[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3100":3,"related-3100":109},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":8,"published_at":11,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":24,"tags":26,"primary_topic":33,"secondary_topics":39,"classification":51,"search_phrases":103,"slug":106,"view_count":107,"doi":8,"paper":8,"created_at":108},3100,"秋实满田 智赋丰年——中国移动(成都)产业研究院\"谷丰登\"农业大模型赋能山西大同玉米单产提升试点亩产 2481 斤较非试点增产 15.5%","https:\u002F\u002Fiot.youth.cn\u002Fyw\u002F202609\u002Ft20260921_16881000.htm","9-21 中国青年网物联网频道报道中国移动(成都)产业研究院秋收赋能：以\"九天大模型\"为底座自主研发\"谷丰登\"农业大模型、构建覆盖土壤分析\u002F品种优选\u002F精准水肥药方案\u002F产量预估全生命周期服务体系；联合中储粮打造行业首个储粮场景大模型\"稷元\"已落地 3 个中央储备粮库，粮食储存损失降至 0.2% 以下、化学药剂年使用量减少 1-2 次；牵头制定《农业大模型高质量数据集》《农业种植大模型应用测评规范》两项团体标准。山西大同玉米单产提升标杆项目依托空天地一体化监测+北斗高精定位+农业大模型实现玉米耕种管收全流程无人化作业，试点田亩产 2481 斤较非试点增产 15.5%，被农业农村部评为\"全国智慧农业建设典型案例\"。",null,"九月的田野，稻菽飘香，又是一年丰收时。\n\n随着“十五五”规划的全面实施，农业农村现代化建设蹄疾步稳。传统“看天吃饭”的农业生产模式正在被改写，数字技术驱动下，丰收迎来科技赋能、全链提质、数智增效的全新发展阶段。\n\n当前正值金秋丰收的关键时期，中国移动（成都）产业研究院（以下简称“成研院”）充分发挥科技引领作用，贯通智慧种养、智能储粮、数字治理、产销对接全业务链路，推动数字技术与农业生产融合走深走实，为国家粮食安全与乡村全面振兴注入强劲数字动能。\n\n**科技强农：打造从田间到粮仓的“智慧大脑”**\n\n成研院聚焦AI育种创新、种植生产管理与粮食仓储等关键环节，以中国移动“九天大模型”为底座，自主研发谷丰登农业大模型，构建起全周期、智能化的农业生产服务体系，打造扎根田野的“身边的农业知识专家”。\n\n“藏粮于技”的关键，在于让技术真正下沉到田间地头。成研院打造国内首个种植综合农业大模型，覆盖土壤分析、品种优选、精准水肥药方案、产量预估等全生命周期服务，实现“信息随手握、种养全程导、在线有人帮”。在山西大同玉米单产提升标杆项目中，成研院构建“良田、良种、良机、良法”融合体系，依托空天地一体化监测、北斗高精定位与农业大模型三大核心能力，实现玉米耕、种、管、收全流程无人化作业。当年秋收，试点田亩产达2481斤，较非试点区域增产15.5％，被农业农村部评为“全国智慧农业建设典型案例”。\n\n丰产更要保收，储粮减损是守住丰收成果、保障粮食安全的关键一环。成研院联合中储粮打造行业首个储粮场景大模型“稷元”，内置二十余万条储粮知识单元及千余本专业文献，结合智能语音助手“粮小助”，实时采集、动态监测各类粮情核心指标，智能预判粮情变化趋势，提前预警仓储虫害风险，自动生成仓储调控最优方案，同时采用一体化集成设计，适配粮库现有系统，实现核心业务在库内闭环处理，严守粮食仓储信息安全底线。目前，“稷元”大模型已在中储粮三个中央储备粮库落地，粮食储存损失降至0.2%以下，化学药剂年使用量减少1—2次，大幅提升粮食仓储安全水平，守住丰收成果、筑牢储粮安全防线。\n\n此外，成研院牵头制定《农业大模型高质量数据集》《农业种植大模型应用测评规范》两项团体标准，填补行业规范空白，为大模型规范化、规模化应用筑牢基础。\n\n![Image 1](https:\u002F\u002Fiot.youth.cn\u002Fyw\u002F202609\u002FW020260921554681104915.png)\n\n![Image 2](https:\u002F\u002Fiot.youth.cn\u002Fyw\u002F202609\u002FW020260921554681314335.png)\n\n**数字惠农：激活基层治理民生温度**\n\n成研院以数字技术创新破解基层治理痛点、赋能民生服务、激活乡村发展动能，推动数字技术真正扎根田野、服务农民。\n\n在传承着焦裕禄精神的河南兰考葡萄架乡，成研院打造的数字乡村平台全面落地应用。平台贯通乡、村两级业务全链条，整合民政、社保、农业等多部门数据资源，实现“一次采集、多方复用”。平台提供20余项高频服务“掌上办”，让“数据多跑路、群众少跑腿”，并依托AI智能预警，推动基层治理高效化、智能化。目前，平台已覆盖全乡20个行政村，累计收录人口数据超3万条，助力基层干部事务性工作量降低60%以上，实现网格管理一屏总览、AI智能安防实时监测、积分激励激活村民参与，打通基层治理“最后一公里”。\n\n在甘肃积石山县团结村，智慧乡村大脑聚焦民族地区灾后重建与乡村振兴，集成灾后重建、乡村振兴、民族团结、服务治理、综合监测五大模块，一屏统揽耕地分布、帮扶进展、集体经济、群众诉求等村庄“家底”，为村委科学决策提供精准支撑。同时，村里为50户重点关爱家庭免费安装一键呼叫终端，直连紧急救助与亲情通话，让乡村治理更有精度与温度。\n\n**产业富农：搭建产销双向联通桥梁**\n\n从培育乡村产业带头人到打通农产品出村进城通道，成研院以数字技术为纽带，一头连着田间地头的生产者，一头连着广阔市场的消费者，让好产品卖出好价钱，让乡村产业真正活起来、强起来。\n\n作为农业农村部“头雁”项目的技术支撑方，成研院为“头雁”人才培育系统提供的全方位信息化支撑。目前，“头雁”平台已覆盖全国31省份，累计服务7万余名乡村产业带头人，为乡村培育“造血种子”。\n\n如果说头雁工程是为乡村培育“造血种子”，那么“陆海优品公共服务平台”就是为乡村产业打通“出村进城、走向世界”的黄金通道。平台开设免费电商、直播、品控等培训，助力800多万农民转型“新农人”，推动中国移动对口帮扶的五省（区）八县、成渝地区双城经济圈特色农产品实现精准产销对接。目前，平台已带动超2000万元消费帮扶、实现3亿元农产品出口额，服务全国超600万用户，以数智力量畅通国内国际双循环。\n\n丰收，不再只是田野里的金黄稻浪，更是数据流中奔涌的智慧动能。当技术真正扎根泥土，当数字红利真正惠及农民，丰收便有了更坚实的支撑，乡村便有了更持久的活力。面向未来，成研院将继续以数智之力深耕沃土，让“藏粮于技”落地生根，让“数字乡村”可感可及，在广袤田野上书写属于新时代的丰收故事。","中国青年网","2026-09-21T00:00:00Z","报道",10,false,84,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},24,22,16,13,9,1,"央媒报道的农业大模型落地标杆案例，含亩产2481斤、增产15.5%、储粮损失降至0.2%等硬数据，产业示范价值突出，值得进入每日精选。",[25],{"name":10,"url":6},[27,28,29,30,31,32],"数字乡村","智慧农业","农业人工智能","农业大模型","玉米单产提升","粮食仓储",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},"A8","yield-food-security","单产提升与粮食安全","\u002Ftopic\u002Ffield-crops\u002Fyield-food-security","A",[40,45],{"code":41,"slug":42,"name":43,"path":44,"parent_code":38},"A3","maize","玉米","\u002Ftopic\u002Ffield-crops\u002Fmaize",{"code":46,"slug":47,"name":48,"path":49,"parent_code":50},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"status":52,"locked":14,"version":53,"method":54,"evidence":55},"classified","2026-10-22.2","rules",{"candidates":56,"agricultural_evidence":58},[57,63,68,73,79,86,92,98],{"code":34,"score":20,"supported":58,"title":59,"abstract":61,"tags":62},true,[60],"单产提升",[60],[31,32],{"code":46,"score":20,"supported":58,"title":64,"abstract":66,"tags":67},[65],"大模型",[65],[29,30],{"code":41,"score":69,"supported":58,"title":70,"abstract":71,"tags":72},12,[43],[43],[31],{"code":74,"score":75,"supported":58,"title":76,"abstract":77,"tags":78},"I1",4,[],[28],[28],{"code":80,"score":81,"supported":58,"title":82,"abstract":83,"tags":85},"H2",3,[],[84],"土壤",[],{"code":87,"score":81,"supported":58,"title":88,"abstract":89,"tags":91},"I5",[],[90],"物联网",[],{"code":93,"score":81,"supported":58,"title":94,"abstract":95,"tags":97},"J4",[],[96],"秋收",[],{"code":99,"score":22,"supported":14,"title":100,"abstract":101,"tags":102},"I6",[],[],[27],[104,105],"中国移动 