细了解该装备在葡萄园智能作业方面的技术特点与应用前景，并与研发单位围绕葡萄智慧装备研发、果园智能化管理等方向开展深入交流。\n\n本次参会是葡萄团队持续推进科研国际化、对标国际前沿开展研究的重要实践。","上海市农业科学院 2026-10-19","2026-10-19T00:00:00Z","报道",65,{"impact":104,"substance":105,"depth":106,"authority":106,"freshness":91,"relevant":21,"comment":107},16,17,13,"国际学术会议交流并涉及葡萄AI作业机器人观摩，有实质科研内容与行业参考价值，但属团队参会动态，影响面有限。",[109],{"name":99,"url":96},[26,111,28,112,113],"智能农机","栽培技术","国际交流",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":117},{"candidates":118,"agricultural_evidence":46},[119,124,129],{"code":32,"score":120,"supported":46,"title":121,"abstract":122,"tags":123},12,[28],[28],[28],{"code":59,"score":125,"supported":46,"title":126,"abstract":127,"tags":128},4,[],[26],[26],{"code":52,"score":21,"supported":14,"title":130,"abstract":131,"tags":132},[],[],[111],[134,135],"上海农科院 葡萄 银川","世界葡萄与葡萄酒大会 奚晓军","上海农科院葡萄银川-5516","2026-10-22T00:07:04.061911Z",{"id":139,"title":140,"url":141,"summary":142,"summary_zh":143,"content":9,"source_name":144,"source_url":141,"published_at":145,"category":12,"cover_url":9,"hotness":146,"is_selected":14,"score":147,"score_detail":148,"sources":151,"tags":155,"primary_topic":159,"secondary_topics":160,"classification":167,"search_phrases":187,"slug":190,"view_count":21,"doi":191,"paper":192,"created_at":206},4241,"Detection of Apple Plant Diseases Using Leaf Images through Convolutional Neural Network","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23142591","Plant diseases have major social, ecological and economic impact on agriculture and frequently results in large financial losses for farmers. Therefore early and precise disease detection is crucial for efficient crop management. The severity of leaf diseases and their direct impact on yield quality have been highlighted in recent studies. Both digital cameras and flatbed scanners can be used to take leaf images but they have certain drawbacks. Although scanners offer a more secure and regulated method of acquiring images they are comparatively slow and may cause edge shadows. This study uses a hybrid deep learning approach to identify diseases in apple plant leaves. To improve classification performance; the proposed approach combines the advantages of Residual Networks (ResNet), Genetic Algorithms (GA) and Convolutional Neural Networks (CNN). CNNs are used to extract features and classify diseases whereas ResNet enhances the learning efficiency of deeper networks. The feature set is optimized using a genetic algorithm which improves accuracy and lowers computational complexity. The model also calculates the percentage of the leaf area that is impacted. According to experimental results the proposed method detects leaf diseases with high accuracy of percentage and low computational cost making it a dependable and effective precision agriculture solution. The proposed model exhibited strong classification efficiency across various leaf conditions that pertaining an accuracy of 0.97 for healthy leaves, 0.994 for apple scab, 0.89 for rust, and 0.91 for powdery mildew.","植物病害对农业产生重大的社会、生态和经济影响，并经常给农民带来巨大的经济损失。因此，早期且精确的病害检测对于高效的作物管理至关重要。近期研究强调了叶部病害的严重程度及其对产量品质的直接影响。数码相机和平板扫描仪均可用于拍摄叶片图像，但它们各有一定缺陷。尽管扫描仪提供了更安全、更可控的图像获取方式，但其速度相对较慢，且可能产生边缘阴影。本研究采用混合深度学习方法识别苹果植株叶部病害。为提高分类性能，所提方法结合了残差网络（ResNet）、遗传算法（GA）和卷积神经网络（CNN）的优势。CNN用于提取特征并分类病害，而ResNet增强了更深网络的学习效率。利用遗传算法对特征集进行优化，从而提高准确率并降低计算复杂度。该模型还计算了受影响的叶面积百分比。实验结果表明，所提方法以高准确率和低计算成本检测叶部病害，使其成为可靠且有效的精准农业解决方案。所提模型在多种叶片条件下均表现出较强的分类效率，健康叶片准确率为0.97，苹果黑星病为0.994，锈病为0.89，白粉病为0.91。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-04T00:00:00Z",25,67,{"impact":120,"substance":17,"depth":104,"authority":120,"freshness":149,"relevant":21,"comment":150},9,"基于ResNet+GA+CNN的苹果叶病识别研究，准确率数据具体，方法有增量，但属实验室论文，产业影响有限。",[152,153],{"name":144,"url":141},{"name":144,"url":154},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23142592",[26,156,157,27,158],"农业人工智能","深度学习","苹果病害",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[161],{"code":162,"slug":163,"name":164,"path":165,"parent_code":166},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"status":39,"locked":14,"version":40,"method":41,"evidence":168},{"candidates":169,"agricultural_evidence":46},[170,176,182],{"code":32,"score":45,"supported":46,"title":171,"abstract":173,"tags":175},[172],"apple",[174,172],"苹果",[158],{"code":59,"score":177,"supported":46,"title":178,"abstract":179,"tags":181},8,[],[27,180],"precision agriculture",[26,27],{"code":162,"score":177,"supported":46,"title":183,"abstract":184,"tags":186},[],[157,185],"deep learning",[156,157],[188,189],"苹果叶片病害 卷积神经网络","苹果锈病 白粉病 识别","苹果叶片病害卷积神经网络-4241","10.5281\u002Fzenodo.23142591",{"doi":191,"openalex_id":193,"authors":194,"venue":144,"cited_by_count":81,"oa_url":141,"card":199,"direction":205,"ingested_from":88},"W7219637192",[195,197],{"name":196,"orcid":9},"Mohd Arshad Raza",{"name":198,"orcid":9},"Kiran Pandey",{"tldr":200,"method":201,"finding":202,"direction":203,"opportunity":204},"提出ResNet+GA+CNN混合模型，用苹果叶片图像检测病害并计算病斑面积占比。","ResNet提取深层特征，遗传算法优化特征集，CNN分类，苹果叶片图像数据。","健康叶、黑星病、锈病、白粉病准确率分别达0.97、0.994、0.89、0.91，计算成本低。","农业人工智能与决策模型","可探索轻量化模型在田间移动端实时检测，并融合多病害严重度分级与产量损失预测。","数字乡村与农业信息化","2026-10-05T23:30:30.830366Z",{"id":208,"title":209,"url":210,"summary":211,"summary_zh":212,"content":9,"source_name":213,"source_url":210,"published_at":214,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":215,"score_detail":216,"sources":219,"tags":221,"primary_topic":223,"secondary_topics":224,"classification":225,"search_phrases":240,"slug":243,"view_count":53,"doi":244,"paper":245,"created_at":257},3019,"PSPE-UNet: Projection-based Similarity Prototype Embedding UNet for Apple Leaf Disease Segmentation","https:\u002F\u002Fdoi.org\u002F10.22266\u002Fijies2026.1031.18","Apple leaf disease segmentation plays a significant role in precision agriculture by enabling the accurate identification and localization of infected regions at the pixel level.However, diverse apple leaf diseases exhibit similar symptoms, such as overlapping lesions makes it challenging to distinguish between various disease classes.In this research, a Projection-based Similarity Prototype Embedding UNet (PSPE-UNet) is proposed to segment apple leaf diseases.Employing a projection head with a similarity prototype embedding in UNet enhances feature discrimination by mapping pixel-level representations into a normalized embedding space.This ensures better separation between healthy and disease regions, even when the regions exhibit similar texture and chromatic characteristics.Three learnable prototypes corresponding to healthy, disease, and boundary regions are used.The boundary prototype act as learnable auxiliary feature prototype within the auxiliary boundary branch to compute boundary probability map during training while disease prediction is based on healthy and disease prototypes.In addition, this method enhances the boundary delineation for irregular and small lesions by refining the feature alignment.Hence, the proposed PSPE-UNet achieves a high Pixel Accuracy (PA) of 98.96%, which is compared to existing methods such as the AS-DeepLabV3+ on the Apple Tree Leaf Disease Segmentation Dataset (ATLDSD).Moreover, proposed PSPE-UNet obtains an inference time of 0.0217s per batch (8 images), corresponding to 0.0027s per image on ATLDSD dataset compared to traditional methods like UNet.","苹果叶片病害分割在精准农业中具有重要意义，能够在像素级别上准确识别和定位感染区域。然而，不同苹果叶片病害表现出相似的症状，例如病灶重叠使得区分不同病害类别具有挑战性。本研究提出了一种基于投影的相似性原型嵌入UNet（PSPE-UNet）用于苹果叶片病害分割。在UNet中采用带有相似性原型嵌入的投影头，通过将像素级表示映射到归一化嵌入空间来增强特征判别能力。这确保了健康和病害区域之间更好的分离，即使这些区域表现出相似的纹理和色彩特征。使用三个可学习原型分别对应健康、病害和边界区域。边界原型在辅助边界分支中作为可学习辅助特征原型，在训练期间计算边界概率图，而病害预测则基于健康和病害原型。此外，该方法通过细化特征对齐增强了对不规则和小病灶的边界描绘。因此，所提出的PSPE-UNet在苹果树叶病害分割数据集（ATLDSD）上达到了98.96%的高像素精度（PA），并与现有方法如AS-DeepLabV3+进行了比较。此外，所提出的PSPE-UNet在ATLDSD数据集上获得了每批次（8张图像）0.0217秒的推理时间，相当于每张图像0.0027秒，与UNet等传统方法相比具有优势。","International journal of intelligent engineering and systems","2026-09-19T00:00:00Z",70,{"impact":120,"substance":217,"depth":105,"authority":120,"freshness":149,"relevant":21,"comment":218},20,"提出基于相似度原型嵌入的UNet分割方法，在苹果叶病害数据集上取得98.96%像素精度，方法新颖、数据明确，但属细分算法研究，产业影响有限。",[220],{"name":213,"url":210},[26,156,27,222,158],"图像分割",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":226},{"candidates":227,"agricultural_evidence":46},[228,232,236],{"code":32,"score":17,"supported":46,"title":229,"abstract":230,"tags":231},[172],[34,174,172],[158],{"code":59,"score":177,"supported":46,"title":233,"abstract":234,"tags":235},[],[27,180],[26,27],{"code":162,"score":60,"supported":14,"title":237,"abstract":238,"tags":239},[],[],[156,222],[241,242],"PSPE-UNet 苹果叶病害 分割","苹果叶病害 数据集 ATLDSD","PSPE-UNet苹果叶病害分割-3019","10.22266\u002Fijies2026.1031.18",{"doi":244,"openalex_id":246,"authors":247,"venue":213,"cited_by_count":81,"oa_url":210,"card":252,"direction":203,"ingested_from":88},"W7213634285",[248,250],{"name":249,"orcid":9},"Vedamurthy Hadavanahalli Kumaraiah",{"name":251,"orcid":9},"Shrinivasacharya Purohit",{"tldr":253,"method":254,"finding":255,"direction":203,"opportunity":256},"提出PSPE-UNet，用投影相似原型嵌入分割苹果叶片病害区域。","UNet+投影头+可学习原型（健康\u002F病害\u002F边界），ATLDSD数据集。","像素精度98.96%，单图推理0.0027秒，优于AS-DeepLabV3+和UNet。","可探索原型嵌入在相似症状多病害区分及轻量化边缘部署中的泛化能力。","2026-09-20T23:30:34.933307Z",{"id":259,"title":260,"url":261,"summary":262,"summary_zh":263,"content":9,"source_name":264,"source_url":261,"published_at":265,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":266,"score_detail":267,"sources":269,"tags":271,"primary_topic":274,"secondary_topics":275,"classification":281,"search_phrases":312,"slug":315,"view_count":60,"doi":316,"paper":317,"created_at":332},2874,"Laboratory Evaluation of Multispectral Grape Quality Grade Prediction with Compensation for Controlled Illumination Variation","https:\u002F\u002Fdoi.org\u002F10.1088\u002F2631-8695\u002Faea952","Abstract Accurate fruit quality assessment is important for selective harvesting in agricultural robotics, where multispectral sensing provides a non-destructive approach but remains sen- sitive to variability in illumination conditions. This study presents a controlled laboratory evaluation conducted in a dark box under halogen illumination with fixed sensor and sam- ple geometry, in which illumination variation was simulated by adjusting lamp intensity. Prediction models for visually assigned grape quality grades were first established under fixed illumination using an AS7265x multispectral sensor with 18 channels from 410 to 940 nm, covering the visible and part of the near-infrared range. Spectral data were prepro- cessed using standard normal variate transformation, standardization, variable importance in projection feature selection, and mixup interpolation. Three machine learning meth- ods, partial least squares regression, support vector regression, and random forest, were evaluated with nested cross-validation, out-of-fold prediction, and 2000-iteration bootstrap resampling. Support vector regression achieved the best performance, with R² = 0.9084 [95% CI: 0.851–0.945], RMSE = 0.3352, and MAE = 0.2374. To address illumination vari- ability, three compensation methods, direct standardization, extended multiplicative scatter correction, and a polynomial per-band fitting method, were implemented and compared across five lamp intensity levels from 20% to 100% of rated power. The three methods and the uncompensated condition reached similar mean accuracy across the five levels, with the exception of extended multiplicative scatter correction, which was lower at every level. Di- rect standardization reduced the spread of the five predictions obtained for the same berry by about 31% relative to no compensation, and gave the highest grade-level accuracy of the four conditions. These results provide laboratory feasibility evidence that direct standard- ization can improve the consistency of grade prediction across controlled illumination levels, and establish a basis for further validation under natural field illumination toward future robotic harvesting applications.","摘要 准确的水果品质评估对于农业机器人中的选择性采收至关重要，其中多光谱传感提供了一种非破坏性方法，但仍对光照条件的变化较为敏感。本研究提出了一种在暗箱中、卤素灯照明下、传感器与样品几何构型固定的受控实验室评估方法，通过调节灯强度来模拟光照变化。首先在固定光照条件下，使用AS7265x多光谱传感器（具有410至940 nm范围内的18个通道，覆盖可见光及部分近红外波段）建立了视觉 assigned 葡萄品质等级的预测模型。光谱数据采用标准正态变量变换、标准化、投影变量重要性特征选择以及mixup插值进行预处理。采用嵌套交叉验证、折外预测和2000次迭代自助重采样，对偏最小二乘回归、支持向量回归和随机森林三种机器学习方法进行了评估。支持向量回归取得了最佳性能，R² = 0.9084 [95% CI: 0.851–0.945]，RMSE = 0.3352，MAE = 0.2374。为解决光照变化问题，实施了直接标准化、扩展多元散射校正和逐波段多项式拟合三种补偿方法，并在额定功率20%至100%的五个灯强度水平下进行了比较。三种方法及未补偿条件在五个水平下达到了相似的平均准确率，但扩展多元散射校正除外，其在每个水平下均较低。与未补偿相比，直接标准化使同一浆果五次预测结果的离散度降低了约31%，并在四种条件中给出了最高的等级准确率。