[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-5031":3,"related-5031":74},{"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":32,"secondary_topics":38,"classification":39,"search_phrases":69,"slug":72,"view_count":22,"doi":8,"paper":8,"created_at":73},5031,"智慧棉田成就新疆新一代科技棉农","https:\u002F\u002Fwww.news.cn\u002Flocal\u002F20261015\u002F048734cea69b466eac74966f7b4d0074\u002Fc.html","金秋十月,天山南北棉花吐絮迎来采收。在塔克拉玛干沙漠北缘的尉犁县'超级棉田',采棉机来回穿梭,朵絮归仓。新疆是中国最大优质棉花生产基地,2025年新疆棉花产量占全国92.8%,总产、单产连续32年居全国第一。2021年极飞科技在尉犁县启动'超级棉田'项目,目前管理环节无人化作业率已达75%,栽培管理模式在全疆推广面积已超200万亩。今年更新的X150新型农业无人机可自动配药、加药、充电、规划航线,自主喷药作业。'90后'家庭农场主郭腾承包2.5万亩棉田,日常管护只需50名工人,返乡种棉成为越来越多年轻人的选择。",null,"  新华社乌鲁木齐10月14日电（记者潘莹、宿传义、高晗）金秋十月，天山南北棉花吐絮，迎来采收。在塔克拉玛干沙漠北缘的尉犁县“超级棉田”，伴随着一台台采棉机来回穿梭，洁白的棉花被采摘、压缩、打包，朵絮归仓。\n\n  “前不久，田里用上一款新款农业无人机，不需要飞手操作，棉花采收环节更加智能化。”“超级棉田”项目负责人艾海鹏说。\n\n  新疆是中国最大优质棉花生产基地，2025年新疆棉花产量占全国产量的92.8%，总产、单产连续32年居全国第一。如今，在新疆，这样的智慧化场景正逐渐贯穿棉花生产全过程，并加速从示范走向普及，一批懂技术、善经营、会管理的“新一代棉农”应运而生。\n\n  在新疆巴音郭楞蒙古自治州尉犁县“超级棉田”中，农业无人机在田间喷洒脱叶剂（2026年9月20日摄，无人机照片）。新华社发（汪志鹏 摄）\n\n  尉犁县气候干燥、光热充足，是中国优质商品棉种植基地，棉花种植面积稳定在100多万亩。2021年，在尉犁县支持下，农业机器人企业极飞科技启动“超级棉田”项目，派出艾海鹏、凌磊两位“90后”管理经营3000亩棉田，探索可复制、可推广的无人化农业管理模式。\n\n  两人从解决棉花管理中最耗人工的灌溉难题入手，持续升级无人化、智能化监测管理的新技术、新设备，让“超级棉田”管理模式与大规模棉花种植高度适配，实现产量更高、品质更优。\n\n  “通过多年努力，‘超级棉田’已实现水、肥、药的智能化管理，目前管理环节无人化作业率已达75%。”艾海鹏说。截至2025年底，“超级棉田”的栽培管理模式在全疆推广面积已超200万亩。\n\n  今年，“超级棉田”又更新了一批新装备：耕种环节用上最新的APC4自驾仪，播种机可自动断膜断带，进一步节省人工。最让管理者津津乐道的，当属X150新型农业无人机。这款无人机可自动配药、加药、充电、规划航线、自主喷药作业，适配除草、防虫、防病等一系列田间需求。\n\n  在棉花吐絮关键期，记者在“超级棉田”作业现场看到，艾海鹏在手机上选好模板、点击“立即开始”，机器人便自主起降、补能，既不需要飞手，也不需要遥控器。手机屏上，药箱空满状态和地面药罐余量一目了然。\n\n  “往年打药虽然也用无人机，但还需要飞手控制轨迹、及时补药换电池。现在把飞机放这儿就不用管了。晚上回去睡觉，第二天早上过来，它已经按要求完成了全部植保任务。”艾海鹏说，目前这款新型农业无人机已在全国推广。\n\n  “我们的目的是想要吸引越来越多年轻人回到农业这一行业。一个行业有年轻人加入，才能有朝气，有更大的发展空间。”艾海鹏说。\n\n  在新疆巴音郭楞蒙古自治州尉犁县“超级棉田”中，采棉机在采收棉花（2026年10月14日摄，无人机照片）。新华社发\n\n  在天山北麓的昌吉市佃坝镇东沟村，“90后”家庭农场主郭腾正盯着屏幕，调度巨大的采棉机在棉海里缓缓行进，源源不断吐出打包好的棉包。\n\n  郭腾是土生土长的昌吉人，一家两代人的种棉经历，正是新疆棉花从“靠人力”到“靠科技”转型发展的鲜活缩影。\n\n  他说，父辈种棉是实打实的苦差事：棉田管护加采收要投入大量人工，采收成本高，种植户收入却很有限。\n\n  “如今农村可发展的空间大了。”郭腾介绍，自己毕业后在城里闯荡过一段时间，随着新疆棉田科技含量越来越高，看到新一代棉农正面临机遇无限的职业图景，决定回乡种棉花。\n\n  如今，郭腾的家庭农场已配齐整套自主农机：4台采棉机、6台植保无人机、十余台播种机、3台大马力拖拉机；棉田用上水肥一体化精准滴灌、无人机植保，犁地、播种、打药、采收，全流程机械化。\n\n  “一台采棉机一天能收400至450亩，每亩采收环节油费加人工成本仅60元。”郭腾说，他承包的2.5万亩棉田日常管护只需50名工人，自己在手机上就能查看田间情况、调度农机作业。\n\n  返乡种棉正成为越来越多年轻人的选择。郭腾发现，与他长期合作的团队里，返乡年轻人越来越多。“只要掌握技术，采棉季每月赚5万元不成问题。”他说。\n\n  随着越来越多科技装备、先进技术在新疆广袤棉田推广普及，新疆棉田加速智慧化，更多年轻人在天山南北的棉田里，找到创业舞台。","新华社 2026 年 10 月 15 日","2026-10-14T23:50:00Z","报道",10,false,84,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},24,20,17,15,8,1,"央媒实地报道，以新疆超级棉田与新一代棉农为样本，呈现农业无人机自主作业与无人化管理模式从示范走向普及的产业级进展，数据与多方信源扎实，具主题聚合价值。