[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3047":3,"related-3047":46},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":8,"source_name":9,"source_url":8,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":15,"sources":23,"tags":25,"search_phrases":31,"slug":34,"view_count":35,"doi":8,"paper":36,"created_at":45},3047,"基于CNN\u002FLR\u002FGA-BP的辣椒叶病识别稳定性与精度多维度视觉特征比较","https:\u002F\u002Fwww.mdpi.com\u002F2311-7524\u002F12\u002F9\u002F1176","塔里木大学Xueting Ma、Yifei Li等联合东北林业大学、南京农业大学，构建1260份辣椒叶数据集（健康、细菌性斑点病、黄化卷叶病），采用Lab b-channel、RGB super-green、Otsu-ACWE三种分割算法系统评估最优预处理方案，提取32个融合视觉特征并以随机森林消除7个低贡献冗余特征保留25个判别变量。20次独立重复试验表明：CNN测试平均精度97.67%、平均AUC 0.999，稳定性最佳；LR计算成本低适合资源受限场景；GA-BP非线性拟合能力弱、预测波动严重。该研究为辣椒叶病识别提供标准化实验框架。",null,"MDPI Horticulturae 12(9):1176","2026-09-19T00:00:00Z","论文",10,false,78,{"impact":16,"substance":17,"depth":18,"authority":19,"freshness":20,"relevant":21,"comment":22},16,22,18,14,8,1,"基于1260份辣椒叶数据集系统比较CNN\u002FLR\u002FGA-BP三种模型，方法规范、结论可靠，对作物病害智能识别有参考价值。",[24],{"name":9,"url":6},[26,27,28,29,30],"智慧农业","农业人工智能","图像识别","辣椒叶病","病害诊断",[32,33],"塔里木大学 辣椒叶病 识别","CNN 辣椒叶病 视觉特征","塔里木大学辣椒叶病识别-3047",0,{"doi":8,"openalex_id":8,"authors":37,"venue":8,"cited_by_count":35,"oa_url":8,"card":38,"direction":42,"ingested_from":44},[],{"tldr":39,"method":40,"finding":41,"direction":42,"opportunity":43},"比较CNN、LR、GA-BP对辣椒叶病的识别精度与稳定性，并评估三种分割预处理方案。","1260份辣椒叶图像，Lab\u002FRGB\u002FOtsu-ACWE分割，32特征经随机森林","CNN平均精度97.67%、AUC 0.999且最稳定；LR成本低适合资源受限；GA-BP波动严重。","农业人工智能与决策模型","可探索轻量化CNN与LR融合的田间实时识别方案，并验证多作物、多病害下的泛化稳定性。","agent","2026-09-21T00:04:39.222838Z",{"total":47,"page":21,"page_size":47,"items":48},6,[49,92,133,175,203,231],{"id":50,"title":51,"url":52,"summary":53,"summary_zh":54,"content":8,"source_name":55,"source_url":52,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":56,"score_detail":57,"sources":63,"tags":65,"search_phrases":68,"slug":71,"view_count":35,"doi":72,"paper":73,"created_at":91},3017,"Automated seedling vigor estimation in cucumbers using digital image processing","https:\u002F\u002Fdoi.org\u002F10.65764\u002Ftjas.2026.267412","Background and Objective: Farmers often rely on experience rather than quantitative indicators to determine seedling quality, resulting in inefficiencies in resource allocation. This study aimed to (1) compare seedling vigor and growth characteristics between open-pollinated (OP) and F1 hybrid cucumber seed types, and (2) develop a non-destructive predictive model for seedling vigor estimation based on digital image analysis.Methodology: Seedling images were acquired under controlled LED lighting using a 5 MP digital camera positioned 30 cm above 14-day-old seedlings. A YOLOv8-based