您好，欢迎访问中国农业科学院 机构知识库！

全部

  • 全部
  • 标题
  • 学者
  • 机构
  • 关键词

Mapping grapevine leafroll disease epidemics from UAV images

文献类型： 外文期刊

作者： Liu, Yixue 1 ; Su, Jinya 4 ; Liu, Dizhu 1 ; Song, Yuyang 6 ; Fang, Yulin 6 ; Su, Baofeng 1 ; Yang, Peng 2 ;

作者机构： 1.Northwest A&F Univ, Coll Mech & Elect Engn, Yangling 712100, Peoples R China

2.Minist Agr & Rural Affairs, Key Lab Agr Internet Things, Yangling 712100, Peoples R China

3.Shaanxi Key Lab Agr Informat Percept & Intelligent, Shaanxi 712100, Peoples R China

4.Southeast Univ, Sch Automat, Nanjing 210096, Peoples R China

5.Southeast Univ, Key Lab Measurement & Control Complex Syst Engn, Minist Educ, Nanjing 210096, Peoples R China

6.Northwest A&F Univ, Coll Enol, Yanglin 712100, Peoples R China

7.Chinese Acad Agr Sci, Key Lab Agr Remote Sensing AGRIRS, Minist Agr & Rural Affairs, Inst Agr Resources & Reg Planning, Beijing 100081, Peoples R China

关键词： Grapevine leafroll disease; UAV; Deep learning; Object detection; Disease mapping

期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE （ 影响因子：8.9； 五年影响因子：9.3 ）

ISSN： 0168-1699

年卷期： 2025 年 237 卷

页码：

收录情况： SCI

摘要： Grapevine Leafroll Disease (GLD) poses a significant economic burden on the wine industry in major wine-producing regions. Conventional methods of phenotyping GLD are inefficient and delay vineyard management decisions. The emergence of affordable Unmanned Aerial Vehicles (UAV) provides unprecedented opportunities for GLD high-throughput phenotyping. However, detecting GLD-infected grapevines at the canopy level using UAV images is still a challenge due to the subtle differences between GLD canopy features and background. In this paper, we propose a GLD Detector (GLDD) for mapping GLD epidemics from UAV images. A new attention mechanism module, namely, Channel Attention with Transformers (CAT) is proposed to alleviate the difficulty of extracting high-resolution features from the elongated canopy. We redesigned YOLOv7-tiny for GLDD and conducted a series of ablation experiments to evaluate its performance. Experimental results show that GLDD outperforms YOLOv7-tiny by 3.1% and YOLOv6-tiny by 6.5%, reaching an accuracy of 88.2%. In comparison to several one-stage object detectors such as YOLOv5, YOLOX, PP-YOLOE, and YOLO-FaceV2, GLDD obtains the best detection results. Additionally, compared to convolutional-based detectors such as Faster-RCNN and transformer-based detector SwinT, GLDD performs better in speed and accuracy. Furthermore, GLD-infected grapevine distribution is also mapped by using GLDD detection results at the field scale.

  • 相关文献
作者其他论文 更多>>
置顶
购物车
反馈

© 京ICP备09089781号-29 主办单位：中国农业科学院

学术资源： 中国农业科学院 农业专业知识服务系统 农科联盟资源共建共享平台 中国农业科技文献与信息服务平台 中国农业期刊集成服务平台