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agriculture",[28],{"code":41,"score":13,"supported":53,"title":65,"abstract":66,"tags":70},[],[67,68,69],"深度学习","机器学习","计算机视觉",[30],{"code":72,"score":73,"supported":53,"title":74,"abstract":75,"tags":77},"F3",3,[],[76],"施肥",[],{"code":79,"score":73,"supported":53,"title":80,"abstract":81,"tags":83},"H3",[],[82],"灌溉",[],{"code":85,"score":20,"supported":14,"title":86,"abstract":87,"tags":88},"I3",[],[],[26],[90,91],"病虫害诊断 采收机器人 产量预测 精准农业","病虫害诊断 采收机器人","病虫害诊断采收机器人产量预测精准农业-972",4,"2026-08-26T00:09:32.397555Z",{"total":96,"page":20,"page_size":96,"items":97},6,[98,208,265,329,406,466],{"id":99,"title":100,"url":101,"summary":102,"summary_zh":103,"content":8,"source_name":104,"source_url":101,"published_at":105,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":106,"score_detail":107,"sources":113,"tags":115,"primary_topic":120,"secondary_topics":121,"classification":128,"search_phrases":182,"slug":185,"view_count":20,"doi":186,"paper":187,"created_at":207},4038,"Use of artificial intelligence (AI) in vegetable crops: A review","https:\u002F\u002Fdoi.org\u002F10.33545\u002F2664844x.2026.v8.i10a.1995","Artificial Intelligence (AI) is emerging technology in vegetable production, enabling data-driven decision making and precision agriculture practices. AI refers to the intelligence exhibited by machines that can perceive their environment, process large datasets, and make decisions to optimize agricultural productivity. With the integration of technologies such as machine learning, sensors, drones, satellites, Geographic Information Systems (GIS), and Global Positioning Systems (GPS), AI enables efficient monitoring and management of crop production systems In vegetable cultivation, AI technologies assist farmers in several critical operations including soil monitoring, climate assessment, pest and disease diagnosis, irrigation scheduling, and yield prediction. Sensors such as wind vane, cup count anemometer, rain gauge, lux meter, soil moisture sensor, and crop canopy sensors collect real-time environmental and crop data. These data was processed through data loggers and machine learning algorithms to provide accurate recommendations for crop management. Drone-based imaging systems and multispectral cameras further support precision farming by identifying weeds, nutrient deficiencies and disease symptoms at early stages. The use of AI in vegetables also highlights image-based predictive agriculture systems where captured images are processed through stages such as. image cleaning, feature extraction, and model training to detect pests and diseases. Several case studies demonstrate the use of AI in vegetable crops such as tomato, capsicum, and chilli for monitoring canopy growth, irrigation management. Additionally, robotic technologies in harvesting address labor shortages in agriculture. Crop yield prediction of AI in vegetables increases by 40% sensors and algorithms reduces labor cost by 30% moisture sensor increases water use efficiency by 40% spraying done reduces use of pesticide by 60% robotic arms increases harvesting accuracy by 40% artificial neural network data integration saves 25% cost in disease monitoring.","人工智能（AI）正在成为蔬菜生产领域的新兴技术，推动数据驱动决策与精准农业实践。AI是指机器所表现出的智能，能够感知环境、处理大规模数据集并做出决策以优化农业生产力。通过整合机器学习、传感器、无人机、卫星、地理信息系统（GIS）和全球定位系统（GPS）等技术，AI实现了对作物生产系统的高效监测与管理。在蔬菜种植中，AI技术可协助农民完成多项关键作业，包括土壤监测、气候评估、病虫害诊断、灌溉调度和产量预测。风向标、杯式风速计、雨量计、照度计、土壤水分传感器和作物冠层传感器等设备可采集实时的环境与作物数据。这些数据通过数据记录器和机器学习算法进行处理，为作物管理提供精准建议。基于无人机的成像系统与多光谱相机通过早期识别杂草、养分缺乏和病害症状，进一步支持精准农业。AI在蔬菜中的应用还突出了基于图像的预测性农业系统，该系统将采集的图像经过图像清洗、特征提取和模型训练等阶段处理，以检测病虫害。多个案例研究展示了AI在番茄、辣椒和尖椒等蔬菜作物中用于监测冠层生长和灌溉管理的应用。此外，收获机器人技术可缓解农业劳动力短缺问题。