object detection model was applied to detect and isolate true leaf regions, from which pixel count and RGB color values were extracted as input features for a multiple linear regression model to predict the seedling vigor index (SVI).Main Results: The YOLOv8 object detection model achieved a mean average precision (mAP@0.5) of 96.00%, precision of 93.40%, and recall of 94.40% in detecting true leaves. Multiple linear regression analysis was conducted using the Scikit-learn library in Python. Scikit-learn provides regression-based machine learning algorithms, including multiple linear regression. The equation was then applied to the training and test sets, using the pixel values of true leaves as the independent variable and SVI as the dependent variable. Using multiple regression analysis, the trained model generated the equation SVI = -2.27 + (2.84 × 10-5 × pixel) + (3.48 × 10-5 × R) + (-7.49 × 10-2 × G) + (2.06 × 10-1 × B), R2 = 0.57 and RMSE = 1.10. The model achieved the test set, r = 0.79 and RMSE = 1.34. The model performance showed a correlation coefficient of 0.85 for OP data and 0.91 for F1 hybrid data.Conclusions: The results demonstrate that digital image processing combined with object detection provides a non-destructive and effective approach to estimate seedling vigor quality. The predictive models developed from OP and F1 hybrid datasets indicate potential application in precision agriculture for automated seedling quality assessment and transplanting decision support.","背景与目标：农民通常依赖经验而非定量指标来判断幼苗质量，导致资源配置效率低下。本研究旨在（1）比较开放授粉（OP）与F1杂交黄瓜种子类型之间的幼苗活力和生长特性，（2）基于数字图像分析开发一种用于幼苗活力评估的无损预测模型。方法：使用500万像素数码相机置于14日龄幼苗上方30 cm处，在受控LED光照下获取幼苗图像。应用基于YOLOv8的目标检测模型检测并分离真叶区域，从中提取像素计数和RGB颜色值作为多元线性回归模型的输入特征，以预测幼苗活力指数（SVI）。主要结果：YOLOv8目标检测模型在检测真叶时达到了96.00%的平均精度均值（mAP@0.5）、93.40%的精确率和94.40%的召回率。使用Python中的Scikit-learn库进行多元线性回归分析。Scikit-learn提供基于回归的机器学习算法，包括多元线性回归。随后将该方程应用于训练集和测试集，以真叶像素值作为自变量，SVI作为因变量。通过多元回归分析，训练模型生成的方程为SVI = -2.27 + (2.84 × 10-5 × 像素) + (3.48 × 10-5 × R) + (-7.49 × 10-2 × G) + (2.06 × 10-1 × B)，R2 = 0.57，RMSE = 1.10。该模型在测试集上达到r = 0.79，RMSE = 1.34。模型性能显示，OP数据的相关系数为0.85，F1杂交数据的相关系数为0.91。结论：结果表明，数字图像处理结合目标检测为评估幼苗活力质量提供了一种无损且有效的方法。基于OP和F1杂交数据集开发的预测模型表明，其在精准农业中具有用于自动化幼苗质量评估和移栽决策支持的潜在应用。","Thai Journal of Agricultural Science",71,{"impact":58,"substance":59,"depth":60,"authority":58,"freshness":61,"relevant":21,"comment":62},12,21,17,9,"基于YOLOv8与多元回归的黄瓜幼苗活力无损估测，方法具体、指标完整，对智慧育苗有参考价值，但属细分作物研究，影响范围有限。",[64],{"name":55,"url":52},[26,27,66,28,67],"无损检测","黄瓜育苗",[69,70],"黄瓜 幼苗活力 图像处理","YOLOv8 幼苗 检测","黄瓜幼苗活力图像处理-3017","10.65764\u002Ftjas.2026.267412",{"doi":72,"openalex_id":74,"authors":75,"venue":55,"cited_by_count":35,"oa_url":52,"card":84,"direction":89,"ingested_from":90},"W7213631983",[76,78,80,82],{"name":77,"orcid":8},"Thanabodee