[1]葛永琪,唐道统,刘瑞,等.AlodgeNet:一种基于无人机RGB图像的紫花苜蓿倒伏识别方法[J].江苏农业学报,2026,42(01):90-98.[doi:doi:10.3969/j.issn.1000-4440.2026.01.010]
　GE Yongqi,TANG Daotong,LIU Rui,et al.AlodgeNet: a lodging identification method for Medicago sativa based on unmanned air vehicle RGB images[J].,2026,42(01):90-98.[doi:doi:10.3969/j.issn.1000-4440.2026.01.010]
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AlodgeNet:一种基于无人机RGB图像的紫花苜蓿倒伏识别方法()

江苏农业学报[ISSN:1006-6977/CN:61-1281/TN]

卷:
42
期数:
2026年01期
页码:
90-98
栏目:
农业信息工程
出版日期:
2026-01-31

文章信息/Info

Title:
AlodgeNet: a lodging identification method for Medicago sativa based on unmanned air vehicle RGB images
作者:
葛永琪1; 2; 唐道统1; 刘瑞1; 朱子欣1; 李昂1
(1.宁夏大学信息工程学院,宁夏银川750021；2.宁夏“东数西算”人工智能与信息安全重点实验室,宁夏银川750021)
Author(s):
GE Yongqi1; 2; TANG Daotong1; LIU Rui1; ZHU Zixin1; LI Ang1
(1.School of Information Engineering, Ningxia University, Yinchuan 750021, China；2.Ningxia Key Laboratory of Artificial Intelligence and Information Security for Channeling Computing Resources from the East to the West, Yinchuan 750021, China)
关键词:
深度学习; YOLO v8x-seg算法; 紫花苜蓿倒伏; 飞行高度
Keywords:
deep learning; YOLO v8x-seg algorithm; alfalfa lodging; flight altitude
分类号:
S541.9
DOI:
doi:10.3969/j.issn.1000-4440.2026.01.010
文献标志码:
A
摘要:
针对复杂大田场景中紫花苜蓿倒伏区域边界模糊、形状不规则及小范围倒伏难以准确识别的问题,本研究提出一种基于无人机RGB（R、G、B分别表示红、绿、蓝）图像的紫花苜蓿倒伏识别方法——AlodgeNet模型。为提升模型对不规则形状与小面积倒伏特征的捕捉能力,并增强空间层次结构学习,在YOLO v8x-seg网络中引入大型可分离卷积核注意力(LSKA)机制和空间深度转化卷积(SPD-Conv),以替换原网络中的部分卷积层。同时在宁夏引黄灌区,通过无人机采集了不同飞行高度（5.0 m、7.5 m、10.0 m）与生育期的紫花苜蓿倒伏RGB图像,并以此构建数据集对模型进行训练。试验结果表明,AlodgeNet模型对飞行高度10.0 m采集图像中紫花苜蓿倒伏区域的识别效果最好,且其对初花期采集图像中紫花苜蓿倒伏区域的识别性能高于分枝期。AlodgeNet模型精确率、召回率、交并比(IoU)阈值为0.50时的平均精度均值(mAP50)和交并比(IoU)阈值从0.50到0.95(步长0.05)的平均精度均值(mAP50∶95)分别达到84.9%、79.2%、83.8%和56.7%,整体性能优于YOLO v5x-seg模型、YOLO v10x-seg模型、YOLO v11x-seg模型、YOLO v8x-seg模型、RT-DETR模型和MASK-RCNN模型。相较于原始模型YOLO v8x-seg,AlodgeNet模型mAP50和mAP50∶95分别提升5.8个百分点和7.3个百分点。本研究结果为复杂大田环境下紫花苜蓿倒伏的快速识别与面积估算提供了一种高效、便捷的监测手段,有助于实现精准农业中的倒伏灾情评估与管理决策支持。
Abstract:
Aiming at the problems of blurry boundaries, irregular shapes, and difficulty in accurately identifying small-scale lodging areas of Medicago sativa in complex field scenarios, this study proposed a lodging identification method for Medicago sativa based on unmanned air vehicle (UAV) RGB images—the AlodgeNet model. To enhance the model’s ability to capture features of irregularly shaped and small-area lodging regions, as well as strengthen the learning of spatial hierarchical structures, the Large Separable Kernel Attention (LSKA) mechanism and Spatial Pyramid Dilated Convolution (SPD-Conv) were introduced into the YOLOv8x-seg network to replace some of the convolution layers in the original network. Meanwhile, in the Yellow River Irrigation Area of Ningxia, RGB images of Medicago sativa lodging at different flight altitudes (5.0 m, 7.5 m,10.0 m) and growth stages were collected by UAV, and a dataset was constructed based on these images for model training. The experimental results showed that the AlodgeNet model achieved the best performance in identifying Medicago sativa lodging regions in images collected at a flight altitude of 10.0 m, and its recognition performance for lodging regions in images collected at the early flowering stage was better than that at the branching stage. The precision, recall, mean average precision at the intersection over union (IoU) threshold of 0.50 (mAP50), and mean average precision at IoU thresholds ranging from 0.50 to 0.95 with a step size of 0.05 (mAP50∶95) of the AlodgeNet model reached 84.9%, 79.2%, 83.8%, and 56.7%, respectively. Its overall performance outperformed the YOLO v5x-seg, YOLO v10x-seg, YOLO v11x-seg, YOLO v8x-seg, RT-DETR, and MASK-RCNN models. Compared with the original YOLO v8x-seg model, the mAP50 and mAP50∶95 of the AlodgeNet model were improved by 5.8 percentage points and 7.3 percentage points, respectively. This study provides an efficient and convenient monitoring method for the rapid identification and area estimation of Medicago sativa lodging in complex field environments, which is conducive to realizing lodging disaster assessment and supporting management decision-making in precision agriculture.

