[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3695":3,"related-3695":66},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":8,"source_name":9,"source_url":8,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":15,"sources":23,"tags":25,"primary_topic":31,"secondary_topics":37,"classification":38,"search_phrases":61,"slug":64,"view_count":45,"doi":8,"paper":8,"created_at":65},3695,"昌吉州：一部手机管万亩良田 数智兴农激活产业升级新动能","https:\u002F\u002Fopen.btzx.com.cn\u002Fopen\u002Farticle\u002Farticle.html?id=ARTI1790222529767861","昌吉回族自治州作为自治区智慧农业综合改革试点，依托2+N场景体系推进智慧农业。一部手机管万亩良田模式推广面积超112万亩，亩均节水30立方米、节肥15%以上、节本增收350元。玛纳斯县建成70万亩数字化农田，棉花数字种植水平领跑全疆。",null,"新疆兵团广播电视台","2026-09-25T00:00:00Z","报道",10,false,75,{"impact":16,"substance":17,"depth":18,"authority":19,"freshness":20,"relevant":21,"comment":22},22,21,16,9,7,1,"自治区智慧农业试点进展，含112万亩推广面积与节水节肥增收等硬数据，产业参考价值较高。",[24],{"name":9,"url":6},[26,27,28,29,30],"智慧农业","新疆昌吉","棉花种植","数字农田","节水节肥",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"A6","cash-crops","棉麻糖与特色经作","\u002Ftopic\u002Ffield-crops\u002Fcash-crops","A",[],{"status":39,"locked":13,"version":40,"method":41,"evidence":42},"classified","2026-10-22.2","rules",{"candidates":43,"agricultural_evidence":46},[44,51,56],{"code":32,"score":45,"supported":46,"title":47,"abstract":48,"tags":50},4,true,[],[49],"棉花",[28],{"code":52,"score":45,"supported":46,"title":53,"abstract":54,"tags":55},"I1",[],[26],[26],{"code":57,"score":21,"supported":13,"title":58,"abstract":59,"tags":60},"H1",[],[],[29],[62,63],"昌吉州 智慧农业 万亩良田","玛纳斯县 数字农田 棉花","昌吉州智慧农业万亩良田-3695","2026-09-29T00:08:18.969197Z",{"total":67,"page":21,"page_size":67,"items":68},6,[69,136,197,254,330,379],{"id":70,"title":71,"url":72,"summary":73,"summary_zh":8,"content":8,"source_name":74,"source_url":8,"published_at":75,"category":76,"cover_url":8,"hotness":12,"is_selected":13,"score":77,"score_detail":78,"sources":81,"tags":83,"primary_topic":88,"secondary_topics":89,"classification":96,"search_phrases":121,"slug":124,"view_count":125,"doi":8,"paper":126,"created_at":135},5532,"精准棉花病害管理：融合无人机影像与定制YOLO深度学习框架","https:\u002F\u002Farxiv.org\u002Fabs\u002F2610.24182","研究提出单阶段全卷积检测器Cotton-Vision，融合幽灵瓶颈通道注意力与跨阶段部分融合三项轻量化设计，参数减少20%。在巴基斯坦旁遮普省7个棉田采集的2700余幅高分辨率无人机影像上测试，精确率97.7%、召回率77.7%、mAP@50为85.6%，较主流基线提升10至20个百分点。预印本，仅在巴基斯坦单一区域验证。","arXiv 预印本 2026-10-16","2026-10-16T00:00:00Z","论文",69,{"impact":18,"substance":17,"depth":79,"authority":19,"freshness":67,"relevant":21,"comment":80},17,"方法轻量化与实测数据有增量，但仅单区域预印本验证，属细分技术进展，可入精选备选。",[82],{"name":74,"url":72},[26,84,85,86,87],"农业人工智能","目标检测","无人机遥感","棉花病害",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[90],{"code":91,"slug":92,"name":93,"path":94,"parent_code":95},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"status":39,"locked":13,"version":40,"method":41,"evidence":97},{"candidates":98,"agricultural_evidence":46},[99,106,112,116],{"code":91,"score":100,"supported":46,"title":101,"abstract":104,"tags":105},18,[102,103],"深度学习","YOLO",[],[84,85],{"code":32,"score":107,"supported":46,"title":108,"abstract":109,"tags":111},11,[49],[110],"cotton",[],{"code":52,"score":21,"supported":13,"title":113,"abstract":114,"tags":115},[],[],[26],{"code":117,"score":21,"supported":13,"title":118,"abstract":119,"tags":120},"I3",[],[],[86],[122,123],"Cotton-Vision 棉花病害","无人机影像 棉花 