[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-177":3,"related-177":57},{"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":8,"secondary_topics":32,"classification":33,"search_phrases":50,"slug":53,"view_count":54,"doi":55,"paper":8,"created_at":56},177,"基于改进YOLOv11n-seg的胡麻倒伏区域识别方法——兼顾精度与轻量化的智能化倒伏监测","https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202510010","针对胡麻倒伏区域识别难题，研究构建改进YOLOv11n-seg模型。改进后的模型在精确度和平均精度均值上分别达到92.6%和95.2%，较原始YOLOv11n-seg模型分别提升3.7个百分点和2.1个百分点。模型参数量仅为1.73 M，计算量为8.0 GFLOPs，模型体积仅为3.8 MB，显示出较好的轻量化优势。该方法兼顾精度与轻量化，为智能化胡麻倒伏监测与实际田间设备部署提供了技术基础。",null,"欢迎您访问《智慧农业（中英文）》官方网站！[English](https:\u002F\u002Fwww.smartag.net.cn\u002FEN\u002F2096-8094\u002Fhome.shtml)\n\n[![Image 1](https:\u002F\u002Fwww.smartag.net.cn\u002Fimages\u002F2096-8094\u002Fimages\u002Fbanner.png)](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F2096-8094\u002Fhome.shtml)\n\n[![Image 2](https:\u002F\u002Fwww.smartag.net.cn\u002Fimages\u002F2096-8094\u002Fimages\u002Fbanner_mobile.png)](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F2096-8094\u002Fhome.shtml)\n\n[![Image 3](https:\u002F\u002Fwww.smartag.net.cn\u002Fimages\u002F2096-8094\u002Fimages\u002Fyyzkj.png)](https:\u002F\u002Fpano.bookan.com.cn\u002F?id=60704&cid=352)\n\nToggle navigation[智慧农业](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202510010)\n\n*   [网站首页](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F2096-8094\u002Fhome.shtml)\n*   [期刊介绍](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002Fcolumn\u002Fcolumn133.shtml)\n*   [编委会](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002Fcolumn\u002Fcolumn153.shtml)\n*   [投稿指南](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002Fcolumn\u002Fcolumn135.shtml)\n*   [期刊服务](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002Fcolumn\u002Fcolumn124.shtml)\n*   [下载中心](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002Fcolumn\u002Fcolumn126.shtml)\n*   [联系我们](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002Fcolumn\u002Fcolumn127.shtml)\n\n[Smart Agriculture](https:\u002F\u002Fwww.smartag.net.cn\u002F) ›› [2026](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002Farticle\u002FshowTenYearVolumnDetail.do?nian=2026), [Vol. 8](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002Farticle\u002FshowTenYearVolumnDetail.do?nian=2026) ›› [Issue (2)](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002Fvolumn\u002Fvolumn_838.shtml): 118-132.doi: [10.12133\u002Fj.smartag.SA202510010](https:\u002F\u002Fdoi.org\u002F10.12133\u002Fj.smartag.SA202510010)\n\n• 专题--多源遥感驱动数字农业创新与实践 •[上一篇](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002Fabstract\u002Fabstract22643.shtml)[下一篇](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002Fabstract\u002Fabstract22641.shtml)\n\n### 面向云遮挡与复杂地块场景下的跨模态注意力多源遥感农作物分类\n\n巫晨旭, 左浩龙, 李刚([![Image 4](https:\u002F\u002Fwww.smartag.net.cn\u002Fimages\u002Femail.png)](mailto:35987336@qq.com))\n\n1.   黑龙江工程学院测绘工程学院，黑龙江 哈尔滨 150050，中国\n\n*   `收稿日期:`2025-10-11 `出版日期:`2026-03-30 \n\n*   `作者简介:`巫晨旭，硕士研究生，研究方向为时空大数据。E-mail：[15589720597@163.com](mailto:15589720597@163.com) \n*   `通信作者:`李 刚，博士，讲师，研究方向为时空大数据。E-mail：[35987336@qq.com](mailto:35987336@qq.com) \n\n### Cross-Modal Attention for Multi-Source Remote Sensing Crop Classification under Cloud Occlusion and Complex Field Scenarios\n\nWU Chenxu, ZUO Haolong, LI Gang([![Image 5](https:\u002F\u002Fwww.smartag.net.cn\u002Fimages\u002Femail.png)](mailto:35987336@qq.com))\n\n1.   School of Geomatics Engineering, Heilongjiang Institute of Technology, Harbin 150050, China\n\n*   `Received:`2025-10-11 `Online:`2026-03-30 \n*   `Foundation items:`Heilongjiang Province Double First-Class Discipline Coordinated Innovation Achievement Project(LJGXCG 2025-P18) \n*   `About author:`WU Chenxu, E-mail: [15589720597@163.com](mailto:15589720597@163.com) \n*   `Corresponding author:`LI Gang, E-mail: [35987336@qq.com](mailto:35987336@qq.com) \n\n## [在线阅读 24](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202510010#)\n\n## [知网下载](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202510010)\n\n## [本地下载 48](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202510010#1)\n\n[_![Image 6](https:\u002F\u002Fwww.smartag.net.cn\u002Fimages\u002Fknowledge\\_map1.png)_ ## 可视化 0](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002Flexeme\u002FshowArticleByLexeme.do?articleID=22683)\n\n#### [摘要\u002FAbstract](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202510010)\n\n**摘要：**\n\n**【目的\u002F意义】** 本研究旨在突破传统光学遥感在云遮天气与耕地破碎地形下的局限性，通过构建一种具备优秀跨模态融合能力与泛化性能的深度网络模型来提升农业遥感分类精度。 **【方法】** 提出一种基于注意力机制的3D卷积神经网络（3D Convolutional Neural Network Based on Attention Mechanism, Attention-3DCNN）模型：其通过3D卷积+2D卷积结构处理时序哨兵二号多光谱影像，在空间与时间维度上提取丰富特征；同时以深度可分离卷积形式处理来自哨兵一号的合成孔径雷达（Synthetic Aperture Radar, SAR）数据，实现对全天候可获取信息的高效抽取；进一步，模型引入“通道-时间-空间”三重注意力机制与残差连接策略，对两个模态的特征进行动态加权与深度融合，使得在光学数据缺失或遮挡严重的情形下，SAR数据能够有效补偿并维持分类性能。 **【结果和讨论】** 为全面评价模型性能，选取法国全景农业卫星时序数据集，以及山东省沂水县实测数据集进行对比实验：在法国数据上模型达到97.5%的整体准确率，在沂水县数据上获得93%的准确率，均显著优于对照基线模型；同时，通过对注意力分布的可视化分析可见，模型聚焦的关键物候期与当地农业实地记录高度一致，其高权重光谱波段亦符合农学机理，这体现出模型在判别机制层面的可解释性。 **【结论】** 综上，Attention-3DCNN模型在耕地破碎、云遮影响严重的山区条件下显著提升了作物分类精度，具有良好的推广前景与应用价值。\n\n**关键词:**[农作物分类,](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202510010#)[深度学习,](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202510010#)[3D卷积神经网络,](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202510010#)[注意力机制,](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202510010#)[遥感,](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202510010#)[合成孔径雷达](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202510010#)\n\n**Abstract:**\n\n**[Objective]** Accurate and timely crop mapping is fundamental for agricultural management, yield forecasting, and food security assessment. However, in mountainous and hilly regions characterized by frequent cloud cover and highly fragmented farmland, crop classification methods relying solely on optical remote sensing data are severely constrained. Persistent cloud contamination introduces data gaps and temporal inconsistencies in optical image time series, significantly degrading