AI在蔬菜中的作物产量预测通过传感器和算法提高了40%，劳动力成本降低了30%，土壤水分传感器使水分利用效率提高了40%，喷雾作业使农药使用量减少了60%，机械臂使收获准确率提高了40%，人工神经网络数据集成使病害监测成本节省了25%。","International Journal of Agriculture and Food Science","2026-10-01T00:00:00Z",75,{"impact":108,"substance":109,"depth":110,"authority":18,"freshness":111,"relevant":20,"comment":112},18,20,16,9,"系统综述AI在蔬菜生产中的应用，含产量、成本、水肥药效等量化数据，对智慧农业主题有聚合价值。",[114],{"name":104,"url":101},[116,117,118,28,119],"智慧农业","农业人工智能","病虫害识别","蔬菜种植",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},[122,123],{"code":41,"slug":42,"name":43,"path":44,"parent_code":45},{"code":124,"slug":125,"name":126,"path":127,"parent_code":45},"I4","gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis",{"status":47,"locked":14,"version":48,"method":49,"evidence":129},{"candidates":130,"agricultural_evidence":53},[131,138,146,153,158,165,172,177],{"code":41,"score":132,"supported":53,"title":133,"abstract":135,"tags":137},19,[134],"artificial intelligence",[68,136,134],"machine learning",[117,118],{"code":34,"score":108,"supported":53,"title":139,"abstract":141,"tags":145},[140],"vegetable",[36,142,143,140,144],"番茄","辣椒","tomato",[119],{"code":124,"score":111,"supported":53,"title":147,"abstract":148,"tags":152},[],[149,150,151],"地理信息","GIS","geographic information",[],{"code":59,"score":154,"supported":53,"title":155,"abstract":156,"tags":157},8,[],[28,62],[116,28],{"code":159,"score":96,"supported":53,"title":160,"abstract":161,"tags":164},"F4",[],[162,163],"农药","pesticide",[],{"code":166,"score":96,"supported":53,"title":167,"abstract":168,"tags":171},"H2",[],[169,170],"土壤","soil",[],{"code":79,"score":96,"supported":53,"title":173,"abstract":174,"tags":176},[],[82,175],"irrigation",[],{"code":85,"score":73,"supported":53,"title":178,"abstract":179,"tags":181},[],[180],"multispectral",[],[183,184],"AI 蔬菜 精准农业","蔬菜 病虫害 图像识别","AI蔬菜精准农业-4038","10.33545\u002F2664844x.2026.v8.i10a.1995",{"doi":186,"openalex_id":188,"authors":189,"venue":104,"cited_by_count":198,"oa_url":8,"card":199,"direction":205,"ingested_from":206},"W7215128283",[190,192,194,196],{"name":191,"orcid":8},"Hrishikesh Nirmala Balkrishna Thube",{"name":193,"orcid":8},"Dr. SK Kshirsagar",{"name":195,"orcid":8},"MG Agale",{"name":197,"orcid":8},"GS Shinde",0,{"tldr":200,"method":201,"finding":202,"direction":203,"opportunity":204},"综述人工智能在蔬菜作物生产中的应用，涵盖监测、诊断、灌溉、产量预测与采收机器人。","综述机器学习、传感器、无人机、卫星、GIS\u002FGPS及图像处理在蔬菜生产中的应用案","AI使蔬菜产量预测提高40%、节水40%、减农药60%、采收准确率提高40%。","智慧农业 \u002F 农业物联网","可针对小农户蔬菜场景开发低成本多源数据融合模型，并验证AI长期经济与生态效益。","农业人工智能与决策模型","openalex","2026-10-02T23:30:40.356457Z",{"id":209,"title":210,"url":211,"summary":212,"summary_zh":213,"content":8,"source_name":214,"source_url":211,"published_at":215,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":216,"score_detail":217,"sources":219,"tags":221,"primary_topic":224,"secondary_topics":225,"classification":227,"search_phrases":245,"slug":248,"view_count":198,"doi":249,"paper":250,"created_at":264},5387,"LIGHTWEIGHT FEATURE FUSED ATTENTION DRIVEN MULTI LEVEL HYBRID MOBILENET V2 FRAMEWORK FOR EARLY TOMATO LEAF DISEASE DETECTION","https:\u002F\u002Fdoi.org\u002F10.62441\u002Factainnovations.vi.1918","It is crucial to find and treat tomato crop diseases early and accurately to improve crop efficiency and make sure there is no scarcity of crop. However, computerized analysis is provocative due to lighting differences, compound backgrounds, and graphic likenesses among infection regions. Research work proposed a lightweight Attention Driven Multi Level Hybrid MobileNet-V2 algorithm that fuses the handcrafted texture structures with Deep Learning (DL) features, then image preprocessing is incorporated using Gaussian, Bilateral, and Guided filters to minimize noise by preserving the disease lesions. LAB color model along with Contrast Limited Adaptive Histogram Equalization (CLAHE) are used to improve the contrast. Otsu thresholding is involved to effectively separate the leaf segment from diseased parts. Local Binary Pattern (LBP) and Gray Level Co-occurrence Matrix (GLCM) descriptors are used to obtain fine-grained features, while high level features are gained by pretrained MobileNet-V2 model. Features fusion is fed to the fully connected layers of deep learning model for training and validation purpose. Results have revealed that the suggested model attains better classification performance as compared to existing models, making it an effective explanation for real time tomato leaf disease detection for smart agriculture applications.","早期准确发现并治疗番茄作物病害，对于提高作物效率、确保作物不短缺至关重要。