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","农业无人机","棉花","无人化农场","新疆棉田",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"A6","cash-crops","棉麻糖与特色经作","\u002Ftopic\u002Ffield-crops\u002Fcash-crops","A",[],{"status":40,"locked":14,"version":41,"method":42,"evidence":43},"classified","2026-10-22.2","rules",{"candidates":44,"agricultural_evidence":47},[45,51,58,64],{"code":33,"score":46,"supported":47,"title":48,"abstract":49,"tags":50},4,true,[],[29],[29],{"code":52,"score":53,"supported":47,"title":54,"abstract":55,"tags":57},"A4",3,[],[56],"花生",[],{"code":59,"score":53,"supported":47,"title":60,"abstract":61,"tags":63},"G2",[],[62],"家庭农场",[],{"code":65,"score":22,"supported":14,"title":66,"abstract":67,"tags":68},"I1",[],[],[27],[70,71],"新疆 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multi-classification and prediction of Pink Bollworm incidence using agro-meteorological variables.The dataset includes maximum and minimum air temperature, relative humidity, rainfall, sunshine hours, evaporation, and wind speed, all of which influence Pink Bollworm development, migration, and reproductive cycles.After preprocessing, applying cyclic encoding to temporal variables, and generating lagged climatic features, multiple machine learning algorithms-including SVM, Neural Networks, KNN, Regression models, Ensemble methods, and Naïve Bayes-were trained and systematically compared.SVM model demonstrated the best test accuracy, effectively capturing dynamic climate-pest interactions.The resulting MATLAB-based classification tool enables rapid assessment and supports climate-informed pest surveillance.A stratified 80:20 train-test split was employed, with 5-fold cross-validation on the training set for hyperparameter tuning, ensuring no data leakage between partitions.The validation and training accuracies of these models are 89.4% and 96 % respectively.Overall, the model offers a practical and interpretable solution for early detection of Pink Bollworm risk, thereby contributing to informed decision-making in integrated pest management (IPM) and precision agriculture.","棉红铃虫（Pectinophora gossypiella）仍是影响棉花产量最具破坏性的害虫之一，其发生程度受气候波动和作物物候的强烈驱动。本研究提出了一种基于机器学习的框架，利用农业气象变量对棉红铃虫的发生进行早期多分类与预测。数据集包括最高和最低气温、相对湿度、降雨量、日照时数、蒸发量和风速，这些因素均影响棉红铃虫的发育、迁移和繁殖周期。经过预处理、对时间变量应用循环编码并生成滞后气候特征后，训练并系统比较了多种机器学习算法，包括支持向量机（SVM）、神经网络、K近邻（KNN）、回归模型、集成方法和朴素贝叶斯。SVM模型表现出最佳的测试准确率，有效捕捉了气候与害虫之间的动态相互作用。所构建的基于MATLAB的分类工具可实现快速评估，并支持气候信息驱动的害虫监测。采用分层80：20的训练-测试划分，并在训练集上进行5折交叉验证以调优超参数，确保各分区之间无数据泄漏。这些模型的验证准确率和训练准确率分别为89.4%和96%。总体而言，该模型为棉红铃虫风险的早期检测提供了一种实用且可解释的解决方案，从而有助于综合害虫管理（IPM）和精准农业中的科学决策。","International Journal of Drug Delivery Technology","2026-10-17T00:00:00Z","论文",55,{"impact":21,"substance":88,"depth":89,"authority":75,"freshness":90,"relevant":22,"comment":91},18,14,9,"研究用多类机器学习模型结合气象与时序特征预测棉铃虫风险，方法扎实但属细分领域应用研究，信源期刊权威性一般，公共影响有限。",[93],{"name":83,"url":80},[27,95,29,96,97],"机器学习","农业气象","病虫害预警",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":101},{"candidates":102,"agricultural_evidence":47},[103,108,115,121],{"code":33,"score":20,"supported":47,"title":104,"abstract":106,"tags":107},[105],"cotton",[29,105],[29],{"code":65,"score":109,"supported":47,"title":110,"abstract":111,"tags":114},7,[],[112,113],"精准农业","precision agriculture",[27],{"code":116,"score":109,"supported":47,"title":117,"abstract":118,"tags":120},"I2",[],[95,119],"machine learning",[95],{"code":122,"score":46,"supported":47,"title":123,"abstract":124,"tags":125},"H7",[],[96],[96],[127,128],"棉铃虫 机器学习 预测","棉花 病虫害 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11n模型的棉花田间复杂环境障碍物检测方法","http:\u002F\u002Fwww.qikanzj.com\u002Fhek\u002Fnyjxxb\u002Fmulu\u002F455619.html","韩科立、王振坤等(中国农业机械化科学研究院集团有限公司;现代农装科技股份有限公司;农业装备技术全国重点实验室)针对棉花田间复杂环境障碍物被遮挡致准确检测难、边缘设备算力有限的问题,提出一种基于改进YOLO 11n模型的田间障碍物检测方法。