检测","Cotton-Vision棉花病害-5532",0,{"doi":8,"openalex_id":8,"authors":127,"venue":8,"cited_by_count":125,"oa_url":8,"card":128,"direction":132,"ingested_from":134},[],{"tldr":129,"method":130,"finding":131,"direction":132,"opportunity":133},"提出轻量化检测器Cotton-Vision，用无人机影像精准识别棉花病害。","基于YOLO改进，融合幽灵瓶颈通道注意力与跨阶段部分融合，用2700余幅无人机影","精确率97.7%、mAP@50达85.6%，较主流基线提升10至20个百分点。","农业遥感与作物表型","仅在巴基斯坦单一区域验证，可扩展至多区域多品种，并探索边缘设备实时部署。","agent","2026-10-22T00:07:09.816790Z",{"id":137,"title":138,"url":139,"summary":140,"summary_zh":141,"content":8,"source_name":142,"source_url":139,"published_at":143,"category":76,"cover_url":8,"hotness":12,"is_selected":13,"score":77,"score_detail":144,"sources":148,"tags":150,"primary_topic":153,"secondary_topics":154,"classification":155,"search_phrases":174,"slug":177,"view_count":21,"doi":178,"paper":179,"created_at":196},5234,"A smart agriculture-oriented cross-attention multiscale CNN for sugarcane disease diagnosis","https:\u002F\u002Fdoi.org\u002F10.1080\u002F23080477.2026.2749739","Sugarcane leaf diseases can adversely affect crop health and yield, making timely disease identification important for smart agriculture. Manual inspection of leaf diseases is time-consuming and requires expert knowledge, particularly when large cultivation areas need to be monitored. To tackle this problem, this study proposes a Cross-Attention Multiscale Parallel Convolutional Neural Network for automatic classification of sugarcane leaf diseases. This framework comprises a Parallel Feature Extraction Stem, cross-attention mechanism, Dense Connection Blocks, and Inception Blocks to learn disease features at various spatial scales. The PFES extracts fine and broad disease patterns using parallel convolutional branches, while the cross-attention mechanism refines the extracted features by emphasizing disease-related information. The DCB supports feature propagation and reuse, and the IB further captures multi-scale feature representations. The proposed model was tested on two benchmark datasets of leaf diseases for sugarcane plants. The proposed framework obtained an accuracy (98.79%), precision (98.31%), recall (97.81%), specificity (98.60%), and F1-Score (98.05%). For assessing the classification accuracy and consistency of the framework, the comparative study, ablation studies, and five-fold cross-validation are carried out. The results show that CA-MPCNN provides accurate sugarcane leaf disease classification and can support automated disease diagnosis in smart agriculture applications.","甘蔗叶片病害会对作物健康与产量造成不利影响，因此及时识别病害对智慧农业具有重要意义。人工检查叶片病害耗时且需要专业知识，尤其是在需要监测大面积种植区域时。为解决该问题，本研究提出一种用于甘蔗叶片病害自动分类的交叉注意力多尺度并行卷积神经网络（Cross-Attention Multiscale Parallel Convolutional Neural Network）。该框架由并行特征提取主干（Parallel Feature Extraction Stem）、交叉注意力机制、密集连接块（Dense Connection Blocks）和Inception块组成，用于学习不同空间尺度上的病害特征。PFES通过并行卷积分支提取细粒度与广域病害模式，而交叉注意力机制通过强调病害相关信息对提取的特征进行细化。DCB支持特征传播与复用，IB进一步捕获多尺度特征表示。所提出的模型在两个甘蔗植株叶片病害基准数据集上进行了测试。该框架取得了准确率（98.79%）、精确率（98.31%）、召回率（97.81%）、特异性（98.60%）和F1分数（98.05%）。为评估该框架的分类准确性和一致性，开展了对比研究、消融研究和五折交叉验证。结果表明，CA-MPCNN能够实现准确的甘蔗叶片病害分类，并可支持智慧农业应用中的自动化病害诊断。","Smart Science","2026-10-17T00:00:00Z",{"impact":145,"substance":146,"depth":18,"authority":145,"freshness":19,"relevant":21,"comment":147},12,20,"该研究提出一种面向甘蔗叶部病害自动分类的交叉注意力多尺度CNN，在两个基准数据集上准确率达98.79%，方法新颖、实验充分，但属于细分作物应用研究，产业影响有限，时效性强，值得进入每日精选。",[149],{"name":142,"url":139},[26,84,151,102,152],"甘蔗","病害诊断",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":13,"version":40,"method":41,"evidence":156},{"candidates":157,"agricultural_evidence":46},[158,164,169],{"code":32,"score":159,"supported":46,"title":160,"abstract":162,"tags":163},15,[161],"sugarcane",[151,161],[151],{"code":52,"score":159,"supported":46,"title":165,"abstract":167,"tags":168},[166],"smart agriculture",[26,166],[26],{"code":91,"score":170,"supported":13,"title":171,"abstract":172,"tags":173},2,[],[],[84,102],[175,176],"甘蔗 叶部病害 智能诊断","CA-MPCNN 甘蔗 