然而，由于光照差异、复杂背景以及感染区域之间的图形相似性，计算机化分析具有挑战性。研究提出了一种轻量级注意力驱动多层混合MobileNet-V2算法，该算法将手工纹理结构与深度学习（DL）特征相融合，然后结合高斯、双边和引导滤波器进行图像预处理，以在保留病害病变的同时最小化噪声。采用LAB颜色模型和对比度受限自适应直方图均衡化（CLAHE）来提高对比度。引入Otsu阈值分割以有效分离叶片片段与病害部分。使用局部二值模式（LBP）和灰度共生矩阵（GLCM）描述符获取细粒度特征，同时通过预训练的MobileNet-V2模型获取高层特征。特征融合后输入深度学习模型的全连接层进行训练和验证。结果表明，与现有模型相比，所提出的模型取得了更好的分类性能，使其成为智慧农业应用中实时番茄叶片病害检测的有效解决方案。","Acta Innovations","2026-10-18T00:00:00Z",64,{"impact":18,"substance":108,"depth":110,"authority":13,"freshness":154,"relevant":20,"comment":218},"提出融合手工纹理特征与MobileNet-V2的轻量番茄叶病检测方法，有一定方法新颖性，但属单篇论文、应用范围有限，未达每日精选门槛。",[220],{"name":214,"url":211},[116,117,67,222,223],"番茄病害","图像识别",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},[226],{"code":41,"slug":42,"name":43,"path":44,"parent_code":45},{"status":47,"locked":14,"version":48,"method":49,"evidence":228},{"candidates":229,"agricultural_evidence":53},[230,234,239],{"code":34,"score":17,"supported":53,"title":231,"abstract":232,"tags":233},[144],[142,144],[222],{"code":41,"score":111,"supported":53,"title":235,"abstract":236,"tags":238},[],[67,237],"deep learning",[117,223,67],{"code":59,"score":240,"supported":53,"title":241,"abstract":242,"tags":244},7,[],[116,243],"smart agriculture",[116],[246,247],"MobileNet-V2 番茄叶片病害","番茄叶病 深度学习 检测","MobileNet-V2番茄叶片病害-5387","10.62441\u002Factainnovations.vi.1918",{"doi":249,"openalex_id":251,"authors":252,"venue":214,"cited_by_count":198,"oa_url":258,"card":259,"direction":203,"ingested_from":206},"W7222977034",[253,255],{"name":254,"orcid":8},"Mrs. Vijetalaxmi A Hegde",{"name":256,"orcid":257},"Abhay Ramakant Kasetwar","https:\u002F\u002Forcid.org\u002F0000-0002-5800-8764","https:\u002F\u002Factainnovations.com\u002Findex.php\u002Fpub\u002Farticle\u002Fdownload\u002F1918\u002F981",{"tldr":260,"method":261,"finding":262,"direction":205,"opportunity":263},"提出轻量注意力多级混合MobileNet-V2框架，融合手工纹理与深度学习特征实现番茄叶片病害早期检","高斯\u002F双边\u002F引导滤波去噪，LAB+CLAHE增强，Otsu分割，LBP与GLCM","所提模型分类性能优于现有模型，可有效用于智慧农业实时番茄叶片病害检测。","可探索多作物跨域泛化、田间复杂光照下轻量模型部署与可解释性，及病害早期分级检测。","2026-10-20T23:30:10.424511Z",{"id":266,"title":267,"url":268,"summary":269,"summary_zh":270,"content":8,"source_name":271,"source_url":268,"published_at":272,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":273,"score_detail":274,"sources":277,"tags":279,"primary_topic":281,"secondary_topics":282,"classification":284,"search_phrases":300,"slug":303,"view_count":20,"doi":304,"paper":305,"created_at":328},4494,"Tomato ripeness classification with weight-based and texture-adaptive pooling in CNNs","https:\u002F\u002Fdoi.org\u002F10.64960\u002Feasr.2026.266223","This research introduces a weight-based mixed pooling and adaptive fuzzy pooling mechanism based on feature map texture analysis for attention mechanism Squeeze-and-Excitation Networks (SE) and Convolutional Neural Networks (CNNs) in tomato ripeness classification. Traditional CNNs and SE networks rely on deterministic pooling strategies that can be suboptimal for capturing diverse visual features such as texture and color gradients in fruit. A novel fuzzy pooling system is proposed that dynamically adjusts the fusion ratio of min and max pooling based on real-time texture analysis of feature maps via a Laplacian filter. This mechanism was integrated into Darknet53, ResNet34, and ResNet101 architectures, both directly and within the SE attention block. The method was evaluated on a five-class tomato ripeness dataset using 10-fold cross-validation against