采用StarNet作为主要特征提取网络并引入DBA模块增强多尺度特征交互能力;使用KAGNConv替换C3k2瓶颈结构增强灵活性与可解释性;集成分离与增强注意力模块SEAM至检测头增强遮挡场景能力。试验结果表明,改进模型YOLO 11n-SKS精确率、召回率、mAP50、mAP50-95分别提升2.3、2.1、1.3、1.4个百分点,达到91.7%、88.3%、91.9%、62.3%。","《农业机械学报》",71,{"impact":152,"substance":153,"depth":88,"authority":89,"freshness":154,"relevant":22,"comment":155},16,21,2,"改进YOLO 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田间障碍物","中国农业机械化科学研究院采棉机障碍物检测-4966",{"doi":8,"openalex_id":8,"authors":198,"venue":8,"cited_by_count":134,"oa_url":8,"card":199,"direction":139,"ingested_from":204},[],{"tldr":200,"method":201,"finding":202,"direction":139,"opportunity":203},"提出改进YOLO 11n模型，实现棉花田间复杂遮挡环境下障碍物高精度检测。","以StarNet为骨干，引入DBA、KAGNConv与SEAM注意力模块，改进Y","改进模型精确率91.7%、召回率88.3%、mAP50达91.9%，各项指标均提升。","可探索轻量化模型在边缘设备实时部署，并扩展至多作物多类障碍物检测。","agent","2026-10-15T00:06:08.577388Z",{"id":207,"title":208,"url":209,"summary":210,"summary_zh":8,"content":211,"source_name":212,"source_url":8,"published_at":213,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":214,"score_detail":215,"sources":219,"tags":221,"primary_topic":225,"secondary_topics":226,"classification":227,"search_phrases":238,"slug":241,"view_count":134,"doi":8,"paper":8,"created_at":242},4766,"棉花关键技术集成助力高温逆境下产量品质效益三丰收","https:\u002F\u002Fwap.sciencenet.cn\u002Fmobile.php?cat=T&id=572667","中国农业科学院西部农业研究中心在昌吉州老龙河综合试验基地举办棉花优质高产关键技术集成示范观摩会。千亩示范方实测籽棉平均亩产601.4公斤,集成中棉113优系、中新棉1号、3号等优质多抗新品种,以及高光效群体调控、智慧棉田管理、绿色病虫害防控等成套技术,节药15%以上、人工成本降低30%以上。","科学网—\n\n## [![Image 1: 科学网](https:\u002F\u002Fwap.sciencenet.cn\u002Fstatic\u002Fimage\u002Fcommon\u002Flogo.png)](https:\u002F\u002Fwap.sciencenet.cn\u002F 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2026-10-10","2026-10-10T00:00:00Z",73,{"impact":216,"substance":88,"depth":89,"authority":217,"freshness":75,"relevant":22,"comment":218},22,13,"报道中国农科院棉花高温逆境关键技术集成取得产量品质效益三丰收，属细分领域实质进展，但时效稍旧、信息增量有限，可入精选备选。",[220],{"name":212,"url":209},[27,222,29,223,224],"中国农科院","技术集成","高温逆境",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":228},{"candidates":229,"agricultural_evidence":47},[230,234],{"code":33,"score":173,"supported":47,"title":231,"abstract":232,"tags":233},[29],[29],[29],{"code":65,"score":22,"supported":14,"title":235,"abstract":236,"tags":237},[],[],[27],[239,240],"中国农科院 棉花 高温逆境","棉花 关键技术集成 产量品质","中国农科院棉花高温逆境-4766","2026-10-13T00:08:35.350655Z",{"id":244,"title":245,"url":246,"summary":247,"summary_zh":248,"content":8,"source_name":249,"source_url":246,"published_at":250,"category":85,"cover_url":8,"hotness":13,"is_selected":14,"score":251,"score_detail":252,"sources":254,"tags":256,"primary_topic":260,"secondary_topics":261,"classification":267,"search_phrases":284,"slug":287,"view_count":53,"doi":288,"paper":289,"created_at":300},3838,"Forecasting