杂草
人工智能
机器学习
适应性
精准农业
深度学习
传感器融合
抗性(生态学)
保险丝(电气)
卷积神经网络
计算机科学
农业
农业工程
模式识别(心理学)
工程类
地理
农学
生物
生态学
考古
电气工程
作者
Fulin Xia,Zhaoxia Lou,Deng Sun,Hailong Li,Longzhe Quan
出处
期刊:International journal of applied earth observation and geoinformation
日期:2023-06-01
卷期号:120: 103352-103352
标识
DOI:10.1016/j.jag.2023.103352
摘要
Herbicide-resistant weeds represent a significant challenge to modern agriculture. The need for innovative and sustainable weed management strategies has become increasingly pressing as the threat of herbicide-resistant weeds continues to escalate. Unmanned aerial vehicles (UAVs) and various sensors have become indispensable tools in plant phenotyping studies. In this study, a comprehensive resistance score (CRS) was proposed to effectively quantify weed resistance in the field. Multimodal data fusion and deep learning were utilized to perform regression of CRS, three different fusion methods for 3D-CNN and 2D-CNN to extract and fuse multimodal information collected by UAVs including spectral, structural, and texture information for weed resistance. Our findings demonstrate that (1) discernible differences in spectral response exist between susceptible and resistant weeds, with the optimal band for the Successive Projections Algorithm (SPA) selection coinciding with the optimal band for resistance expression band; (2) resistance assessment accuracy is enhanced through multimodal data fusion, with the late deep fusion network exhibiting the best accuracy, R2 of 0.777 and RMSE of 0.547; (3) the multimodal fusion network model displays robust adaptability in resistance assessment across varying densities and effectively generates weed resistance map. Overall, this research demonstrates the effectiveness of using multimodal data fusion and CRS, combined with deep learning for achieving accurate and reliable weed resistance assessment in agricultural fields.
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