病害","甘蔗叶部病害智能诊断-5234","10.1080\u002F23080477.2026.2749739",{"doi":178,"openalex_id":180,"authors":181,"venue":142,"cited_by_count":125,"oa_url":8,"card":188,"direction":194,"ingested_from":195},"W7222832245",[182,184,186],{"name":183,"orcid":8},"Bala Subramanian Chokkalingam",{"name":185,"orcid":8},"Uvarani Meenrajan",{"name":187,"orcid":8},"Shanmugapriya Sankaran",{"tldr":189,"method":190,"finding":191,"direction":192,"opportunity":193},"提出交叉注意力多尺度并行CNN，实现甘蔗叶病自动分类。","并行卷积分支、交叉注意力、密集连接与Inception块，两个基准数据集验证。","模型准确率达98.79%，消融与五折交叉验证证明其有效稳定。","农业人工智能与决策模型","可探索田间复杂背景与轻量化部署，并扩展到多作物病害实时诊断。","智慧农业 \u002F 农业物联网","openalex","2026-10-18T23:30:07.777036Z",{"id":198,"title":199,"url":200,"summary":201,"summary_zh":202,"content":8,"source_name":203,"source_url":200,"published_at":143,"category":76,"cover_url":8,"hotness":12,"is_selected":13,"score":204,"score_detail":205,"sources":209,"tags":211,"primary_topic":215,"secondary_topics":216,"classification":217,"search_phrases":240,"slug":243,"view_count":21,"doi":244,"paper":245,"created_at":253},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",55,{"impact":206,"substance":100,"depth":207,"authority":67,"freshness":19,"relevant":21,"comment":208},8,14,"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。",[210],{"name":203,"url":200},[26,212,49,213,214],"机器学习","农业气象","病虫害预警",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":13,"version":40,"method":41,"evidence":218},{"candidates":219,"agricultural_evidence":46},[220,224,230,235],{"code":32,"score":159,"supported":46,"title":221,"abstract":222,"tags":223},[110],[49,110],[49],{"code":52,"score":20,"supported":46,"title":225,"abstract":226,"tags":229},[],[227,228],"精准农业","precision agriculture",[26],{"code":91,"score":20,"supported":46,"title":231,"abstract":232,"tags":234},[],[212,233],"machine learning",[212],{"code":236,"score":45,"supported":46,"title":237,"abstract":238,"tags":239},"H7",[],[213],[213],[241,242],"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":244,"openalex_id":246,"authors":247,"venue":203,"cited_by_count":125,"oa_url":200,"card":248,"direction":194,"ingested_from":195},"W7222826491",[],{"tldr":249,"method":250,"finding":251,"direction":192,"opportunity":252},"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。","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。","2026-10-18T23:30:07.725427Z",{"id":255,"title":256,"url":257,"summary":258,"summary_zh":259,"content":8,"source_name":260,"source_url":257,"published_at":75,"category":76,"cover_url":8,"hotness":12,"is_selected":13,"score":261,"score_detail":262,"sources":265,"tags":267,"primary_topic":270,"secondary_topics":271,"classification":273,"search_phrases":290,"slug":293,"view_count":21,"doi":294,"paper":295,"created_at":329},5150,"MSFANet: a multi-scale feature aggregation network for tobacco pest recognition","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffpls.2026.1871587","Introduction Tobacco is a globally significant economic crop, and pest infestation represents one of the primary factors contributing to declines in both yield and quality. However, tobacco pests encompass a wide variety of species that differ substantially in morphology and scale. Field-captured images are frequently subject to complex background interference and class imbalance, presenting considerable challenges for automated recognition. Existing deep learning approaches remain limited in their capacity for multi-scale feature extraction and discriminative feature representation under such conditions. To address these challenges, this study proposes a Multi-Scale Feature Aggregation Network (MSFANet) for tobacco pest