参考文献/References:

[1]LIU R, LU L F, GE Y Q, et al. An approach for multi-depth soil moisture prediction in alfalfa based on a dual-branch combined model[J]. Computers and Electronics in Agriculture,2024,222:109038.
[2]JIA Z C, OU C M, SUN S J, et al. Optimizing drip irrigation managements to improve alfalfa seed yield in semiarid region[J]. Agricultural Water Management,2024,297:108830.
[3]王显国,韩建国,刘富渊,等. 疏枝对紫花苜蓿倒伏状况、种子产量和产量构成因子的影响[J]. 草业科学,2006(2):27-30.
[4]耿智广,韩建国,刘荣堂,等. 倒伏对紫花苜蓿种子产量及质量的影响[C]//第三届中国紫花苜蓿发展大会论文集. 北京:2010.
[5]HAN L, YANG G J, FENG H K, et al. Quantitative identification of maize lodging-causing feature factors using unmanned aerial vehicle images and a nomogram computation[J]. Remote Sensing,2018,10:1528-1537.
[6]CAO W X, QIAO Z Y, GAO Z Y, et al. Use of unmanned aerial vehicle imagery and a hybrid algorithm combining a watershed algorithm and adaptive threshold segmentation to extract wheat lodging[J]. Physics and Chemistry of the Earth,2021,123:103016.
[7]LIU T, LI R, ZHONG X C, et al. Estimates of rice lodging using indices derived from UAV visible and thermal infrared images[J]. Agricultural and Forest Meteorology,2018,252:144-154.
[8]ZHAO X, YUAN Y T, SONG M D, et al. Use of unmanned aerial vehicle imagery and deep learning UNet to extract rice lodging[J]. Sensors,2019,19:3859.
[9]YANG M D, TSENG H H, HSU Y C, et al. Semantic segmentation using deep learning with vegetation indices for rice lodging identification in multi-date UAV visible images[J]. Remote Sens,2020,12(4):633.
[10]JACOB S, FRANCESCO. What is YOLOv8? a complete guide [EB/OL].
[2024-10-23]. https://blog.roboflow.com/guide-to-yolo-models/.
[11]ALI N, MOHAMMED A, BAIS A, et al. LodgeNet:an automated framework for precise detection and classification of wheat lodging severity levels in precision farming[J]. Frontiers in Plant Science,2023,14:1255961.
[12]GU M, SHEN H Y, LING J, et al. Online detection of broken and impurity rates in half-feed peanut combine harvesters based on improved YOLOv8-Seg[J]. Computers and Electronics in Agriculture,2025,237:110494.
[13]GE Y Q, LIU R, CAO L Z, et al. Research on ACPS architecture of precision prataculture:a case of water precision regulation for alfalfa[J]. Journal of the ASABE,2023,66:497-511.
[14]周蓉. 大豆抗倒伏性评价体系的建立及主要农艺性状QTL定位[D]. 武汉:华中农业大学,2009.
[15]吴宇通,张伟,石文强,等. 基于无人机遥感的大豆倒伏识别研究[J]. 中国农机化学报,2024,45(9):209-257.
[16]LAU K W, PO L M, REHMAN Y A U. Large separable kernel attention:rethinking the large kernel attention design in CNN[J]. Journal of Latex Class Files,2023,14(8):1-16.
[17]GUO M H, LU C Z, LIU Z N, et al. Visual attention network[C]// Computer Vision and Pattern Recognition. New Orleans:IEEE,2022.
[18]SUNKARA R, LUO T. No more strided convolutions or pooling:a new CNN building block for low-resolution images and small objects[C]//Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Cham:Springer,2023.
[19]吕文坤,沈静,曹致中,等. 2个苜蓿品种(系)茎秆力学性能与纤维成分研究[J]. 草原与草坪,2011,31(2):60-73.
[20]ZHANG D Y, DING Y, CHEN P F, et al. Automatic extraction of wheat lodging area based on transfer learning method and deeplabv3+ network[J]. Computers and Electronics in Agriculture,2020,179:105845.
[21]SUGANDH C H, DARVISHZADEH R, BOSCHETTI M, et al. Remote sensing-based crop lodging assessment:current status and perspectives[J]. ISPRS Journal of Photogrammetry and Remote Sensing,2019,151:124-140.
[22]SUN Q, SUN L, SHU M Y, et al. Monitoring maize lodging grades via unmanned aerial vehicle multispectral image[J]. Plant Phenomics,2019,5704154.
[23]魏永康,杨天聪,丁信尧,等. 基于不同空间分辨率无人机多光谱遥感影像的小麦倒伏区域识别方法[J]. 智慧农业(中英文),2023,5(2):56-67.
[24]GE Y Q, WANG J, LIU R, et al. A dataset of shallow soil moisture for alfalfa in the Ningxia irrigation area of the Yellow River[J]. Frontiers in Plant Science,2024,1472930.

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备注/Memo

备注/Memo:
收稿日期：2025-06-05基金项目：国家自然科学基金地区项目(62162052、62262052)作者简介：葛永琪(1981-),男,宁夏青铜峡人,博士,副教授,研究方向为智能物联网。(E-mail)geyongqi@nxu.edu.cn通讯作者：刘瑞,(E-mail)ruiliu@nxu.edu.cn
更新日期/Last Update: 2026-02-09