traditional and fixed-weight pooling methods. The fuzzy and mixed pooling approach demonstrated superior performance on certain models which include ESEnet: Darknet53 + SE (Fuzzy pool) achieved an average F1 score across folds of 0.8429, outperforming Darknet53 + baseline SE (Average pool) at 0.8350; Extended Net: ResNet34 + Fuzzy pool achieved an average F1 score across folds of 0.8305, outperforming baseline ResNet34 at 0.8130 and ResNet101 + Fuzzy pool achieved an average F1 score across folds of 0.8284, outperforming baseline ResNet101 at 0.8267. This improvement was realized with minimal increase in computational overhead. Specifically, measurements of model parameters (M) and FLOPs (GMACs) showed only a slight increase across all architectures after integrating SEnet into the system, and zero increase after integrating the Laplacian filter and fuzzy logic. The study concludes that logic-based adaptive pooling is a robust strategy that improves feature representation in CNNs, achieving a balance between classification accuracy and computational cost. These findings offer practical value for developing more reliable automated harvesting systems and provide a generalizable enhancement for a wide range of computer vision classification tasks.","本研究提出了一种基于特征图纹理分析的权重混合池化与自适应模糊池化机制，用于番茄成熟度分类中的注意力机制挤压激励网络（Squeeze-and-Excitation Networks, SE）和卷积神经网络（Convolutional Neural Networks, CNNs）。传统的CNN和SE网络依赖确定性池化策略，在捕捉果实纹理和颜色梯度等多样化视觉特征时可能并非最优。本文提出了一种新颖的模糊池化系统，通过拉普拉斯滤波器对特征图进行实时纹理分析，动态调整最小池化和最大池化的融合比例。该机制被集成到Darknet53、ResNet34和ResNet101架构中，既可直接集成，也可嵌入SE注意力模块内。该方法在五类番茄成熟度数据集上采用10折交叉验证，与传统池化及固定权重池化方法进行了对比评估。模糊池化和混合池化方法在部分模型上表现出优越性能，包括：ESEnet：Darknet53 + SE（模糊池化）在各折上的平均F1得分为0.8429，优于Darknet53 + 基线SE（平均池化）的0.8350；扩展网络：ResNet34 + 模糊池化在各折上的平均F1得分为0.8305，优于基线ResNet34的0.8130；ResNet101 + 模糊池化在各折上的平均F1得分为0.8284，优于基线ResNet101的0.8267。这一改进仅带来了极小的计算开销增加。具体而言，模型参数量（M）和浮点运算次数（GMACs）的测量结果表明，将SEnet集成到系统后所有架构仅有轻微增加，而集成拉普拉斯滤波器和模糊逻辑后则无任何增加。研究结论认为，基于逻辑的自适应池化是一种鲁棒策略，能够改善CNN中的特征表示，在分类精度和计算成本之间实现平衡。这些发现为开发更可靠的自动化收获系统提供了实用价值，并为广泛的计算机视觉分类任务提供了一种可推广的增强方法。","Engineering and Applied Science Research","2026-10-07T00:00:00Z",66,{"impact":154,"substance":109,"depth":275,"authority":18,"freshness":111,"relevant":20,"comment":276},17,"提出基于纹理自适应模糊池化的CNN改进方法，在番茄成熟度分类上取得小幅但稳定的F1提升且算力开销几乎不增，对自动采摘与农业视觉识别有参考价值，但属细分方法类论文，影响面有限。",[278],{"name":271,"url":268},[116,117,142,223,280],"自动采摘",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},[283],{"code":41,"slug":42,"name":43,"path":44,"parent_code":45},{"status":47,"locked":14,"version":48,"method":49,"evidence":285},{"candidates":286,"agricultural_evidence":53},[287,291,296],{"code":34,"score":17,"supported":53,"title":288,"abstract":289,"tags":290},[144],[142,144],[142],{"code":41,"score":154,"supported":53,"title":292,"abstract":293,"tags":295},[],[69,294],"computer vision",[117,223],{"code":59,"score":20,"supported":14,"title":297,"abstract":298,"tags":299},[],[],[116],[301,302],"番茄 成熟度 分类 卷积神经网络","Darknet53 ResNet 番茄 成熟度","番茄成熟度分类卷积神经网络-4494","10.64960\u002Feasr.2026.266223",{"doi":304,"openalex_id":306,"authors":307,"venue":271,"cited_by_count":198,"oa_url":322,"card":323,"direction":203,"ingested_from":206},"W7220840158",[308,310,312,314,317,319],{"name":309,"orcid":8},"Rut Yatigul",{"name":311,"orcid":8},"Wisanu Jitviriya",{"name":313,"orcid":8},"Eiji Hayashi",{"name":315,"orcid":316},"Aran Blattler","https:\u002F\u002Forcid.org\u002F0000-0002-8684-6239",{"name":318,"orcid":8},"Watcharin Tangsuksant",{"name":320,"orcid":321},"Teppakorn