谷丰登 农业大模型","山西大同 玉米 亩产2481斤","中国移动谷丰登农业大模型-3100",8,"2026-09-22T00:05:33.912382Z",{"total":110,"page":22,"page_size":110,"items":111},6,[112,201,260,335,417,491],{"id":113,"title":114,"url":115,"summary":116,"summary_zh":117,"content":8,"source_name":118,"source_url":115,"published_at":119,"category":120,"cover_url":8,"hotness":13,"is_selected":14,"score":121,"score_detail":122,"sources":125,"tags":127,"primary_topic":131,"secondary_topics":132,"classification":134,"search_phrases":158,"slug":161,"view_count":22,"doi":162,"paper":163,"created_at":200},5164,"Lightweight Hybrid CNN-Transformer Network for Robust Maize Leaf Disease Detection and Edge Deployment","https:\u002F\u002Fdoi.org\u002F10.22266\u002Fijies2026.1130.50","Early and accurate detection of maize leaf diseases is essential for improving crop productivity and supporting precision agriculture.However, deep learning models trained on controlled leaf-image datasets often experience performance degradation under real-world agricultural conditions because of illumination variation, background clutter, occlusion, leaf orientation, and imaging noise.To address these challenges, this study develops a lightweight deep learning framework for robust maize leaf disease classification and edge-oriented deployment.The framework employs MobileNetV3-Small for efficient extraction of disease-discriminative visual features and investigates lightweight feature-learning strategies for improving classification under challenging field conditions.Experiments were conducted using PlantVillage-Maize and Field-Maize datasets comprising 6,188 images from four categories: Maize Leaf Blight (MLB), Common Rust, Healthy, and Leaf Spot.The image-only MobileNetV3-Small model achieved 97.8% accuracy and a 97.4% F1-score on the controlled PlantVillage-Maize dataset, while its performance decreased to 86.1% accuracy and an 83.9% F1-score on Field-Maize, highlighting the domain gap between controlled and real-field imagery.Robustness was further evaluated under Gaussian noise, illumination variation, and partial occlusion, together with computational-efficiency and edge-deployment analyses.The findings demonstrate the importance of lightweight and robust visual feature learning for practical maize disease diagnosis under real-world agricultural imaging conditions.","早期准确检测玉米叶片病害对于提高作物生产力和支持精准农业至关重要。然而，在受控叶片图像数据集上训练的深度学习模型，在真实农业条件下往往会出现性能下降，原因包括光照变化、背景杂乱、遮挡、叶片朝向以及成像噪声。为应对这些挑战，本研究开发了一个轻量级深度学习框架，用于稳健的玉米叶片病害分类和面向边缘的部署。该框架采用MobileNetV3-Small高效提取具有病害判别力的视觉特征，并研究轻量级特征学习策略，以提升在复杂田间条件下的分类性能。实验使用PlantVillage-Maize和Field-Maize数据集进行，共包含来自四个类别的6，188张图像：玉米大斑病（Maize Leaf Blight，MLB）、普通锈病（Common Rust）、健康（Healthy）和叶斑病（Leaf Spot）。仅使用图像的MobileNetV3-Small模型在受控的PlantVillage-Maize数据集上达到了97.8%的准确率和97.4%的F1分数，而在Field-Maize上的性能下降至86.1%的准确率和83.9%的F1分数，凸显了受控图像与真实田间图像之间的域差距。研究进一步在高斯噪声、光照变化和部分遮挡条件下评估了稳健性，并进行了计算效率和边缘部署分析。