In Pest management science
BACKGROUND : Spatial-explicit weed information is critical for controlling weed infestation and reducing corn yield losses. The development of unmanned aerial vehicle (UAV) based remote sensing presents an unprecedented opportunity for efficient, timely weed mapping. Spectral, textural, and structural measurements have been used for weed mapping, while thermal measurements (e.g., canopy temperature, CT) were seldom considered and used. In this study, we quantified the optimal combination of spectral, textural, structural, and CT measurements based on different machine learning algorithms for weed mapping.
RESULTS : (1) CT improved weed mapping accuracies as complementary information for spectral, textural, and structural features (up to 5% and 0.051 improvements in overall accuracy (OA) and Marco-F1, respectively); (2) the fusion of textural, structural, and thermal features achieved the best performance in weed mapping (OA = 96.4%, Marco-F1 = 0.964), followed by the fusion of structural and thermal features (OA = 93.6%, Marco-F1 = 0.936); (3) Support Vector Machine based model achieved the best performance in weed mapping, with 3.5% and 7.1% improvements in OA and 0.036 and 0.071 in Marco-F1 respectively, compared to the best models of Random Forest and Naïve Bayes Classifier.
CONCLUSION : Thermal measurement can complement other types of remote sensing measurements and improve the weed mapping accuracy within the data fusion framework. Importantly, integrating textural, structural, and thermal features achieved the best performance for weed mapping. Our study provides a novel method for weed mapping using UAV-based multi-source remote sensing measurements, which is critical for ensuring crop production in precision agriculture. This article is protected by copyright. All rights reserved.
Xu Binyuan, Meng Ran, Chen Gengshen, Liang Linlin, Lv Zhengang, Zhou Longfei, Sun Rui, Zhao Feng, Yang Wanneng
2023-Mar-08
Machine learning, Multi-source remote sensing, Smart agriculture, Thermal feature, Weed mapping
