UAV-Multispectral Based Maize Lodging Stress Assessment with Machine and Deep Learning Methods

Minghu Zhao, Dashuai Wang (), Qing Yan, Zhuolin Li and Xiaoguang Liu
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Minghu Zhao: School of Microelectronics, Southern University of Science and Technology, Shenzhen 518005, China
Dashuai Wang: School of Microelectronics, Southern University of Science and Technology, Shenzhen 518005, China
Qing Yan: School of Mechanical & Automotive Engineering, Liaocheng University, Liaocheng 252000, China
Zhuolin Li: School of Microelectronics, Southern University of Science and Technology, Shenzhen 518005, China
Xiaoguang Liu: School of Microelectronics, Southern University of Science and Technology, Shenzhen 518005, China

Agriculture, 2024, vol. 15, issue 1, 1-19

Abstract: Maize lodging is a prevalent stress that can significantly diminish corn yield and quality. Unmanned aerial vehicles (UAVs) remote sensing is a practical means to quickly obtain lodging information at field scale, such as area, severity, and distribution. However, existing studies primarily use machine learning (ML) methods to qualitatively analyze maize lodging (lodging and non-lodging) or estimate the maize lodging percentage, while there is less research using deep learning (DL) to quantitatively estimate maize lodging parameters (type, severity, and direction). This study aims to introduce advanced DL algorithms into the maize lodging classification task using UAV-multispectral images and investigate the advantages of DL compared with traditional ML methods. This study collected a UAV-multispectral dataset containing non-lodging maize and lodging maize with different lodging types, severities, and directions. Additionally, 22 vegetation indices (VIs) were extracted from multispectral data, followed by spatial aggregation and image cropping. Five ML classifiers and three DL models were trained to classify the maize lodging parameters. Finally, we compared the performance of ML and DL models in evaluating maize lodging parameters. The results indicate that the Random Forest (RF) model outperforms the other four ML algorithms, achieving an overall accuracy (OA) of 89.29% and a Kappa coefficient of 0.8852. However, the maize lodging classification performance of DL models is significantly better than that of ML methods. Specifically, Swin-T performs better than ResNet-50 and ConvNeXt-T, with an OA reaching 96.02% and a Kappa coefficient of 0.9574. This can be attributed to the fact that Swin-T can more effectively extract detailed information that accurately characterizes maize lodging traits from UAV-multispectral data. This study demonstrates that combining DL with UAV-multispectral data enables a more comprehensive understanding of maize lodging type, severity, and direction, which is essential for post-disaster rescue operations and agricultural insurance claims.

Keywords: maize lodging; UAV; multispectral; machine learning; deep learning (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: 2024
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