研究结果表明，轻量级且稳健的视觉特征学习对于真实农业成像条件下的实际玉米病害诊断具有重要意义。","International journal of intelligent engineering and systems","2026-10-16T00:00:00Z","论文",69,{"impact":69,"substance":123,"depth":19,"authority":69,"freshness":21,"relevant":22,"comment":124},20,"该研究提出轻量级玉米叶病检测框架，并揭示受控数据集与田间数据之间的性能差距，方法扎实、结论有参考价值，但属细分技术论文，公共影响有限。",[126],{"name":118,"url":115},[28,29,128,129,130],"边缘计算","深度学习","玉米病害",{"code":41,"slug":42,"name":43,"path":44,"parent_code":38},[133],{"code":46,"slug":47,"name":48,"path":49,"parent_code":50},{"status":52,"locked":14,"version":53,"method":54,"evidence":135},{"candidates":136,"agricultural_evidence":58},[137,142,147,154],{"code":41,"score":138,"supported":58,"title":139,"abstract":140,"tags":141},14,[42],[43,42],[],{"code":46,"score":107,"supported":58,"title":143,"abstract":144,"tags":146},[],[129,145],"deep learning",[29,129],{"code":74,"score":148,"supported":58,"title":149,"abstract":150,"tags":153},7,[],[151,152],"精准农业","precision agriculture",[28],{"code":87,"score":22,"supported":14,"title":155,"abstract":156,"tags":157},[],[],[128],[159,160],"MobileNetV3 玉米叶部病害","玉米叶片病害 边缘部署","MobileNetV3玉米叶部病害-5164","10.22266\u002Fijies2026.1130.50",{"doi":162,"openalex_id":164,"authors":165,"venue":118,"cited_by_count":191,"oa_url":115,"card":192,"direction":198,"ingested_from":199},"W7222602850",[166,169,172,174,176,179,181,183,186,188],{"name":167,"orcid":168},"Awwab Mohammad","https:\u002F\u002Forcid.org\u002F0000-0002-1148-5678",{"name":170,"orcid":171},"Punith kumar","https:\u002F\u002Forcid.org\u002F0009-0005-5676-4194",{"name":173,"orcid":8},"Bhavani K",{"name":175,"orcid":8},"Sailee N. Panchbhai",{"name":177,"orcid":178},"Sagarkumar S. Badhiye","https:\u002F\u002Forcid.org\u002F0000-0002-0710-3761",{"name":180,"orcid":8},"Manoj Kumar S B",{"name":182,"orcid":8},"Dharamvir",{"name":184,"orcid":185},"Sumit Kumar Sar","https:\u002F\u002Forcid.org\u002F0000-0003-0384-9088",{"name":187,"orcid":8},"Rani Chhagan Dalvi",{"name":189,"orcid":190},"Yogesh Harishchandra Bhosale","https:\u002F\u002Forcid.org\u002F0000-0001-6901-1419",0,{"tldr":193,"method":194,"finding":195,"direction":196,"opportunity":197},"构建轻量MobileNetV3-Small框架实现玉米叶片病害分类并评估边缘部署。","MobileNetV3-Small，PlantVillage-Maize与Fie","受控数据准确率97.8%，田间降至86.1%，揭示明显域差距。","农业人工智能与决策模型","可研究跨域自适应与田间鲁棒特征学习，缩小受控与真实场景性能差距。","农业遥感与作物表型","openalex","2026-10-17T23:30:28.248539Z",{"id":202,"title":203,"url":204,"summary":205,"summary_zh":206,"content":8,"source_name":207,"source_url":204,"published_at":208,"category":120,"cover_url":8,"hotness":13,"is_selected":14,"score":209,"score_detail":210,"sources":213,"tags":215,"primary_topic":218,"secondary_topics":219,"classification":221,"search_phrases":240,"slug":243,"view_count":191,"doi":244,"paper":245,"created_at":259},4759,"Machine Learning-based Prediction of SVI Parameter in Maize: A Comparative Study and Feature Importance Analysis","https:\u002F\u002Fdoi.org\u002F10.24925\u002Fturjaf.v14i10.2870-2879.9043","Background: Accurate estimation of seedling vigor indices (SVI) is critical for monitoring plant health and predicting yields in precision agriculture. This study comparatively evaluates the performance of different machine learning algorithms in estimating the SVI parameter. Methods: Germination data were collected from 39 maize genotypes under three different salinity conditions (0 mM, 100 mM, and 200 mM NaCl), including the parameters Gmax, MGT, GI, SL, RL, SdL, SMR, RMR, and SVI. The dataset was preprocessed through normalization to the [0–1] range. The SVI parameter was estimated using four different machine learning methods: Random Forest, CatBoost, LightGBM, and Support Vector Regression (SVR). To evaluate the performance of the models, both 10-fold cross-validation and 80%–20% hold-out validation techniques were employed. The model performance was examined using RMSE, MSE, MAE, and R² metrics. Results: The findings show that all models have a high explanatory power in the training dataset. Specifically, the CatBoost model showed the highest success in the training phase (R²=0.9962), whereas the SVR model achieved the highest generalization performance on the test dataset (R²=0.9712, RMSE=0.0582). Feature importance analysis results revealed that the 'App' parameter had the highest impact on the target variable in most models, but different structural interactions were prominent in the LightGBM model. The results demonstrate that machine learning techniques provide high accuracy in SVI estimation and that the tendency towards overfitting is limited. Conclusions: In conclusion, this study confirms that machine learning techniques are powerful and reliable tools for estimating spectral indices in precision agriculture applications. The integration of SVR and ensemble-based models into agricultural decision support systems is expected to significantly contribute to optimizing resource use and improving yield predictions.","背景：准确估计幼苗活力指数（SVI）对于精准农业中监测植物健康和预测产量至关重要。