Monitoring the damage of armyworm as a pest in summer corn by unmanned aerial vehicle imaging

• Published in: Pest management science Vol. 78; no. 6; pp. 2265 - 2276
• Main Authors: Tao, Wancheng, Wang, Xinsheng, Xue, Jing‐Hao, Su, Wei, Zhang, Mingzheng, Yin, Dongqin, Zhu, Dehai, Xie, Zixuan, Zhang, Ying
• Format: Journal Article
• Language: English
• Published: Chichester, UK John Wiley & Sons, Ltd 01.06.2022; Wiley Subscription Services, Inc
• Subjects: Algorithms; Animals; armyworm; Bayes Theorem; Bayesian analysis; Corn; Damage detection; Datasets; Learning algorithms; Machine learning; Multilayer perceptrons; Pests; Random Forest; Seasons; Sentinel‐2; Spodoptera; summer corn; Support vector machines; Unmanned Aerial Devices; unmanned aerial vehicle; Unmanned aerial vehicles; Vegetables; Zea mays; armyworm; unmanned aerial vehicle; summer corn; Sentinel-2; Random Forest
• ISSN: 1526-498X; 1526-4998
• DOI: 10.1002/ps.6852

BACKGROUND The timely, rapid, and accurate near real‐time observations are urgent to monitor the damage of corn armyworm, because the rapid expansion of armyworm would lead to severe yield losses. Therefore, the potential of machine learning algorithms for identifying the armyworm infected areas automatically and accurately by multispectral unmanned aerial vehicle (UAV) dataset is explored in this study. The study area is in Beicuizhuang Village, Langfang City, Hebei Province, which is 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 four kinds of machine learning methods including Random Forest (RF), Multilayer Perceptron (MLP), Naive Bayesian (NB) and Support Vector Machine (SVM) was done. And RF was proved to be the most potential with the highest Kappa and OA of 0.9709 and 0.9850, respectively. Secondly, the armyworm infected areas and healthy corn areas were predicted by an optimized RF model in the UAV dataset, and the armyworm incidence levels were classified subsequently. Thirdly, the relationship between the spectral 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, the Sentinel‐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 damage by armyworm using UAV images accurately and automatically in field‐scale. © 2022 Society of Chemical Industry. The optimized unmanned aerial vehicle (UAV) dataset and Random Forest model are effective and reliable, which can be used for identifying the corn damage by armyworm accurately and automatically in field‐scale.