recognition, formulated here as a single-label image classification task. Methods To address scale variation and complex field backgrounds, two task-oriented modules were developed. The Multi-Scale Pyramid Pooling (MSPP) module aggregates contextual information across multiple receptive fields through parallel feature extraction and adaptive cross-branch fusion. The Hierarchical Feature Fusion Block (HFFB) subsequently refines the feature representations through spatial gating and partial convolution. An auxiliary classification head and Focal Loss were incorporated during training to provide intermediate supervision and address class imbalance, respectively. Results The effectiveness of MSFANet was evaluated on a dataset comprising 13 tobacco-associated pest and natural-enemy categories. The model achieved accuracy, precision, recall, and F1-score of 97.63%, 97.89%, 97.59%, and 97.71%, respectively, with a parameter count of 1.21 M. Among the models trained from random initialization, MSFANet shared the highest accuracy while using the fewest parameters. Discussion By combining multi-scale contextual aggregation with hierarchical feature refinement, MSFANet achieved accurate classification across the 13 tobacco-associated categories under complex field conditions. The proposed method provides effective technical support for automated and precision-oriented pest monitoring in tobacco fields, offering practical value for advancing precise pest management and the broader development of smart agriculture.","引言 烟草是全球重要的经济作物，虫害侵染是导致其产量和品质下降的主要因素之一。然而，烟草害虫种类繁多，形态和尺度差异显著。田间采集的图像常受到复杂背景干扰和类别不平衡的影响，给自动化识别带来了相当大的挑战。现有深度学习方法在此类条件下进行多尺度特征提取和判别性特征表示的能力仍然有限。为应对这些挑战，本研究提出了一种用于烟草害虫识别的多尺度特征聚合网络（Multi-Scale Feature Aggregation Network，MSFANet），并将其形式化为单标签图像分类任务。方法 针对尺度变化和复杂田间背景，开发了两个面向任务的模块。多尺度金字塔池化（Multi-Scale Pyramid Pooling，MSPP）模块通过并行特征提取和自适应跨分支融合，聚合多个感受野的上下文信息。分层特征融合模块（Hierarchical Feature Fusion Block，HFFB）随后通过空间门控和部分卷积对特征表示进行细化。训练过程中引入辅助分类头和Focal Loss，分别提供中间监督和处理类别不平衡。结果 在包含13个烟草相关害虫及天敌类别的数据集上评估了MSFANet的有效性。模型的准确率、精确率、召回率和F1分数分别为97.63%、97.89%、97.59%和97.71%，参数量为1.21 M。在随机初始化训练的模型中，MSFANet以最少的参数量取得了最高准确率。讨论 通过结合多尺度上下文聚合与分层特征细化，MSFANet在复杂田间条件下实现了对13个烟草相关类别的准确分类。所提方法为烟草田间害虫的自动化和精准监测提供了有效的技术支持，对推进精准害虫管理和智慧农业的更广泛发展具有实用价值。","Frontiers in Plant Science",76,{"impact":18,"substance":17,"depth":79,"authority":263,"freshness":19,"relevant":21,"comment":264},13,"该论文提出轻量化多尺度特征聚合网络，在烟草害虫识别上取得97%以上精度且参数量仅1.21M，方法新颖、数据扎实，对智慧农业虫情监测有实用价值，值得进入每日精选。",[266],{"name":260,"url":257},[26,84,102,268,269],"病虫害识别","烟草",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[272],{"code":91,"slug":92,"name":93,"path":94,"parent_code":95},{"status":39,"locked":13,"version":40,"method":41,"evidence":274},{"candidates":275,"agricultural_evidence":46},[276,281,286],{"code":32,"score":159,"supported":46,"title":277,"abstract":279,"tags":280},[278],"tobacco",[269,278],[269],{"code":91,"score":19,"supported":46,"title":282,"abstract":283,"tags":285},[],[102,284],"deep learning",[84,102,268],{"code":52,"score":20,"supported":46,"title":287,"abstract":288,"tags":289},[],[26,166],[26],[291,292],"MSFANet 烟草害虫识别","多尺度特征聚合 害虫识别","MSFANet烟草害虫识别-5150","10.3389\u002Ffpls.2026.1871587",{"doi":294,"openalex_id":296,"authors":297,"venue":260,"cited_by_count":125,"oa_url":323,"card":324,"direction":194,"ingested_from":195},"W7222603139",[298,300,302,304,306,308,310,312,315,318,321],{"name":299,"orcid":8},"Shun He",{"name":301,"orcid":8},"Sifan Li",{"name":303,"orcid":8},"Xinjian Li",{"name":305,"orcid":8},"Qianqian Luo",{"name":307,"orcid":8},"Dong Sufen",{"name":309,"orcid":8},"Lu Lei",{"name":311,"orcid":8},"Xingjiang