本研究比较评估了不同机器学习算法在估计SVI参数方面的性能。方法：从39个玉米基因型在三种不同盐度条件（0 mM、100 mM和200 mM NaCl）下收集发芽数据，包括Gmax、MGT、GI、SL、RL、SdL、SMR、RMR和SVI等参数。数据集通过归一化至[0–1]范围进行预处理。采用四种不同的机器学习方法估计SVI参数：随机森林、CatBoost、LightGBM和支持向量回归（SVR）。为评估模型性能，采用了10折交叉验证和80%–20%留出验证技术。使用RMSE、MSE、MAE和R²指标检验模型性能。结果：研究结果表明，所有模型在训练数据集中均具有较高的解释力。具体而言，CatBoost模型在训练阶段表现出最高的成功率（R²=0.9962），而SVR模型在测试数据集上取得了最高的泛化性能（R²=0.9712，RMSE=0.0582）。特征重要性分析结果显示，在大多数模型中，“App”参数对目标变量的影响最大，但在LightGBM模型中不同的结构交互作用更为突出。结果表明，机器学习技术在SVI估计中提供了高精度，且过拟合倾向有限。结论：总之，本研究证实机器学习技术是精准农业应用中估计光谱指数的强大而可靠的工具。将SVR和基于集成的模型整合到农业决策支持系统中，预计将显著有助于优化资源利用和改善产量预测。","Turkish Journal of Agriculture - Food Science and Technology","2026-10-11T00:00:00Z",63,{"impact":107,"substance":211,"depth":19,"authority":69,"freshness":21,"relevant":22,"comment":212},18,"方法对比扎实、结论可靠，但属细分领域论文，产业影响有限，适合主题聚合而非头条精选。",[214],{"name":207,"url":204},[28,29,216,43,217],"机器学习","耐盐育种",{"code":41,"slug":42,"name":43,"path":44,"parent_code":38},[220],{"code":46,"slug":47,"name":48,"path":49,"parent_code":50},{"status":52,"locked":14,"version":53,"method":54,"evidence":222},{"candidates":223,"agricultural_evidence":58},[224,231,236],{"code":46,"score":225,"supported":58,"title":226,"abstract":228,"tags":230},19,[227],"machine learning",[216,227,229],"decision support",[29,216],{"code":41,"score":232,"supported":58,"title":233,"abstract":234,"tags":235},15,[42],[43,42],[43],{"code":74,"score":148,"supported":58,"title":237,"abstract":238,"tags":239},[],[151,152],[28],[241,242],"玉米 SVI 机器学习","玉米 耐盐 幼苗活力指数","玉米SVI机器学习-4759","10.24925\u002Fturjaf.v14i10.2870-2879.9043",{"doi":244,"openalex_id":246,"authors":247,"venue":207,"cited_by_count":191,"oa_url":204,"card":254,"direction":196,"ingested_from":199},"W7221462188",[248,251],{"name":249,"orcid":250},"Mustafa Turan Arslan","https:\u002F\u002Forcid.org\u002F0000-0001-5498-6571",{"name":252,"orcid":253},"Gülay Zülkadir","https:\u002F\u002Forcid.org\u002F0000-0003-3488-4011",{"tldr":255,"method":256,"finding":257,"direction":196,"opportunity":258},"用四种机器学习模型基于39个玉米基因型盐胁迫发芽数据预测幼苗活力指数SVI。","39个玉米基因型在三种盐浓度下的发芽数据，归一化后用RF、CatBoost、Li","SVR泛化最优（R²=0.9712），CatBoost训练拟合最高；App参数对SVI影响最大。","可扩展到多作物多胁迫环境，结合光谱或图像表型数据提升SVI预测的田间适用性。","2026-10-12T23:30:44.762497Z",{"id":261,"title":262,"url":263,"summary":264,"summary_zh":8,"content":265,"source_name":266,"source_url":8,"published_at":267,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":268,"score_detail":269,"sources":272,"tags":274,"primary_topic":277,"secondary_topics":278,"classification":280,"search_phrases":330,"slug":333,"view_count":191,"doi":8,"paper":8,"created_at":334},4460,"中国农科院资划所牵头智慧农作体系观摩会在山东桓台举行 千亩玉米单产提升8%—12%","https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Fgdxw\u002F2026-10\u002F03\u002Fcontent_591573.html","中国农业科学院农业资源与农业区划研究所牵头粮食作物精准智慧化生产管理关键技术与集成示范项目黄淮海麦玉轮作主产区智慧农作体系应用示范观摩会在山东桓台中化MAP桓台农场举行；项目构建以多源感知为基础、智能诊断为核心、农机精准执行为保障、数据平台为中枢的智慧农作技术体系，千亩示范方玉米单产较周边常规种植对照提升8%—12%，优质品率稳定在88%以上，化肥、农药、灌溉用水量较常规种植分别减少10%—15%、8%—10%、12%—18%。","![Image 4](https:\u002F\u002Fstatics.kjrb.com.cn\u002Fresource\u002Fkjw\u002Findex\u002Fimages\u002Fshare_logo.jpg)\n\n[![Image 5](https:\u002F\u002Fstatics.kjrb.com.cn\u002Fresource\u002Fkjw\u002Findex\u002Fimages\u002Flogo.png)](http:\u002F\u002Fwww.stdaily.com\u002F)\n\n*   [时政](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Fszxw\u002Fnode_357.html)\n*   [热点](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Frdxw\u002Fnode_327.html)\n*   [政务](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Fzwxw\u002Fnode_362.html)\n*   [深瞳](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Fstbd\u002Fnode_434.html)\n*   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马爱平\n\n近日，由中国农业科学院农业资源与农业区划研究所牵头的“粮食作物精准智慧化生产管理关键技术与集成示范”项目黄淮海麦玉轮作主产区智慧农作体系应用示范观摩会，在山东桓台中化MAP桓台农场举行。