Improved weed mapping in corn fields by combining UAV-based spectral, textural, structural, and thermal measurements.

Abstract

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, whereas thermal measurements-for example, 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. 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). 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). The 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 with the best models of Random Forest and Naïve Bayes Classifier. 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 multisource remote sensing measurements, which is critical for ensuring crop production in precision agriculture. © 2023 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.