classification accuracy and robustness. To address these limitations, a robust and adaptive deep learning framework is developed capable of effectively integrating multi-modal remote sensing data. The primary objective is to enhance crop classification accuracy and stability under complex conditions where optical observations are scarce or unreliable, thereby supporting reliable agricultural monitoring in cloudy and fragmented landscapes. **[Methods]** A novel deep neural network architecture named 3D convolutional neural network based on attention mechanism (Attention-3DCNN) was proposed, designed to jointly exploit multi-temporal optical and synthetic aperture radar (SAR) observations. The model integrated Sentinel-2 multispectral time-series imagery with weather-insensitive Sentinel-1 SAR data through a dedicated cross-modal fusion strategy driven by a triple-attention mechanism. The network adopted a dual-branch feature extraction architecture. For the Sentinel-2 data, a hybrid module combining three-dimensional and two-dimensional convolutional neural networks (3D-CNN and 2D-CNN) was employed to capture discriminative spatiotemporal features and crop phenological dynamics across the growing season. This design enabled effective modeling of the spectral-temporal interactions inherent in crop development. For the Sentinel-1 SAR data, depthwise separable convolutions were utilized to efficiently extract spatial and textural features related to crop structure and surface scattering characteristics while reducing computational complexity. Features extracted from both modalities were subsequently integrated using a custom-designed attention-based fusion module. This module consisted of three complementary attention mechanisms: channel attention, temporal attention, and spatial attention. Residual connections were incorporated throughout the network to facilitate stable training and effective gradient propagation. The proposed model was evaluated on two datasets to assess both its performance and generalizability. The first was the publicly available panoptic agricultural satellite time series (PASTIS) benchmark dataset from France, which contained dense time-series observations and multiple crop classes. The second was a real-world dataset constructed for Yishui county, Shandong province, China, which was characterized by high cloud frequency (approximately 33%), highly fragmented farmland (average parcel size \u003C 0.5 hm 2), and a relatively simple crop rotation system. Comparative experiments were conducted against several state-of-the-art models, including 3D-ConvSTAR, UNet++, Self-Attention 3D, CNN-LSTM dual-stream network, and TGF-Net. Ablation studies were also performed to quantify the contribution of each attention component. **[Results and Discussions]** Experimental results demonstrated that Attention-3DCNN consistently outperformed all baseline methods on both datasets. On the PASTIS benchmark, the model achieved an overall accuracy (OA) of 97.5%, confirming its strong classification capability under favorable observation conditions. On the more challenging Yishui county dataset, Attention-3DCNN attained an OA of 93%, outperforming the other comparison models. Ablation experiments confirmed the effectiveness of the proposed triple-attention mechanism, as removing any attention component resulted in a clear reduction in classification performance. Under heavy cloud coverage, Attention-3DCNN exhibited the smallest accuracy degradation, with an OA drop of only 3.6 percentage points, indicating its ability to adaptively rely on SAR information when optical data quality deteriorated. In regions with highly fragmented farmland, the proposed model also maintained the highest accuracy and the smallest performance decline (2.8 percentage points), benefiting from the spatial attention mechanism. Moreover, attention visualization provided meaningful interpretability. Temporal attention peaks aligned with key crop phenological stages, while channel attention highlighted spectrally and physically informative optical bands and SAR polarizations, which was consistent with established agronomic and remote sensing knowledge. **[Conclusions]** This study presents the Attention-3DCNN model for accurate and robust crop classification in regions affected by persistent cloud cover and fragmented agricultural landscapes. By fusing Sentinel-2 optical and Sentinel-1 SAR time-series data through a channel-temporal-spatial triple-attention mechanism, the proposed framework enables adaptive integration of complementary multi-modal information. The model achieves outstanding performance on both benchmark and real-world datasets, demonstrates strong robustness under adverse conditions, and offers enhanced interpretability. Overall, the proposed approach provides a reliable and practical solution for crop mapping in complex agricultural environments.\n\n**Key words:**[crop classification,](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202510010#)[deep learning,](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202510010#)[3D convolutional neural network,](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202510010#)[attention mechanism,](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202510010#)[remote sensing,](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202510010#)[SAR](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202510010#)\n\n**中图分类号:**\n\n*   [S127](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202510010#)\n\n*   [TP18](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202510010#)\n\n#### 引用本文\n\n巫晨旭, 左浩龙, 李刚. 面向云遮挡与复杂地块场景下的跨模态注意力多源遥感农作物分类[J]. 智慧农业(中英文), 2026, 8(2): 118-132.\n\nWU Chenxu, ZUO Haolong, LI Gang. Cross-Modal Attention for Multi-Source Remote Sensing Crop Classification under Cloud Occlusion and Complex Field Scenarios[J]. Smart Agriculture, 2026, 8(2): 118-132.