\n\n与会人员实地观摩项目针对黄淮海小麦—玉米轮作精准智慧生产现实需求，围绕麦玉种、肥、水、药管理关键环节研发的作物表型监测、精量播种、养分诊断与变量追肥、精准灌溉、病虫害监测与变量喷施等系列新技术、新装备，现场考察了集实时监测、智能诊断、定量调控、变量作业于一体，农艺农机信息深度融合的区域智慧农作技术体系及示范成效。\n\n![Image 8](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Fgdxw\u002Fpic\u002F2026-10\u002F03\u002F591573_4296d9a8-883a-4a2a-98e1-7290f3adff4fcopy.jpg)\n\n据中化MAP桓台农场负责人介绍，这套数字技术解决方案通过精准播种、变量施肥和智能灌溉等核心环节的优化，实现千亩示范方玉米单产较周边常规种植对照提升8%—12%，优质品率稳定在88%以上；同时，实现农业资源的节约，化肥、农药和灌溉用水量较常规种植分别减少10%—15%、8%—10%、12%—18%，资源利用效率得到显著提升，目前已形成面向周边普通农户和新型经营主体的技术培训、推广和辐射服务能力。\n\n![Image 9](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Fgdxw\u002Fpic\u002F2026-10\u002F03\u002F591573_e7499078-dee9-4a40-bf66-504e09cef576copy.jpg)\n\n项目首席科学家吴文斌研究员表示，玉米是我国种植面积最大、产量最高的粮食作物。但长期以来，我国玉米种植生产管理整体偏粗放。播种质量参差不齐，关键农情信息获取滞后，水肥药施用多依赖传统经验，不仅造成玉米单产与先进国家相比有较大差距，也带来农业资源浪费、农田生态压力加大等问题。为此，项目组联合中化MAP团队，选择灌溉条件完善、机械化基础扎实的MAP桓台农场作为核心基地，打造玉米生产方式数字化转型升级示范区。该项目构建一套以多源感知为基础、智能诊断为核心、农机精准执行为保障、数据平台为中枢的智慧农作技术体系，实现玉米种植全过程的数据驱动和精准管控。\n\n（中国农业科学院农业资源与农业区划研究所供图）\n\n责任编辑：陈可轩\n\n相关稿件：\n\n![Image 10](https:\u002F\u002Fstatics.kjrb.com.cn\u002Fresource\u002Fkjw\u002Fimages\u002Fpinglun.png)网友评论\n\n评论\n\n最热评论\n\n没有更多评论了\n\n[热点](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Frdxw\u002Fnode_327.html)\n\n[![Image 11](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Fgdxw\u002Fpic\u002F2026-10\u002F08\u002F592769_1df74a15-67d5-4910-aece-debcc638606a_1009x6734106423copy.JPG)](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Fgdxw\u002F2026-10\u002F08\u002Fcontent_592769.html)\n\n[国庆假期服务消费旺盛 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2026-10-03","2026-10-03T00:00:00Z",79,{"impact":18,"substance":270,"depth":19,"authority":138,"freshness":110,"relevant":22,"comment":271},21,"国家级科研机构牵头、有明确增产与节本数据的智慧农作示范报道，专业性与信息增量俱佳，值得进入每日精选。",[273],{"name":266,"url":263},[28,275,151,276,31],"农业遥感","水肥一体化",{"code":41,"slug":42,"name":43,"path":44,"parent_code":38},[279],{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},{"status":52,"locked":14,"version":53,"method":54,"evidence":281},{"candidates":282,"agricultural_evidence":58},[283,287,291,297,303,309,315,320,325],{"code":41,"score":69,"supported":58,"title":284,"abstract":285,"tags":286},[43],[43],[31],{"code":34,"score":21,"supported":58,"title":288,"abstract":289,"tags":290},[60],[],[31],{"code":292,"score":81,"supported":58,"title":293,"abstract":294,"tags":296},"A7",[],[295],"轮作",[],{"code":298,"score":81,"supported":58,"title":299,"abstract":300,"tags":302},"F4",[],[301],"农药",[],{"code":304,"score":81,"supported":58,"title":305,"abstract":306,"tags":308},"F5",[],[307],"农机",[],{"code":310,"score":81,"supported":58,"title":311,"abstract":312,"tags":314},"H3",[],[313],"灌溉",[],{"code":74,"score":316,"supported":14,"title":317,"abstract":318,"tags":319},2,[],[],[28,151],{"code":321,"score":22,"supported":14,"title":322,"abstract":323,"tags":324},"F3",[],[],[276],{"code":326,"score":22,"supported":14,"title":327,"abstract":328,"tags":329},"I3",[],[],[275],[331,332],"中国农科院资划所 智慧农作 桓台","中化MAP 玉米 单产提升","中国农科院资划所智慧农作桓台-4460","2026-10-09T00:04:56.174089Z",{"id":336,"title":337,"url":338,"summary":339,"summary_zh":340,"content":8,"source_name":341,"source_url":338,"published_at":342,"category":120,"cover_url":8,"hotness":13,"is_selected":58,"score":343,"score_detail":344,"sources":346,"tags":348,"primary_topic":350,"secondary_topics":351,"classification":353,"search_phrases":378,"slug":381,"view_count":382,"doi":383,"paper":384,"created_at":416},3842,"Raspberry Pi Lightweight Deep Learning Models Deployed for Corn Growth Stage Classification Using PhenoCam Images","https:\u002F\u002Fdoi.org\u002F10.3390\u002Frs18193337","Identifying crop growth stages is essential for comprehensive assessment that facilitates timely and precise agronomic management. However, conventional field-based manual scouting is labor-intensive, time-consuming, and lacks scalability. Digital field images with advanced models deployed on portable edge-computing devices offer an alternative solution for real-time crop monitoring and decision support. Therefore, in this study, the standard 8 corn growth stages were defined as 8 classes based on distinct