Withunchettanan",{"name":79,"orcid":8},"Raksak Sermsak",{"name":81,"orcid":8},"Pichittra Kaewsorn",{"name":83,"orcid":8},"Kriengkri Kaewtrakulpong",{"tldr":85,"method":86,"finding":87,"direction":42,"opportunity":88},"用YOLOv8检测黄瓜真叶并结合多元回归，实现幼苗活力指数无损预测。","LED下拍摄14天幼苗，YOLOv8分割真叶，提取像素与RGB做多元线性回归。","YOLOv8检测mAP@0.5达96%，模型测试r=0.79，F1杂交种相关性达0.91。","可扩展多品种、多环境数据，融合时序图像与深度学习提升活力预测泛化性。","数字乡村与农业信息化","openalex","2026-09-20T23:30:26.929607Z",{"id":93,"title":94,"url":95,"summary":96,"summary_zh":97,"content":8,"source_name":98,"source_url":95,"published_at":99,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":100,"score_detail":101,"sources":104,"tags":106,"search_phrases":109,"slug":112,"view_count":35,"doi":113,"paper":114,"created_at":132},1730,"PAT: An Image Analysis Tool for Automated Scoring of Pollen in Alexander-Stained Anthers","https:\u002F\u002Fdoi.org\u002F10.1093\u002Fjxb\u002Ferag436","Abstract Quantitative pollen viability analysis is a critical but labor-intensive step in plant reproductive biology. Existing deep-learning Segment Anything Models (SAM) fail to reliably segment viable pollen in Alexander-stained anthers. To address this, we fine-tuned an existing Cellpose-SAM model for pollen segmentation. We integrated it into PAT (Pollen Analysis Tool), a cross-platform desktop application. PAT features instance segmentation with interactive quality control, an in-app model retraining module, and publication-ready statistical outputs. We deployed PAT in an EMS suppressor screen of semi-sterile Arabidopsis smg7-6 mutants, enabling efficient candidate prioritization for whole genome sequencing and mapping candidate mutation. This screen led to the identification of a point mutation in CAP-D2 ( capd2-2 ), a Condensin I subunit, that rescues the smg7-6 meiotic phenotype. Notably, mutation in a Condensin II subunits (CAP-D3 and CAP-H2) does not confer rescue. Further characterization suggests the capd2-2 allele is hypomorphic, showing no defects in vegetative growth, chromocenter compaction, or transposable element silencing. Collectively, we demonstrate that accessible AI tools have the potential to bridge gaps in plant phenotyping and accelerate the pace of biological discovery. Highlight We combined AI-powered image analysis with an easy-to-use desktop app to automate plant pollen counting, then used it to identify a new genetic suppressor of meiotic defects.","定量花粉活力分析是植物生殖生物学中一个关键但劳动密集的步骤。现有的深度学习分割一切模型（SAM）在亚历山大染色花药中无法可靠地分割有活力的花粉。为解决此问题，我们对现有的Cellpose-SAM模型进行了微调，用于花粉分割。我们将其整合到PAT（花粉分析工具）中，这是一个跨平台的桌面应用程序。