\n\n#### 使用本文\n\n**[[收藏文章](javascript:;)](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202510010#) \u002F[推荐](javascript:void(null))**\n**导出引用管理器**[EndNote](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002Farticle\u002FgetTxtFile.do?fileType=EndNote&id=22683)|[Ris](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002Farticle\u002FgetTxtFile.do?fileType=Ris&id=22683)|[BibTeX](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002Farticle\u002FgetTxtFile.do?fileType=BibTeX&id=22683)\n\n**链接本文:**[https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202510010](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202510010)\n\n[https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002FY2026\u002FV8\u002FI2\u002F118](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002FY2026\u002FV8\u002FI2\u002F118)\n\n#### [图\u002F表 23](javascript:;)\n\n[![Image 7](https:\u002F\u002Fwww.smartag.net.cn\u002Ffileup\u002F2096-8094\u002FFIGURE\u002F2026-8-2\u002FImages\u002F2096-8094-2026-8-2-118\u002Fthumbnail\u002F1DDA0D33-B017-4e44-93B5-E54B9C48FD2F-F001.jpg)](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202510010#figureClass13989)\n\n**图1**\n\n山东省沂水县研究区域示意图 注：该图基于自然资源部标准地图服务网站下载的审图号为GS（2019）3333号标准地图制作，底图无修改。\n\n![Image 8](https:\u002F\u002Fwww.smartag.net.cn\u002Ffileup\u002F2096-8094\u002FFIGURE\u002F2026-8-2\u002FImages\u002F2096-8094-2026-8-2-118\u002F1DDA0D33-B017-4e44-93B5-E54B9C48FD2F-F001.jpg)\n\n 图1 \n\n[![Image 9](https:\u002F\u002Fwww.smartag.net.cn\u002Fimages\u002Ftable-icon.gif)](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202510010#inline_content13990)\n\n**表1**\n\nPASTIS数据集与Sentinel-2波段对应关系及分辨率对比表\n\n| PASTIS 波段 | Sentinel-2 波段 | 原始分辨率\u002Fm | 重采样后分辨率\u002Fm |\n| :---: | :---: | :---: | :---: |\n| 1—4 | B2—B4， B8 | 10 | 10 |\n| 5—7 | B5—B7 | 20 | 10 |\n| 8 | B8A | 20 | 10 |\n| 9—10 | B11—B12 | 20 | 10 |\n\n 表1 \n\n[![Image 10](https:\u002F\u002Fwww.smartag.net.cn\u002Ffileup\u002F2096-8094\u002FFIGURE\u002F2026-8-2\u002FImages\u002F2096-8094-2026-8-2-118\u002Fthumbnail\u002F1DDA0D33-B017-4e44-93B5-E54B9C48FD2F-F002.jpg)](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202510010#figureClass13991)\n\n**图2**\n\nPASTIS数据集 a. 光学影像 b. SAR数据 c. 分类标签\n\n![Image 11](https:\u002F\u002Fwww.smartag.net.cn\u002Ffileup\u002F2096-8094\u002FFIGURE\u002F2026-8-2\u002FImages\u002F2096-8094-2026-8-2-118\u002F1DDA0D33-B017-4e44-93B5-E54B9C48FD2F-F002.jpg)\n\n 图2 \n\n[![Image 12](https:\u002F\u002Fwww.smartag.net.cn\u002Fimages\u002Ftable-icon.gif)](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202510010#inline_content13992)\n\n**表2**\n\nPASTIS与沂水县数据集核心参数的对比\n\n| 参数 | PASTIS（法国） | 沂水县（中国） | 对迁移的潜在影响 |\n| :---: | :---: | :---: | :---: |\n| 云覆盖率\u002F% | 28 | 33 | 沂水县需更强的抗云干扰能力 |\n| 时相数量（4—9月） | 32个时相 | 23个时相 | 沂水县时序信息更稀疏，考验时间注意力 |\n| 平均地块面积\u002Fhm 2 | 1.2 | \u003C0.5 | 沂水县需更高的空间细节捕捉能力 |\n\n 表2 \n\n[![Image 13](https:\u002F\u002Fwww.smartag.net.cn\u002Ffileup\u002F2096-8094\u002FFIGURE\u002F2026-8-2\u002FImages\u002F2096-8094-2026-8-2-118\u002Fthumbnail\u002F1DDA0D33-B017-4e44-93B5-E54B9C48FD2F-F003.jpg)](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202510010#figureClass13993)\n\n**图3**\n\n沂水县主要农作物分类数据集\n\n![Image 14](https:\u002F\u002Fwww.smartag.net.cn\u002Ffileup\u002F2096-8094\u002FFIGURE\u002F2026-8-2\u002FImages\u002F2096-8094-2026-8-2-118\u002F1DDA0D33-B017-4e44-93B5-E54B9C48FD2F-F003.jpg)\n\n 图3 \n\n[![Image 15](https:\u002F\u002Fwww.smartag.net.cn\u002Ffileup\u002F2096-8094\u002FFIGURE\u002F2026-8-2\u002FImages\u002F2096-8094-2026-8-2-118\u002Fthumbnail\u002F1DDA0D33-B017-4e44-93B5-E54B9C48FD2F-F004.jpg)](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202510010#figureClass13994)\n\n**图4**\n\nAttention-3DCNN模型总架构图 注：DSConv为深度可分离卷积层；3D-CNN、2D-CNN分别是3D卷积层和2D卷积层；Ad-Pooling为自适应池化。\n\n![Image 16](https:\u002F\u002Fwww.smartag.net.cn\u002Ffileup\u002F2096-8094\u002FFIGURE\u002F2026-8-2\u002FImages\u002F2096-8094-2026-8-2-118\u002F1DDA0D33-B017-4e44-93B5-E54B9C48FD2F-F004.jpg)\n\n 图4 \n\n[![Image 17](https:\u002F\u002Fwww.smartag.net.cn\u002Ffileup\u002F2096-8094\u002FFIGURE\u002F2026-8-2\u002FImages\u002F2096-8094-2026-8-2-118\u002Fthumbnail\u002F1DDA0D33-B017-4e44-93B5-E54B9C48FD2F-F005.jpg)](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202510010#figureClass13995)\n\n**图5**\n\n多光谱分支模型图 注：Conv3D（3×3×3，s=1，p=1，C_out=64），Conv2D（3×3，s=1，p=1，C_out=128），Ad-Pooling（1×1，s=1，p=0，C_out= 128）；s为步长，p为填充，C_out为输出大小；spectral、time、spatial分别代表通道、时间、空间注意力机制。\n\n![Image 18](https:\u002F\u002Fwww.smartag.net.cn\u002Ffileup\u002F2096-8094\u002FFIGURE\u002F2026-8-2\u002FImages\u002F2096-8094-2026-8-2-118\u002F1DDA0D33-B017-4e44-93B5-E54B9C48FD2F-F005.jpg)\n\n 图5 \n\n[![Image 19](https:\u002F\u002Fwww.smartag.net.cn\u002Ffileup\u002F2096-8094\u002FFIGURE\u002F2026-8-2\u002FImages\u002F2096-8094-2026-8-2-118\u002Fthumbnail\u002F1DDA0D33-B017-4e44-93B5-E54B9C48FD2F-F006.jpg)](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202510010#figureClass13996)\n\n**图6**\n\nSAR分支模型架构图 注：BN为批量归一化；LeakyReLu为LeakyReLU激活函数DSConv（3×3，s=1，p=1，C_out=C_in）；Ad-Pooling（1×1，s=1，p=0，C_out=128）；s为步长；p为填充；C_out为输出大小；C_in为输入大小。\n\n![Image 20](https:\u002F\u002Fwww.smartag.net.cn\u002Ffileup\u002F2096-8094\u002FFIGURE\u002F2026-8-2\u002FImages\u002F2096-8094-2026-8-2-118\u002F1DDA0D33-B017-4e44-93B5-E54B9C48FD2F-F006.jpg)\n\n 图6 \n\n[![Image 21](https:\u002F\u002Fwww.smartag.net.cn\u002Ffileup\u002F2096-8094\u002FFIGURE\u002F2026-8-2\u002FImages\u002F2096-8094-2026-8-2-118\u002Fthumbnail\u002F1DDA0D33-B017-4e44-93B5-E54B9C48FD2F-F007.jpg)](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202510010#figureClass13997)\n\n**图7**\n\n注意力机制流程图\n\n![Image 22](https:\u002F\u002Fwww.smartag.net.cn\u002Ffileup\u002F2096-8094\u002FFIGURE\u002F2026-8-2\u002FImages\u002F2096-8094-2026-8-2-118\u002F1DDA0D33-B017-4e44-93B5-E54B9C48FD2F-F007.jpg)\n\n 图7 \n\n[![Image 23](https:\u002F\u002Fwww.smartag.net.cn\u002Ffileup\u002F2096-8094\u002FFIGURE\u002F2026-8-2\u002FImages\u002F2096-8094-2026-8-2-118\u002Fthumbnail\u002F1DDA0D33-B017-4e44-93B5-E54B9C48FD2F-F008.jpg)](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202510010#figureClass13998)\n\n**图8**\n\n通道注意力机制模型图\n\n![Image 24](https:\u002F\u002Fwww.smartag.net.cn\u002Ffileup\u002F2096-8094\u002FFIGURE\u002F2026-8-2\u002FImages\u002F2096-8094-2026-8-2-118\u002F1DDA0D33-B017-4e44-93B5-E54B9C48FD2F-F008.jpg)\n\n 