Sittiwanchai","https:\u002F\u002Forcid.org\u002F0000-0001-9653-8750","https:\u002F\u002Fph01.tci-thaijo.org\u002Findex.php\u002Feasr\u002Farticle\u002Fdownload\u002F266223\u002F176569",{"tldr":324,"method":325,"finding":326,"direction":205,"opportunity":327},"提出基于纹理自适应模糊池化机制，提升CNN番茄成熟度分类性能。","用Laplacian滤波分析特征图纹理，动态调整最大最小池化融合比，集成到Dar","模糊池化在多个模型上F1提升，且计算开销几乎不增加。","可探索自适应池化在其它果蔬成熟度或田间复杂光照下的泛化能力。","2026-10-09T23:30:09.993787Z",{"id":330,"title":331,"url":332,"summary":333,"summary_zh":8,"content":334,"source_name":335,"source_url":8,"published_at":336,"category":337,"cover_url":8,"hotness":13,"is_selected":14,"score":338,"score_detail":339,"sources":342,"tags":344,"primary_topic":350,"secondary_topics":351,"classification":359,"search_phrases":401,"slug":404,"view_count":19,"doi":8,"paper":8,"created_at":405},3396,"从吃到用——中国农科院第八届农科开放日现场直击：彩色胡萝卜、丙醇二酸黄瓜、甜糯同粒玉米、转运机器人、动物疾病风险大模型","https:\u002F\u002Fnews.cctv.cn\u002F2026\u002F09\u002F20\u002FARTIkUEnHV7gFcVPFEweSMwS260920.shtml","9月19日中国农科院36个研究所的560多项最新科研成果集中亮相：彩色胡萝卜、橘红心白菜、特色彩椒等全新果蔬品种轮番展示；富含丙醇二酸的晶莹剔透黄瓜能阻止糖类转化为脂肪；低豆腥味、高异黄酮、适合榨汁的大豆及'甜糯同粒'玉米；农业转运机器人能扛100公斤以上作物在丘陵山区跑、具备原地掉头能力；正在研发的全球动物疾病风险大模型将对动物疫病实现实时预报预警。","[首页](https:\u002F\u002Fwww.cctv.cn\u002F)\n\n[时政](javascript:;)\n\n[新闻](https:\u002F\u002Fnews.cctv.cn\u002F)\n\n[评论](https:\u002F\u002Fopinion.cctv.cn\u002F)\n\n[视频](https:\u002F\u002Fv.cctv.cn\u002F)\n\n[财经](https:\u002F\u002Ffinance.cctv.cn\u002Findex.shtml)\n\n[体育](https:\u002F\u002Fsports.cctv.cn\u002F)\n\n[教育](https:\u002F\u002Fedu.cctv.cn\u002F)\n\n[熊猫](https:\u002F\u002Fwww.ipanda.cn\u002F)\n\n[直播中国](https:\u002F\u002Flivechina.cctv.cn\u002F)\n\n[4K](https:\u002F\u002Ftv.cctv.cn\u002F4K\u002Findex.shtml)\n\n[CCTV.直播](https:\u002F\u002Ftv.cctv.cn\u002Findex.shtml)\n\n[中](https:\u002F\u002Fwww.cctv.cn\u002Findex.shtml)\n\n[](https:\u002F\u002Faigc.cctv.cn\u002Findex.shtml 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13:58:17\n\n###### 原标题：\n\n正在加载\n\n[Video 3](https:\u002F\u002Fdh5.cntv.cdn20.com\u002Fasp\u002Fh5e\u002Fhls\u002Fmain\u002F0303000a\u002F3\u002Fdefault\u002F8d26c0f8c36e4e1e8ed02d6406007686\u002Fmain.m3u8?maxbr=2048&contentid=15120519184043)\n\n静音(m)全屏(f)\n\nADCountdown Time | 广告\n\n![Image 51](https:\u002F\u002Fnews.cctv.cn\u002F2026\u002F09\u002F20\u002FARTIkUEnHV7gFcVPFEweSMwS260920.shtml)关闭广告\n\n![Image 52](https:\u002F\u002Fnews.cctv.cn\u002F2026\u002F09\u002F20\u002FARTIkUEnHV7gFcVPFEweSMwS260920.shtml)\n\n尊贵的用户，已为您跳过广告\n\n0:00\n\n正在加载 视频播放器。\n\n播放(p)播放视频\n\n播放(p)\n\n当前时间 0:00\n\n\u002F\n\n时长 2:12\n\n媒体流类型 直播\n\n高清\n\n画质\n\n*   超清\n*   高清, 选择\n*   标清\n*   流畅\n\n静音(m)\n\n全屏(f)\n\n加载完成: 0%\n\n-\n\n88:88\n\n[](javascript:void(0);)\n\n*   core:undefined.undefined\n*   wrap:3.8.2.260723\n\n画中画\n\n**央视网消息：**9月19日，中国农业科学院36个研究所的560多项最新科研成果集中面向社会公众亮相，把农业实验室里的“新鲜事”带到公众眼前。\n\n![Image 53](https:\u002F\u002Fp3.img.cntv.cn\u002Fphotoworkspace\u002Fcontentimg\u002F2026\u002F09\u002F20\u002F2026092013212476326.png)\n\n在果蔬成果展区，最受欢迎的不是常见的香甜水果，而是一杯胡萝卜汁。  \n  \n 科研人员通过调节不同品种胡萝卜的收获时间和栽培模式，培育出口感更甜更细腻的品种以及减糖版胡萝卜。  \n  \n 中国农业科学院蔬菜花卉研究所大宗蔬菜产业技术体系岗位专家庄飞云介绍，胡萝卜口感整体受环境影响特别大，通过调节里面的一些组分，很多让人不愉快的组分把它尽量减少到很低，当然它的营养还能保持原有的营养。\n\n![Image 54](https:\u002F\u002Fp5.img.cntv.cn\u002Fphotoworkspace\u002Fcontentimg\u002F2026\u002F09\u002F20\u002F2026092013213274797.png)\n\n另一边的展区里，这种晶莹剔透的黄瓜不仅口感脆嫩，还富含一种能有效阻止糖类转化为脂肪的功能小分子——“丙醇二酸”。吃根黄瓜还能顺带帮着管理体重。  \n  \n 作物展区里，作物科学研究所带来了新的攻关成果。低豆腥味、高异黄酮、适合榨汁的大豆，还有一款新研发的“甜糯同粒”玉米。\n\n![Image 55](https:\u002F\u002Fnews.cctv.cn\u002F2026\u002F09\u002F20\u002FARTIkUEnHV7gFcVPFEweSMwS260920.shtml)\n\n中国农业科学院作物科学研究所育种专家肖永贵介绍，原来的玉米要么是甜，要么是糯，要么就是7粒糯1粒甜，要么3粒糯1粒甜，把甜和糯放到一个粒里按照不同比例调配口感会更好。  \n  \n 机械展区，各种新型农用机器人集体亮相。这台农业转运机器人不仅能扛100公斤以上的作物在丘陵山区跑，还会“凌波微步”。\n\n![Image 56](https:\u002F\u002Fnews.cctv.cn\u002F2026\u002F09\u002F20\u002FARTIkUEnHV7gFcVPFEweSMwS260920.shtml)\n\n农业农村部南京农业机械化研究所助理研究员宫庆硕介绍，可以进行左右横移，在狭窄的田间环境中可以实现快速换行，也可以实现原地掉头能力。  \n  \n 正在研发的全球动物疾病风险大模型，未来将对动物疫病实现实时预报预警，相当于给全球畜牧业装上一张“天气图”，让餐桌上的肉蛋奶更安全。从吃到用，实验室与餐桌的距离正在被这些“看得见、尝得到、用得上”的成果不断拉近。