Classification of Maize Lodging Extents Using Deep Learning Algorithms by UAV-Based RGB and Multispectral Images

Xin Yang, Shichen Gao, Qian Sun, Xiaohe Gu, Tianen Chen, Jingping Zhou and Yuchun Pan
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Xin Yang: Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100089, China
Shichen Gao: School of Science, China University of Geosciences, Beijing 100089, China
Qian Sun: Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100089, China
Xiaohe Gu: Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100089, China
Tianen Chen: National Engineering Research Center for Information Technology in Agriculture, Beijing 100089, China
Jingping Zhou: Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100089, China
Yuchun Pan: Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100089, China

Agriculture, 2022, vol. 12, issue 7, 1-16

Abstract: Lodging depresses the grain yield and quality of maize crop. Previous machine learning methods are used to classify crop lodging extents through visual interpretation and sensitive features extraction manually, which are cost-intensive, subjective and inefficient. The analysis on the accuracy of subdivision categories is insufficient for multi-grade crop lodging. In this study, a classification method of maize lodging extents was proposed based on deep learning algorithms and unmanned aerial vehicle (UAV) RGB and multispectral images. The characteristic variation of three lodging extents in RGB and multispectral images were analyzed. The VGG-16, Inception-V3 and ResNet-50 algorithms were trained and compared depending on classification accuracy and Kappa coefficient. The results showed that the more severe the lodging, the higher the intensity value and spectral reflectance of RGB and multispectral image. The reflectance variation in red edge band were more evident than that in visible band with different lodging extents. The classification performance using multispectral images was better than that of RGB images in various lodging extents. The test accuracies of three deep learning algorithms in non-lodging based on RGB images were high, i.e., over 90%, but the classification performance between moderate lodging and severe lodging needed to be improved. The test accuracy of ResNet-50 was 96.32% with Kappa coefficients of 0.9551 by using multispectral images, which was superior to VGG-16 and Inception-V3, and the accuracies of ResNet-50 on each lodging subdivision category all reached 96%. The ResNet-50 algorithm of deep learning combined with multispectral images can realize accurate lodging classification to promote post-stress field management and production assessment.

Keywords: lodging classification; unmanned aerial vehicle (UAV); sensitive band; ResNet algorithm (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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