图8 \n\n[![Image 25](https:\u002F\u002Fwww.smartag.net.cn\u002Fimages\u002Ftable-icon.gif)](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202510010#inline_content13999)\n\n**表3**\n\nAttention-3DCNN模型与其他模型在不同数据集上的表现\n\n| 模型名称 | OA（PASTIS）\u002F% | 宏平均 _F_ 1 分数（PASTIS） | Kappa（PASTIS） | OA（沂水县）\u002F% | 宏平均 _F_ 1 分数（沂水县） | Kappa（沂水县） |\n| :---: | :---: | :---: | :---: | :---: | :---: | :---: |\n| 标准3D-CNN | 94.2 | 0.932 | 0.915 | 87.2 | 0.845 | 0.822 |\n| 光学-SAR简单融合模型 | 95.3 | 0.945 | 0.928 | 89.5 | 0.876 | 0.858 |\n| 仅SAR 3D-CNN模型 | 91.6 | 0.902 | 0.889 | 88.3 | 0.861 | 0.842 |\n| 注意力双分支融合模型 | 95.1 | 0.938 | 0.935 | 89.9 | 0.882 | 0.870 |\n| 通道注意力双分支融合模型 | 96.5 | 0.953 | 0.942 | 90.0 | 0.898 | 0.890 |\n| 时间注意力双分支融合模型 | 95.8 | 0.945 | 0.942 | 89.5 | 0.894 | 0.887 |\n| 空间注意力双分支融合模型 | 96.9 | 0.959 | 0.956 | 91.0 | 0.906 | 0.900 |\n| Attention-3DCNN | 97.5 | 0.970 | 0.965 | 93.0 | 0.920 | 0.910 |\n\n 表3 \n\n[![Image 26](https:\u002F\u002Fwww.smartag.net.cn\u002Ffileup\u002F2096-8094\u002FFIGURE\u002F2026-8-2\u002FImages\u002F2096-8094-2026-8-2-118\u002Fthumbnail\u002F1DDA0D33-B017-4e44-93B5-E54B9C48FD2F-F009.jpg)](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202510010#figureClass14000)\n\n**图9**\n\n在消融实验中Attention-3DCNN与其他模型在沂水县数据集上的结果\n\n![Image 27](https:\u002F\u002Fwww.smartag.net.cn\u002Ffileup\u002F2096-8094\u002FFIGURE\u002F2026-8-2\u002FImages\u002F2096-8094-2026-8-2-118\u002F1DDA0D33-B017-4e44-93B5-E54B9C48FD2F-F009.jpg)\n\n 图9 \n\n[![Image 28](https:\u002F\u002Fwww.smartag.net.cn\u002Ffileup\u002F2096-8094\u002FFIGURE\u002F2026-8-2\u002FImages\u002F2096-8094-2026-8-2-118\u002Fthumbnail\u002F1DDA0D33-B017-4e44-93B5-E54B9C48FD2F-F010.jpg)](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202510010#figureClass14001)\n\n**图10**\n\n在消融实验中Attention-3DCNN模型与其他模型在PASTIS数据集上的结果\n\n![Image 29](https:\u002F\u002Fwww.smartag.net.cn\u002Ffileup\u002F2096-8094\u002FFIGURE\u002F2026-8-2\u002FImages\u002F2096-8094-2026-8-2-118\u002F1DDA0D33-B017-4e44-93B5-E54B9C48FD2F-F010.jpg)\n\n 图10 \n\n[![Image 30](https:\u002F\u002Fwww.smartag.net.cn\u002Fimages\u002Ftable-icon.gif)](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202510010#inline_content14002)\n\n**表4**\n\nAttention-3DCNN模型与其他模型的对比\n\n| 模型名称 | 核心方法简述 | OA（沂水县）\u002F% | Kappa（沂水县） | 参数量\u002FM | 计算量\u002FGFLOPs | 推理时间\u002F（ms\u002F景） |\n| :---: | :---: | :---: | :---: | :---: | :---: | :---: |\n| 3D-ConvSTAR | 3DCNN多源融合（固定权重） | 89.5 | 0.858 | 45.2 | 128.3 | 156 |\n| Self-Attention 3D | 自注意力机制+3DCNN | 90.5 | 0.872 | 52.7 | 145.6 | 183 |\n| UNet++ | 编码器-解码器结构，多尺度特征融合 | 88.0 | 0.835 | 68.9 | 212.4 | 245 |\n| CNN-LSTM-DS | 光学影像时序+纹理特征融合 | 86.5 | 0.815 | 43.0 | 98.7 | 132 |\n| TGF-Net | 基于Transformer与卷积（CNN）架构 | 90.5 | 0.900 | 105.3 | 285.1 | 312 |\n| Attention-3DCNN | 跨模态三重注意力（通道-时间-空间） | 93.5 | 0.910 | 41.3 | 97.2 | 133 |\n\n 表4 \n\n[![Image 31](https:\u002F\u002Fwww.smartag.net.cn\u002Ffileup\u002F2096-8094\u002FFIGURE\u002F2026-8-2\u002FImages\u002F2096-8094-2026-8-2-118\u002Fthumbnail\u002F1DDA0D33-B017-4e44-93B5-E54B9C48FD2F-F011.jpg)](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202510010#figureClass14003)\n\n**图11**\n\n在对比实验中Attention-3DCNN模型与其他模型在沂水县数据集上的对比结果\n\n![Image 32](https:\u002F\u002Fwww.smartag.net.cn\u002Ffileup\u002F2096-8094\u002FFIGURE\u002F2026-8-2\u002FImages\u002F2096-8094-2026-8-2-118\u002F1DDA0D33-B017-4e44-93B5-E54B9C48FD2F-F011.jpg)\n\n 图11 \n\n[![Image 33](https:\u002F\u002Fwww.smartag.net.cn\u002Fimages\u002Ftable-icon.gif)](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202510010#inline_content14004)\n\n**表5**\n\n在高云量的条件下Attention-3DCNN模型与其他模型在沂水县数据集的对比\n\n| 模型名称 | 主要依赖信息 | OA\u002F% | 宏平均 _F_ 1 | Kappa |\n| :---: | :---: | :---: | :---: | :---: |\n| 3D-ConvSTAR | 光学时序为主 | 83.6 | 0.802 | 0.775 |\n| Self-Attention 3D | 光学 + 时序注意力 | 85.1 | 0.821 | 0.796 |\n| UNet++ | 光学空间特征 | 82.4 | 0.789 | 0.761 |\n| CNN-LSTM-DS | 光学时序 + 手工特征 | 81.9 | 0.781 | 0.754 |\n| TGF-Net | Transformer + CNN融合 | 86.3 | 0.836 | 0.812 |\n| Attention-3DCNN | 跨模态三重注意力（S2+S1） | 89.4 | 0.872 | 0.846 |\n\n 表5 \n\n[![Image 34](https:\u002F\u002Fwww.smartag.net.cn\u002Fimages\u002Ftable-icon.gif)](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202510010#inline_content14005)\n\n**表6**\n\n多源遥感农作物分类研究中不同模型在高云量条件下的下降幅度\n\n| 模型 | 常规OA\u002F% | 高云量OA\u002F% | 下降幅度\u002F百分点 |\n| :---: | :---: | :---: | :---: |\n| 3D-ConvSTAR | 89.5 | 83.6 | 5.9 |\n| Self-Attention 3D | 90.5 | 85.1 | 5.4 |\n| UNet++ | 88.0 | 82.4 | 5.6 |\n| CNN-LSTM-DS | 86.5 | 81.9 | 4.6 |\n| TGF-Net | 90.0 | 86.3 | 3.7 |\n| Attention-3DCNN | 93.0 | 89.4 | 3.6 |\n\n 表6 \n\n[![Image 35](https:\u002F\u002Fwww.smartag.net.cn\u002Ffileup\u002F2096-8094\u002FFIGURE\u002F2026-8-2\u002FImages\u002F2096-8094-2026-8-2-118\u002Fthumbn","智慧农业(中英文) 2026年第8卷 第2期","2026-07-08T00:00:00Z","论文",10,false,67,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},15,20,18,12,2,1,"核心期刊论文，提出改进YOLOv11n-seg模型用于胡麻倒伏识别，方法新颖且兼顾精度与轻量化，但发表于数月前，时效性较低。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","YOLOv11n-seg","胡麻倒伏","轻量化","实例分割",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":37},"needs_review","2026-10-22.2","rules",{"candidates":38,"agricultural_evidence":14,"reason":49},[39,44],{"code":40,"score":22,"supported":14,"title":41,"abstract":42,"tags":43},"I1",[],[],[27],{"code":45,"score":22,"supported":14,"title":46,"abstract":47,"tags":48},"I2",[],[],[31],"农业相关性需要复核",[51,52],"实例分割 智慧农业 胡麻倒伏 轻量化","实例分割 智慧农业","实例分割智慧农业胡麻倒伏轻量化-177",9,"10.12133\u002Fj.smartag.sa202510010","2026-08-01T23:53:44.590710Z",{"total":58,"page":22,"page_size":58,"items":59},6,[60,132,196,262,314,365],{"id":61,"title":62,"url":63,"summary":64,"summary_zh":65,"content":8,"source_name":66,"source_url":63,"published_at":67,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":68,"score_detail":69,"sources":73,"tags":75,"primary_topic":79,"secondary_topics":84,"classification":85,"search_phrases":110,"slug":113,"view_count":114,"doi":115,"paper":116,"created_at":131},5075,"An integrated instance segmentation and deep feature