Cotton Productivity in the Mil-Mugan Economic Region using Climate Variables and Remote Sensing Data","https:\u002F\u002Fdoi.org\u002F10.52171\u002Fherald.342","Satellite imagery is considered as one of the most valuable and functional data sources for evaluating and forecasting production in agricultural systems. This study aims to forecast productivity in cotton fields within the Mil-Mugan economic region by incorporating vegetation indices and climatic variables. NDVI indicators were derived from Sentinel-2 data, temperature (T) was obtained from ERA5-Land data, and precipitation (R) was collected from the CHIRPS database. The data were analyzed using Google Earth Engine, ArcGIS Pro, and Python environments, consolidating them into an integrated temporal-spatial dimension. The correlation and regression analysis assessed the link among NDVI, temperature, precipitation, and productivity, leading to the development of a multivariate model. The findings derived from the model indicate that NDVI is the primary factor influencing productivity. The model developed for one region was implemented in the other three locations, and the correlation between the actual and predicted indicators was assessed using R² and RMSE metrics. The results indicate that the model's forecast accuracy fluctuates within the 90% range, considered appropriate for predicting regional cotton yield. The study's findings are significant for enhancing operational monitoring and planning of agrotechnical decisions with satellite data.","卫星影像被认为是评估和预测农业系统产量最有价值且最实用的数据来源之一。本研究旨在通过整合植被指数与气候变量，预测米尔-穆甘经济区棉田的生产力。NDVI指标源自Sentinel-2数据，温度（T）取自ERA5-Land数据，降水（R）来自CHIRPS数据库。数据在Google Earth Engine、ArcGIS Pro和Python环境中进行分析，并将其整合为统一的时空维度。通过相关性与回归分析评估了NDVI、温度、降水与生产力之间的关系，进而构建了多变量模型。模型结果表明，NDVI是影响生产力的主要因素。将为某一区域开发的模型应用于其他三个地点，并使用R²和RMSE指标评估实际值与预测值之间的相关性。结果表明，模型的预测精度在90%范围内波动，被认为适用于预测区域棉花产量。本研究结果对于利用卫星数据加强农艺决策的业务化监测与规划具有重要意义。","Herald of the Azerbaijan Engineering Academy","2026-09-29T00:00:00Z",63,{"impact":21,"substance":18,"depth":152,"authority":13,"freshness":90,"relevant":22,"comment":253},"基于Sentinel-2与气候变量的棉花产量预测模型，方法扎实、精度达90%，但属区域案例研究，公共影响有限。",[255],{"name":249,"url":246},[27,257,29,258,259],"产量预测","遥感","NDVI",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[262],{"code":263,"slug":264,"name":265,"path":266,"parent_code":168},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"status":40,"locked":14,"version":41,"method":42,"evidence":268},{"candidates":269,"agricultural_evidence":47},[270,274,280],{"code":33,"score":20,"supported":47,"title":271,"abstract":272,"tags":273},[105],[29,105],[29],{"code":263,"score":275,"supported":47,"title":276,"abstract":278,"tags":279},11,[277],"remote sensing",[],[259,257,258],{"code":65,"score":22,"supported":14,"title":281,"abstract":282,"tags":283},[],[],[27],[285,286],"Mil-Mugan 