PAT具备实例分割及交互式质量控制、应用内模型再训练模块以及可直接用于发表的统计输出功能。我们在半不育拟南芥smg7-6突变体的EMS抑制子筛选中部署了PAT，从而能够高效地对候选突变体进行优先级排序，以进行全基因组测序和候选突变定位。该筛选鉴定出CAP-D2（capd2-2）中的一个点突变，该基因编码凝缩蛋白I的一个亚基，该突变能够挽救smg7-6的减数分裂表型。值得注意的是，凝缩蛋白II亚基（CAP-D3和CAP-H2）的突变并未赋予挽救效果。进一步的特征分析表明，capd2-2等位基因属于亚效等位基因，在营养生长、染色质中心压缩或转座子沉默方面未显示任何缺陷。总体而言，我们证明了易于使用的人工智能工具具有弥合植物表型分析差距并加速生物学发现的潜力。亮点：我们将人工智能驱动的图像分析与易于使用的桌面应用相结合，实现了植物花粉计数的自动化，并利用该方法鉴定了一个新的减数分裂缺陷遗传抑制子。","Journal of Experimental Botany","2026-09-04T00:00:00Z",79,{"impact":18,"substance":17,"depth":18,"authority":19,"freshness":102,"relevant":21,"comment":103},7,"AI工具用于花粉活力自动分析，加速遗传筛选，对育种和表型分析有实用价值。",[105],{"name":98,"url":95},[26,27,107,108,28],"育种","表型分析",[110,111],"农业人工智能 图像识别 智慧农业 表型分析","农业人工智能 图像识别","农业人工智能图像识别智慧农业表型分析-1730","10.1093\u002Fjxb\u002Ferag436",{"doi":113,"openalex_id":115,"authors":116,"venue":98,"cited_by_count":35,"oa_url":95,"card":126,"direction":131,"ingested_from":90},"W7160664980",[117,120,123],{"name":118,"orcid":119},"Darya Volkava","https:\u002F\u002Forcid.org\u002F0009-0003-3578-6592",{"name":121,"orcid":122},"Karel Říha","https:\u002F\u002Forcid.org\u002F0000-0002-6124-0118",{"name":124,"orcid":125},"Vivek K. Raxwal","https:\u002F\u002Forcid.org\u002F0000-0002-5182-6377",{"tldr":127,"method":128,"finding":129,"direction":42,"opportunity":130},"开发了花粉分析工具PAT，自动化评估花粉活力，并用于发现新的减数分裂抑制因子。","微调Cellpose-SAM模型，集成到跨平台桌面应用PAT，支持交互式质量控制","PAT高效筛选拟南芥突变体，发现CAP-D2点突变可挽救smg7-6表型，而Condensin II","将AI图像分析工具扩展到其他作物表型，如种子活力、果实发育，结合交互式平台加速遗传筛选。","农业遥感与作物表型","2026-09-05T23:30:19.596351Z",{"id":134,"title":135,"url":136,"summary":137,"summary_zh":138,"content":8,"source_name":139,"source_url":136,"published_at":140,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":56,"score_detail":141,"sources":143,"tags":145,"search_phrases":148,"slug":150,"view_count":35,"doi":151,"paper":152,"created_at":174},1381,"From Visual Words to Vision Transformers: Dual Approaches to Water Stress Classification in Maize","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.atech.2026.102519","Early water stress in maize can quietly reduce growth and yield, making early detection from simple RGB images valuable for timely irrigation. This study compares two fundamentally different representation strategies for maize water stress classification: deep features learned from localized image regions using a custom Swin transformer and handcrafted visual features classified using a backpropagation neural network (BPNN). The Swin transformer learns stress-related representations from localized cropped image regions, with patch-level predictions aggregated into image-level classifications through majority voting, whereas the BPNN relies on handcrafted color- and texture-based