fusion model for robust leaf species classification in natural environments","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffpls.2026.1893779","Introduction The recognition of leaf segments and species in nature is a difficult process due to the changes in brightness, too much clutter in the background, and the overlapping of leaves due to which the accuracy falls down for 20-40% when using laboratory scans instead of field-ready applications. Methods This research presents a unified framework of deep learning that includes Mask R-CNN -based instance segmentation and deep feature fusion in order to overcome this problem. The process of extracting a number of leaves took place through Detectron2 and the excessive stream extractor that included ResNet-50 and VGG-16 used in conjunction with other descriptive methods allowed providing a joint representation of 6,148 dimensions. The classifier was trained according to three methods: Baseline, PSO (particle swarm optimization), and Grid search. Results The masked fusion system cut off the noise in the background while retaining the important botanical features with the help of achieving the mean Intersection-over-Union equal to 0.752, detection precision of 98.39%, and mAP@0.5 of 0.8291. The ResNet-50 model enhanced with PSO techniques has the best performance among other technologies based on ResNet-50 (90% accuracy, F1 score 0.9002), while the technology of Grid search revealed a little higher result for the special techniques such as ResNet-50 plus traditional features (F1 score = 0.9135) which means that the efficiency of the methodology is contingent to the type of the features not the fact if the technology is better than the other. Discussion Comparative and ablation data analysis proved the efficiency of the instance segmentation used in combination with multi-mode fusion especially when used in occlusion-prone conditions. The system offers a scalable application for smart agriculture and biodiversity monitoring while being a good example of bridging the gap in automated environmental intelligence.","引言 由于亮度变化、背景杂乱以及叶片重叠等因素，在自然环境中识别叶片片段和物种是一项困难的过程，当使用实验室扫描而非野外应用时，准确率会下降20-40%。方法 本研究提出了一个统一的深度学习框架，包括基于Mask R-CNN（掩码区域卷积神经网络）的实例分割和深度特征融合，以克服这一问题。通过Detectron2和包含ResNet-50与VGG-16的过度流提取器，结合其他描述性方法，提取了多个叶片，提供了6，148维的联合表示。分类器根据三种方法进行训练：基线、PSO（粒子群优化）和网格搜索。结果 掩码融合系统在保留重要植物学特征的同时消除了背景噪声，实现了平均交并比为0.752、检测精度为98.39%、mAP@0.5为0.8291。经PSO技术增强的ResNet-50模型在基于ResNet-50的其他技术中表现最佳（准确率90%，F1分数0.9002），而网格搜索技术在ResNet-50加传统特征等特定技术中显示出略高的结果（F1分数=0.9135），这意味着该方法的效率取决于特征类型，而非某种技术是否优于其他技术。讨论 比较和消融数据分析证明了实例分割与多模态融合相结合的有效性，尤其是在易遮挡条件下。该系统为智能农业和生物多样性监测提供了可扩展的应用，同时是弥合自动化环境智能差距的良好范例。","Frontiers in Plant Science","2026-10-15T00:00:00Z",74,{"impact":17,"substance":18,"depth":70,"authority":71,"freshness":54,"relevant":22,"comment":72},17,13,"方法新颖、数据扎实的叶片识别论文，对智慧农业与生物多样性监测有参考价值，但属细分技术进展，非重大突破。",[74],{"name":66,"url":63},[27,76,31,77,78],"农业人工智能","叶片识别","生物多样性监测",{"code":45,"slug":80,"name":81,"path":82,"parent_code":83},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[],{"status":86,"locked":14,"version":35,"method":36,"evidence":87},"classified",{"candidates":88,"agricultural_evidence":91},[89,97,104],{"code":45,"score":90,"supported":91,"title":92,"abstract":93,"tags":96},8,true,[],[94,95],"深度学习","deep learning",[76,31],{"code":98,"score":58,"supported":91,"title":99,"abstract":100,"tags":103},"H6",[],[101,102],"生物多样性","biodiversity",[],{"code":40,"score":105,"supported":91,"title":106,"abstract":107,"tags":109},4,[],[108],"smart agriculture",[27],[111,112],"Mask R-CNN 叶片识别","ResNet-50 粒子群优化 叶片分类","MaskR-CNN叶片识别-5075",0,"10.3389\u002Ffpls.2026.1893779",{"doi":115,"openalex_id":117,"authors":118,"venue":66,"cited_by_count":114,"oa_url":63,"card":123,"direction":129,"ingested_from":130},"W7222005311",[119,121],{"name":120,"orcid":8},"K. Arunkumar",{"name":122,"orcid":8},"S. Leninisha",{"tldr":124,"method":125,"finding":126,"direction":127,"opportunity":128},"提出实例分割与深度特征融合框架，在自然环境下实现鲁棒叶片物种分类。","Mask R-CNN分割叶片，ResNet-50与VGG-16融合成6148维特","掩膜融合去背景噪声，mAP@0.5达0.8291，ResNet-50+PSO准确率90%，F1达0.","农业遥感与作物表型","可探索轻量化分割与多模态融合在移动端实时叶片识别及遮挡场景下的泛化能力。","智慧农业 \u002F 农业物联网","openalex","2026-10-16T23:30:07.962310Z",{"id":133,"title":134,"url":135,"summary":136,"summary_zh":137,"content":8,"source_name":138,"source_url":135,"published_at":139,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":140,"score_detail":141,"sources":144,"tags":146,"primary_topic":150,"secondary_topics":156,"classification":158,"search_phrases":175,"slug":178,"view_count":114,"doi":179,"paper":180,"created_at":195},5592,"Evaluation of Concept-Rank Collapse Audits for Computer Vision Agriculture Disease Classifiers Under Low-Label Regimes: An Interpretability Capacity Bound for Smallholder Fungicide Selectivity","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23545193","Concept-based interpretability methods for plant-disease classifiers are typically evaluated by whether their concept activations correlate with human-annotated labels, but this criterion is silent on a prior question: whether the classifier's internal representation retains enough concept-rank structure for any audit to be informative. the present analysis formalises that prior question as an interpretability capacity bound. A classifier is modelled as a concept-rank operator whose singular values govern how much concept-level signal survives the projection from pixel space to class logits, and a collapse audit is defined as the ratio of retained concept-rank energy to total concept energy. Under a low-label regime, where concept annotations are scarce and the audit must be estimated from few labelled examples, the bound predicts that audit reliability degrades as a function of the concept-rank spectrum and the label budget. The analysis is a theoretical and methodological contribution: it derives the bound, states the conditions under which a collapse audit is identifiable, and characterises the operating characteristics of the audit as a decision procedure under a declared variance structure (between-cluster SD = 6.0, residual SD = 4.5, intraclass correlation = 0.64). Power was estimated across 2000 design cells for cluster counts of 8, 16, 24 and 32 and effect sizes of 3, 5 and 8 percentage points; 52 clusters were required for 5 percentage points at 80% power. The bound reframes concept audits as capacity tests rather than fidelity tests, with consequences for how smallholder fungicide-selectivity decisions should be supported by interpretable disease classifiers.","面向植物病害分类器的基于概念的可解释性方法，通常以其概念激活是否与人工标注标签相关作为评价标准，但该标准对一个更先前的问题保持沉默：分类器的内部表征是否保留了足够的概念秩结构，从而使任何审计具有信息量。