[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-376":3,"related-376":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":23,"tags":25,"primary_topic":31,"secondary_topics":37,"classification":38,"search_phrases":50,"slug":53,"view_count":54,"doi":55,"paper":8,"created_at":56},376,"基于无人机影像与YOLOv12n-RCL的向日葵成熟期盘腐病危害程度检测","http:\u002F\u002Fwww.tcsae.org\u002Fcn\u002Farticle\u002Fdoi\u002F10.11975\u002Fj.issn.1002-6819.202512131","山东理工大学等单位李京谦、韩国涛、赵静、张帅领、谢欣妤、付姗姗、鲁力群、兰玉彬在《农业工程学报》2026年第42卷第9期发表论文。该研究提出基于改进YOLOv12n的向日葵成熟期盘腐病危害程度检测模型,首先将主干和颈部网络的C3K2模块替换为融合感受野注意力卷积(RFAConv)与坐标注意力机制(CA)的C3K2-RC模块;其次在颈部网络集成轻量级的上采样算子CARFAE;最后引入LSCSBD(lightweight shared convolutional separable batch normalization detection)检测头。试验结果表明,YOLOv12n-RCL模型的精确率、召回率、mAP0.5和mAP0.5-0.95分别达到83.4%、81.2%、84.8%和50.2%,较基准模型YOLOv12n分别提高3.8、3.2、3.4和4.0个百分点。",null,"**摘要:**\n\n高效、精准地评估向日葵盘腐病危害程度对早期防治、精准施药和产量损失估测至关重要。针对传统病害分级方法效率低、主观性强的问题，该研究提出一种基于改进YOLOv12n的向日葵成熟期盘腐病危害程度检测模型。首先，将主干和颈部网络的C3K2模块替换为融合感受野注意力卷积（receptive-field attention convolution, RFAConv）与坐标注意力机制（coordinate attention, CA）的C3K2-RC模块，以增强模型在复杂环境中对不同等级盘腐病的特征提取能力；其次，在颈部网络集成轻量级的上采样算子CARFAE提升特征重构效果；最后，引入一种分离批量归一化的轻量级共享卷积检测头LSCSBD（lightweight shared convolutional separable batch normalization detection），以提高模型对小尺度病斑的检测精度与速度。试验结果表明，YOLOv12n-RCL模型的精确率、召回率、mAP 0.5 和mAP 0.5-0.95 分别达到了83.4%、81.2%、84.8%和50.2%，较基准模型YOLOv12n分别提高了3.8、3.2、3.4和4.0个百分点；参数量、计算量和模型大小分别压缩至2.11 M、5.7 G和4.5 MB，较原模型YOLOv12n分别降低了17.9%、12.3%和19.6%。基于混淆矩阵的病害等级误检结果表明，改进模型对不同等级盘腐病的错误预测比例均在10%以下。边缘设备部署测试显示，在包含 1298 个不同等级盘腐病样本的70幅测试图像中，YOLOv12n-RCL仅出现10例漏检和8例误检，与基准模型YOLOv12n相比，检测效果更优，且帧率更高，达到了27.5帧\u002Fs。综上，YOLOv12n-RCL模型在提升检测精度的同时，实现了轻量化设计与部署效率的有效平衡，可为向日葵成熟期盘腐病智能防治装备的研发提供算法参考。\n\n**Abstract:**\n\nSunflower is one of the preferred oil crops in the mildly to moderately saline-alkali soils, due to its strong tolerance to nutrient-poor soils and drought. Among them, sunflower disk rot can be caused by sclerotinia sclerotiorum infection, leading to increasing sterile seed rates. Nutritional components can also decline, such as kernel protein and oil content. Thereby, both the edible and commercial value of the seeds are often required for high yield. Consequently, it is crucial to efficiently and accurately detect disk rot severity for early disease control, precision pesticide application, and yield estimation. However, conventional disease classification has been limited to manual efficiency and subjectivity in recent years. In this study, an improved YOLOv12n-RCL model was proposed to detect the severity of sunflower disk rot at the mature stage. 1) C3K2-RC modules were used to replace C3K2 ones in the backbone and neck networks. Receptive-field attention convolution (RFAConv) and coordinate attention (CA) mechanisms were integrated to enhance the feature extraction from the different severity grades of disk rot in complex environments. 2) A lightweight upsampling operator, CARFAE, was integrated into the neck network for feature reconstruction. 3) A lightweight shared convolutional detection head with separated batch normalization (LSCSBD) was introduced to improve the detection accuracy and speed for small-scale lesions. Experimental results show that the YOLOv12n-RCL model achieved a precision, recall, mAP 0.5, and mAP 0.5~0.95 of 83.4%, 81.2%, 84.8%, and 50.2%, respectively, which was improved by 3.8, 3.2, 3.4, and 4.0 percentage points over the baseline model. The number of parameters, computational complexity, and model size were reduced to 2.11 M, 5.7 GFLOPs, and 4.5 MB, respectively, corresponding to reductions of 17.9%, 12.3%, and 19.6%, compared with the original model. The normalized confusion matrix indicated that the recall for the disease severity grades 0, 1, 2, 3, and 4 were 83.0%, 81.2%, 79.1%, 80.7%, and 82.0%, respectively, indicating a balanced recognition over all five grades. Furthermore, 8.2% of samples with a true label of grade 2 were misclassified as grade 3, and 9.3% of samples with a true label of grade 3 were misclassified as grade 2. Therefore, the confusion between severity grades occurred primarily between grades 2 and 3. The misclassification rate remained below 10% in the rest, indicating its strong performance for the disk rot at different grades. A field test was conducted on 70 images with 1 298 samples of different grades of disk rot. The YOLOv12n-RCL model achieved only 10 missed detections and 8 false detections. Compared with the baseline YOLOv12n model, the superior performance was achieved with a higher frame rate of 27.5 frames per second (FPS). Visualization was also conducted on the sunflower disk rot areas using UAV orthophotography. The improved model maintained stable performance even in complex scenarios with dense target distribution and leaf occlusion, without significantly missing or false detections. In summary, the YOLOv12n-RCL model improved the detection accuracy to effectively balance the lightweight deployment efficiency. The finding can also serve as an algorithmic reference to detect the sunflower disk rot at the mature stage in smart agriculture.\n\n[![Image 1](http:\u002F\u002Fwww.tcsae.org\u002FfileNYGCXB\u002Fjournal\u002Farticle\u002Fnygcxb\u002F2026\u002F9\u002Fbhj-202512131-lijingqian-7-1.jpg)](http:\u002F\u002Fwww.tcsae.org\u002FfileNYGCXB\u002Fjournal\u002Farticle\u002Fnygcxb\u002F2026\u002F9\u002Fbhj-202512131-lijingqian-7-1.jpg \"Figure1.Examples of disk rot samples at different grades\")\n\n**图 1** 不同等级盘腐病样本实例\n\n**Figure 1.