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. 2022 Jul 5;13:938604. doi: 10.3389/fpls.2022.938604

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Copyright © 2022 Xia, Quan, Lou, Sun, Li and Lv.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

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Workflow of the unmanned aerial vehicle (UAV) high-throughput field weed resistance approach. (A) DJI Phantom 4 Multispectral, DJI Phantom 4 RTK, DJI Terra, and RTK GPS instrument for collecting field images. (B) Digital orthophoto maps (DOM) of three maize field densities (low, moderate, high) for weed resistance research. (C) Gram–Schmidt sharpening for improving spectral image information. (D) Reflectance values of four objects in the orthophoto (soil, maize, barnyardgrass, and velvetleaf). (E) Soil and maize removal and two types of weed segmentation, including barnyardgrass and velvetleaf. (F) Two weed image datasets from 6 days after atrazine application (6 DAA) used in the classification models. (G) Deep convolutional neural network (DCNN) architecture.