本分析将该先前问题形式化为一个可解释性容量上界。分类器被建模为概念秩算子，其奇异值支配着从像素空间到类别logits的投影中概念级信号得以保留的程度，并将坍缩审计定义为保留的概念秩能量与总概念能量之比。在低标签情形下，概念标注稀缺且审计必须由少量标注样本估计，该上界预测审计可靠性会随概念秩谱和标签预算的函数而退化。本分析是一项理论和方法论贡献：推导出该上界，陈述坍缩审计可识别的条件，并在声明的方差结构（簇间SD = 6.0，残差SD = 4.5，组内相关系数 = 0.64）下刻画审计作为决策程序的操作特征。在2000个设计单元中，针对簇数为8、16、24和32以及效应量为3、5和8个百分点的情形估计了功效；在80%功效下达到5个百分点需要52个簇。该上界将概念审计重新框定为容量检验而非保真度检验，并对可解释病害分类器应如何支持小农杀菌剂选择性决策具有启示意义。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-21T00:00:00Z",69,{"impact":90,"substance":142,"depth":19,"authority":20,"freshness":13,"relevant":22,"comment":143},21,"理论方法论文，提出可解释性容量上界并给出审计功效分析，对智慧农业病害识别可信度有参考价值，但属小众方法学研究、缺乏田间实证，暂不宜进入每日精选。",[145],{"name":138,"url":135},[27,76,147,148,149],"小农户","植物病害识别","可解释性",{"code":151,"slug":152,"name":153,"path":154,"parent_code":155},"G2","farm-services","社会化服务与规模经营","\u002Ftopic\u002Frural-services\u002Ffarm-services","G",[157],{"code":45,"slug":80,"name":81,"path":82,"parent_code":83},{"status":86,"locked":14,"version":35,"method":36,"evidence":159},{"candidates":160,"agricultural_evidence":91},[161,166,171],{"code":151,"score":20,"supported":91,"title":162,"abstract":164,"tags":165},[163],"smallholder",[163],[147],{"code":45,"score":13,"supported":91,"title":167,"abstract":169,"tags":170},[168],"computer vision",[],[76,148],{"code":40,"score":22,"supported":14,"title":172,"abstract":173,"tags":174},[],[],[27],[176,177],"农业人工智能 植物病害识别 可解释性 智慧农业","农业人工智能 植物病害识别","农业人工智能植物病害识别可解释性智慧农业-5592","10.5281\u002Fzenodo.23545193",{"doi":179,"openalex_id":181,"authors":182,"venue":138,"cited_by_count":114,"oa_url":135,"card":189,"direction":193,"ingested_from":130},"W7223137323",[183,185,187],{"name":184,"orcid":8},"Akshay Ramgoolam",{"name":186,"orcid":8},"Priya Bundhoo Appadoo",{"name":188,"orcid":8},"Jean-Noël LEspérance",{"tldr":190,"method":191,"finding":192,"direction":193,"opportunity":194},"提出可解释性容量上界，评估低标签下概念秩崩溃审计的可靠性。","将分类器建模为概念秩算子，推导容量上界并做2000格功效模拟。","审计可靠性随概念秩谱与标签预算下降；5个百分点效应需52个聚类达80%功效。","农业人工智能与决策模型","可将容量上界用于小农户杀菌剂选择性决策的可解释模型审计与标签预算设计。","2026-10-22T11:21:10.425290Z",{"id":197,"title":198,"url":199,"summary":200,"summary_zh":201,"content":8,"source_name":138,"source_url":199,"published_at":139,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":140,"score_detail":202,"sources":205,"tags":207,"primary_topic":211,"secondary_topics":217,"classification":219,"search_phrases":244,"slug":247,"view_count":114,"doi":248,"paper":249,"created_at":261},5591,"Hierarchical Conformal Risk Control for Artificial Intelligence Agriculture Irrigation Scheduling Under Unmodeled Infiltration: A Computer Safety-Guarantee Evaluation","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23543665","Irrigation scheduling algorithms that rely on soil-moisture forecasts are vulnerable to unmodeled infiltration, the process by which water bypasses the root zone through preferential pathways that resistance-based soil models cannot represent. The framework is evaluated through a replication-based design study that characterises the operating characteristics of the before-after-control-impact estimator used to audit scheduler performance, under a declared variance structure with between-cluster standard deviation 6.0, residual standard deviation 4.5 and intraclass correlation 0.64. Power was estimated across 2000 design cells spanning cluster counts of 8, 16, 24 and 32 and effect sizes of 3, 5 and 8 percentage points. The analysis indicates that 54 clusters are required to detect a 5-percentage-point effect at 80% power, and that the minimum detectable effect declines steeply as cluster count rises from 8 to 32. The contribution is a formal safety guarantee for irrigation decision support that remains valid when the infiltration model is wrong, together with an audit design that makes the guarantee empirically checkable.","依赖土壤湿度预报的灌溉调度算法容易受到未建模入渗的影响，即水分通过优先路径绕过根区的过程，而基于阻力的土壤模型无法表征这一过程。该框架通过一项基于重复的设计研究进行评估，该研究刻画了用于审计调度器性能的前后对照影响估计量的运行特性，其所依据的声明方差结构为：组间标准差6.0、残差标准差4.5、组内相关系数0.64。功效估计覆盖2000个设计单元，涵盖聚类数8、16、24和32以及效应量3、5和8个百分点。分析表明，在80%功效下检测5个百分点效应需要54个聚类，且最小可检测效应随聚类数从8增至32而急剧下降。本文的贡献在于为灌溉决策支持提供了一种正式的安全保证，该保证在入渗模型错误时仍然有效，同时提出了一种使该保证可经实证检验的审计设计。",{"impact":90,"substance":203,"depth":19,"authority":20,"freshness":54,"relevant":22,"comment":204},22,"该研究提出在入渗模型失配条件下仍有效的灌溉决策安全保证框架，并给出可审计的试验设计，方法新颖、结论可靠，但属细分方法学研究，产业影响有限，适合作为专业精选而非头条。",[206],{"name":138,"url":199},[27,76,208,209,210],"灌溉决策","安全保证","试验设计",{"code":212,"slug":213,"name":214,"path":215,"parent_code":216},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",[218],{"code":45,"slug":80,"name":81,"path":82,"parent_code":83},{"status":86,"locked":14,"version":35,"method":36,"evidence":220},{"candidates":221,"agricultural_evidence":91},[222,227,233,240],{"code":212,"score":17,"supported":91,"title":223,"abstract":224,"tags":226},[213],[225,213],"灌溉",[208],{"code":45,"score":20,"supported":91,"title":228,"abstract":230,"tags":232},[229],"artificial intelligence",[231],"decision support",[76],{"code":234,"score":58,"supported":91,"title":235,"abstract":236,"tags":239},"H2",[],[237,238],"土壤","soil",[],{"code":40,"score":22,"supported":14,"title":241,"abstract":242,"tags":243},[],[],[27],[245,246],"灌溉调度 安全保证","农业人工智能 安全保证 智慧农业 灌溉决策","灌溉调度安全保证-5591","10.5281\u002Fzenodo.23543665",{"doi":248,"openalex_id":250,"authors":251,"venue":138,"cited_by_count":114,"oa_url":199,"card":256,"direction":193,"ingested_from":130},"W7223131205",[252,254],{"name":253,"orcid":8},"Nassima Benkhelifa",{"name":255,"orcid":8},"Yacine Aït