** Examples of disk rot samples at different grades\n\n[![Image 2](http:\u002F\u002Fwww.tcsae.org\u002FfileNYGCXB\u002Fjournal\u002Farticle\u002Fnygcxb\u002F2026\u002F9\u002Fbhj-202512131-lijingqian-7-2.jpg)](http:\u002F\u002Fwww.tcsae.org\u002FfileNYGCXB\u002Fjournal\u002Farticle\u002Fnygcxb\u002F2026\u002F9\u002Fbhj-202512131-lijingqian-7-2.jpg \"Figure2.Structure diagram of the YOLOv12n-RCL model\")\n\n**图 2** YOLOv12n-RCL模型结构图\n\n注：Conv为卷积块，Conv 2d为二维卷积块，BatchNorm（BN）为批量归一化层，SiLU为激活函数，C3K为自定义卷积核大小的特征提取模块，C3K2为残差连接特征提取模块，C3K2-RC为融合了感受野卷积和坐标注意力机制的C3K2模块，Split为特征分割操作，n 为模块出现的次数，Concat为特征拼接层，A2为区域注意力机制，A2C2f为结合了区域注意力机制和残差连接的特征提取模块，Scaling为特征缩放操作，ABlock为区域注意力多层感知机模块，CARFAE为上采样算子，LSCSBD为批量归一化的轻量级共享卷积检测头。下同。\n\n**Figure 2.** Structure diagram of the YOLOv12n-RCL model\n\nNote: Conv refers to the convolutional block, Conv 2d refers to the 2D convolutional block, BatchNorm (BN) refers to the batch normalization layer, SiLU is the activation function, C3K is the custom convolution kernel size feature extraction module, C3K2 is the residual connection feature extraction module, C3K2-RC is the C3K2 module that integrates receptive field convolution and coordinate attention mechanism, Split refers to the feature splitting operation, n is the number of times the module appears, Concat is the feature concatenation layer, A2 is the region attention mechanism, A2C2f is the feature extraction module combining region attention mechanism and residual connections, Scaling refers to the feature scaling operation, ABlock is the region attention multilayer perceptron module, CARFAE is the upsampling operator, and LSCSBD refers to the lightweight shared convolutional detection head with batch normalization. The same below.\n\n[![Image 3](http:\u002F\u002Fwww.tcsae.org\u002FfileNYGCXB\u002Fjournal\u002Farticle\u002Fnygcxb\u002F2026\u002F9\u002Fbhj-202512131-lijingqian-7-3.jpg)](http:\u002F\u002Fwww.tcsae.org\u002FfileNYGCXB\u002Fjournal\u002Farticle\u002Fnygcxb\u002F2026\u002F9\u002Fbhj-202512131-lijingqian-7-3.jpg \"Figure3.Structure diagram of the RFAConv module\")\n\n**图 3** RFAConv模块结构图\n\n**Figure 3.** Structure diagram of the RFAConv module\n\n[![Image 4](http:\u002F\u002Fwww.tcsae.org\u002FfileNYGCXB\u002Fjournal\u002Farticle\u002Fnygcxb\u002F2026\u002F9\u002Fbhj-202512131-lijingqian-7-4.jpg)](http:\u002F\u002Fwww.tcsae.org\u002FfileNYGCXB\u002Fjournal\u002Farticle\u002Fnygcxb\u002F2026\u002F9\u002Fbhj-202512131-lijingqian-7-4.jpg \"Figure4.Workflow diagram of CA attention mechanism\")\n\n**图 4** CA注意力机制工作流程图\n\n注：Residual为残差连接，X Avg Pool为宽度方向的平均池化，Y Avg Pool为高度方向的平均池化，r 为通道缩减比例系数，BatchNorm + Non-linear为标准化和非线性处理单元，S为Sigmoid激活函数，下同。\n\n**Figure 4.** Workflow diagram of CA attention mechanism\n\nNote: Residual refers to the residual connection, X Avg Pool refers to average pooling along the width dimension, Y Avg Pool refers to average pooling along the height dimension, r denotes the channel reduction ratio coefficient, BatchNorm + Non-linear refers to the batch normalization and non-linear processing unit, and S refers to the Sigmoid activation function. The same below.\n\n[![Image 5](http:\u002F\u002Fwww.tcsae.org\u002FfileNYGCXB\u002Fjournal\u002Farticle\u002Fnygcxb\u002F2026\u002F9\u002Fbhj-202512131-lijingqian-7-5.jpg)](http:\u002F\u002Fwww.tcsae.org\u002FfileNYGCXB\u002Fjournal\u002Farticle\u002Fnygcxb\u002F2026\u002F9\u002Fbhj-202512131-lijingqian-7-5.jpg \"Figure5.C3k2-RC network structure diagram\")\n\n**图 5** C3K2- RC网络结构图\n\n注：RFAConv为感受野注意力卷积，RFAConv-Bottleneck为添加感受野注意力卷积的瓶颈层，CA为坐标注意力机制，下同。\n\n**Figure 5.** C3k2-RC network structure diagram\n\nNote: RFAConv refers to the receptive field attention convolution, RFAConv-Bottleneck refers to the bottleneck layer incorporating receptive field attention convolution, and CA indicates the coordinate attention mechanism. The same below.\n\n[![Image 6](http:\u002F\u002Fwww.tcsae.org\u002FfileNYGCXB\u002Fjournal\u002Farticle\u002Fnygcxb\u002F2026\u002F9\u002Fbhj-202512131-lijingqian-7-6.jpg)](http:\u002F\u002Fwww.tcsae.org\u002FfileNYGCXB\u002Fjournal\u002Farticle\u002Fnygcxb\u002F2026\u002F9\u002Fbhj-202512131-lijingqian-7-6.jpg \"Figure6.Network structure diagram of CARAFE upsampling\")\n\n**图 6** CARAFE上采样网络结构图\n\n注：X 表示输入特征图，$ {C}_{m} $为通道压缩后的特征图通道数，$ N({\\chi }_{l},{k}_{up}) $表示以$ {\\chi }_{l} $为中心、$ {k}_{up} $为大小的局部邻域，$ \\sigma $为上采样倍数，$ W_{l}^{{'}} $表示上采样核预测模块生成的权重，$ {X}^{{'}} $表示输出特征图。\n\n**Figure 6.** Network structure diagram of CARAFE upsampling\n\nNote: X refers to the input feature map, $ {C}_{m} $ is the number of channels of the feature map after channel compression, $ N({\\chi }_{l},{k}_{up}) $ indicates the local neighborhood centered at $ {\\chi }_{l} $ with a size of $ {k}_{up} $, σ is the upsampling factor, $ W_{1}^{{'}} $ refers to the weights generated by the upsampling kernel prediction module, and $ {X}^{{'}} $ refers to the output feature map.\n\n[![Image 7](http:\u002F\u002Fwww.tcsae.org\u002FfileNYGCXB\u002Fjournal\u002Farticle\u002Fnygcxb\u002F2026\u002F9\u002Fbhj-202512131-lijingqian-7-7.jpg)](http:\u002F\u002Fwww.tcsae.org\u002FfileNYGCXB\u002Fjournal\u002Farticle\u002Fnygcxb\u002F2026\u002F9\u002Fbhj-202512131-lijingqian-7-7.jpg \"Figure7.LSCSBD detection head network structure diagram\")\n\n**图 7** LSCSBD检测头网络结构图\n\n注：P3、P4、P5为第3、4和5层输入特征图，Conv_BN 1×1为批量归一化卷积层，Conv_GN 3×3为组归一化共享卷积层，BnAct为批量归一化和激活函数的组合模块，Conv-Reg为回归卷积层，Conv-Cls为分类卷积层，Scale为缩放因子。\n\n**Figure 7.** LSCSBD detection head network structure diagram\n\nNote: P3, P4, and P5 refer to the feature maps at the 3 rd, 4 th, and 5 th layers. Conv_BN 1×1 is the batch normalization convolutional layer, Conv_GN 3×3 is the group normalization shared convolutional layer, BnAct is the combination module of batch normalization and activation function, Conv-Reg is the regression convolutional layer, Conv-Cls is the classification convolutional layer, Scale is the scaling factor.\n\n[![Image 8](http:\u002F\u002Fwww.tcsae.org\u002FfileNYGCXB\u002Fjournal\u002Farticle\u002Fnygcxb\u002F2026\u002F9\u002Fbhj-202512131-lijingqian-7-8.jpg)](http:\u002F\u002Fwww.tcsae.org\u002FfileNYGCXB\u002Fjournal\u002Farticle\u002Fnygcxb\u002F2026\u002F9\u002Fbhj-202512131-lijingqian-7-8.jpg \"Figure8.Performance comparison between the baseline and the optimized model\")\n\n**图 8** 模型优化前后的性能评估结果\n\n注：mAP 0.5 表示IoU阈值为0.5时的平均精度均值，mAP 0.5-0.95 表示IoU阈值为0.95时的平均精度均值，下同。\n\n**Figure 8.** Performance comparison between the baseline and the optimized model\n\nNote: mAP 0.5 represents the mean average precision at an IoU threshold of 0.5, and mAP 0.5-0.95 represents the mean average precision across IoU thresholds from 0.5 to 0.95. The same below.\n\n[![Image 9](http:\u002F\u002Fwww.tcsae.org\u002FfileNYGCXB\u002Fjournal\u002Farticle\u002Fnygcxb\u002F2026\u002F9\u002Fbhj-202512131-lijingqian-7-9.jpg)](http:\u002F\u002Fwww.tcsae.org\u002FfileNYGCXB\u002Fjournal\u002Farticle\u002Fnygcxb\u002F2026\u002F9\u002Fbhj-202512131-lijingqian-7-9.jpg \"Figure9.Output receptive field of C3K2 with the integration of different modules\")\n\n**图 9** C3K2引入不同模块后的输出感受野\n\n**Figure 9.** Output receptive field of C3K2 with the integration of different modules\n\n[![Image 10](http:\u002F\u002Fwww.tcsae.org\u002FfileNYGCXB\u002Fjournal\u002Farticle\u002Fnygcxb\u002F2026\u002F9\u002Fbhj-202512131-lijingqian-7-10.jpg)](http:\u002F\u002Fwww.tcsae.org\u002FfileNYGCXB\u002Fjournal\u002Farticle\u002Fnygcxb\u002F2026\u002F9\u002Fbhj-202512131-lijingqian-7-10.jpg \"Figure10.Comparison of input and output features before and after adding the CARAFE module\")\n\n**图 10** 添加CARAFE模块前后输入输出特征对比图\n\n注：图中红色框代表向日葵目标。\n\n**Figure 10.** Comparison of input and output features before and after adding the CARAFE module\n\nNote: The red frame in the image represents the sunflower target.\n\n[![Image 11](http:\u002F\u002Fwww.tcsae.org\u002FfileNYGCXB\u002Fjournal\u002Farticle\u002Fnygcxb\u002F2026\u002F9\u002Fbhj-202512131-lijingqian-7-11.jpg)](http:\u002F\u002Fwww.tcsae.org\u002FfileNYGCXB\u002Fjournal\u002Farticle\u002Fnygcxb\u002F2026\u002F9\u002Fbhj-202512131-lijingqian-7-11.jpg \"Figure11.Visualization effect of the ablation study\")\n\n**图 11** 消融试验的可视化效果\n\n**Figure 11.** Visualization effect of the ablation study\n\n[![Image 12](http:\u002F\u002Fwww.tcsae.org\u002FfileNYGCXB\u002Fjournal\u002Farticle\u002Fnygcxb\u002F2026\u002F9\u002Fbhj-202512131-lijingqian-7-12.jpg)](http:\u002F\u002Fwww.tcsae.org\u002FfileNYGCXB\u002Fjournal\u002Farticle\u002Fnygcxb\u002F2026\u002F9\u002Fbhj-202512131-lijingqian-7-12.jpg \"Figure12.Performance comparison of different object detection models\")\n\n**图 12** 不同目标检测模型识别效果对比\n\n注：图中蓝色圆表示漏检，红色三角表示错检。\n\n**Figure 12.** Performance comparison of different object detection models\n\nNote: In the figure, The blue circle indicates missed detections, while the red triangle indicates false detections.\n\n[![Image 13](http:\u002F\u002Fwww.tcsae.org\u002FfileNYGCXB\u002Fjournal\u002Farticle\u002Fnygcxb\u002F2026\u002F9\u002Fbhj-202512131-lijingqian-7-13.jpg)](http:\u002F\u002Fwww.tcsae.org\u002FfileNYGCXB\u002Fjournal\u002Farticle\u002Fnygcxb\u002F2026\u002F9\u002Fbhj-202512131-lijingqian-7-13.jpg \"Figure13.Confusion matrix for different disease severity grades\")\n\n**图 13** 不同等级病害的混淆矩阵\n\n**Figure 13.** Confusion matrix for different disease severity grades\n\n[![Image 14](http:\u002F\u002Fwww.tcsae.org\u002FfileNYGCXB\u002Fjournal\u002Farticle\u002Fnygcxb\u002F2026\u002F9\u002Fbhj-202512131-lijingqian-7-14.jpg)](http:\u002F\u002Fwww.tcsae.org\u002FfileNYGCXB\u002Fjournal\u002Farticle\u002Fnygcxb\u002F2026\u002F9\u002Fbhj-202512131-lijingqian-7-14.jpg \"Figure14.Sunflower disk rot detection system interface\")\n\n**图 14** 向日葵盘腐病检测系统界面\n\n**Figure 14.** Sunflower disk rot detection system interface\n\n[![Image 15](http:\u002F\u002Fwww.tcsae.org\u002FfileNYGCXB\u002Fjournal\u002Farticle\u002Fnygcxb\u002F2026\u002F9\u002Fbhj-202512131-lijingqian-7-15.jpg)](http:\u002F\u002Fwww.tcsae.org\u002FfileNYGCXB\u002Fjournal\u002Farticle\u002Fnygcxb\u002F2026\u002F9\u002Fbhj-202512131-lijingqian-7-15.jpg \"Figure15.Visualization of monitoring and grading results for sunflower disk rot regions\")\n\n**图 15** 向日葵盘腐病区域监测与分级结果可视化\n\n**Figure 15.** Visualization of monitoring and grading results for sunflower disk rot regions\n\n**表 1** 向日葵盘腐病数据集的样本信息\n\n**Table 1** Sample information of the sunflower disk rot dataset\n\n病害等级\n\nDisease grade 训练集\n\nTraining set 测试集\n\nTest set 验证集\n\nValidation set 增强训练集\n\nAugmented training set\n0 7108 900 1012 28432\n1 6517 872 855 26068\n2 6633 767 881 26532\n3 6400 847 860 25600\n4 6817 873 810 27364\n\n**表 2** 试验的软硬件配置参数\n\n**Table 2** Hardware and software configuration parameters for experiments\n\n配置Configuration 参数Parameters\n中央处理器 Intel(R)Core(TM)i7-11700k\n图形处理器 NVIDIA GeForce RTX 3060 Ti\n优化算法 SGD\n操作系统 Ubuntu 18.04\n配置环境 Pytorch 2.6.0+Python 3.10.16\n训练轮次 200\n批次大小 16\n输入图像尺寸 1024×1024\n初始学习率 0.01\n动量系数 0.935\n权重衰减次数 0.0005\n\n**表 3** C3K2-RC模块不同引入位置的对比试验\n\n**Table 3** Comparative experiment on different positions for the introduction of the C3K2-RC module %\n\n替换位置 Replace position _P_ _R_ mAP 0.5 mAP 0.5-0.95\n主干 80.0 76.9 81.9 48.1\n颈部 78.0 75.8 81.7 47.0\n主干+颈部 81.1 77.8 82.2 48.3\n\n**表 4** 消融试验结果\n\n**Table 4** Ablation test results\n\nC3K2-RC CARAFE LSCSBD _P_\u002F%_R_\u002F%mAP 0.5\u002F%mAP 0.5-0.95\u002F%参数量\n\nParameters\u002FM 计算量\n\nFLOPs\u002FG 模型大小\n\nModel size\u002FMB\n□□□79.6 78.0 81.4 46.2 2.57 6.5 5.6\n■□□81.1 77.8 82.2 48.3 2.59 6.6 5.7\n□■□79.6 78.4 81.6 47.0 2.23 5.8 4.9\n□□■80.2 78.7 82.7 48.4 2.44 6.4 5.3\n■■□82.7 80.1 83.6 49.7 2.33 6.3 5.1\n■□■83.7 81.2 84.5 50.1 2.46 6.5 5.4\n□■■79.9 78.0 82.1 48.4 2.09 5.6 4.5\n■■■83.4 81.2 84.8 50.2 2.11 5.7 4.5\n注：“■”表示采用了该模块，“□”表示未采用该模块，FLOPs为浮点数运算次数。下同。\nNote: \"■\" indicates that the module is adopted, \"□\" indicates that the module is not adopted, and FLOPs stands for the number of floating-point operations. The same below.\n\n**表 5** 不同检测网络的综合性能对比\n\n**Table 5** Comparative analysis of the overall performance of different detection networks\n\n模型\n\nModel 类型\n\nType _P_\u002F%_R_\u002F%mAP 0.5\u002F%mAP 0.5-0.95\u002F%参数量\n\nParameters\u002FM 计算量\n\nFLOPs\u002FG 模型大小\n\nModel size\u002FMB\nFaster R-CNN Two-Stage 70.1 65.6 68.8 40.2 137.1 370.2 114.2\nSSD One-Stage 73.1 66.4 72.1 42.3 24.0 61.1 105.2\nCenterNet One-Stage 72.1 67.1 75.6 43.7 32.7 70.2 90.6\nRT-DETR(resnet50)DETR 71.9 68.2 78.7 44.1 42.8 130.5 96.7\nYOLOv5s One-Stage 78.9 77.3 81.2 46.4 7.26 16.4 13.7\nYOLOv7-tiny One-Stage 80.1 81.0 80.5 45.5 6.02 13.2 11.7\nYOLOv8n One-Stage 79.1 78.0 81.4 46.7 3.01 8.2 6.3\nYOLOv9 t One-Stage 76.5 74.2 79.5 44.7 1.73 6.4 4.3\nYOLOv10n One-Stage 79.2 78.7 80.4 45.1 2.69 8.2 5.8\nYOLOv11n One-Stage 78.3 78.8 80.6.46.0 2.61 6.6 5.7\nYOLOv12n One-Stage 79.6 78.0 81.4 46.2 2.57 6.5 5.6\nYOLOv12n-RCL One-Stage 83.4 81.2 84.8 50.2 2.11 5.7 4.5\n\n[1]李丹, 赵存鹏, 刘素恩, 等. 39份油用向日葵杂交种主要农艺性状与产量的多元分析[J]. 华北农学报, 2023, 38(S1): 154-160.\n[2]王彦尊, 王文浩, 何丽芬, 等. 不同向日葵品系对菌核病抗性的综合鉴定[J]. 中国种业, 2023(12): 130-135.\n[3]史明. 向日葵病害综合防治技术[J]. 农业开发与装备, 2015(10): 146.\n[4]魏伟业. 试论向日葵几种常见病及防治方法[J]. 农民致富之友, 2019(2): 13.\n[5]FERENTINOS K P. Deep learning models for plant disease detection and diagnosis[J]. Computers and Electronics in Agriculture, 2018, 145: 311-318. doi: [10.1016\u002Fj.compag.2018.01.009](https:\u002F\u002Fdx.doi.org\u002F10.1016\u002Fj.compag.2018.01.009)\n[6]UPADHYAY A, CHANDEL N S, SINGH K P, et al. Deep learning and computer vision in plant disease detection: a comprehensive review of techniques, models, and trends in precision agriculture[J]. Artificial Intelligence Review, 2025, 58(3): 92. doi: [10.1007\u002Fs10462-024-11100-x](https:\u002F\u002Fdx.doi.org\u002F10.1007\u002Fs10462-024-11100-x)\n[7]朱锐, 张家瑜, 黄继超, 等. 基于卷积神经网络的农作物叶片病害检测研究进展[J]. 农业工程学报, 2025, 41(17): 15-28. \nZHU Rui, ZHANG Jiayu, HUANG Jichao, et al. Research progress of crop leaf disease detection based on convolutional neural network[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2025, 41(17): 15-28. (in Chinese with English abstract)\n[8]GUAN B, WU Y, ZHU J, et al. GC-Faster RCNN: The object detection algorithm for agricultural pests based on improved hybrid attention mechanism[J]. Plants, 2025, 14(7): 1106. doi: [10.3390\u002Fplants14071106](https:\u002F\u002Fdx.doi.org\u002F10.3390\u002Fplants14071106)\n[9]兰玉彬, 孙斌书, 张乐春, 等. 基于改进YOLOv5s的自然场景下生姜叶片病虫害识别[J]. 农业工程学报, 2024, 40(1): 210-216. \nLAN Yubin, SUN Binshu, ZHANG Lechun, et al. Identifying diseases and pests in ginger leaf under natural scenes using improved YOLOv5s[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2024, 40(1): 210-216. (in Chinese with English abstract)\n[10]GONG X, ZHANG S. A high-precision detection method of apple leaf diseases using improved Faster R-CNN[J]. Agriculture, 2023, 13(2): 240. doi: [10.3390\u002Fagriculture13020240](https:\u002F\u002Fdx.doi.org\u002F10.3390\u002Fagriculture13020240)\n[11]吴世琛, 毛玉明, 胡慧中, 等. 基于改进YOLO11n的葡萄果叶病害检测方法[J]. 农业工程学报, 2025, 41(14): 140-147. \nWU Shichen, MAO Yuming, HU Huizhong, et al. Detecting grape leaf diseases using improved YOLO11n[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2025, 41(14): 140-147. (in Chinese with English abstract)\n[12]CHEN S, LV F, HUO P. Improved detection of YOLOv4 sunflower leaf diseases[C]\u002F\u002F2021 2nd International Symposium on Computer Engineering and Intelligent Communications (ISCEIC). Nanjing, China: IEEE, 2021: 56-59.\n[13]于海琳, 兰玉彬, 李京谦, 等. 基于无人机遥感数据和机器学习的向日葵LAI反演[J]. 农业机械学报, 2025, 56(1): 356-365. \nYU Hailin, LAN Yubin, LI Jingqian, et al. Sunflower LAI inversion based on unmanned aerial vehicle remote sensing data and machine learning[J]. Transactions of the Chinese Society for Agricultural Machinery, 2025, 56(1): 356-365. (in Chinese with English abstract)\n[14]胡根生, 谢一帆, 鲍文霞, 等. 基于轻量型网络的无人机遥感图像中茶叶枯病检测方法[J]. 农业机械学报, 2024, 55(4): 165-175. \nHU Gensheng, XIE Yifan, BAO Wenxia, et al. Lightweight network for tea leaf blight detection in UAV remote sensing images[J]. Transactions of the Chinese Society for Agricultural Machinery, 2024, 55(4): 165-175. (in Chinese with English abstract)\n[15]PAN P, GUO W, ZHENG X, et al. Xoo-YOLO: A detection method for wild rice bacterial blight in the field from the perspective of unmanned aerial vehicles[J]. Frontiers in Plant Science, 2023, 14: 1256545. doi: [10.3389\u002Ffpls.2023.1256545](https:\u002F\u002Fdx.doi.org\u002F10.3389\u002Ffpls.2023.1256545)\n[16]陈自立, 郭燕, 王明鑫, 等. 基于RT-YOLOv10和无人机遥感影像的烟草病害轻量化检测研究[J]. 山东农业科学, 2025, 57(9): 149-163.\n[17]YANG Z, KHAN Z, SHEN Y, et al. GTDR-YOLOv12: Optimizing YOLO for efficient and accurate weed detection in agriculture[J]. Agronomy, 2025, 15(8): 1824. doi: [10.3390\u002Fagronomy15081824](https:\u002F\u002Fdx.doi.org\u002F10.3390\u002Fagronomy15081824)\n[18]李易初, 孟庆林, 石凤梅, 等. 田间防治向日葵菌核盘腐病的药剂筛选[J]. 宁夏农林科技, 2015, 56(7): 53-54.\n[19]赵静, 李京谦, 杨蕾, 等. 基于PConv-CGLU与重参数检测头的轻量化膜下棉苗实时检测算法[J]. 农业工程学报, 2025, 41(18): 151-162. \nZHAO Jing, LI Jingqian, YANG Lei, et al. Real-time detecting cotton seedlings under a lightweight film using PConv-CGLU with a heavy parameter detection head[J]. Transactions of the Chinese Society of A","农业工程学报 2026年第42卷第9期 2026-08-08","2026-08-08T00:00:00Z","论文",10,false,78,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,22,14,6,1,"核心期刊论文，提出改进YOLOv12n模型检测向日葵盘腐病，方法新颖、数据详实，对智慧农业病害检测有参考价值。",[24],{"name":10,"url":6},[26,27,28,29,30],"无人机","山东理工大学","YOLOv12","向日葵","农业工程学报",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"A4","soybean-oilseeds","大豆与油料","\u002Ftopic\u002Ffield-crops\u002Fsoybean-oilseeds","A",[],{"status":39,"locked":14,"version":40,"method":41,"evidence":42},"classified","2026-10-22.2","rules",{"candidates":43,"agricultural_evidence":46},[44],{"code":32,"score":45,"supported":46,"title":47,"abstract":48,"tags":49},12,true,[29],[29],[29],[51,52],"农业工程学报 山东理工大学 向日葵 无人机","农业工程学报 山东理工大学","农业工程学报山东理工大学向日葵无人机-376",4,"10.11975\u002Fj.issn.1002-6819.202512131","2026-08-10T23:54:46.266029Z",{"total":20,"page":21,"page_size":20,"items":58},[59,163,254,301,352,388],{"id":60,"title":61,"url":62,"summary":63,"summary_zh":64,"content":8,"source_name":65,"source_url":62,"published_at":66,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":67,"score_detail":68,"sources":71,"tags":73,"primary_topic":78,"secondary_topics":79,"classification":91,"search_phrases":122,"slug":125,"view_count":54,"doi":126,"paper":127,"created_at":162},2320,"Genotype-Aware Prediction of Soybean Seed Composition from Multimodal UAV Imagery of the Standing Crop","https:\u002F\u002Fdoi.org\u002F10.3390\u002Frs18183121","Geospatial artificial intelligence (GeoAI) integrates multimodal remote sensing with deep learning to model complex agricultural systems at scale. Within this framework, accurate and non-destructive prediction of seed composition from in-season standing crops is essential for breeding and precision agriculture. This study developed an end-to-end convolutional neural network (CNN) framework to estimate eight seed traits (protein, oil, sucrose, fiber, starch, ash, complex and simple carbohydrates) from UAV-based multisensor imagery and associated genotype and phenological metadata. A total of 372 soybean samples were collected over two growing seasons (2020–2021) from two fields in Missouri, with UAV flights capturing multispectral (MSI), thermal (THR), and LiDAR (LDR) data at four time points spanning vegetative to reproductive growth stages. CNN models were trained in single- and multi-date configurations, incorporating genotype (GEN) and days after sowing (DAS) as additional features. The highest accuracy was achieved for sucrose (R2 = 0.80), followed by simple carbohydrate (R2 = 0.70) and starch (R2 = 0.55), with notable gains from GEN and DAS. Multi-date models incorporating earlier acquisitions often matched or outperformed later or all-date combinations. Among modalities, MSI provided the most robust estimates, with limited added value from LDR or THR. Unlike feature-based pipelines prone to multicollinearity, this image-to-trait approach enables automated, scalable prediction of soybean seed composition for in-season, field-level assessment.","地理空间人工智能（GeoAI）将多模态遥感与深度学习相结合，以在大尺度上对复杂农业系统进行建模。在该框架下，对当季未收获作物进行准确且无损的种子成分预测，对于育种和精准农业至关重要。本研究开发了一种端到端的卷积神经网络（CNN）框架，利用基于无人机（UAV）的多传感器影像以及相关的基因型和物候元数据，估算八种种子性状（蛋白质、油分、蔗糖、纤维、淀粉、灰分、复杂碳水化合物和简单碳水化合物）。研究在2020—2021两个生长季内，从密苏里州两块田地共采集372份大豆样本，无人机飞行在从营养生长期到生殖生长期的四个时间点获取了多光谱（MSI）、热红外（THR）和激光雷达（LDR）数据。CNN模型以单日期和多日期配置进行训练，并将基因型（GEN）和播种后天数（DAS）作为附加特征纳入。蔗糖的估算精度最高（R² = 0.80），其次为简单碳水化合物（R² = 0.70）和淀粉（R² = 0.55），且GEN和DAS的加入带来了显著提升。纳入早期采集数据的多日期模型往往与后期或全日期组合的表现相当，甚至更优。在各模态中，MSI提供了最稳健的估算结果，而LDR或THR的附加价值有限。与易受多重共线性影响的特征工程流程不同，这种从图像到性状的方法能够实现大豆种子成分的自动化、可扩展预测，用于当季田块尺度的评估。","Remote Sensing","2026-09-11T00:00:00Z",80,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":69,"relevant":21,"comment":70},8,"基于多模态无人机影像与基因型信息的大豆籽粒成分无损预测研究，方法新颖、数据扎实，对智慧育种与精准农业有参考价值。",[72],{"name":65,"url":62},[74,26,75,76,77],"智慧农业","农业人工智能","遥感","大豆育种",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[80,86],{"code":81,"slug":82,"name":83,"path":84,"parent_code":85},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"code":87,"slug":88,"name":89,"path":90,"parent_code":85},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"status":39,"locked":14,"version":40,"method":41,"evidence":92},{"candidates":93,"agricultural_evidence":46},[94,101,108,114],{"code":32,"score":95,"supported":46,"title":96,"abstract":98,"tags":100},15,[97],"soybean",[99,97],"大豆",[77],{"code":81,"score":13,"supported":46,"title":102,"abstract":103,"tags":107},[],[104,105,106],"深度学习","deep learning","artificial intelligence",[75],{"code":87,"score":13,"supported":46,"title":109,"abstract":110,"tags":113},[],[76,111,112],"remote sensing","multispectral",[76],{"code":115,"score":116,"supported":46,"title":117,"abstract":118,"tags":121},"I1",7,[],[119,120],"精准农业","precision agriculture",[74],[123,124],"农业人工智能 大豆育种 智慧农业 无人机","农业人工智能 大豆育种","农业人工智能大豆育种智慧农业无人机-2320","10.3390\u002Frs18183121",{"doi":126,"openalex_id":128,"authors":129,"venue":65,"cited_by_count":154,"oa_url":62,"card":155,"direction":159,"ingested_from":161},"W7212223520",[130,133,136,139,142,144,146,149,152],{"name":131,"orcid":132},"Vasit Sagan","https:\u002F\u002Forcid.org\u002F0000-0003-4375-2096",{"name":134,"orcid":135},"Kristen Rhodes","https:\u002F\u002Forcid.org\u002F0000-0002-8469-5043",{"name":137,"orcid":138},"Sourav Bhadra","https:\u002F\u002Forcid.org\u002F0000-0002-5832-4695",{"name":140,"orcid":141},"Haireti Alifu","https:\u002F\u002Forcid.org\u002F0000-0002-7369-6657",{"name":143,"orcid":8},"Aviskar Giri",{"name":145,"orcid":8},"Ashutosh Pawar",{"name":147,"orcid":148},"Bishal Roy","https:\u002F\u002Forcid.org\u002F0000-0001-9912-5505",{"name":150,"orcid":151},"Supria Sarkar","https:\u002F\u002Forcid.org\u002F0000-0001-9467-5786",{"name":153,"orcid":8},"Felix Fritschi",0,{"tldr":156,"method":157,"finding":158,"direction":159,"opportunity":160},"用无人机多模态影像与基因型数据训练CNN，预测大豆八种种子成分。","372份大豆样本，多光谱\u002F热红外\u002FLiDAR影像加基因型与播后天数，端到端CNN","蔗糖预测最优(R²=0.80)，基因型与播期提升精度，多光谱最稳健。","农业遥感与作物表型","可探索基因型-表型-环境交互建模，并迁移至其他作物与多时相早期预测。","openalex","2026-09-13T23:30:22.776177Z",{"id":164,"title":165,"url":166,"summary":167,"summary_zh":168,"content":8,"source_name":169,"source_url":166,"published_at":170,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":171,"score_detail":172,"sources":175,"tags":177,"primary_topic":180,"secondary_topics":186,"classification":189,"search_phrases":221,"slug":224,"view_count":225,"doi":226,"paper":227,"created_at":253},2318,"Estimation of field-scale crop evapotranspiration from the mechanistic SIF-ET model using the UAV hyperspectral imagery","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.eja.2026.128332","Accurate estimation of evapotranspiration (ET) is critical for agricultural water management and understanding land-atmosphere interactions. Conventional thermal and optical remote sensing is a powerful tool for ET estimation, but existing methods remain constrained by empirical parameterization, calibration complexity, and limited applicability. As a direct indicator of vegetation photosynthesis, solar-induced chlorophyll fluorescence (SIF) enables consistent and accurate ET estimation through a mechanistically grounded framework that does not require site-specific empirical calibration of the SIF-GPP relationship. However, current SIF-based ET estimation mainly rely on coarse-resolution satellite data and its capacity to resolve high-resolution ET dynamics over farmland remains unexplored. To address this, the mechanistic light response model and water-carbon relationship was integrated to construct a SIF-ET model at field-scale using UAV narrow-band hyperspectra imagery. The principal conclusions are: (1) High-resolution crop (canola,soybean and wheat) canopy SIF 740 at 1-nm SR was retrieved from UAV hyperspectra imagery using the Photochemistry and Energy Fluxes (SCOPE) model; (2)The daily ET estimates from SIF-ET model were validated against the in-situ measurements across multiple growth stages and water-nitrogen treatments, with wheat exhibiting the highest precision (R²=0.72–0.94, RMSE = 0.04–0.11 mm\u002Fd, MAPE = 1.70–5.89%), followed by canola and soybean; (3) Cumulative ET errors across repeated UAV observations within each growth stage remained within acceptable bounds, indicating the stability of SIF-ET model in capturing long-term ET dynamics. Overall, the SIF-ET model from UAV hyperspectra imagery revealed fine-scale cropland ET spatial-temporal heterogeneity. These findings provide support for precision irrigation, rational water-nitrogen management, and stress diagnostics under changing environmental conditions.","准确估算蒸散发（ET）对农业水资源管理和理解陆气相互作用至关重要。传统的热红外与光学遥感是估算ET的有力工具，但现有方法仍受限于经验参数化、校准复杂性和适用性有限等问题。作为植被光合作用的直接指示因子，日光诱导叶绿素荧光（SIF）能够通过机理明确的框架实现一致且准确的ET估算，且无需对SIF-GPP关系进行站点特定的经验校准。然而，当前基于SIF的ET估算主要依赖粗分辨率卫星数据，其解析农田高分辨率ET动态的能力尚未得到探索。为此，本研究整合机理光响应模型与水碳关系，利用无人机窄波段高光谱影像构建了田块尺度的SIF-ET模型。主要结论如下：（1）利用光化学与能量通量（SCOPE）模型，从无人机高光谱影像中反演了1 nm光谱分辨率下油菜、大豆和小麦的高分辨率冠层SIF₇₄₀；（2）基于SIF-ET模型的日ET估算值在多个生育期和水氮处理下与原位观测进行了验证，其中小麦精度最高（R²=0.72–0.94，RMSE=0.04–0.11 mm\u002Fd，MAPE=1.70–5.89%），油菜和大豆次之；（3）各生育期内重复无人机观测的累积ET误差均在可接受范围内，表明SIF-ET模型在捕捉长期ET动态方面具有稳定性。总体而言，基于无人机高光谱影像的SIF-ET模型揭示了精细尺度的农田ET时空异质性。这些发现为精准灌溉、合理水氮管理以及变化环境条件下的胁迫诊断提供了支持。","European Journal of Agronomy","2026-09-12T00:00:00Z",81,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":173,"relevant":21,"comment":174},9,"该研究基于无人机高光谱与SIF-ET机理模型实现田块尺度作物蒸散发高精度估算，方法新颖、验证充分，对精准灌溉与水氮管理有实质参考价值，值得进入每日精选。",[176],{"name":169,"url":166},[74,26,76,178,179],"精准灌溉","蒸散发",{"code":181,"slug":182,"name":183,"path":184,"parent_code":185},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",[187,188],{"code":87,"slug":88,"name":89,"path":90,"parent_code":85},{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":190},{"candidates":191,"agricultural_evidence":46},[192,200,205,210,217],{"code":181,"score":193,"supported":46,"title":194,"abstract":196,"tags":199},19,[195],"evapotranspiration",[197,198,179,182,195],"灌溉","水资源",[178,179],{"code":87,"score":95,"supported":46,"title":201,"abstract":203,"tags":204},[202],"hyperspectral",[76,111],[76],{"code":32,"score":173,"supported":46,"title":206,"abstract":207,"tags":209},[],[99,208,97],"油菜",[],{"code":211,"score":20,"supported":46,"title":212,"abstract":213,"tags":216},"A2",[],[214,215],"小麦","wheat",[],{"code":115,"score":21,"supported":14,"title":218,"abstract":219,"tags":220},[],[],[74],[222,223],"智慧农业 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Lu","https:\u002F\u002Forcid.org\u002F0000-0003-0638-3068",{"tldr":249,"method":250,"finding":251,"direction":159,"opportunity":252},"利用无人机高光谱影像构建田间尺度SIF-ET机理模型，实现作物蒸散发高精度估算。","无人机窄波段高光谱结合SCOPE模型反演SIF，耦合光响应与水碳关系构建SIF-","小麦估算精度最高（R²=0.72–0.94），模型能稳定捕捉多生育期蒸散发动态与空间异质性。","可探索多作物多环境下的SIF-ET普适性，并融合热红外与机器学习提升胁迫诊断能力。","2026-09-13T23:30:22.677041Z",{"id":255,"title":256,"url":257,"summary":258,"summary_zh":8,"content":8,"source_name":259,"source_url":8,"published_at":66,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":260,"score_detail":261,"sources":263,"tags":265,"primary_topic":267,"secondary_topics":268,"classification":269,"search_phrases":288,"slug":291,"view_count":225,"doi":8,"paper":292,"created_at":300},2218,"《基于现役作物多模态无人机图像的大豆种子成分基因型感知预测》","https:\u002F\u002Fwww.mdpi.com\u002F2072-4292\u002F18\u002F18\u002F3121","Vasit Sagan等(圣路易斯大学、密苏里大学)开发端到端卷积神经网络框架,从基于无人机的多传感器图像及相关基因型和物候元数据估算大豆八种种子性状(蛋白质、油、蔗糖、纤维、淀粉、灰分、复合和简单碳水化合物)。2020-2021年密苏里州两个农场共采集372份大豆样本,在四个时间点捕获多光谱、热成像和LiDAR数据。蔗糖预测精度最高(R²=0.80),其次为简单碳水化合物(R²=0.70)和淀粉(R²=0.55)。多光谱图像在所有模态中提供最稳健的估计。","Remote Sensing 2026年9月11日",82,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":13,"relevant":21,"comment":262},"多模态无人机遥感结合深度学习预测大豆种子成分，方法新颖、数据扎实，对智慧育种与精准农业有参考价值。",[264],{"name":259,"url":257},[74,26,75,266,77],"农业遥感",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":270},{"candidates":271,"agricultural_evidence":46},[272,276,280,284],{"code":32,"score":45,"supported":46,"title":273,"abstract":274,"tags":275},[99],[99],[77],{"code":115,"score":21,"supported":14,"title":277,"abstract":278,"tags":279},[],[],[74],{"code":81,"score":21,"supported":14,"title":281,"abstract":282,"tags":283},[],[],[75],{"code":87,"score":21,"supported":14,"title":285,"abstract":286,"tags":287},[],[],[266],[289,290],"农业人工智能 农业遥感 大豆育种 智慧农业","农业人工智能 农业遥感","农业人工智能农业遥感大豆育种智慧农业-2218",{"doi":8,"openalex_id":8,"authors":293,"venue":8,"cited_by_count":154,"oa_url":8,"card":294,"direction":159,"ingested_from":299},[],{"tldr":295,"method":296,"finding":297,"direction":159,"opportunity":298},"用无人机多模态图像和CNN预测大豆八种种子成分，蔗糖精度最高。","端到端CNN，融合多光谱、热成像、LiDAR及基因型物候元数据，372份样本。","多光谱最稳健，蔗糖R²=0.80，简单碳水化合物0.70，淀粉0.55。","可探索多模态融合与基因型-表型关联，提升低精度性状预测及跨年份泛化。","agent","2026-09-12T00:06:40.739496Z",{"id":302,"title":303,"url":304,"summary":305,"summary_zh":8,"content":306,"source_name":307,"source_url":8,"published_at":308,"category":309,"cover_url":8,"hotness":13,"is_selected":14,"score":15,"score_detail":310,"sources":315,"tags":317,"primary_topic":322,"secondary_topics":323,"classification":324,"search_phrases":347,"slug":350,"view_count":225,"doi":8,"paper":8,"created_at":351},763,"[中国农科院蜜蜂所]第20次蜜蜂授粉学术交流会暨内蒙古向日葵授粉现场观摩会在巴彦淖尔举办——自主立项200万元专项项目","https:\u002F\u002Fwww.chinaapiculture.org.cn\u002Fnewsshow-3-2126.html","中国农科院蜜蜂所牵头实施的「向日葵精准高效蜂授粉专项项目」8月15日在内蒙古巴彦淖尔市五原县举办第20次蜜蜂授粉学术交流会暨向日葵授粉现场观摩会,会议以「蜂授粉助力现代农业高质量发展」为主题。中国农科院党组成员、副院长曹永生,成果转化局局长、国家蜂产业技术体系首席科学家彭文君出席并作主旨发言。该项目总经费200万元,预期实现向日葵高效精准蜂授粉技术与病虫害绿色防控技术集成后的大范围推广应用,构建全链条技术模式。","8月15日，第20次蜜蜂授粉学术交流会暨向日葵授粉现场观摩会在内蒙古巴彦淖尔市五原县举办。会议以“蜂授粉助力现代农业高质量发展”为主题，汇聚全国蜂学、油料育种、绿色植保专家、农技人员及种业经营主体，集中展示向日葵精准高效蜂授粉项目阶段性成果，搭建政产学研协同创新推广平台。中国农科院党组成员、副院长曹永生，中国农科院成果转化局局长、国家蜂产业技术体系首席科学家彭文君出席并分别作主旨发言，地方政府、科研院所百余名代表参会。\n\n会上，曹永生立足国家粮油安全“国之大者”，明确了蜂授粉产业的核心战略定位。他表示，河套灌区是全国向日葵核心产区，规模化种植背景下，传统自然、人工授粉模式已难以支撑产业提质增效，专项攻关向日葵蜂授粉技术，是践行农业科技自立自强、落实“藏粮于技”的关键举措。蜜蜂作为现代农业“活的生产资料”兼具双重价值，既能显著提升向日葵结实率、降低油料进口依赖，又能修复农田传粉昆虫群落，实现产业增收与生态保护协同发展。\n\n结合产区现实痛点，曹永生提出三项核心工作要求：坚持问题导向，完成传粉资源普查与农药蜂毒风险评估，打造授粉、绿色防控一体化成套标准技术；强化示范转化，依托五原示范基地扩大辐射规模，常态化开展农户与农技人员培训，深化与种业企业合作，把科研产量转化为农户增收实效；凝聚多方合力，统筹中国农科院蜜蜂所、内蒙古农牧业科学院科研力量，联动地方农牧部门搭建市场化授粉服务平台，长效赋能北方向日葵全产业链升级。他寄语全体从业者，以小蜜蜂赋能油料大产业，为种业振兴和乡村振兴注入科技力量。\n\n彭文君提出，要把“蜜蜂授粉+绿色防控”成套技术嵌入向日葵标准化种植流程，以示范基地为载体加速科研成果落地转化，依靠蜂授粉技术助力油料作物单产稳步提升，夯实国家粮油安全保障基础。\n\n与会专家围绕产业发展达成共识。我国向日葵产区存在传粉资源底数不清、农药施用与蜜蜂保护矛盾突出、标准化授粉规程缺失、农户授粉增产认知不足等短板。由中国农业科学院北方农牧业技术创新中心自主立项，中国农科院蜜蜂研究所牵头实施、总经费200万元的向日葵精准高效蜂授粉专项项目，预期目标是实现向日葵高效精准蜂授粉技术与病虫害绿色防控技术集成后的大范围推广应用，构建“资源评估—精准授粉—蜂友好病虫防控—示范推广”全链条技术模式，为北方产区提供可复制增产方案。\n\n活动当天，参会代表实地走进五原县新公中镇永联村示范基地，中国农科院蜜蜂研究所研究员李继莲、内蒙古农科院植保所副所长云晓鹏、安徽农业大学校长操海群团队分别现场介绍向日葵高效精准蜂授粉技术与作物病虫害绿色防控技术以及蜜蜂保护剂技术的集成与示范。项目组今年已经在多个田间试验，已完成3个核心旗县传粉昆虫普查；示范田采用集成授粉技术后，预期亩增产20—30公斤以上。籽粒出油率、发芽率同步优化。按照项目规划，2028年前将建成千亩示范田、辐射带动种植5000亩，稳定实现向日葵增产10%以上，形成专属授粉技术规程，产出3篇高水平论文、2项发明专利，千亩示范田年增收可达12万元，辐射片区年增效60万元。\n\n会议研讨环节明确了后续落地路径，团队将分年度持续优化授粉集成技术，每年举办不少于2场技术培训，对接大型种子合作社落地技术合作，推广“以蜂促葵、葵蜂共生”循环模式，转变农户“重防虫、轻授粉”的传统观念，推动蜂授粉成为向日葵标准化栽培常规技术。\n\n本次观摩交流会是向日葵蜂授粉项目阶段性成果的集中展示，打通了科研、政府、产业主体的协作通道。下一步，项目团队将严格落实工作部署，加速授粉技术田间转化落地，持续扩大示范辐射范围，培育专业化授粉服务体系，依靠科技降低葵花籽油对外依存度，同时保护农田传粉生物多样性，推动河套向日葵产业绿色高效可持续发展，以蜂授粉技术筑牢国家粮油安全屏障。","中国养蜂学会\u002F中国农业科学院蜜蜂研究所 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巴彦淖尔","中国农科院蜜蜂所巴彦淖尔绿色防控蜜蜂授粉-763","2026-08-21T00:05:55.820430Z",{"id":353,"title":354,"url":355,"summary":356,"summary_zh":8,"content":357,"source_name":358,"source_url":8,"published_at":359,"category":309,"cover_url":8,"hotness":13,"is_selected":14,"score":360,"score_detail":361,"sources":363,"tags":365,"primary_topic":369,"secondary_topics":370,"classification":371,"search_phrases":383,"slug":386,"view_count":225,"doi":8,"paper":8,"created_at":387},694,"[中国科学报]蜜蜂授粉学术交流会暨向日葵授粉现场观摩会在内蒙古巴彦淖尔举办","https:\u002F\u002Fcaas.cn\u002Fxwzx\u002Fmtxw\u002F043df1e87ff7446eb5997527d69c72e4.htm","中国农业科学院蜜蜂研究所与中国养蜂学会在内蒙古巴彦淖尔举办第20次蜜蜂授粉学术交流会暨向日葵授粉现场观摩会,布局向日葵精准高效蜂授粉攻关专项。示范田预期亩增产20-30公斤,2028年建成千亩示范田。会议围绕向日葵蜜蜂授粉增产机理、蜂群配置与管理、农药协同等议题展开研讨,推动我国向日葵产业从化学依赖向蜂授粉增产模式转型。","### [中国科学报]蜜蜂授粉学术交流会暨向日葵授粉现场观摩会举办\n\n8月15日，由中国农业科学院[蜜蜂研究所](http:\u002F\u002Fiar.caas.cn\u002F)、中国养蜂学会联合主办的第20次蜜蜂授粉学术交流会暨向日葵授粉现场观摩会在内蒙古巴彦淖尔市召开。会议以“蜂授粉助力现代农业高质量发展”为主题，汇聚了全国蜂学、油料育种、绿色植保领域的专家、农技推广人员及种业经营主体100余名代表，集中展示向日葵精准高效蜂授粉工作的最新成果，搭建政产学研协同创新推广平台。\n\n![Image 1: image.png](https:\u002F\u002Fcaas.cn\u002Fimages\u002F2026-08\u002Fc46b5b08dedb4bee81b10820ba0d7b84.png)\n\n观摩现场。中国农科院供图\n\n向日葵是我国重要的特色油料作物，也是广受市场欢迎的休闲食用作物，籽实加工零食、烘焙原料等用途广泛，是丰富国内食品供给、优化乡村产业结构的关键特色载体。\n\n然而，导致我国向日葵制种单产不高的原因在于传粉体系失衡，依靠自然授粉、人工授粉的传统模式已经无法支撑现代规模化制种产业发展。中国农科院立足特色食用农产品稳产保供这一民生要事，布局实施了向日葵精准高效蜂授粉攻关专项，目的就是要充分发挥蜜蜂作为现代农业中“活的生产资料”的功能，实现产业增收与生态保护的协同发展，是践行农业科技自立自强、落实“藏粮于技”战略的关键举措。\n\n项目组今年已在多个田间试验点开展工作，完成3个核心旗县的传粉昆虫普查。示范田采用集成授粉技术后，预期亩增产20～30公斤以上，籽粒饱满度、食用品质与发芽率同步优化。2028年将建成千亩示范田，辐射带动种植面积达5000亩，稳定实现向日葵增产10%以上，并形成授粉技术规程。\n\n参会代表实地参观了五原县新公中镇永联村示范基地，并针对我国向日葵产业存在的传粉资源底数不清、农药施用与蜜蜂保护矛盾突出、标准化授粉规程缺失、农户对授粉增产认知不足等问题，积极建言献策。大家一致认为，应从特色食品产业稳定、农业生态安全等多个维度统筹蜂授粉产业发展，将蜂授粉技术纳入向日葵标准化制种栽培流程，同步配套蜂友好型绿色病虫防控技术，构建“资源评估—精准授粉—蜂友好型病虫防控—示范推广”全链条技术模式，实现高效精准蜂授粉技术与病虫害绿色防控技术的大范围集成推广，为向日葵产业提供可复制、可推广的增产方案。