Monitoring the damage of armyworm as a pest in summer corn by unmanned aerial vehicle imaging
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 aut...
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Published in | Pest management science Vol. 78; no. 6; pp. 2265 - 2276 |
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Main Authors | , , , , , , , , |
Format | Journal Article |
Language | English |
Published |
Chichester, UK
John Wiley & Sons, Ltd
01.06.2022
Wiley Subscription Services, Inc |
Subjects | |
Online Access | Get full text |
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Abstract | 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. |
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AbstractList | BACKGROUNDThe 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. RESULTSFirstly, 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. CONCLUSIONSThe 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. Abstract 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. 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. 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. 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. 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. |
Author | Xue, Jing‐Hao Yin, Dongqin Zhu, Dehai Wang, Xinsheng Zhang, Ying Zhang, Mingzheng Xie, Zixuan Tao, Wancheng Su, Wei |
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The timely, rapid, and accurate near real‐time observations are urgent to monitor the damage of corn armyworm, because the rapid expansion of... 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... Abstract BACKGROUND The timely, rapid, and accurate near real‐time observations are urgent to monitor the damage of corn armyworm, because the rapid expansion... BACKGROUNDThe timely, rapid, and accurate near real‐time observations are urgent to monitor the damage of corn armyworm, because the rapid expansion of... BACKGROUNDThe timely, rapid, and accurate near real-time observations are urgent to monitor the damage of corn armyworm, because the rapid expansion of... |
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SubjectTerms | 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 |
Title | Monitoring the damage of armyworm as a pest in summer corn by unmanned aerial vehicle imaging |
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