这些结果为直接标准化能够提高受控光照水平下等级预测的一致性提供了实验室可行性证据，并为未来机器人采收应用中在自然田间光照下进一步验证奠定了基础。","Engineering Research Express","2026-09-17T00:00:00Z",66,{"impact":177,"substance":217,"depth":105,"authority":106,"freshness":177,"relevant":21,"comment":268},"实验室条件下多光谱葡萄品质分级与光照补偿研究，方法扎实、结论可靠，但属实验室可行性验证，产业影响有限。",[270],{"name":264,"url":261},[26,156,28,272,273],"多光谱检测","水果品质分级",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[276],{"code":277,"slug":278,"name":279,"path":280,"parent_code":166},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"status":39,"locked":14,"version":40,"method":41,"evidence":282},{"candidates":283,"agricultural_evidence":46},[284,288,294,301,308],{"code":32,"score":45,"supported":46,"title":285,"abstract":286,"tags":287},[48],[28,48],[28],{"code":277,"score":289,"supported":46,"title":290,"abstract":292,"tags":293},11,[291],"multispectral",[291],[],{"code":162,"score":295,"supported":46,"title":296,"abstract":297,"tags":300},7,[],[298,299],"机器学习","machine learning",[156],{"code":302,"score":91,"supported":46,"title":303,"abstract":304,"tags":307},"F6",[],[305,306],"农业机器人","robotic harvesting",[],{"code":59,"score":21,"supported":14,"title":309,"abstract":310,"tags":311},[],[],[26],[313,314],"多光谱 葡萄 品质分级","AS7265x 多光谱传感器","多光谱葡萄品质分级-2874","10.1088\u002F2631-8695\u002Faea952",{"doi":316,"openalex_id":318,"authors":319,"venue":264,"cited_by_count":81,"oa_url":261,"card":327,"direction":203,"ingested_from":88},"W7213509882",[320,322,324],{"name":321,"orcid":9},"Guodong Xiu",{"name":323,"orcid":9},"Sára Strakošová",{"name":325,"orcid":326},"Ikuo Mizuuchi","https:\u002F\u002Forcid.org\u002F0000-0003-4657-2613",{"tldr":328,"method":329,"finding":330,"direction":86,"opportunity":331},"在暗箱中评估多光谱葡萄品质分级预测，并比较三种光照补偿方法。","AS7265x多光谱传感器、SNV与VIP预处理、SVR\u002FRF建模、直接标准化等","SVR预测最佳（R²=0.9084）；直接标准化使同果预测离散度降低约31%。","可延伸至自然田间光照下的验证，并融合机器人采摘的实时多光谱分级系统。","2026-09-18T23:30:52.790038Z",{"id":334,"title":335,"url":336,"summary":337,"summary_zh":338,"content":9,"source_name":339,"source_url":336,"published_at":340,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":341,"score_detail":342,"sources":345,"tags":347,"primary_topic":351,"secondary_topics":352,"classification":353,"search_phrases":380,"slug":383,"view_count":81,"doi":384,"paper":385,"created_at":401},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":104,"substance":343,"depth":17,"authority":20,"freshness":149,"relevant":21,"comment":344},22,"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。",[346],{"name":339,"url":336},[26,348,27,349,350],"农户采纳","遥感监测","葡萄种植",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":354},{"candidates":355,"agricultural_evidence":46},[356,360,367,371,376],{"code":59,"score":104,"supported":46,"title":357,"abstract":358,"tags":359},[180],[27,180],[26,27],{"code":361,"score":91,"supported":46,"title":362,"abstract":363,"tags":366},"G",[],[364,365],"农村发展","rural development",[],{"code":32,"score":125,"supported":46,"title":368,"abstract":369,"tags":370},[],[28],[350],{"code":372,"score":21,"supported":14,"title":373,"abstract":374,"tags":375},"G2",[],[],[348],{"code":277,"score":21,"supported":14,"title":377,"abstract":378,"tags":379},[],[],[349],[381,382],"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":384,"openalex_id":386,"authors":387,"venue":339,"cited_by_count":81,"oa_url":336,"card":396,"direction":205,"ingested_from":88},"W7212151394",[388,390,393],{"name":389,"orcid":9},"Adriano