descriptors extracted from segmented full-plant images. Both methods are evaluated on a maize water stress image dataset, using an independent test set that is not used during training or cross-validation for the final performance comparison. While the Swin transformer achieved 98% image-level accuracy after aggregation, the BPNN achieved 97% accuracy. Despite relying on fundamentally different representation strategies, both methods achieved high classification performance on the independently held-out test set. The results show that high-accuracy maize water stress classification can be achieved using low-cost RGB imagery and provide insight into the tradeoffs between learned and handcrafted representations in terms of classification performance and computational characteristics. These findings demonstrate the potential of computer-vision approaches for RGB-based agricultural water stress monitoring.","玉米早期水分胁迫会悄然降低生长和产量，因此利用简单的RGB图像进行早期检测对于及时灌溉具有重要意义。本研究比较了两种根本不同的玉米水分胁迫分类表示策略：一种是通过自定义Swin变换器从局部图像区域学习深度特征，另一种是利用反向传播神经网络（BPNN）对手工视觉特征进行分类。Swin变换器从局部裁剪图像区域学习与胁迫相关的表示，通过多数投票将补丁级预测聚合为图像级分类；而BPNN则依赖于从分割的全株图像中提取的基于颜色和纹理的手工描述符。两种方法均在玉米水分胁迫图像数据集上进行了评估，并使用独立测试集（训练或交叉验证中未使用）进行最终性能比较。尽管Swin变换器在聚合后实现了98%的图像级准确率，BPNN也达到了97%的准确率。尽管依赖根本不同的表示策略，两种方法在独立保留的测试集上均取得了较高的分类性能。结果表明，利用低成本RGB图像即可实现高精度的玉米水分胁迫分类，并揭示了学习表示与手工表示在分类性能和计算特性方面的权衡。这些发现展示了基于计算机视觉方法在RGB农业水分胁迫监测中的潜力。","Smart Agricultural Technology","2026-09-01T00:00:00Z",{"impact":58,"substance":17,"depth":18,"authority":58,"freshness":102,"relevant":21,"comment":142},"研究对比Swin Transformer与手工特征在玉米水分胁迫分类中的表现，均达97%以上准确率，为低成本RGB图像监测提供新思路。",[144],{"name":139,"url":136},[26,27,146,147,28],"玉米","水分胁迫",[149,111],"农业人工智能 图像识别 智慧农业 水分胁迫","农业人工智能图像识别智慧农业水分胁迫-1381","10.1016\u002Fj.atech.2026.102519",{"doi":151,"openalex_id":153,"authors":154,"venue":139,"cited_by_count":35,"oa_url":168,"card":169,"direction":42,"ingested_from":90},"W7204901486",[155,157,159,162,165],{"name":156,"orcid":8},"Sumaira Ghazal",{"name":158,"orcid":8},"Sardar Ali Abbas",{"name":160,"orcid":161},"Arslan Munir","https:\u002F\u002Forcid.org\u002F0000-0002-3126-8945",{"name":163,"orcid":164},"Ignacio A. Ciampitti","https:\u002F\u002Forcid.org\u002F0000-0001-9619-5129",{"name":166,"orcid":167},"Waqar S. Qureshi","https:\u002F\u002Forcid.org\u002F0000-0003-0176-8145","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS2772375526007446\u002Fpdf",{"tldr":170,"method":171,"finding":172,"direction":42,"opportunity":173},"比较Swin Transformer与手工特征BPNN在玉米水分胁迫RGB图像分类中的表现，两者均达","使用自定义Swin Transformer学习局部特征，BPNN结合手工颜色纹理","Swin Transformer准确率98%，BPNN 