棉花 遥感","Sentinel-2 NDVI 棉花产量","Mil-Mugan棉花遥感-3838","10.52171\u002Fherald.342",{"doi":288,"openalex_id":290,"authors":291,"venue":249,"cited_by_count":134,"oa_url":246,"card":294,"direction":298,"ingested_from":142},"W7214748696",[292],{"name":293,"orcid":8},"B.H. Babashli",{"tldr":295,"method":296,"finding":297,"direction":298,"opportunity":299},"结合Sentinel-2 NDVI与气候变量，构建多元模型预测米尔-穆甘地区棉花产量。","Sentinel-2 NDVI、ERA5-Land温度、CHIRPS降水，GEE","NDVI是产量主控因子，模型跨区预测精度约90%，可用于区域估产。","农业遥感与作物表型","可引入多时相NDVI与机器学习，提升跨区域迁移性和极端气候下的估产鲁棒性。","2026-09-30T23:30:27.786044Z",{"id":302,"title":303,"url":304,"summary":305,"summary_zh":306,"content":8,"source_name":307,"source_url":304,"published_at":308,"category":85,"cover_url":8,"hotness":13,"is_selected":14,"score":15,"score_detail":309,"sources":313,"tags":315,"primary_topic":318,"secondary_topics":319,"classification":321,"search_phrases":349,"slug":352,"view_count":46,"doi":353,"paper":354,"created_at":386},3014,"Detecting diurnal dynamics of cotton leaf inclination angle under water-salt stress","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.rse.2026.115674","Leaf inclination angle (LIA) dynamics act as a rapid response mechanism to abiotic stress, regulating canopy energy balance and water use efficiency. While the adaptive value of diurnal LIA plasticity (e.g., paraheliotropism) is well-recognized in ecology, most current remote sensing algorithms and ecosystem models still treat canopy architecture as static and neglect stress-induced geometric adjustments. Furthermore, the diurnal dynamics of LIA under combined abiotic stresses, such as concurrent water deficit and salinity, still remain poorly understood. Recent advances in unmanned aerial vehicle (UAV) photogrammetry offer a promising approach for capturing LIA dynamics at high spatial and temporal resolution. However, accurately resolving fine scale, dynamic leaf movements in real environments using UAVs remains challenging. To address these gaps, we developed the Constraint-Assisted Point cloud fusion for Leaf scale Analysis (CAPLA), an integrated UAV analytical workflow that combines deep learning with Structure from Motion (SfM). CAPLA employs 2D semantic masks to strictly constrain 3D mesh reconstruction, effectively mitigating motion-induced artifacts. Independent validation against 19 plot level mean leaf angle (MLA) observations collected at 9:30 am and 12:00 pm yielded an R 2 of 0.89 and an RMSE of 0.9°, supporting plot level MLA estimation under the validated acquisition conditions. CAPLA was subsequently applied across five observation times to characterize diurnal canopy structural dynamics. Importantly, repeated measures analysis of the high frequency observations revealed significant effects of irrigation, salinity, and observation time on MLA, together with a significant irrigation × time interaction ( P = 0.0109), indicating that diurnal MLA trajectories differed among irrigation levels. In contrast, neither