Identification and Comprehensive Evaluation of Resistant Weeds Using Unmanned Aerial Vehicle-Based Multispectral Imagery

Fulin Xia; Longzhe Quan; Zhaoxia Lou; Deng Sun; Hailong Li; Xiaolan Lv

doi:10.3389/fpls.2022.938604

Identification and Comprehensive Evaluation of Resistant Weeds Using Unmanned Aerial Vehicle-Based Multispectral Imagery

Front Plant Sci. 2022 Jul 5:13:938604. doi: 10.3389/fpls.2022.938604. eCollection 2022.

Authors

Fulin Xia¹, Longzhe Quan^{2

1}, Zhaoxia Lou¹, Deng Sun¹, Hailong Li², Xiaolan Lv³

Affiliations

¹ College of Engineering, Northeast Agricultural University, Harbin, China.
² College of Engineering, Anhui Agricultural University, Anhui, China.
³ Institute of Agricultural Facilities and Equipment, Jiangsu Academy of Agricultural Sciences (JAAS), Jiangsu, China.

Abstract

Atrazine is one of the most widely used herbicides in weed management. However, the widespread use of atrazine has concurrently accelerated the evolution of weed resistance mechanisms. Resistant weeds were identified early to contribute to crop protection in precision agriculture before visible symptoms of atrazine application to weeds in actual field environments. New developments in unmanned aerial vehicle (UAV) platforms and sensor technologies promote cost-effective data collection by collecting multi-modal data at very high spatial and spectral resolution. In this study, we obtained multispectral and RGB images using UAVs, increased available information with the help of image fusion technology, and developed a weed spectral resistance index, WSRI = (RE-R)/(RE-B), based on the difference between susceptible and resistant weed biotypes. A deep convolutional neural network (DCNN) was applied to evaluate the potential for identifying resistant weeds in the field. Comparing the WSRI introduced in this study with previously published vegetation indices (VIs) shows that the WSRI is better at classifying susceptible and resistant weed biotypes. Fusing multispectral and RGB images improved the resistance identification accuracy, and the DCNN achieved high field accuracies of 81.1% for barnyardgrass and 92.4% for velvetleaf. Time series and weed density influenced the study of weed resistance, with 4 days after application (4DAA) identified as a watershed timeframe in the study of weed resistance, while different weed densities resulted in changes in classification accuracy. Multispectral and deep learning proved to be effective phenotypic techniques that can thoroughly analyze weed resistance dynamic response and provide valuable methods for high-throughput phenotyping and accurate field management of resistant weeds.

Keywords: atrazine-resistant weed; deep convolutional neural networks (DCNNs); multispectral reflectance; unmanned aerial vehicle (UAV); vegetation indices (VIs).