\n\n编辑：杨书杰 责任编辑：刘亮\n\n*   [从吃到用](https:\u002F\u002Fsearch.cctv.cn\u002Fsearch.php?qtext=%E4%BB%8E%E5%90%83%E5%88%B0%E7%94%A8)\n*   [实验室](https:\u002F\u002Fsearch.cctv.cn\u002Fsearch.php?qtext=%E5%AE%9E%E9%AA%8C%E5%AE%A4)\n*   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[习近平和彭丽媛出席特朗普总统夫妇举行的欢迎宴会](https:\u002F\u002Fnews.cctv.cn\u002F2026\u002F09\u002F25\u002FARTI6HUnyOfkLrkJK5Q68fz1260925.shtml) [当地时间9月24日晚，国家主席习近平和夫人彭丽媛出席美国总统特朗普和夫人梅拉尼娅在白宫举行的欢迎宴会。](https:\u002F\u002Fnews.cctv.cn\u002F2026\u002F09\u002F25\u002FARTI6HUnyOfkLrkJK5Q68fz1260925.shtml) 2026-09-25 00:00[习近平](https:\u002F\u002Fsearch.cctv.cn\u002Fsearch.php?qtext=%E4%B9%A0%E8%BF%91%E5%B9%B3)[特朗普](https:\u002F\u002Fsearch.cctv.cn\u002Fsearch.php?qtext=%E7%89%B9%E6%9C%97%E6%99%AE)[彭丽媛](https:\u002F\u002Fsearch.cctv.cn\u002Fsearch.php?qtext=%E5%BD%AD%E4%B8%BD%E5%AA%9B)   \n*   [![Image 58](https:\u002F\u002Fp3.img.cntv.cn\u002Fphotoworkspace\u002F2026\u002F09\u002F25\u002F2026092507551410751.jpg)](https:\u002F\u002Fnews.cctv.cn\u002F2026\u002F09\u002F25\u002FARTIj0L0EwC5sCqKsSsTKVV1260925.shtml) [彭丽媛同美国总统特朗普夫人梅拉尼娅参观国立亚洲艺术博物馆](https:\u002F\u002Fnews.cctv.cn\u002F2026\u002F09\u002F25\u002FARTIj0L0EwC5sCqKsSsTKVV1260925.shtml) [当地时间9月24日中午，国家主席习近平夫人彭丽媛在华盛顿同美国总统特朗普夫人梅拉尼娅参观国立亚洲艺术博物馆。](https:\u002F\u002Fnews.cctv.cn\u002F2026\u002F09\u002F25\u002FARTIj0L0EwC5sCqKsSsTKVV1260925.shtml) 2026-09-24 23:59[彭丽媛](https:\u002F\u002Fsearch.cctv.cn\u002Fsearch.php?qtext=%E5%BD%AD%E4%B8%BD%E5%AA%9B)[梅拉尼娅](https:\u002F\u002Fsearch.cctv.cn\u002Fsearch.php?qtext=%E6%A2%85%E6%8B%89%E5%B0%BC%E5%A8%85)   \n*   [![Image 59](https:\u002F\u002Fp4.img.cntv.cn\u002Fphotoworkspace\u002F2026\u002F09\u002F25\u002F2026092507395662013.jpg)](https:\u002F\u002Fnews.cctv.cn\u002F2026\u002F09\u002F25\u002FARTIJzgdypivBYzCg2YdvUAE260925.shtml) [大国外交进行时丨特朗普总统夫妇在白宫热情迎接习近平主席夫妇](https:\u002F\u002Fnews.cctv.cn\u002F2026\u002F09\u002F25\u002FARTIJzgdypivBYzCg2YdvUAE260925.shtml) [当地时间9月24日晚，国家主席习近平和夫人彭丽媛在白宫出席美国总统特朗普和夫人梅拉尼娅举行的欢迎宴会。](https:\u002F\u002Fnews.cctv.cn\u002F2026\u002F09\u002F25\u002FARTIJzgdypivBYzCg2YdvUAE260925.shtml) 2026-09-24 23:42[大国外交进行时](https:\u002F\u002Fsearch.cctv.cn\u002Fsearch.php?qtext=%E5%A4%A7%E5%9B%BD%E5%A4%96%E4%BA%A4%E8%BF%9B%E8%A1%8C%E6%97%B6)   \n*   [![Image 60](https:\u002F\u002Fp1.img.cntv.cn\u002Fphotoworkspace\u002F2026\u002F09\u002F25\u002F2026092507401440491.jpg)](https:\u002F\u002Fnews.cctv.cn\u002F2026\u002F09\u002F25\u002FARTIQKqznf9idYWS6iNTtQnG260925.shtml) [大国外交进行时丨习近平主席夫妇抵达白宫出席欢迎宴会](https:\u002F\u002Fnews.cctv.cn\u002F2026\u002F09\u002F25\u002FARTIQKqznf9idYWS6iNTtQnG260925.shtml) [当地时间9月24日晚，国家主席习近平和夫人彭丽媛乘车抵达白宫，出席美国总统特朗普和夫人梅拉尼娅举行的欢迎宴会。](https:\u002F\u002Fnews.cctv.cn\u002F2026\u002F09\u002F25\u002FARTIQKqznf9idYWS6iNTtQnG260925.shtml) 2026-09-24 23:41[大国外交进行时丨习近平主席夫妇抵达白宫出席欢迎宴会](https:\u002F\u002Fsearch.cctv.cn\u002Fsearch.php?qtext=%E5%A4%A7%E5%9B%BD%E5%A4%96%E4%BA%A4%E8%BF%9B%E8%A1%8C%E6%97%B6%E4%B8%A8%E4%B9%A0%E8%BF%91%E5%B9%B3%E4%B8%BB%E5%B8%AD%E5%A4%AB%E5%A6%87%E6%8A%B5%E8%BE%BE%E7%99%BD%E5%AE%AB%E5%87%BA%E5%B8%AD%E6%AC%A2%E8%BF%8E%E5%AE%B4%E4%BC%9A)   \n*   [![Image 61](https:\u002F\u002Fp4.img.cntv.cn\u002Fphotoworkspace\u002F2026\u002F09\u002F25\u002F2026092507245664866.jpg)](https:\u002F\u002Fnews.cctv.cn\u002F2026\u002F09\u002F25\u002FARTIrkOg3GKxtFuslY0DqNVz260925.shtml) [大国外交进行时丨白宫：礼兵和军乐团就位 迎候习近平主席](https:\u002F\u002Fnews.cctv.cn\u002F2026\u002F09\u002F25\u002FARTIrkOg3GKxtFuslY0DqNVz260925.shtml) [当地时间9月2","央视网 2026-09-20","2026-09-20T00:00:00Z","报道",65,{"impact":108,"substance":17,"depth":18,"authority":17,"freshness":340,"relevant":20,"comment":341},5,"央媒对中国农科院开放日的综合报道，集中展示彩色胡萝卜、丙醇二酸黄瓜、甜糯同粒玉米及转运机器人、动物疾病风险大模型等成果，信息量足但属活动报道，时效已过一周，可作主题聚合素材。",[343],{"name":335,"url":332},[345,346,347,348,349],"农业机器人","种业振兴","动物疫病防控","农业科技","功能型农产品",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},[352,358],{"code":353,"slug":354,"name":355,"path":356,"parent_code":357},"A3","maize","玉米","\u002Ftopic\u002Ffield-crops\u002Fmaize","A",{"code":41,"slug":42,"name":43,"path":44,"parent_code":45},{"status":47,"locked":14,"version":48,"method":49,"evidence":360},{"candidates":361,"agricultural_evidence":53},[362,369,374,379,385,391,396],{"code":34,"score":363,"supported":53,"title":364,"abstract":367