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Remote Sens. 2018, 10(10), 1528; https://doi.org/10.3390/rs10101528

Quantitative Identification of Maize Lodging-Causing Feature Factors Using Unmanned Aerial Vehicle Images and a Nomogram Computation

1
College of Architecture and Geomatics Engineering, Shanxi Datong University, Datong 037003, China
2
Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture, Beijing Research Center for Information Technology in Agriculture, Beijing 100097, China
3
National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
4
School of Electronics and Information Engineering, Anhui University, Hefei 230601, China
*
Authors to whom correspondence should be addressed.
Received: 26 August 2018 / Revised: 19 September 2018 / Accepted: 20 September 2018 / Published: 23 September 2018
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Abstract

Maize (zee mays L.) is one of the most important grain crops in China. Lodging is a natural disaster that can cause significant yield losses and threaten food security. Lodging identification and analysis contributes to evaluate disaster losses and cultivates lodging-resistant maize varieties. In this study, we collected visible and multispectral images with an unmanned aerial vehicle (UAV), and introduce a comprehensive methodology and workflow to extract lodging features from UAV imagery. We use statistical methods to screen several potential feature factors (e.g., texture, canopy structure, spectral characteristics, and terrain), and construct two nomograms (i.e., Model-1 and Model-2) with better validation performance based on selected feature factors. Model-2 was superior to Model-1 in term of its discrimination ability, but had an over-fitting phenomenon when the predicted probability of lodging went from 0.2 to 0.4. The results show that the nomogram could not only predict the occurrence probability of lodging, but also explore the underlying association between maize lodging and the selected feature factors. Compared with spectral features, terrain features, texture features, canopy cover, and genetic background, canopy structural features were more conclusive in discriminating whether maize lodging occurs at the plot scale. Using nomogram analysis, we identified protective factors (i.e., normalized difference vegetation index, NDVI and canopy elevation relief ratio, CRR) and risk factors (i.e., Hcv) related to maize lodging, and also found a problem of terrain spatial variability that is easily overlooked in lodging-resistant breeding trials. View Full-Text
Keywords: lodging; nomogram; logistic regression; UAV; maize; features lodging; nomogram; logistic regression; UAV; maize; features
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Han, L.; Yang, G.; Feng, H.; Zhou, C.; Yang, H.; Xu, B.; Li, Z.; Yang, X. Quantitative Identification of Maize Lodging-Causing Feature Factors Using Unmanned Aerial Vehicle Images and a Nomogram Computation. Remote Sens. 2018, 10, 1528.

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