Chen",{"name":313,"orcid":314},"Liuti Cai","https:\u002F\u002Forcid.org\u002F0000-0002-9151-8002",{"name":316,"orcid":317},"Jian‐Yu Meng","https:\u002F\u002Forcid.org\u002F0000-0002-5796-1086",{"name":319,"orcid":320},"Ning Lu","https:\u002F\u002Forcid.org\u002F0000-0003-0214-5038",{"name":322,"orcid":8},"Hancheng Wang","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fplant-science\u002Farticles\u002F10.3389\u002Ffpls.2026.1871587\u002Fpdf",{"tldr":325,"method":326,"finding":327,"direction":192,"opportunity":328},"提出MSFANet多尺度特征聚合网络，实现复杂田间背景下烟草害虫的自动识别。","多尺度金字塔池化MSPP、分层特征融合HFFB、辅助分类头与Focal Loss","模型准确率97.63%、参数量仅1.21M，在随机初始化模型中精度最高且参数最少。","可探索轻量多尺度网络在边缘设备部署及跨作物害虫迁移识别，结合类不平衡与复杂背景的鲁棒性研究。","2026-10-17T23:30:08.695455Z",{"id":331,"title":332,"url":333,"summary":334,"summary_zh":8,"content":335,"source_name":336,"source_url":8,"published_at":337,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":338,"score_detail":339,"sources":342,"tags":344,"primary_topic":348,"secondary_topics":349,"classification":350,"search_phrases":374,"slug":377,"view_count":21,"doi":8,"paper":8,"created_at":378},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":340,"substance":146,"depth":79,"authority":159,"freshness":206,"relevant":21,"comment":341},24,"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。",[343],{"name":336,"url":333},[26,345,49,346,347],"农业无人机","无人化农场","新疆棉田",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":13,"version":40,"method":41,"evidence":351},{"candidates":352,"agricultural_evidence":46},[353,357,364,370],{"code":32,"score":45,"supported":46,"title":354,"abstract":355,"tags":356},[],[49],[49],{"code":358,"score":359,"supported":46,"title":360,"abstract":361,"tags":363},"A4",3,[],[362],"花生",[],{"code":365,"score":359,"supported":46,"title":366,"abstract":367,"tags":369},"G2",[],[368],"家庭农场",[],{"code":52,"score":21,"supported":13,"title":371,"abstract":372,"tags":373},[],[],[26],[375,376],"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":380,"title":381,"url":382,"summary":383,"summary_zh":8,"content":8,"source_name":384,"source_url":8,"published_at":8,"category":76,"cover_url":8,"hotness":12,"is_selected":13,"score":385,"score_detail":386,"sources":388,"tags":390,"primary_topic":393,"secondary_topics":394,"classification":396,"search_phrases":418,"slug":421,"view_count":125,"doi":8,"paper":422,"created_at":429},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":18,"substance":17,"depth":100,"authority":207,"freshness":170,"relevant":21,"comment":387},"改进YOLO 11n实现棉花田间遮挡障碍物检测，方法新颖、指标提升明确，属农业人工智能细分领域实质进展，但发布日期未知、时效性偏弱。",[389],{"name":384,"url":382},[26,84,49,391,392],"障碍物检测","采棉机",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[395],{"code":91,"slug":92,"name":93,"path":94,"parent_code":95},{"status":39,"locked":13,"version":40,"method":41,"evidence":397},{"candidates":398,"agricultural_evidence":46},[399,403,407,414],{"code":32,"score":145,"supported":46,"title":400,"abstract":401,"tags":402},[49],[49],[49],{"code":91,"score":145,"supported":46,"title":404,"abstract":405,"tags":406},[103],[103],[84],{"code":408,"score":67,"supported":46,"title":409,"abstract":410,"tags":413},"F5",[],[411,412],"农业机械","机械化",[],{"code":52,"score":21,"supported":13,"title":415,"abstract":416,"tags":417},[],[],[26],[419,420],"中国农业机械化科学研究院 采棉机 障碍物检测","YOLO 11n 棉花 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":423,"venue":8,"cited_by_count":125,"oa_url":8,"card":424,"direction":192,"ingested_from":134},[],{"tldr":425,"method":426,"finding":427,"direction":192,"opportunity":428},"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。","2026-10-15T00:06:08.577388Z"]