您好，欢迎访问江苏省农业科学院 机构知识库！
江苏省农业科学院机构知识库

全部

  • 全部
  • 标题
  • 学者
  • 机构
  • 关键词

Multimodal remote sensing application for weed competition time series analysis in maize farmland ecosystems

文献类型： 外文期刊

作者： Quan, Longzhe 1 ; Lou, Zhaoxia 3 ; Lv, Xiaolan 2 ; Sun, Deng 3 ; Xia, Fulin 3 ; Li, Hailong 3 ; Sun, Wenfeng 3 ;

作者机构： 1.Anhui Agr Univ, Coll Engn, Hefei 230036, Anhui, Peoples R China

2.Jiangsu Acad Agr Sci JAAS, Inst Agr Facil & Equipment, Nanjing 210014, Jiangsu, Peoples R China

3.Northeast Agr Univ, Coll Engn, Harbin 150030, Peoples R China

关键词： Deep learning; UAV time Series analysis; Hyperspectral remote sensing; Lidar remote sensing; Weed competition; Farmland ecosystems

期刊名称：JOURNAL OF ENVIRONMENTAL MANAGEMENT （ 影响因子：8.7； 五年影响因子：8.4 ）

ISSN： 0301-4797

年卷期： 2023 年 344 卷

页码：

收录情况： SCI

摘要： Although weeds cause serious harm to crops through competition for resources, they also have ecological functions. We need to study the change law of competition between crops and weeds, and achieve scientific farmland weed management under the premise of protecting weed biodiversity. In the research, we perform a competitive experiment in Harbin, China, in 2021, with five periods of maize as the study subjects. Comprehensive competition indices (CCI-A) based on maize phenotypes were used to describe the dynamic processes and results of weeds competition. The relation between in structural and biochemical information of maize and weed competitive intensity (Levels 1-5) at different periods and the effects on yield parameters were analyzed. The results showed that the differences of maize plant height, stalk thickness, and N and P elements among different competition levels (Levels 1-5) changed significantly with increasing competition time. This directly resulted in 10%, 31%, 35% and 53% decrease in maize yield; and 3%, 7%, 9% and 15% decrease in hundred grain weight. Compared to the conventional competition indices, CCI-A had better dispersion in the last four periods and was more suitable for quantifying the time-series response of competition. Then, multi-source remote sensing technologies are applied to reveal the temporal response of spectral and lidar information to community competition. The first-order derivatives of the spectra indicate that the red edge (RE) of competition stressed plots biased in short-wave direction in each period. With increasing competition time, RE of Levels 1-5 shifted towards the long wave direction as a whole. The coefficients of variation of canopy height model (CHM) indicate that weed competition had a significant effect on CHM. Finally, the deep learning model with multimodal data (Mul3DCNN) is created to achieve a large range of CCI-A predictions for different periods, and achieves a prediction accuracy of R2 = 0.85 and RMSE = 0.095. Overall, this study use of CCI-A indices combined with multimodal temporal remote sensing imagery and DL to achieve large scale prediction of weed competitiveness in different periods of maize.

  • 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：江苏省农业科学院

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