the irrigation × salinity interaction ( P = 0.8800) nor the irrigation × salinity × time interaction ( P = 0.9086) was significant. Descriptive differences in within-day variability were nevertheless observed among individual treatment combinations, highlighting the value of time-resolved structural monitoring for characterizing canopy responses to combined water and salinity stresses. These findings highlight the complex structural plasticity of canopies under interacting stresses, emphasizing the critical need to transition from static canopy assumptions to dynamic structural monitoring for improving ecosystem models and precision agriculture.","叶片倾角（LIA）动态变化是植物对非生物胁迫的快速响应机制，调控冠层能量平衡与水分利用效率。尽管昼夜LIA可塑性（如避日运动）的适应价值在生态学中已得到广泛认可，但当前大多数遥感算法和生态系统模型仍将冠层结构视为静态，忽略了胁迫诱导的几何调整。此外，在水分亏缺与盐分胁迫等复合非生物胁迫条件下，LIA的昼夜动态变化仍知之甚少。近年来无人机（UAV）摄影测量技术的进展为在高时空分辨率下捕捉LIA动态提供了有前景的方法。然而，利用无人机在真实环境中精确解析精细尺度的动态叶片运动仍具挑战性。为弥补上述不足，我们开发了约束辅助点云融合叶片尺度分析流程（CAPLA），这是一种集成了深度学习与运动恢复结构（SfM）的无人机综合分析工作流。CAPLA利用二维语义掩膜严格约束三维网格重建，有效减轻了运动诱导的伪影。基于上午9：30和中午12：00采集的19个样地水平平均叶倾角（MLA）观测值进行独立验证，结果R²为0.89，RMSE为0.9°，支持在验证采集条件下进行样地水平MLA估算。随后将CAPLA应用于五个观测时段以表征冠层结构的昼夜动态变化。重要的是，对高频观测的重复测量分析揭示了灌溉、盐分和观测时间对MLA的显著影响，以及显著的灌溉×时间交互效应（P = 0.0109），表明不同灌溉水平下MLA的昼夜变化轨迹存在差异。相比之下，灌溉×盐分交互效应（P = 0.8800）和灌溉×盐分×时间交互效应（P = 0.9086）均不显著。尽管如此，在各处理组合之间仍观察到日内变异性的描述性差异，凸显了时间分辨结构监测在表征冠层对水分与盐分复合胁迫响应方面的价值。这些发现揭示了冠层在交互胁迫下的复杂结构可塑性，强调亟需从静态冠层假设转向动态结构监测，以改进生态系统模型和精准农业。","Remote Sensing of Environment","2026-09-19T00:00:00Z",{"impact":88,"substance":310,"depth":311,"authority":20,"freshness":90,"relevant":22,"comment":312},23,19,"该研究提出CAPLA无人机点云融合方法，实现水盐胁迫下棉花叶倾角昼夜动态的高精度监测，方法新颖、数据可靠，对作物表型与精准农业有实质参考价值。",[314],{"name":307,"url":304},[27,316,29,112,317],"农业遥感","无人机遥感",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[320],{"code":263,"slug":264,"name":265,"path":266,"parent_code":168},{"status":40,"locked":14,"version":41,"method":42,"evidence":322},{"candidates":323,"agricultural_evidence":47},[324,328,332,336,343],{"code":33,"score":90,"supported":47,"title":325,"abstract":326,"tags":327},[105],[],[29],{"code":65,"score":21,"supported":47,"title":329,"abstract":330,"tags":331},[],[112,113],[27,112],{"code":263,"score":21,"supported":47,"title":333,"abstract":334,"tags":335},[],[258,277],[316,317],{"code":337,"score":75,"supported":47,"title":338,"abstract":339,"tags":342},"H3",[],[340,341],"灌溉","irrigation",[],{"code":116,"score":75,"supported":47,"title":344,"abstract":345,"tags":348},[],[346,347],"深度学习","deep learning",[],[350,351],"无人机 棉花 叶倾角 水盐胁迫","CAPLA 冠层结构 动态监测","无人机棉花叶倾角水盐胁迫-3014","10.1016\u002Fj.rse.2026.115674",{"doi":353,"openalex_id":355,"authors":356,"venue":307,"cited_by_count":134,"oa_url":304,"card":381,"direction":298,"ingested_from":142},"W7213661690",[357,360,362,364,366,368,371,374,376,379],{"name":358,"orcid":359},"Qing Li","https:\u002F\u002Forcid.org\u002F0009-0004-4580-7761",{"name":361,"orcid":8},"Dalei Hao",{"name":363,"orcid":8},"Jan Pisek",{"name":365,"orcid":8},"Zicheng Ji",{"name":367,"orcid":8},"Yanan