Ahmed",{"tldr":257,"method":258,"finding":259,"direction":193,"opportunity":260},"提出分层保形风险控制，为未建模入渗下的灌溉调度提供安全保证。","分层保形风险控制与基于重复设计的BACI审计，2000个设计单元功效分析。","检测5个百分点效应需54个集群，最小可检测效应随集群数增加陡降。","可将保形风险控制扩展到其他未建模扰动下的农业决策安全保证与审计设计。","2026-10-22T11:21:10.283561Z",{"id":263,"title":264,"url":265,"summary":266,"summary_zh":267,"content":8,"source_name":138,"source_url":265,"published_at":139,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":268,"score_detail":269,"sources":271,"tags":273,"primary_topic":276,"secondary_topics":277,"classification":279,"search_phrases":294,"slug":297,"view_count":114,"doi":298,"paper":299,"created_at":313},5590,"Evaluation of Concept-Probe Temporal-Stability Audits for Computer Vision Agriculture Disease Classifiers Under Seasonal Phenology Drift: An Interpretability Decay Bound for Smallholder Fungicide Timing","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23545234","Concept-probe audits are increasingly used to certify that a crop-disease classifier's internal representations track the pathological concepts a smallholder would act on, but a probe certified at one point in the season may silently decouple from those concepts as phenology shifts the visual statistics of the field. the present analysis treats that decoupling as a formal object. It defines a temporal-stability audit as a sequence of concept-probe accuracies measured across defined phenological windows, and it derives an interpretability decay bound: the rate at which probe accuracy can fall before the fungicide-timing decision it is meant to support becomes unreliable. The bound is expressed as a function of the probe's initial margin, the drift in concept prevalence between windows, and the classifier's sensitivity to the drifted features, and it yields a certified audit interval rather than a single-point certificate. The paper then characterises the operating characteristics of the audit design under a declared variance structure, using a replication-based evaluation of the before-after-control-impact estimator with between-cluster SD = 6.0, residual SD = 4.5 and intraclass correlation = 0.64. Power was estimated across 2000 design cells over cluster counts of 8, 16, 24 and 32 and effect sizes of 3, 5 and 8 percentage points; 54 clusters were required for 5 percentage points at 80% power. The contribution is a decision-theoretic account of when a concept-probe certificate expires, and a design rule for scheduling re-audits across the season.","概念探针审计（concept-probe audit）日益被用于验证作物病害分类器的内部表征是否追踪小农户会据以采取行动的病理学概念，但在生长季某一时点获得认证的探针，可能随着物候变化改变田间的视觉统计特征而悄然与这些概念脱钩。本分析将这种脱钩视为一个形式化对象。它将时间稳定性审计（temporal-stability audit）定义为在既定物候窗口内测量的一系列概念探针准确率，并推导出一个可解释性衰减界限（interpretability decay bound）：即探针准确率在其所支持的杀菌剂施用时机决策变得不可靠之前所能下降的速率。该界限表示为探针初始裕度、窗口间概念流行率漂移以及分类器对漂移特征敏感度的函数，并给出一个经认证的审计区间而非单点认证。随后，本文在声明的方差结构下刻画该审计设计的运行特征，采用基于重复的前后对照影响估计量评估，其中组间标准差 = 6.0、残差标准差 = 4.5、组内相关系数 = 0.64。功效在 2000 个设计单元上估计，聚类数取 8、16、24 和 32，效应量取 3、5 和 8 个百分点；在 80% 功效下达到 5 个百分点需要 54 个聚类。本文的贡献在于从决策论角度阐明概念探针认证何时失效，并给出在整个生长季内安排重新审计的设计规则。",70,{"impact":90,"substance":203,"depth":19,"authority":20,"freshness":13,"relevant":22,"comment":270},"该论文提出概念探针审计的时序稳定性框架与可解释性衰减界，方法新颖、统计设计扎实，但属小众方法论研究，产业影响有限，适合作为专业精选而非头条。",[272],{"name":138,"url":265},[27,76,147,274,275],"作物病害识别","模型审计",{"code":151,"slug":152,"name":153,"path":154,"parent_code":155},[278],{"code":45,"slug":80,"name":81,"path":82,"parent_code":83},{"status":86,"locked":14,"version":35,"method":36,"evidence":280},{"candidates":281,"agricultural_evidence":91},[282,286,290],{"code":151,"score":17,"supported":91,"title":283,"abstract":284,"tags":285},[163],[147,163],[147],{"code":45,"score":13,"supported":91,"title":287,"abstract":288,"tags":289},[168],[],[274,76],{"code":40,"score":22,"supported":14,"title":291,"abstract":292,"tags":293},[],[],[27],[295,296],"概念探针 时序稳定性 作物病害分类器","杀菌剂 施药时机 可解释性衰减","概念探针时序稳定性作物病害分类器-5590","10.5281\u002Fzenodo.23545234",{"doi":298,"openalex_id":300,"authors":301,"venue":138,"cited_by_count":114,"oa_url":265,"card":308,"direction":193,"ingested_from":130},"W7223088030",[302,304,306],{"name":303,"orcid":8},"Abdi-Basid Idriss Farah",{"name":305,"orcid":8},"Amina Abdillahi Guedi",{"name":307,"orcid":8},"Moussa Omar Youssouf",{"tldr":309,"method":310,"finding":311,"direction":193,"opportunity":312},"提出概念探针时间稳定性审计与可解释性衰减界，判断何时需重新审计。","定义跨物候窗口探针精度序列，推导衰减界，用BACI复制评估统计功效。","5个百分点效应需54个聚类达80%功效，审计证书会随物候漂移过期。","可将衰减界与再审计调度规则嵌入小农户杀菌剂时机决策支持系统，并验证跨作物泛化。","2026-10-22T11:21:08.978348Z",{"id":315,"title":316,"url":317,"summary":318,"summary_zh":319,"content":8,"source_name":138,"source_url":317,"published_at":139,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":320,"score_detail":321,"sources":323,"tags":325,"primary_topic":328,"secondary_topics":329,"classification":330,"search_phrases":349,"slug":352,"view_count":114,"doi":353,"paper":354,"created_at":364},5589,"Conformal-Risk Evaluation for Artificial Intelligence Agriculture Spray-Nozzle Fault Detection Under Label-Delay Feedback: A Computer Coverage Audit","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23544107","Spray-nozzle fault detection in precision agriculture is a classification problem in which the label that confirms a fault arrives after the spray pass, so the detector must act on unlabelled evidence and its error rate must be certified before the label exists. Conformal prediction offers distribution-free coverage guarantees, but the guarantee is stated for exchangeable calibration and test data, an assumption that label-delayed feedback violates because the calibration set is always older than the deployment set. the present analysis formalises label-delay feedback as a distribution shift in the conformity score and derives a coverage bound for split-conformal fault detectors under that shift. The bound separates the nominal miscoverage rate into an exchangeable component and a delay-induced component that scales with the total variation distance between the calibration and deployment score distributions. A replication-based evaluation of the ['Intercept', 'exposure', 'group', 'severity'] estimator was conducted: 600 data sets of N = 500 were drawn from a declared data-generating process (intercept 12.0, exposure 0.42, group -3.1, severity 1.35, error SD 4.2), and the study characterises the behaviour of the ordinary least squares estimator under the stated process. The estimator recovered the declared coefficients with estimated bias 0.01122 for the intercept and -0.0002 for exposure, root mean squared error 0.9217 and 0.01614 respectively, and empirical coverage of the nominal 95% intervals of 1.0 for both parameters across 600 replications. The coverage audit shows that the delay-induced term, not the exchangeable term, dominates the risk of a conformal spray-nozzle detector, and that the audit procedure can certify a detector before field labels return.","在精准农业中，喷雾喷嘴故障检测是一个分类问题，其中确认故障的标签在喷雾作业结束后才到达，因此检测器必须基于未标记的证据采取行动，并且其错误率必须在标签存在之前得到认证。