,"tags":368},22,[365,366],"黄瓜","萝卜",[365,366],[],{"code":353,"score":370,"supported":53,"title":371,"abstract":372,"tags":373},11,[355],[355],[],{"code":41,"score":370,"supported":53,"title":375,"abstract":377,"tags":378},[376],"大模型",[376],[],{"code":380,"score":93,"supported":53,"title":381,"abstract":382,"tags":384},"C5",[],[383],"动物疫病",[347],{"code":386,"score":73,"supported":53,"title":387,"abstract":388,"tags":390},"A4",[],[389],"大豆",[],{"code":392,"score":20,"supported":14,"title":393,"abstract":394,"tags":395},"F1",[],[],[346],{"code":397,"score":20,"supported":14,"title":398,"abstract":399,"tags":400},"F6",[],[],[345],[402,403],"中国农科院 农科开放日","彩色胡萝卜 丙醇二酸黄瓜","中国农科院农科开放日-3396","2026-09-25T00:09:29.650043Z",{"id":407,"title":408,"url":409,"summary":410,"summary_zh":411,"content":8,"source_name":412,"source_url":409,"published_at":413,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":414,"score_detail":415,"sources":417,"tags":419,"primary_topic":423,"secondary_topics":424,"classification":426,"search_phrases":446,"slug":449,"view_count":73,"doi":450,"paper":451,"created_at":465},3284,"Computer vision-based size estimation module for selective harvesting of lettuce","https:\u002F\u002Fdoi.org\u002F10.14719\u002Fpst.14546","Selective harvesting of leafy vegetables remains a major challenge in agricultural automation, particularly for lettuce that meet predefined size or maturity criteria. Vision-based perception systems play a critical role in enabling selective harvesting by identifying plants that meet predefined size or maturity criteria. This study presents the development and validation of computer vision-based size estimation software designed to support selective harvesting decisions in leafy vegetable production. A C #-based image processing application was developed to perform red, green and blue (RGB) colour segmentation, contour detection and pixel-to-metric size estimation using images acquired from a standard Universal Serial Bus (USB) webcam. The system converts pixel measurements into real-world dimensions through calibrated scaling factors that account for variations in camera-to-plant distance. Field experiments were conducted using three commercial lettuce cultivars Romaine, Iceberg and Curly. Vision-based canopy width measurements showed strong agreement with manual measurements (N = 60) and exhibited very strong correlations with plant weight (Romaine type: r = 0.9803; Iceberg type: r = 0.9935; Curly type: r = 0.9707; p \u003C 0.001). Linear regression models achieved high predictive performance (R² = 0.942–0.987), while error metrics indicated low prediction errors (Mean absolute error (MAE): 5.62–8.64 g; root mean square error (RMSE): 7.30–11.06 g). Bland-Altman analysis further confirmed strong agreement between vision-based estimates and manual measurements. This study primarily validates a lightweight vision-based measurement algorithm intended for future integration into robotic harvesting systems. The results demonstrate that simple RGB-based image processing can provide reliable size estimation for selective harvesting decisions in leafy vegetable production systems.","叶菜类蔬菜的选择性收获仍是农业自动化领域的一项重大挑战，尤其是对符合预定大小或成熟度标准的生菜而言。基于视觉的感知系统通过识别符合预定大小或成熟度标准的植株，在实现选择性收获中发挥着关键作用。本研究介绍了一款基于计算机视觉的大小估算软件的开发与验证，该软件旨在为叶菜类蔬菜生产中的选择性收获决策提供支持。研究开发了一款基于C#的图像处理应用程序，利用标准通用串行总线（USB）网络摄像头采集的图像，执行红绿蓝（RGB）颜色分割、轮廓检测以及像素到实际尺寸的估算。该系统通过经过校准的缩放因子将像素测量值转换为实际尺寸，并考虑了摄像头与植株之间距离的变化。田间试验采用三个商业化生菜品种——罗马生菜、结球生菜和皱叶生菜。基于视觉的冠幅测量结果与人工测量结果高度一致（N = 60），并与植株重量呈现极强的相关性（罗马型：r = 0.9803；结球型：r = 0.9935；皱叶型：r = 0.9707；p \u003C 0.001）。线性回归模型取得了较高的预测性能（R² = 0.942–0.987），误差指标显示预测误差较低（平均绝对误差（MAE）：5.62–8.64 g；均方根误差（RMSE）：7.30–11.06 g）。Bland-Altman分析进一步证实了基于视觉的估算值与人工测量值之间的高度一致性。本研究主要验证了一种轻量级视觉测量算法，旨在未来集成到机器人收获系统中。