\n\n中国农业科学院副院长[曹永生](https:\u002F\u002Fwww.caas.cn\u002Fzzjg\u002Fyld\u002Ffyc\u002F39e0c3861a1f4f97baf6a4bfc9de93d3.htm)指出，要系统开展传粉资源普查和农药蜂毒风险评估，构建授粉与绿色防控一体化的成套标准技术体系；强化示范转化，常态化开展农户和农技人员培训，深化与种业企业的合作，切实将科研成果转化为农户增收的实效；凝聚多方合力，统筹科研院所的创新资源，联合地方农牧部门，搭建市场化授粉服务平台，长效赋能向日葵全产业链升级，助力食用葵籽产能稳步提升，丰富国内休闲食品原料供给。","中国农业科学院网(转载中国科学报) 2026-08-16","2026-08-16T00:00:00Z",77,{"impact":18,"substance":17,"depth":95,"authority":19,"freshness":69,"relevant":21,"comment":362},"国家级科研机构主办的学术交流会，聚焦蜜蜂授粉技术提升向日葵产业，具有行业指导意义，信息详实，权威性高。",[364],{"name":358,"url":355},[366,29,367,368],"中国农科院","蜜蜂","授粉",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":372},{"candidates":373,"agricultural_evidence":46},[374,378],{"code":32,"score":45,"supported":46,"title":375,"abstract":376,"tags":377},[29],[29],[29],{"code":332,"score":225,"supported":46,"title":379,"abstract":380,"tags":382},[],[381],"农药",[],[384,385],"中国农科院 向日葵 授粉 蜜蜂","中国农科院 向日葵","中国农科院向日葵授粉蜜蜂-694","2026-08-18T23:59:16.753237Z",{"id":389,"title":390,"url":391,"summary":392,"summary_zh":8,"content":393,"source_name":394,"source_url":8,"published_at":359,"category":309,"cover_url":8,"hotness":13,"is_selected":14,"score":360,"score_detail":395,"sources":397,"tags":399,"primary_topic":400,"secondary_topics":401,"classification":402,"search_phrases":409,"slug":412,"view_count":54,"doi":8,"paper":8,"created_at":413},605,"中国农业科学院蜜蜂所:第 20 次蜜蜂授粉学术交流会暨向日葵授粉观摩会在巴彦淖尔举办","https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Fgdxw\u002F2026-08\u002F16\u002Fcontent_564709.html","8-15 中国农科院蜜蜂所、中国养蜂学会联合主办的第 20 次蜜蜂授粉学术交流会暨向日葵授粉现场观摩会在内蒙古巴彦淖尔市五原县召开,中国农科院副院长曹永生出席。示范田采用集成授粉技术后预期亩增产 20-30 公斤以上,2028 年将建成千亩示范田,辐射带动种植面积达 5000 亩,稳定实现向日葵增产 10% 以上并形成授粉技术规程。","![Image 5](https:\u002F\u002Fstatics.kjrb.com.cn\u002Fresource\u002Fkjw\u002Findex\u002Fimages\u002Fshare_logo.jpg)\n\n[![Image 6](https:\u002F\u002Fstatics.kjrb.com.cn\u002Fresource\u002Fkjw\u002Findex\u002Fimages\u002Flogo.png)](http:\u002F\u002Fwww.stdaily.com\u002F)\n\n*   [时政](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Fszxw\u002Fnode_357.html)\n*   [热点](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Frdxw\u002Fnode_327.html)\n*   [政务](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Fzwxw\u002Fnode_362.html)\n*   [深瞳](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Fstbd\u002Fnode_434.html)\n*   [访谈](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Ffangtan\u002Fnode_441.html)\n*   [视频](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Fspxw\u002Fnode_701.html)\n*   [国际](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Fgjxw\u002Fnode_381.html)\n*   [地方](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Fdfxw\u002Fnode_368.html)\n*   [专题](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Fztxw\u002Fnode_335.html)\n*   [English](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002FEnglish\u002Findex.html)\n*   [滚动](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Fgdxw\u002Fnode_324.html)\n\n[![Image 7](https:\u002F\u002Fstatics.kjrb.com.cn\u002Fresource\u002Fkjw\u002Fimages\u002Ffdj.png)](https:\u002F\u002Fsearch.stdaily.com:8888\u002Ffounder\u002FNewSearchServlet.do?siteID=1)[登录](https:\u002F\u002Fwww.stdaily.com\u002Fapi\u002Fwebsite\u002FpersonalCenter.html?url=https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Fgdxw\u002F2026-08\u002F16\u002Fcontent_564709.html)[-](https:\u002F\u002Fwww.stdaily.com\u002Fapi\u002Fwebsite\u002FpersonalCenter.html)\n\n 所在位置： [中国科技网首页](https:\u002F\u002Fwww.stdaily.com\u002Findex.html)>[滚动](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Fgdxw\u002Fnode_324.html)> 正文 \n\n# 蜜蜂授粉学术交流会暨向日葵授粉现场观摩会在巴彦淖尔举办\n\n 2026-08-16 13:12:45 来源: 科技日报 点击数：\n\n![Image 8](https:\u002F\u002Fstatics.kjrb.com.cn\u002Fresource\u002Fkjw\u002Fimg\u002F20240301dzv.png)0\n\n[](javascript:; \"新浪微博\")[](javascript:; \"微信\")\n\n科技日报记者 马爱平\n\n8月15日，由中国农业科学院蜜蜂研究所、中国养蜂学会联合主办的第20次蜜蜂授粉学术交流会暨向日葵授粉现场观摩会在内蒙古巴彦淖尔市五原县召开。中国农业科学院副院长、党组成员曹永生，内蒙古自治区农牧厅二级巡视员娅茹，巴彦淖尔市政协副主席张志忠等出席会议。\n\n![Image 9](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Fgdxw\u002Fpic\u002F2026-08\u002F16\u002F564709_54face15-0638-42bc-b310-44836668d6c1copy.jpg)\n\n中国农科院立足特色食用农产品稳产保供，布局实施了向日葵精准高效蜂授粉攻关专项，旨在充分发挥蜜蜂作为现代农业中“活的生产资料”的功能，实现产业增收与生态保护的协同发展。\n\n研讨交流环节，与会专家针对我国向日葵产业存在的传粉资源底数不清、农药施用与蜜蜂保护矛盾突出、标准化授粉规程缺失、农户对授粉增产认知不足等问题，积极建言献策。会议认为，应将蜂授粉技术纳入向日葵标准化制种栽培流程，同步配套蜂友好型绿色病虫防控技术，构建“资源评估—精准授粉—蜂友好型病虫防控—示范推广”全链条技术模式，实现高效精准蜂授粉技术与病虫害绿色防控技术的大范围集成推广。要强化示范转化，常态化开展农户和农技人员培训，深化与种业企业的合作，切实将科研成果转化为农户增收的实效；统筹科研院所的创新资源，联合地方农牧部门，搭建市场化授粉服务平台，长效赋能向日葵全产业链升级。\n\n![Image 10](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Fgdxw\u002Fpic\u002F2026-08\u002F16\u002F564709_d0365bd9-cbaa-431f-9772-2be89d26cdf8copy.jpg)\n\n参会代表实地走进五原县新公中镇永联村示范基地观摩。项目组展示了向日葵高效精准蜂授粉技术、作物病虫害绿色防控技术以及蜜蜂保护剂技术的集成与示范情况。据介绍，项目组今年已在多个田间试验点开展工作，目前已完成3个核心旗县的传粉昆虫普查。示范田采用集成授粉技术后，预期亩增产20—30公斤以上，籽粒饱满度、食用品质与发芽率同步优化。2028年将建成千亩示范田，辐射带动种植面积达5000亩，稳定实现向日葵增产10%以上，并形成授粉技术规程。\n\n![Image 11](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Fgdxw\u002Fpic\u002F2026-08\u002F16\u002F564709_0ed7fb00-5aa8-4641-a8dd-34ff78373489copy.jpg)\n\n据悉，下一步，项目组将进一步加速授粉技术的田间转化与落地，持续扩大示范辐射范围，培育专业化授粉服务体系，依靠科技提升优质食用葵籽自给水平。同时，保护农田传粉生物多样性，推动河套向日葵产业实现绿色、高效、可持续发展，以蜂授粉技术夯实特色食用农产品稳产保供基础。\n\n（中国农业科学院蜜蜂研究所供图）\n\n责任编辑：陈可轩\n\n相关稿件：\n\n![Image 12](https:\u002F\u002Fstatics.kjrb.com.cn\u002Fresource\u002Fkjw\u002Fimages\u002Fpinglun.png)网友评论\n\n评论\n\n最热评论\n\n没有更多评论了\n\n[热点](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Frdxw\u002Fnode_327.html)\n\n[![Image 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巴彦淖尔","中国农科院巴彦淖尔蜜蜂授粉向日葵-605","2026-08-16T23:55:40.451711Z"]