保形预测（conformal prediction）提供了无分布覆盖保证，但该保证是针对可交换的校准数据和测试数据而言的，而标签延迟反馈违反了这一假设，因为校准集总是比部署集更旧。本分析将标签延迟反馈形式化为一致性分数中的分布偏移，并推导了在该偏移下分裂保形故障检测器的覆盖界。该界将名义错误覆盖 rate 分解为可交换分量和延迟诱导分量，后者随校准与部署分数分布之间的全变差距离而缩放。对 ['Intercept', 'exposure', 'group', 'severity'] 估计器进行了基于重复的评估：从声明的数据生成过程（截距 12.0，暴露 0.42，分组 -3.1，严重程度 1.35，误差标准差 4.2）中抽取了 600 个 N = 500 的数据集，研究刻画了普通最小二乘估计器在所述过程下的行为。该估计器恢复了声明的系数，截距的估计偏差为 0.01122，暴露为 -0.0002，均方根误差分别为 0.9217 和 0.01614，两个参数在 600 次重复中名义 95% 区间的经验覆盖率为 1.0。覆盖审计表明，延迟诱导项而非可交换项主导了保形喷雾喷嘴检测器的风险，并且该审计程序可以在田间标签返回之前对检测器进行认证。",62,{"impact":90,"substance":19,"depth":70,"authority":54,"freshness":13,"relevant":22,"comment":322},"论文提出标签延迟下共形预测覆盖界，方法有新意但属预印本、应用面窄，可作为专业补充而非每日精选头条。",[324],{"name":138,"url":317},[27,76,326,327],"精准施药","病虫害监测",{"code":45,"slug":80,"name":81,"path":82,"parent_code":83},[],{"status":86,"locked":14,"version":35,"method":36,"evidence":331},{"candidates":332,"agricultural_evidence":91},[333,337,344],{"code":45,"score":54,"supported":91,"title":334,"abstract":335,"tags":336},[229],[],[76],{"code":40,"score":338,"supported":91,"title":339,"abstract":340,"tags":343},7,[],[341,342],"精准农业","precision agriculture",[27],{"code":345,"score":22,"supported":14,"title":346,"abstract":347,"tags":348},"F4",[],[],[326],[350,351],"农业人工智能 病虫害监测 智慧农业 精准施药","农业人工智能 病虫害监测","农业人工智能病虫害监测智慧农业精准施药-5589","10.5281\u002Fzenodo.23544107",{"doi":353,"openalex_id":355,"authors":356,"venue":138,"cited_by_count":114,"oa_url":317,"card":359,"direction":193,"ingested_from":130},"W7223116087",[357],{"name":358,"orcid":8},"Achieng' Atim Okello",{"tldr":360,"method":361,"finding":362,"direction":193,"opportunity":363},"将标签延迟反馈建模为共形分数分布偏移，推导覆盖率界并审计喷雾喷嘴故障检测器。","分裂共形预测、总变差距离、600次重复模拟（N=500）评估OLS估计。","延迟诱导项而非可交换项主导检测器风险，审计可在田间标签返回前认证检测器。","可将该覆盖率审计框架扩展到其他标签延迟的农业传感器故障检测，并研究延迟分布估计误差对认证的影响。","2026-10-22T11:21:02.845718Z",{"id":366,"title":367,"url":368,"summary":369,"summary_zh":370,"content":8,"source_name":138,"source_url":368,"published_at":139,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":68,"score_detail":371,"sources":373,"tags":375,"primary_topic":379,"secondary_topics":380,"classification":381,"search_phrases":409,"slug":412,"view_count":114,"doi":413,"paper":414,"created_at":424},5588,"Certified Temporal Fusion of Artificial Intelligence Climate Analytics for Crop Failure Early Warning Under Sparse Agricultural Sensors: Error Bounds and Famine Relief Targeting","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23545489","Sparse agricultural sensor networks across sub-Saharan Africa leave crop-failure early-warning systems with too little ground observation to support reliable famine relief targeting. The framework treats sensor sparsity as a missing-data problem with a known observation operator and propagates climate-analytics uncertainty through a temporal Kalman-Buys filter whose gain is constrained by a certified risk envelope. Using NASA POWER monthly reanalysis for Accra, Ghana (2000-2023, 312 observations), the analysis establishes a mean temperature of 26.521 C and a warming trend of 0.1622 C per decade, which calibrates the climate-analytics prior. The derived bounds show that relief-targeting error scales with the square root of sensor density and with the Lipschitz constant of the crop-response function, yielding an explicit sensor-placement rule for a target misclassification rate. The contribution is a certified, assumption-explicit bridge between climate analytics and famine relief targeting that states the conditions under which early warning is trustworthy.","撒哈拉以南非洲稀疏的农业传感器网络使得作物歉收预警系统缺乏足够的地面观测数据，难以支撑可靠的饥荒救援目标定位。该框架将传感器稀疏性视为具有已知观测算子的缺失数据问题，并通过时间Kalman-Buys滤波器传播气候分析不确定性，该滤波器的增益受经认证的风险包络约束。利用NASA POWER对加纳阿克拉的月度再分析数据（2000—2023年，312个观测值），分析确定了平均气温为26.521°C、每十年0.1622°C的增暖趋势，据此校准了气候分析先验。推导出的界限表明，救援目标定位误差随传感器密度的平方根以及作物响应函数的Lipschitz常数缩放，从而为目标误分类率给出了明确的传感器布设规则。该研究的贡献在于构建了一个经认证的、假设明确的气候分析与饥荒救援目标定位之间的桥梁，阐明了早期预警可信赖的条件。",{"impact":20,"substance":203,"depth":19,"authority":20,"freshness":13,"relevant":22,"comment":372},"该研究提出一种带误差界认证的时序融合方法，用于稀疏传感器条件下的作物歉收预警与饥荒救援瞄准，方法新颖、结论明确，但案例局限于加纳阿克拉，产业影响有限，适合作为技术前沿条目收录。",[374],{"name":138,"url":368},[27,76,376,377,378],"粮食安全","遥感","气候分析",{"code":45,"slug":80,"name":81,"path":82,"parent_code":83},[],{"status":86,"locked":14,"version":35,"method":36,"evidence":382},{"candidates":383,"agricultural_evidence":91},[384,388,395,400,404],{"code":45,"score":54,"supported":91,"title":385,"abstract":386,"tags":387},[229],[],[76],{"code":389,"score":390,"supported":91,"title":391,"abstract":392,"tags":394},"I5",3,[],[393],"农业传感",[],{"code":396,"score":22,"supported":14,"title":397,"abstract":398,"tags":399},"A8",[],[],[376],{"code":40,"score":22,"supported":14,"title":401,"abstract":402,"tags":403},[],[],[27],{"code":405,"score":22,"supported":14,"title":406,"abstract":407,"tags":408},"I3",[],[],[377],[410,411],"NASA POWER 加纳 气候分析","作物歉收 早期预警 传感器","NASAPOWER加纳气候分析-5588","10.5281\u002Fzenodo.23545489",{"doi":413,"openalex_id":415,"authors":416,"venue":138,"cited_by_count":114,"oa_url":368,"card":419,"direction":193,"ingested_from":130},"W7223111766",[417],{"name":418,"orcid":8},"Fatima-Zahra El Khattabi",{"tldr":420,"method":421,"finding":422,"direction":127,"opportunity":423},"提出带误差界的时间融合框架，在稀疏传感器下用气候分析预警作物歉收。","卡尔曼-布西滤波+NASA POWER再分析数据，风险包络约束增益。","救济瞄准误差随传感器密度平方根和作物响应Lipschitz常数缩放，给出布点规则。","可将该认证误差界框架扩展到多作物多区域，结合卫星遥感弥补地面传感器稀疏。","2026-10-22T11:21:02.682497Z"]