97%，证明低成本RGB图像可实现高精度水分","可探索轻量化模型或融合两种表征策略，以平衡精度与计算效率，适应田间实时监测需求。","2026-09-02T23:30:04.449177Z",{"id":176,"title":177,"url":178,"summary":179,"summary_zh":8,"content":8,"source_name":180,"source_url":8,"published_at":181,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":100,"score_detail":182,"sources":185,"tags":187,"search_phrases":191,"slug":194,"view_count":35,"doi":8,"paper":195,"created_at":202},3048,"基于双路径注意力与多尺度融合的作物病虫害识别网络DPMFNet","https:\u002F\u002Fwww.mdpi.com\u002F1099-4300\u002F28\u002F9\u002F1032","盐城工学院Hong Zhang、Fagen Song等联合江苏开放大学提出DPMFNet轻量级双路径网络，集成空间-通道双注意力（SCDA）与多尺度深度可分离卷积（MDSC）模块，构建AttMDSCBlock残差结构。在PlantVillage与AI Challenger 2018数据集上DPMFNet仅14.24M参数和2.55G FLOPs，跨注意力机制融合局部细节与全局上下文，轻量化金字塔策略自适应整合多分辨率特征，在复杂农田场景下兼顾精度与可部署性，为嵌入式田间设备提供高性价比方案。","MDPI Entropy 28(9):1032","2026-09-20T00:00:00Z",{"impact":16,"substance":17,"depth":18,"authority":183,"freshness":12,"relevant":21,"comment":184},13,"轻量级双路径注意力网络在两大公开数据集上兼顾精度与可部署性，对嵌入式田间设备落地有实质参考价值。",[186],{"name":180,"url":178},[26,27,188,189,190],"病虫害识别","作物监测","轻量化模型",[192,193],"盐城工学院 DPMFNet 病虫害识别","PlantVillage AI Challenger 作物病害","盐城工学院DPMFNet病虫害识别-3048",{"doi":8,"openalex_id":8,"authors":196,"venue":8,"cited_by_count":35,"oa_url":8,"card":197,"direction":42,"ingested_from":44},[],{"tldr":198,"method":199,"finding":200,"direction":42,"opportunity":201},"提出轻量级双路径网络DPMFNet，实现复杂农田场景下的作物病虫害高精度识别。","空间-通道双注意力与多尺度深度可分离卷积，构建AttMDSCBlock残差结构。","仅14.24M参数、2.55G FLOPs，在PlantVillage与AI Challenger ","可探索真实田间多病虫害并发与边缘设备实时推理的轻量化自适应识别研究。","2026-09-21T00:04:39.305757Z",{"id":204,"title":205,"url":206,"summary":207,"summary_zh":8,"content":8,"source_name":208,"source_url":8,"published_at":209,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":210,"score_detail":211,"sources":214,"tags":216,"search_phrases":219,"slug":222,"view_count":35,"doi":8,"paper":223,"created_at":230},3046,"基于Flor-YOLO的香石竹鲜切花分级轻量化检测方法","https:\u002F\u002Ffinance.sina.com.cn\u002Froll\u002F2026-09-20\u002Fdoc-inisnssw6528969.shtml","西南林业大学李传孟、杨洁副教授、张晓宇在《智慧农业（中英文）》2026,8(4):85-99发表Flor-YOLO模型，针对香石竹鲜切花开放度人工分级主观性强、效率低问题，以YOLO11n为基线进行骨干网络、下采样方式、检测头结构针对性改进。Flor-YOLO在自建香石竹数据集上mAP@50达到96.10%，较基准模型提升3.25个百分点；模型参数量与计算量分别为1.26M和1.1GFLOPs，同比降低51.2%和82.5%。","智慧农业(中英文)2026,8(4):85-99","2026-09-20T12:26:00Z",83,{"impact":18,"substance":212,"depth":18,"authority":19,"freshness":12,"relevant":21,"comment":213},23,"核心期刊论文，提出轻量化Flor-YOLO模型实现香石竹鲜切花自动分级，数据翔实、方法有创新，对花卉产业智能化有参考价值。",[215],{"name":208,"url":206},[26,27,217,190,218],"花卉产业","鲜切花分级",[220,221],"西南林业大学 香石竹 鲜切花分级","Flor-YOLO 香石竹 