visual canopy appearance patterns observed in PhenoCam (near-surface [proximal] remote sensing network camera) imagery collected from 10 PhenoCam sites. These classes do not correspond directly to standard agronomic vegetation and reproductive growth stages. For modeling, four lightweight deep learning (DL) models, ELiteCrop0, ELiteCrop1, ELiteCrop4, and MobNetCropV2, were developed and evaluated across five image vertical clipping levels (0–40%) using a supercomputer (NDSU CCAST). The optimized model was subsequently deployed on a single-board Raspberry Pi 5 computer for edge inference. Model training accounted for the majority of the total CPU time, exceeding 97%, while the testing times ranged from 0.01–0.12 min, enabling real-time applications. Among the models, ELiteCrop0 achieved the most balanced performance with a confusion-matrix diagonal ratio (CMDR) of 0.93, followed by ELiteCrop1 (CMDR = 0.92). Overall, model performance decreased with increasing vertical clipping; therefore, a moderate image clipping (0–10%) was recommended for improved computational efficiency. Analysis with a supercomputer produced an intrasite (same training sites) accuracy of 0.90–0.93 (Raspberry Pi: 0.78–0.81) and an intersite (unseen test sites) accuracy of 0.48–0.50 (Raspberry Pi: 0.41–0.43), indicating challenges with model generalization. Raspberry Pi processed ≈ 1000 images\u002Fmin at 68 °C, with a PhenoCam:Raspberry Pi processing ratio of 1:2500 at 50% capacity over an 8 h day. Overall, this study presents a scalable and cost-effective solution for real-time corn growth stage monitoring in precision agriculture, which could be extended to other crops.","识别作物生长阶段对于全面评估、促进及时精准的农艺管理至关重要。然而，传统的田间人工巡查劳动强度大、耗时且缺乏可扩展性。部署在便携式边缘计算设备上的数字田间图像与先进模型为实时作物监测和决策支持提供了替代方案。因此，本研究基于从10个PhenoCam（近地面[近端]遥感网络相机）站点采集的影像中观察到的不同冠层视觉外观模式，将标准的8个玉米生长阶段定义为8个类别。这些类别并不直接对应于标准的农艺营养生长和生殖生长阶段。在建模方面，开发了四种轻量级深度学习（DL）模型——ELiteCrop0、ELiteCrop1、ELiteCrop4和MobNetCropV2，并利用超级计算机（NDSU CCAST）在五种图像垂直裁剪水平（0–40%）下进行了评估。随后，优化后的模型被部署到单板Raspberry Pi 5计算机上进行边缘推理。模型训练占总CPU时间的大部分，超过97%，而测试时间为0.01–0.12分钟，支持实时应用。在各模型中，ELiteCrop0实现了最均衡的性能，混淆矩阵对角线比率（CMDR）为0.93，其次是ELiteCrop1（CMDR = 0.92）。总体而言，模型性能随垂直裁剪比例的增加而下降；因此，建议采用适度的图像裁剪（0–10%）以提高计算效率。使用超级计算机的分析得出站内（相同训练站点）准确率为0.90–0.93（Raspberry Pi：0.78–0.81），站间（未见测试站点）准确率为0.48–0.50（Raspberry Pi：0.41–0.43），表明模型泛化面临挑战。Raspberry Pi在68 °C下处理约1000张图像\u002F分钟，在8小时工作日50%容量下，PhenoCam与Raspberry Pi的处理比为1:2500。总体而言，本研究为精准农业中的实时玉米生长阶段监测提供了一种可扩展且经济高效的解决方案，并可扩展至其他作物。","Remote Sensing","2026-09-29T00:00:00Z",81,{"impact":211,"substance":18,"depth":211,"authority":138,"freshness":21,"relevant":22,"comment":345},"该研究将轻量深度学习模型部署于树莓派实现玉米生育期实时识别，方法新颖、数据扎实，对精准农业边缘监测有参考价值。",[347],{"name":341,"url":338},[28,29,128,43,349],"遥感",{"code":41,"slug":42,"name":43,"path":44,"parent_code":38},[352],{"code":46,"slug":47,"name":48,"path":49,"parent_code":50},{"status":52,"locked":14,"version":53,"method":54,"evidence":354},{"candidates":355,"agricultural_evidence":58},[356,360,365,369,374],{"code":46,"score":211,"supported":58,"title":357,"abstract":358,"tags":359},[145],[129,145,229],[29],{"code":41,"score":232,"supported":58,"title":361,"abstract":363,"tags":364},[362],"corn",[43,362],[43],{"code":74,"score":148,"supported":58,"title":366,"abstract":367,"tags":368},[],[151,152],[28],{"code":326,"score":148,"supported":58,"title":370,"abstract":371,"tags":373},[],[349,372],"remote sensing",[349],{"code":87,"score":75,"supported":58,"title":375,"abstract":376,"tags":377},[],[128],[128],[379,380],"Raspberry Pi 玉米 生长阶段","PhenoCam 玉米 深度学习","RaspberryPi玉米生长阶段-3842",5,"10.3390\u002Frs18193337",{"doi":383,"openalex_id":385,"authors":386,"venue":341,"cited_by_count":191,"oa_url":338,"card":411,"direction":198,"ingested_from":199},"W7214787420",[387,390,393,396,399,402,405,408],{"name":388,"orcid":389},"Anne Joice","https:\u002F\u002Forcid.org\u002F0000-0001-7499-9222",{"name":391,"orcid":392},"Humeera Tazeen","https:\u002F\u002Forcid.org\u002F0000-0003-2411-0725",{"name":394,"orcid":395},"Talha Tufaique","https:\u002F\u002Forcid.org\u002F0000-0002-4490-5341",{"name":397,"orcid":398},"Mathala