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Article

Detection and Analysis of Degree of Maize Lodging Using UAV-RGB Image Multi-Feature Factors and Various Classification Methods

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College of Earth Science and Resources, Chang’an University, Xi’an 710054, China
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Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Key Laboratory of Crop Physiology and Ecology, Ministry of Agriculture, Beijing 100081, China
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Institute of Crop Sciences, National Key Facility of Crop Gene Resources and Genetic Improvement, Chinese Academy of Agricultural Sciences, Beijing 100081, China
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College of Land Engineering, Chang’an University, Shaanxi Provincial Key Laboratory of Land Engineering, Xi’an 710054, China
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School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454150, China
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College of Earth Science and Engineering, Hebei University of Technology, Handan 056000, China
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College of Agricultural Science and Engineering, Hohai University, Nanjing 210098, China
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School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
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Faculty of Geomatics, East China University of Technology, Nanchang 330013, China
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School of Earth Sciences, China University of Geosciences, Wuhan 430074, China
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Author to whom correspondence should be addressed.
Academic Editors: Panagiotis Partsinevelos and Wolfgang Kainz
ISPRS Int. J. Geo-Inf. 2021, 10(5), 309; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10050309
Received: 10 March 2021 / Revised: 27 April 2021 / Accepted: 2 May 2021 / Published: 6 May 2021
Maize (Zea mays L.), one of the most important agricultural crops in the world, which can be devastated by lodging, which can strike maize during its growing season. Maize lodging affects not only the yield but also the quality of its kernels. The identification of lodging is helpful to evaluate losses due to natural disasters, to screen lodging-resistant crop varieties, and to optimize field-management strategies. The accurate detection of crop lodging is inseparable from the accurate determination of the degree of lodging, which helps improve field management in the crop-production process. An approach was developed that fuses supervised and object-oriented classifications on spectrum, texture, and canopy structure data to determine the degree of lodging with high precision. The results showed that, combined with the original image, the change of the digital surface model, and texture features, the overall accuracy of the object-oriented classification method using random forest classifier was the best, which was 86.96% (kappa coefficient was 0.79). The best pixel-level supervised classification of the degree of maize lodging was 78.26% (kappa coefficient was 0.6). Based on the spatial distribution of degree of lodging as a function of crop variety, sowing date, densities, and different nitrogen treatments, this work determines how feature factors affect the degree of lodging. These results allow us to rapidly determine the degree of lodging of field maize, determine the optimal sowing date, optimal density and optimal fertilization method in field production. View Full-Text
Keywords: unmanned aerial vehicles (UAVs); digital surface model; lodging level; object-oriented classification; color and texture features unmanned aerial vehicles (UAVs); digital surface model; lodging level; object-oriented classification; color and texture features
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MDPI and ACS Style

Wang, Z.; Nie, C.; Wang, H.; Ao, Y.; Jin, X.; Yu, X.; Bai, Y.; Liu, Y.; Shao, M.; Cheng, M.; Liu, S.; Wang, S.; Tuohuti, N. Detection and Analysis of Degree of Maize Lodging Using UAV-RGB Image Multi-Feature Factors and Various Classification Methods. ISPRS Int. J. Geo-Inf. 2021, 10, 309. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10050309

AMA Style

Wang Z, Nie C, Wang H, Ao Y, Jin X, Yu X, Bai Y, Liu Y, Shao M, Cheng M, Liu S, Wang S, Tuohuti N. Detection and Analysis of Degree of Maize Lodging Using UAV-RGB Image Multi-Feature Factors and Various Classification Methods. ISPRS International Journal of Geo-Information. 2021; 10(5):309. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10050309

Chicago/Turabian Style

Wang, Zixu, Chenwei Nie, Hongwu Wang, Yong Ao, Xiuliang Jin, Xun Yu, Yi Bai, Yadong Liu, Mingchao Shao, Minghan Cheng, Shuaibing Liu, Siyu Wang, and Nuremanguli Tuohuti. 2021. "Detection and Analysis of Degree of Maize Lodging Using UAV-RGB Image Multi-Feature Factors and Various Classification Methods" ISPRS International Journal of Geo-Information 10, no. 5: 309. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10050309

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