Biondo",{"name":391,"orcid":392},"Antonino Galati","https:\u002F\u002Forcid.org\u002F0000-0003-0753-2934",{"name":394,"orcid":395},"Francesco Caracciolo","https:\u002F\u002Forcid.org\u002F0000-0001-9430-7529",{"tldr":397,"method":398,"finding":399,"direction":205,"opportunity":400},"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":403,"title":404,"url":405,"summary":406,"summary_zh":407,"content":9,"source_name":10,"source_url":405,"published_at":408,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":409,"score_detail":410,"sources":412,"tags":414,"primary_topic":416,"secondary_topics":417,"classification":423,"search_phrases":444,"slug":447,"view_count":53,"doi":448,"paper":449,"created_at":474},1278,"ERG-Mask: An edge–region collaborative instance segmentation model for greenhouse table-grape clusters","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112369","ERG-Mask: An edge–region collaborative instance segmentation model for greenhouse table-grape clusters。Computers and Electronics in Agriculture","ERG-Mask：面向温室鲜食葡萄果串的边缘-区域协同实例分割模型。《Computers and Electronics in Agriculture》","2026-08-31T00:00:00Z",62,{"impact":120,"substance":17,"depth":104,"authority":20,"freshness":60,"relevant":21,"comment":411},"针对温室葡萄簇的实例分割新方法，发表于核心期刊，技术有创新性，但影响范围限于细分领域，时效性较低。",[413],{"name":10,"url":405},[26,156,415,28],"设施农业",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[418],{"code":419,"slug":420,"name":421,"path":422,"parent_code":36},"B7","protected-horticulture","设施园艺与植物工厂","\u002Ftopic\u002Fhorticulture\u002Fprotected-horticulture",{"status":39,"locked":14,"version":40,"method":41,"evidence":424},{"candidates":425,"agricultural_evidence":46},[426,430,436,440],{"code":32,"score":45,"supported":46,"title":427,"abstract":428,"tags":429},[48],[28,48],[28],{"code":419,"score":20,"supported":46,"title":431,"abstract":433,"tags":435},[432],"greenhouse",[434,432],"温室",[],{"code":59,"score":21,"supported":14,"title":437,"abstract":438,"tags":439},[],[],[26],{"code":162,"score":21,"supported":14,"title":441,"abstract":442,"tags":443},[],[],[156],[445,446],"农业人工智能 智慧农业 设施农业 葡萄","农业人工智能 智慧农业","农业人工智能智慧农业设施农业葡萄-1278","10.1016\u002Fj.compag.2026.112369",{"doi":448,"openalex_id":450,"authors":451,"venue":10,"cited_by_count":81,"oa_url":9,"card":469,"direction":203,"ingested_from":88},"W7204777248",[452,455,458,461,463,466],{"name":453,"orcid":454},"Zhengtong Ning","https:\u002F\u002Forcid.org\u002F0000-0002-9218-6425",{"name":456,"orcid":457},"Jian Li","https:\u002F\u002Forcid.org\u002F0000-0002-4187-317X",{"name":459,"orcid":460},"Haidong Li","https:\u002F\u002Forcid.org\u002F0000-0002-6634-9593",{"name":462,"orcid":9},"Liangkuan Zhu",{"name":464,"orcid":465},"Qingjie Wang","https:\u002F\u002Forcid.org\u002F0009-0001-9038-7821",{"name":467,"orcid":468},"Yi Wang","https:\u002F\u002Forcid.org\u002F0000-0002-0192-3195",{"tldr":470,"method":471,"finding":472,"direction":203,"opportunity":473},"提出ERG-Mask模型，用于温室鲜食葡萄串的实例分割，提升边缘与区域协同精度。","结合边缘与区域信息的协同实例分割模型，基于深度学习。","ERG-Mask在葡萄串分割上优于现有方法，精度更高。","可探索该模型在复杂遮挡、不同光照下的鲁棒性，或扩展至其他簇状果实（如番茄、蓝莓）的检测。","2026-09-01T23:30:01.626379Z"]