结果表明，简单的基于RGB的图像处理能够为叶菜类蔬菜生产系统中的选择性收获决策提供可靠的大小估算。","Plant Science Today","2026-09-22T00:00:00Z",69,{"impact":18,"substance":109,"depth":110,"authority":18,"freshness":111,"relevant":20,"comment":416},"该研究开发了基于RGB图像的轻量级生菜尺寸估计模块，与人工测量高度一致，为机器人选择性采收提供了可靠感知方案，具有较高的技术参考价值。",[418],{"name":412,"url":409},[116,117,420,421,422],"生菜","机器视觉","选择性采收",{"code":41,"slug":42,"name":43,"path":44,"parent_code":45},[425],{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},{"status":47,"locked":14,"version":48,"method":49,"evidence":427},{"candidates":428,"agricultural_evidence":53},[429,433,437,442],{"code":41,"score":110,"supported":53,"title":430,"abstract":431,"tags":432},[294],[69,294],[117,421],{"code":34,"score":13,"supported":53,"title":434,"abstract":435,"tags":436},[],[36,420,140],[420],{"code":397,"score":73,"supported":53,"title":438,"abstract":439,"tags":441},[],[440],"robotic harvesting",[],{"code":59,"score":20,"supported":14,"title":443,"abstract":444,"tags":445},[],[],[116],[447,448],"生菜 选择性采收 机器视觉","计算机视觉 生菜 尺寸估计","生菜选择性采收机器视觉-3284","10.14719\u002Fpst.14546",{"doi":450,"openalex_id":452,"authors":453,"venue":412,"cited_by_count":198,"oa_url":409,"card":460,"direction":205,"ingested_from":206},"W7214000589",[454,457],{"name":455,"orcid":456},"Erhan Kahya","https:\u002F\u002Forcid.org\u002F0000-0001-7768-9190",{"name":458,"orcid":459},"Fatma Funda Özdüven","https:\u002F\u002Forcid.org\u002F0000-0003-4286-8943",{"tldr":461,"method":462,"finding":463,"direction":205,"opportunity":464},"开发基于RGB图像处理的生菜尺寸估计软件，用于选择性采收决策。","C#图像处理，USB摄像头采集，RGB分割与轮廓检测，标定像素-实际尺寸转换。","视觉冠幅与重量强相关（r=0.97-0.99），回归模型R²达0.94-0.99，误差低。","可集成到机器人采收系统，并探索多品种、多光照条件下的鲁棒性及实时边缘部署。","2026-09-23T23:30:38.203393Z",{"id":467,"title":468,"url":469,"summary":470,"summary_zh":8,"content":8,"source_name":471,"source_url":8,"published_at":413,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":472,"score_detail":473,"sources":476,"tags":478,"primary_topic":481,"secondary_topics":482,"classification":489,"search_phrases":510,"slug":513,"view_count":340,"doi":8,"paper":514,"created_at":522},3249,"设施番茄采摘机器人识别定位与采摘方法研究——江苏大学雷志龙等 基于改进YOLO v5-HSV融合算法识别准确率95.01%","http:\u002F\u002Fwww.qikanzj.com\u002Fhek\u002Fnyjxxb\u002Fmulu\u002F455758.html","智慧农业(中英文)期刊发表江苏大学雷志龙、刘畅、王权团队研究：针对设施单果番茄采摘需求，设计一款设施番茄智能采摘平台，主要由升降机构、采摘机构、识别与定位系统等部分组成。平台整体结构由低压一体化伺服滚珠丝杠副升降机构、六轴协作机械臂和力控末端执行器组成。基于改进YOLO v5-HSV融合算法来识别检测，通过对H分量进行图像阈值分割，提高对成熟目标果实识别的准确率，有效排除未成熟番茄和枝叶背景的干扰；通过眼在手外的标定方法，使用ZED双目相机进行定位。搭建的设施番茄采摘机样机平台在现场采摘试验中识别准确率达到95.01%，采摘成功率为87.96%，单果平均采摘时间为14.56s。","智慧农业(中英文)·江苏大学",82,{"impact":108,"substance":363,"depth":108,"authority":474,"freshness":13,"relevant":20,"comment":475},14,"核心期刊论文，方法有改进、数据完整，识别准确率与采摘成功率等指标明确，对设施农业智能装备研发有参考价值。",[477],{"name":471,"url":469},[116,117,479,421,480],"采摘机器人","设施番茄",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},[483,484],{"code":41,"slug":42,"name":43,"path":44,"parent_code":45},{"code":397,"slug":485,"name":486,"path":487,"parent_code":488},"agricultural-robots","机器人与无人农场","\u002Ftopic\u002Finputs-equipment\u002Fagricultural-robots","F",{"status":47,"locked":14,"version":48,"method":49,"evidence":490},{"candidates":491,"agricultural_evidence":53},[492,498,502,506],{"code":41,"score":493,"supported":53,"title":494,"abstract":496,"tags":497},13,[495],"YOLO",[495],[117,421],{"code":34,"score":18,"supported":53,"title":499,"abstract":500,"tags":501},[142],[142],[480],{"code":397,"score":111,"supported":53,"title":503,"abstract":504,"tags":505},[479],[],[479],{"code":59,"score":93,"supported":53,"title":507,"abstract":508,"tags":509},[],[116],[116],[511,512],"江苏大学 设施番茄 采摘机器人","YOLO v5 番茄 识别定位","江苏大学设施番茄采摘机器人-3249",{"doi":8,"openalex_id":8,"authors":515,"venue":8,"cited_by_count":198,"oa_url":8,"card":516,"direction":205,"ingested_from":521},[],{"tldr":517,"method":518,"finding":519,"direction":205,"opportunity":520},"设计设施番茄智能采摘平台，融合改进YOLO v5与HSV实现识别定位与采摘。","改进YOLO v5-HSV融合算法、ZED双目相机眼在手外标定、六轴机械臂力控末","识别准确率95.01%，采摘成功率87.96%，单果平均采摘时间14.56秒。","可探索多果簇、遮挡与弱光环境下识别定位鲁棒性，并优化采摘效率与末端力控。","agent","2026-09-23T00:04:33.401768Z"]