Wei",{"name":369,"orcid":370},"Youngryel Ryu","https:\u002F\u002Forcid.org\u002F0000-0001-6238-2479",{"name":372,"orcid":373},"Jiarui Xu","https:\u002F\u002Forcid.org\u002F0000-0003-4925-2770",{"name":375,"orcid":8},"Yangmin Feng",{"name":377,"orcid":378},"Shaozhong Kang","https:\u002F\u002Forcid.org\u002F0000-0002-8019-2537",{"name":380,"orcid":8},"Yelu Zeng",{"tldr":382,"method":383,"finding":384,"direction":298,"opportunity":385},"提出CAPLA无人机点云融合方法，监测水盐胁迫下棉花叶倾角昼夜动态。","结合深度学习与SfM，用2D语义掩膜约束3D网格重建，无人机高频观测。","灌溉、盐分和时间显著影响叶倾角，灌溉×时间交互显著，昼夜轨迹因灌溉而异。","可将动态叶倾角参数化嵌入作物模型，提升水盐胁迫下冠层结构与蒸散模拟精度。","2026-09-20T23:30:21.262698Z",{"id":388,"title":389,"url":390,"summary":391,"summary_zh":392,"content":8,"source_name":393,"source_url":390,"published_at":394,"category":85,"cover_url":8,"hotness":13,"is_selected":14,"score":395,"score_detail":396,"sources":398,"tags":400,"primary_topic":403,"secondary_topics":404,"classification":406,"search_phrases":431,"slug":434,"view_count":53,"doi":435,"paper":436,"created_at":466},2288,"Field-based deep learning classification of cotton and weeds for machine-vision-assisted intra-row weed management","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.atech.2026.102567","Timely and precise weed management is essential for reducing crop-weed competition, labour requirements, and unnecessary weed-control inputs in cotton production. Reliable discrimination between cotton plants and weeds is a prerequisite for automated intra-row weed management. This study evaluated two pretrained convolutional neural network models, MobileNetV2 and DenseNet169, for binary classification of cotton and weeds using field-acquired RGB images. Images were collected from a farmer-managed cotton field in Haryana and an experimental cotton field at ICAR-Indian Agricultural Research Institute, New Delhi, during May-September 2025 under natural field conditions. The dataset comprised 2,195 weed images representing six predominant weed species and 1,211 cotton images. Images were classified into two operational classes, cotton and weed. Training images were augmented to address the original class imbalance, whereas validation and test images were retained without augmentation. Transfer learning was used to fine-tune both models, and performance was evaluated using accuracy, precision, recall, F1-score, confusion matrices, and inference time. Both models achieved high classification performance. DenseNet169 attained a test accuracy of 99.47%, compared with 98.27% for MobileNetV2. The results indicate that pretrained CNNs can provide accurate image-level discrimination between cotton and weed vegetation under the investigated field conditions. DenseNet169 therefore shows promise as a visual-perception component for machine-vision-assisted intra-row weed management. However, the present study is limited to image-level classification and does not demonstrate plant localization, continuous machine operation, actuator coordination, or field-scale weed removal. Further validation under independent locations, varying weed densities, illumination conditions, machine travel speeds, and complete perception-decision-actuation pipelines is required.","及时、精准的杂草管理对于减少棉花生产中的棉草竞争、劳动力需求和不必要的除草投入至关重要。