Next Article in Journal
Study of Fast and Reliable Time Transfer Methods Using Low Earth Orbit Enhancement
Previous Article in Journal
Using Ground-Penetrating Radar (GPR) to Investigate the Exceptionally Thick Deposits from the Storegga Tsunami in Northeastern Scotland
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Remote Sensing
Volume 16
Issue 11
10.3390/rs16112043
remotesensing-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Evaluation of Soybean Drought Tolerance Using Multimodal Data from an Unmanned Aerial Vehicle and Machine Learning

by
Heng Liang
1,2,†,
Yonggang Zhou
3,†,
Yuwei Lu
4,†,
Shuangkang Pei
3,
Dong Xu
1,2,
Zhen Lu
1,2,
Wenbo Yao
3,
Qian Liu
1,2,
Lejun Yu
1,2,* and
Haiyan Li
3
1
State Key Laboratory of Digital Medical Engineering, School of Biomedical Engineering, Hainan University, Haikou 570228, China
2
Key Laboratory of Biomedical Engineering of Hainan Province, School of Biomedical Engineering, Hainan University, Haikou 570228, China
3
School of Breeding and Multiplication (Sanya Institute of Breeding and Multiplication), Hainan University, Sanya 572025, China
4
Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan 430074, China
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Remote Sens. 2024, 16(11), 2043; https://doi.org/10.3390/rs16112043
Submission received: 9 May 2024 / Revised: 24 May 2024 / Accepted: 28 May 2024 / Published: 6 June 2024
(This article belongs to the Topic Challenges, Development and Frontiers of Smart Agriculture and Forestry—2nd Volume)
Browse Figures
Versions Notes

Abstract

Drought stress is a significant factor affecting soybean growth and yield. A lack of suitable high-throughput phenotyping techniques hinders the drought tolerance evaluation of multi-genotype samples. A method for evaluating drought tolerance in soybeans is proposed based on multimodal remote sensing data from an unmanned aerial vehicle (UAV) and machine learning. Hundreds of soybean genotypes were repeatedly planted under well water (WW) and drought stress (DS) in different years and locations (Jiyang and Yazhou, Sanya, China), and UAV multimodal data were obtained in multiple fertility stages. Notably, data from Yazhou were repeatedly obtained during five significant fertility stages, which were selected based on days after sowing. The geometric mean productivity (GMP) index was selected to evaluate the drought tolerance of soybeans. Compared with the results of manual measurement after harvesting, support vector regression (SVR) provided better results (N = 356, R2 = 0.75, RMSE = 29.84 g/m2). The model was also migrated to the Jiyang dataset (N = 427, R2 = 0.68, RMSE = 15.36 g/m2). Soybean varieties were categorized into five Drought Injury Scores (DISs) based on the manually measured GMP. Compared with the results of the manual DIS, the accuracy of the predicted DIS gradually increased with the soybean growth period, reaching a maximum of 77.12% at maturity. This study proposes a UAV-based method for the rapid high-throughput evaluation of drought tolerance in multi-genotype soybean at multiple fertility stages, which provides a new method for the early judgment of drought tolerance in individual varieties, improving the efficiency of soybean breeding, and has the potential to be extended to other crops.
Keywords: UAV; imaging spectroscopy; soybean; drought tolerance; machine learning UAV; imaging spectroscopy; soybean; drought tolerance; machine learning

Share and Cite

MDPI and ACS Style

Liang, H.; Zhou, Y.; Lu, Y.; Pei, S.; Xu, D.; Lu, Z.; Yao, W.; Liu, Q.; Yu, L.; Li, H. Evaluation of Soybean Drought Tolerance Using Multimodal Data from an Unmanned Aerial Vehicle and Machine Learning. Remote Sens. 2024, 16, 2043. https://doi.org/10.3390/rs16112043

AMA Style

Liang H, Zhou Y, Lu Y, Pei S, Xu D, Lu Z, Yao W, Liu Q, Yu L, Li H. Evaluation of Soybean Drought Tolerance Using Multimodal Data from an Unmanned Aerial Vehicle and Machine Learning. Remote Sensing. 2024; 16(11):2043. https://doi.org/10.3390/rs16112043

Chicago/Turabian Style

Liang, Heng, Yonggang Zhou, Yuwei Lu, Shuangkang Pei, Dong Xu, Zhen Lu, Wenbo Yao, Qian Liu, Lejun Yu, and Haiyan Li. 2024. "Evaluation of Soybean Drought Tolerance Using Multimodal Data from an Unmanned Aerial Vehicle and Machine Learning" Remote Sensing 16, no. 11: 2043. https://doi.org/10.3390/rs16112043

APA Style

Liang, H., Zhou, Y., Lu, Y., Pei, S., Xu, D., Lu, Z., Yao, W., Liu, Q., Yu, L., & Li, H. (2024). Evaluation of Soybean Drought Tolerance Using Multimodal Data from an Unmanned Aerial Vehicle and Machine Learning. Remote Sensing, 16(11), 2043. https://doi.org/10.3390/rs16112043

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop