Wancheng Tao Xinsheng Wang Jing-Hao Xue Wei Su Mingzheng Zhang Dongqin Yin Dehai Zhu Zixuan Xie Ying Zhang
Abstract
BACKGROUND: The timely, rapid, and accurate near real-time observations are urgent to monitor the damage of corn army-worm, because the rapid expansion of armyworm would lead to severe yield losses. Therefore, the potential of machine learn-ing algorithms for identifying the armyworm infected areas automatically and accurately by multispectral unmanned aerialvehicle (UAV) dataset is explored in this study. The study area is in Beicuizhuang Village, Langfang City, Hebei Province, whichis the main corn-producing area in the North China Plain.RESULTS: Firstly, we identified the optimal combination of image features by Gini-importance and the comparation of fourkinds of machine learning methods including Random Forest (RF), Multilayer Perceptron (MLP), Naive Bayesian (NB) and Sup-port Vector Machine (SVM) was done. And RF was proved to be the most potential with the highest Kappa and OA of 0.9709 and0.9850, respectively. Secondly, the armyworm infected areas and healthy corn areas were predicted by an optimized RF modelin the UAV dataset, and the armyworm incidence levels were classified subsequently. Thirdly, the relationship between thespectral characteristics of different bands and pest incidence levels within the Sentinel-2 and UAV images were analyzed,and the B3 in UAV images and the B6 in Sentinel-2 image were less sensitive for armyworm incidence levels. Therefore, theSentinel-2 image was used to monitor armyworm in two towns.CONCLUSIONS: The optimized dataset and RF model are effective and reliable, which can be used for identifying the corn dam-age by armyworm using UAV images accurately and automatically infield-scale.
Keywords: armyworm; summer corn; unmanned aerial vehicle; Random Forest; Sentinel-2
Monitoring the damage of armyworm as a pest in summer corn by unmanned aerial vehicle imaging.pdf
