[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1469":3,"related-1469":62},{"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":22,"tags":24,"primary_topic":8,"secondary_topics":30,"classification":31,"search_phrases":56,"slug":59,"view_count":60,"doi":8,"paper":8,"created_at":61},1469,"新疆昌吉市 54.55 万亩机采棉陆续进入田间管理关键期：65 名农技人员、256 架无人机完成 95% 喷施作业","https:\u002F\u002Fnews.qq.com\u002Frain\u002Fa\u002F20260902A03W8N00","眼下昌吉市 54.55 万亩优质机采棉已陆续裂铃吐絮、进入田间管理关键期。全市组织 65 名农业技术人员、协调 256 架植保无人机在各乡镇开展技术指导和田管作业，预计 9 月 5 日左右全部完成。每亩施用 20-30 克乙烯利促进棉桃成熟又不伤棉花；新型植保无人机搭载北斗导航系统日作业面积可达 700 亩。榆树沟镇 6.83 万亩机采棉主要集中在曙光村、榆树沟村等地，已完成 90% 作业、剩余预计一周内完成。",null,"腾讯新闻（昌吉市融媒体中心）","2026-09-02T00:00:00Z","报道",10,false,55,{"impact":16,"substance":17,"depth":16,"authority":18,"freshness":19,"relevant":20,"comment":21},12,16,6,9,1,"地方农业无人机应用动态，数据具体，但影响范围有限。",[23],{"name":9,"url":6},[25,26,27,28,29],"智慧农业","棉花","田间管理","植保无人机","昌吉",[],{"status":32,"locked":13,"version":33,"method":34,"evidence":35},"needs_review","2026-10-22.2","rules",{"candidates":36,"agricultural_evidence":40,"reason":55},[37,44,50],{"code":38,"score":39,"supported":40,"title":41,"abstract":42,"tags":43},"A6",4,true,[],[26],[26],{"code":45,"score":39,"supported":40,"title":46,"abstract":47,"tags":49},"F4",[],[48],"植保",[28],{"code":51,"score":20,"supported":13,"title":52,"abstract":53,"tags":54},"I1",[],[],[25],"多个产业证据相当，需要确认主归属",[57,58],"植保无人机 智慧农业 田间管理 昌吉","植保无人机 智慧农业","植保无人机智慧农业田间管理昌吉-1469",3,"2026-09-03T00:06:40.656867Z",{"total":18,"page":20,"page_size":18,"items":63},[64,136,186,246,283,348],{"id":65,"title":66,"url":67,"summary":68,"summary_zh":69,"content":8,"source_name":70,"source_url":67,"published_at":71,"category":72,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":73,"sources":78,"tags":80,"primary_topic":84,"secondary_topics":89,"classification":90,"search_phrases":118,"slug":121,"view_count":20,"doi":122,"paper":123,"created_at":135},5233,"Cotton pest and disease multi-class classification with weather and temporal predictors features across Indian agroclimatic zones","https:\u002F\u002Fdoi.org\u002F10.25258\u002Fijddt.16.73s.45","The Pink Bollworm (Pectinophora gossypiella) remains one of the most destructive pests affecting cotton productivity, with infestation levels strongly driven by climatic fluctuations and crop phenology.This study presents a machine learning-based framework for early 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",{"impact":74,"substance":75,"depth":76,"authority":18,"freshness":19,"relevant":20,"comment":77},8,18,14,"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。",[79],{"name":70,"url":67},[25,81,26,82,83],"机器学习","农业气象","病虫害预警",{"code":38,"slug":85,"name":86,"path":87,"parent_code":88},"cash-crops","棉麻糖与特色经作","\u002Ftopic\u002Ffield-crops\u002Fcash-crops","A",[],{"status":91,"locked":13,"version":33,"method":34,"evidence":92},"classified",{"candidates":93,"agricultural_evidence":40},[94,100,107,113],{"code":38,"score":95,"supported":40,"title":96,"abstract":98,"tags":99},15,[97],"cotton",[26,97],[26],{"code":51,"score":101,"supported":40,"title":102,"abstract":103,"tags":106},7,[],[104,105],"精准农业","precision agriculture",[25],{"code":108,"score":101,"supported":40,"title":109,"abstract":110,"tags":112},"I2",[],[81,111],"machine learning",[81],{"code":114,"score":39,"supported":40,"title":115,"abstract":116,"tags":117},"H7",[],[82],[82],[119,120],"棉铃虫 机器学习 预测","棉花 病虫害 气象因子","棉铃虫机器学习预测-5233","10.25258\u002Fijddt.16.73s.45",{"doi":122,"openalex_id":124,"authors":125,"venue":70,"cited_by_count":126,"oa_url":67,"card":127,"direction":133,"ingested_from":134},"W7222826491",[],0,{"tldr":128,"method":129,"finding":130,"direction":131,"opportunity":132},"用农业气象变量与时间特征构建机器学习模型，实现印度棉区棉铃虫多级发生预测。","气象数据加周期编码与滞后特征，比较SVM等模型，5折交叉验证。","SVM测试精度最高达89.4%，能有效刻画气候与虫害动态关系。","农业人工智能与决策模型","可引入遥感植被指数与实时气象物联网数据，构建跨区域可迁移的病虫害预警系统。","智慧农业 \u002F 农业物联网","openalex","2026-10-18T23:30:07.725427Z",{"id":137,"title":138,"url":139,"summary":140,"summary_zh":8,"content":141,"source_name":142,"source_url":8,"published_at":143,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":144,"score_detail":145,"sources":150,"tags":152,"primary_topic":156,"secondary_topics":157,"classification":158,"search_phrases":181,"slug":184,"view_count":20,"doi":8,"paper":8,"created_at":185},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。","  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",84,{"impact":146,"substance":147,"depth":148,"authority":95,"freshness":74,"relevant":20,"comment":149},24,20,17,"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。",[151],{"name":142,"url":139},[25,153,26,154,155],"农业无人机","无人化农场","新疆棉田",{"code":38,"slug":85,"name":86,"path":87,"parent_code":88},[],{"status":91,"locked":13,"version":33,"method":34,"evidence":159},{"candidates":160,"agricultural_evidence":40},[161,165,171,177],{"code":38,"score":39,"supported":40,"title":162,"abstract":163,"tags":164},[],[26],[26],{"code":166,"score":60,"supported":40,"title":167,"abstract":168,"tags":170},"A4",[],[169],"花生",[],{"code":172,"score":60,"supported":40,"title":173,"abstract":174,"tags":176},"G2",[],[175],"家庭农场",[],{"code":51,"score":20,"supported":13,"title":178,"abstract":179,"tags":180},[],[],[25],[182,183],"新疆 超级棉田 极飞科技","尉犁县 智慧棉田 无人机","新疆超级棉田极飞科技-5031","2026-10-16T00:13:02.993245Z",{"id":187,"title":188,"url":189,"summary":190,"summary_zh":8,"content":8,"source_name":191,"source_url":8,"published_at":8,"category":72,"cover_url":8,"hotness":12,"is_selected":13,"score":192,"score_detail":193,"sources":197,"tags":199,"primary_topic":203,"secondary_topics":204,"classification":210,"search_phrases":233,"slug":236,"view_count":126,"doi":8,"paper":237,"created_at":245},4966,"基于改进YOLO 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":17,"substance":194,"depth":75,"authority":76,"freshness":195,"relevant":20,"comment":196},21,2,"改进YOLO 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