可靠地区分棉花植株与杂草是实现行内自动化杂草管理的前提。本研究评估了两种预训练卷积神经网络模型——MobileNetV2和DenseNet169，利用田间采集的RGB图像对棉花与杂草进行二分类。图像于2025年5月至9月期间，在自然田间条件下，采集自哈里亚纳邦一处农民管理的棉田以及新德里ICAR-印度农业研究所的实验棉田。数据集包含2，195幅杂草图像，涵盖六种主要杂草种类，以及1，211幅棉花图像。图像被分为棉花和杂草两个操作类别。训练图像经过增强处理以解决原始类别不平衡问题，而验证和测试图像则保留未增强状态。采用迁移学习对两种模型进行微调，并使用准确率、精确率、召回率、F1分数、混淆矩阵和推理时间评估性能。两种模型均取得了较高的分类性能。DenseNet169的测试准确率达到99.47%，而MobileNetV2为98.27%。结果表明，预训练卷积神经网络能够在所研究的田间条件下实现棉花与杂草植被在图像层面的准确区分。因此，DenseNet169有望作为机器视觉辅助行内杂草管理的视觉感知组件。然而，本研究仅限于图像层面的分类，并未展示植株定位、机器连续作业、执行器协调或田间规模除草。仍需在独立地点、不同杂草密度、光照条件、机器行进速度以及完整的感知-决策-执行流程下进行进一步验证。","Smart Agricultural Technology","2026-09-11T00:00:00Z",67,{"impact":173,"substance":88,"depth":152,"authority":217,"freshness":21,"relevant":22,"comment":397},"基于田间RGB图像的棉花与杂草深度学习分类研究，DenseNet169测试准确率达99.47%，方法扎实但仅限图像级分类，尚缺定位与执行环节验证，属细分领域技术进展。",[399],{"name":393,"url":390},[27,159,29,401,402],"杂草识别","机器视觉",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[405],{"code":116,"slug":165,"name":166,"path":167,"parent_code":168},{"status":40,"locked":14,"version":41,"method":42,"evidence":407},{"candidates":408,"agricultural_evidence":47},[409,413,417,423,427],{"code":33,"score":20,"supported":47,"title":410,"abstract":411,"tags":412},[105],[29,105],[29],{"code":116,"score":13,"supported":47,"title":414,"abstract":415,"tags":416},[347],[],[159,402],{"code":418,"score":46,"supported":47,"title":419,"abstract":420,"tags":422},"F4",[],[421],"除草",[401],{"code":52,"score":53,"supported":47,"title":424,"abstract":425,"tags":426},[],[56],[],{"code":65,"score":22,"supported":14,"title":428,"abstract":429,"tags":430},[],[],[27],[432,433],"农业人工智能 智慧农业 机器视觉 杂草识别","农业人工智能 智慧农业","农业人工智能智慧农业机器视觉杂草识别-2288","10.1016\u002Fj.atech.2026.102567",{"doi":435,"openalex_id":437,"authors":438,"venue":393,"cited_by_count":134,"oa_url":390,"card":461,"direction":139,"ingested_from":142},"W7212315230",[439,441,444,447,450,452,455,457,459],{"name":440,"orcid":8},"Shaik Nasreen",{"name":442,"orcid":443},"Roaf Ahmad Parray","https:\u002F\u002Forcid.org\u002F0000-0002-8303-1990",{"name":445,"orcid":446},"Parveen Dhanger","https:\u002F\u002Forcid.org\u002F0000-0001-5250-0275",{"name":448,"orcid":449},"Rishi Raj","https:\u002F\u002Forcid.org\u002F0009-0002-7918-1141",{"name":451,"orcid":8},"Tapan Kumar Khura",{"name":453,"orcid":454},"P. Sahoo","https:\u002F\u002Forcid.org\u002F0000-0002-3888-8506",{"name":456,"orcid":8},"Tushar Dhar",{"name":458,"orcid":8},"Prajwal R",{"name":460,"orcid":8},"Sripriyanka S. Nalla",{"tldr":462,"method":463,"finding":464,"direction":139,"opportunity":465},"用MobileNetV2和DenseNet169对田间棉花与杂草图像做二分类，验证机器视觉除草可行性","迁移学习微调两种预训练CNN，使用2025年田间RGB图像共3406张。","DenseNet169测试准确率达99.47%，优于MobileNetV2的98.27%。","可延伸至植株定位、多光照与密度条件下的感知-决策-执行全流程田间验证。","2026-09-13T23:30:04.183782Z"]