Juliet Gupta","https:\u002F\u002Forcid.org\u002F0000-0002-3886-2429",{"name":400,"orcid":401},"Cannayen Igathinathane","https:\u002F\u002Forcid.org\u002F0000-0001-8884-7959",{"name":403,"orcid":404},"Nitin Rai","https:\u002F\u002Forcid.org\u002F0000-0002-9350-9601",{"name":406,"orcid":407},"Craig W. Whippo","https:\u002F\u002Forcid.org\u002F0000-0002-7626-6919",{"name":409,"orcid":410},"David Walter Archer","https:\u002F\u002Forcid.org\u002F0000-0002-4816-7040",{"tldr":412,"method":413,"finding":414,"direction":198,"opportunity":415},"用PhenoCam图像训练轻量深度学习模型，在树莓派上实时分类玉米8个生长阶段。","基于10个PhenoCam站点图像，开发4种轻量DL模型，在树莓派5上边缘推理。","ELiteCrop0表现最佳（CMDR=0.93），站点内精度0.90-0.93，但站点间泛化差（0","站点间泛化差是主要瓶颈，可研究跨站点迁移学习或域适应以提升模型鲁棒性。","2026-09-30T23:30:29.168194Z",{"id":418,"title":419,"url":420,"summary":421,"summary_zh":422,"content":8,"source_name":423,"source_url":420,"published_at":424,"category":120,"cover_url":8,"hotness":13,"is_selected":14,"score":425,"score_detail":426,"sources":428,"tags":430,"primary_topic":434,"secondary_topics":435,"classification":443,"search_phrases":473,"slug":476,"view_count":81,"doi":477,"paper":478,"created_at":490},3613,"AI-Assisted Demand Forecasting and Production Scheduling for High-Volume Food Manufacturing in Saudi Arabia: A Food Security Framework for Vision 2030","https:\u002F\u002Fdoi.org\u002F10.64388\u002Firev10i3-1723457","High-volume food manufacturing in Saudi Arabia is increasingly exposed to volatile demand, short product life cycles, promotion-driven peaks, imported input dependencies, capacity bottlenecks, and the operational consequences of seasonal and religious events. These conditions make demand forecasting and production scheduling inseparable rather than sequential planning tasks. This review synthesizes research published between 2020 and 2025 on artificial intelligence-assisted demand forecasting, perishable-product planning, finite-capacity scheduling, digital-twin-enabled decision support, and food supply-chain resilience. The study develops an integrated framework in which demand sensing produces probabilistic forecasts that are translated into production decisions through capacity, shelf-life, sanitation, changeover, labour, inventory, and service constraints. Evidence indicates that machine-learning and deep-learning methods can improve forecast accuracy when promotions, weather, calendar events, regional variation, and censored demand are represented appropriately, while optimization models can convert these signals into feasible schedules that reduce changeovers, waste, shortages, and unstable resource use [1-8]. The review also identifies a persistent implementation gap: forecasting studies often stop at predictive accuracy, whereas scheduling studies frequently assume demand inputs are fixed and reliable. The proposed framework closes this gap by linking forecast uncertainty, rolling-horizon scheduling, execution feedback, and food-security outcomes. For Saudi manufacturers, the value of this integration lies not only in lower cost, but also in improving product availability, freshness, localized processing capacity, shock recovery, and evidence-based management under Vision 2030. The paper concludes with a staged implementation roadmap and research priorities for explainability, cross-site scheduling, event-aware forecasting, data governance, and resilient planning.","沙特阿拉伯的大规模食品制造日益面临需求波动、产品生命周期短、促销驱动的峰值、进口原料依赖、产能瓶颈以及季节性和宗教事件的运营影响。这些条件使得需求预测与生产调度成为不可分割的任务，而非顺序规划任务。本文综述了2020年至2025年间发表的相关研究，涵盖人工智能辅助需求预测、易腐产品规划、有限产能调度、数字孪生赋能的决策支持以及食品供应链韧性。研究构建了一个集成框架，其中需求感知产生概率预测，并通过产能、保质期、卫生、换产、劳动力、库存和服务约束转化为生产决策。证据表明，当促销、天气、日历事件、区域差异和截尾需求得到适当表征时，机器学习和深度学习方法能够提高预测精度，而优化模型可将这些信号转化为可行的调度方案，从而减少换产、浪费、缺货和不稳定的资源使用[1-8]。本综述还指出了一个持续存在的实施缺口：预测研究往往止步于预测精度，而调度研究则常常假设需求输入是固定且可靠的。所提出的框架通过将预测不确定性、滚动时域调度、执行反馈和食品安全结果联系起来，弥合了这一缺口。对于沙特制造商而言，这种集成的价值不仅在于降低成本，还在于改善产品可得性、新鲜度、本地化加工能力、冲击恢复能力以及“2030愿景”下的循证管理。