检测","西南林业大学香石竹鲜切花分级-3046",{"doi":8,"openalex_id":8,"authors":224,"venue":8,"cited_by_count":35,"oa_url":8,"card":225,"direction":42,"ingested_from":44},[],{"tldr":226,"method":227,"finding":228,"direction":42,"opportunity":229},"提出轻量化Flor-YOLO模型，实现香石竹鲜切花开放度自动分级检测。","以YOLO11n为基线，改进骨干网络、下采样方式与检测头，自建香石竹数据集。","mAP@50达96.10%，较基准提升3.25个百分点，参数量与计算量分别降低51.2%和82.5%","可迁移至其他花卉或果蔬的轻量化分级，并探索边缘设备实时部署与多任务联合检测。","2026-09-21T00:04:39.154232Z",{"id":232,"title":233,"url":234,"summary":235,"summary_zh":8,"content":236,"source_name":237,"source_url":8,"published_at":238,"category":239,"cover_url":8,"hotness":12,"is_selected":13,"score":240,"score_detail":241,"sources":244,"tags":246,"search_phrases":250,"slug":253,"view_count":35,"doi":8,"paper":8,"created_at":254},3039,"江苏沿江地区农科所参加华东地区农学会学术年会：以\"AI赋能农业新质生产力发展\"为主题","https:\u002F\u002Fyj.jaas.ac.cn\u002Fxww\u002Fxwzx\u002Fart\u002F2026\u002Fart_c0ccb7c5ca5547768d1e29ed9427f12a.html","2026年华东地区农学会学术年会9-17至19在江苏扬州召开，以\"AI赋能农业新质生产力发展\"为主题，汇聚华东六省一市农业科研院所、高等院校、农技推广部门及农业科技企业专家代表。江苏沿江地区农科所王顺祥所长、陆兵副所长带队，组织10余名科研人员参会交流。中国工程院院士张洪程作《水稻绿色丰产无人化栽培技术》报告；日本工程院院士邓明聪分享人工智能与非线性控制技术融合及其在农业工程领域的应用。年会同步开设四大平行专题论坛：智慧种植·丰产提质、智慧养殖·节本增效、智造农机·装备赋能、智驱加工·延链强链。","所内要闻|AI赋能聚力发展农业新质生产力——沿江农科所参加华东地区农学会学术年会\n\n作者：唐明霞 文章来源：沿江所 点击数： 发布时间：2026-09-20 16:09\n\n为学习农业领域前沿科技创新成果，加强跨区域科研交流合作，9月17日至19日，2026年华东地区农学会学术年会在江苏扬州中兴天成国际酒店召开。本次年会以“AI赋能农业新质生产力发展”为主题，汇聚华东六省一市农业科研院所、高等院校、农技推广部门及农业科技企业专家代表。江苏沿江地区农科所王顺祥所长、陆兵副所长带队，组织10余名科研人员赴扬州参会交流。\n\n会议由江苏省农学会、扬州大学承办。开幕式后，主旨报告环节大咖云集。中国工程院院士张洪程作《水稻绿色丰产无人化栽培技术》报告；日本工程院院士邓明聪分享人工智能与非线性控制技术融合及其在农业工程领域的应用；南京农业大学陆明洲教授围绕畜禽养殖智能化技术、装备与农业大模型展开交流；南京工业大学徐虹教授介绍基于AI赋能的新型生物刺激素绿色生物制造研究。专家们从智慧种植、智能养殖、生物制造等方面，展现AI技术赋能现代农业的最新进展。\n\n9月18日下午，年会同步开设四大平行专题论坛：智慧种植·丰产提质、智慧养殖·节本增效、智造农机·装备赋能、智驱加工·延链强链。智慧种植专场聚焦AI大田生产管控、农业碳汇、水肥智能决策、数字化育种、大豆抗逆新品种选育等研究；智慧养殖专场研讨畜禽水产智能感知、稻虾生态种养智慧管控、畜禽行为智能识别与养殖环境调控技术；智造农机专场交流名优茶采摘机器人、设施蔬菜装备、智能育种加速器、丘陵山地农机装备研发实践；智驱加工专场围绕食品加工数字孪生、全溶性植物蛋白开发、果蔬及特色农产品精深加工技术开展研讨，覆盖农业生产全链条数字化转型。\n\n参会期间，我所科研人员结合自身研究方向，分赴各专题会场认真聆听报告，积极参与学术讨论，与华东地区同行专家深入交流，重点学习智慧农业装备、绿色高效栽培、农产品加工增值等领域创新成果，探讨科研项目协同合作路径。\n\n此次参会，有效拓宽了科研人员学术视野，进一步搭建起跨区域科研协作桥梁。下一步，江苏沿江地区农科所将充分吸收本次年会的新理念、新技术，立足南通沿江农业产业实际需求，持续推进农业科技创新，加快科技成果示范转化，为培育农业新质生产力、推动区域农业高质量发展贡献科研力量。\n\n[![Image 1: 1.jpeg](https:\u002F\u002Fyj.jaas.ac.cn\u002Fcms_files\u002Ffilemanager\u002F695893685\u002Fpicture\u002F20268\u002FS254e30553383497089ded7cfd802aec4-800.jpeg)](https:\u002F\u002Fyj.jaas.ac.cn\u002Fcms_files\u002Ffilemanager\u002F695893685\u002Fpicture\u002F20268\u002F254e30553383497089ded7cfd802aec4.jpeg)\n\n（图文：唐明霞；责任编辑：陈满峰；审核人：陈国清）","江苏省农业科学院沿江地区农业科学研究所","2026-09-20T10:00:00Z","报道",58,{"impact":58,"substance":19,"depth":58,"authority":242,"freshness":61,"relevant":21,"comment":243},11,"区域性学术年会参会通稿，信息以会议议程与报告概览为主，无新增数据或独家结论，但AI赋能农业主题契合度高、时效新，可作为智慧农业主题页的聚合素材。",[245],{"name":237,"url":234},[26,27,247,248,249],"新质生产力","智能农机","数字育种",[251,252],"华东地区农学会 学术年会","沿江农科所 扬州 AI赋能","华东地区农学会学术年会-3039","2026-09-21T00:04:34.285572Z"]