本文最后提出了分阶段实施路线图以及可解释性、跨厂区调度、事件感知预测、数据治理和韧性规划方面的研究优先事项。","Iconic Research and Engineering Journals","2026-09-25T00:00:00Z",60,{"impact":69,"substance":211,"depth":19,"authority":110,"freshness":107,"relevant":22,"comment":427},"综述性论文，提出AI需求预测与生产排程整合框架，对智慧农业与粮食安全有参考价值，但属境外研究且期刊权威性一般。",[429],{"name":423,"url":420},[28,29,431,432,433],"粮食安全","供应链韧性","生产调度",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},[436,442],{"code":437,"slug":438,"name":439,"path":440,"parent_code":441},"E1","food-processing","加工与食品制造","\u002Ftopic\u002Ffood-supply\u002Ffood-processing","E",{"code":46,"slug":47,"name":48,"path":49,"parent_code":50},{"status":52,"locked":14,"version":53,"method":54,"evidence":444},{"candidates":445,"agricultural_evidence":58},[446,452,457,462,467],{"code":437,"score":447,"supported":58,"title":448,"abstract":450,"tags":451},11,[449],"food manufacturing",[449],[],{"code":46,"score":13,"supported":58,"title":453,"abstract":454,"tags":456},[],[129,216,455,229],"artificial intelligence",[29],{"code":34,"score":21,"supported":58,"title":458,"abstract":460,"tags":461},[459],"food security",[],[431],{"code":74,"score":75,"supported":58,"title":463,"abstract":464,"tags":466},[],[465],"数字孪生",[28],{"code":468,"score":81,"supported":58,"title":469,"abstract":470,"tags":472},"E4",[],[471],"食品安全",[],[474,475],"沙特阿拉伯 食品制造 需求预测","Vision 2030 粮食安全","沙特阿拉伯食品制造需求预测-3613","10.64388\u002Firev10i3-1723457",{"doi":477,"openalex_id":479,"authors":480,"venue":423,"cited_by_count":191,"oa_url":483,"card":484,"direction":489,"ingested_from":199},"W7214295236",[481],{"name":482,"orcid":8},"Atif Maqbool Syed","https:\u002F\u002Fwww.irejournals.com\u002Fformatedpaper\u002F1723457.pdf",{"tldr":485,"method":486,"finding":487,"direction":196,"opportunity":488},"综述2020-2025年AI需求预测与生产调度研究，提出沙特食品制造整合框架以保障Vision 20","文献综述2020-2025年AI预测、易腐品规划、有限产能调度与数字孪生研究。","预测研究止于精度、调度研究假设需求固定，整合框架可弥合实施鸿沟并提升粮食安全。","可研究事件感知预测与滚动调度耦合、跨工厂协同及可解释AI在食品供应链韧性中的应用。","智慧农业 \u002F 农业物联网","2026-09-27T23:30:14.516185Z",{"id":492,"title":493,"url":494,"summary":495,"summary_zh":8,"content":8,"source_name":496,"source_url":494,"published_at":497,"category":120,"cover_url":8,"hotness":13,"is_selected":14,"score":121,"score_detail":498,"sources":500,"tags":502,"primary_topic":504,"secondary_topics":505,"classification":511,"search_phrases":536,"slug":539,"view_count":75,"doi":540,"paper":541,"created_at":549},3152,"An integrated IoT and machine learning framework for maize yield prediction and smart agriculture in Tanzania","https:\u002F\u002Fdoi.org\u002F10.1080\u002F23311932.2026.2725400","An integrated IoT and machine learning framework for maize yield prediction and smart agriculture in Tanzania。Cogent Food & Agriculture","Cogent Food & Agriculture","2026-09-22T00:00:00Z",{"impact":69,"substance":211,"depth":19,"authority":20,"freshness":13,"relevant":22,"comment":499},"论文提出物联网与机器学习融合的玉米产量预测框架，方法有新意但属区域性应用研究，影响力有限，时效性强。",[501],{"name":496,"url":494},[28,29,503,90,43],"产量预测",{"code":41,"slug":42,"name":43,"path":44,"parent_code":38},[506,507],{"code":46,"slug":47,"name":48,"path":49,"parent_code":50},{"code":87,"slug":508,"name":509,"path":510,"parent_code":50},"agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",{"status":52,"locked":14,"version":53,"method":54,"evidence":512},{"candidates":513,"agricultural_evidence":58},[514,518,523,527,532],{"code":41,"score":69,"supported":58,"title":515,"abstract":516,"tags":517},[42],[42],[43],{"code":74,"score":69,"supported":58,"title":519,"abstract":521,"tags":522},[520],"smart agriculture",[520],[28],{"code":46,"score":69,"supported":58,"title":524,"abstract":525,"tags":526},[227],[227],[29],{"code":87,"score":69,"supported":58,"title":528,"abstract":530,"tags":531},[529],"IoT",[529],[90],{"code":326,"score":22,"supported":14,"title":533,"abstract":534,"tags":535},[],[],[503],[537,538],"坦桑尼亚 玉米 产量预测","物联网 机器学习 智慧农业","坦桑尼亚玉米产量预测-3152","10.1080\u002F23311932.2026.2725400",{"doi":540,"openalex_id":542,"authors":543,"venue":496,"cited_by_count":191,"oa_url":494,"card":8,"direction":489,"ingested_from":199},"W7213978365",[544,547],{"name":545,"orcid":546},"Alcardo Alex Barakabitze","https:\u002F\u002Forcid.org\u002F0000-0001-8960-8415",{"name":548,"orcid":8},"Yasinta Nzogera","2026-09-22T23:30:10.178097Z"]