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Agronomy
Volume 15
Issue 10
10.3390/agronomy15102384
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Article

Evaluating the Performance of Winter Wheat Under Late Sowing Using UAV Multispectral Data

by
Yuanyuan Zhao
1,2,†,
Hui Wang
3,†,
Wei Wu
4,
Yi Sun
1,2,
Ying Wang
1,2,
Weijun Zhang
1,2,
Jianliang Wang
1,2,
Fei Wu
5,
Wouter H. Maes
6,
Jinfeng Ding
1,2,
Chunyan Li
1,2,
Chengming Sun
1,2,
Tao Liu
1,2,* and
Wenshan Guo
1,2,*
1
Jiangsu Key Laboratory of Crop Genetics and Physiology/Jiangsu Key Laboratory of Crop Cultivation and Physiology, Agricultural College of Yangzhou University, Yangzhou 225009, China
2
Jiangsu Co-Innovation Center for Modern Production Technology of Grain Crops, Yangzhou University, Yangzhou 225009, China
3
Lixiahe Institute of Agricultural Sciences of Jiangsu/Key Laboratory of Wheat Biology and Genetic Improvement for Low & Middle Yangtze Valley, Ministry of Agriculture and Rural Affairs, Yangzhou 225012, China
4
Key Laboratory of Agricultural Blockchain Application, Ministry of Agriculture and Rural Affairs, Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China
5
Precision Agriculture Lab, School of Life Sciences, Technical University of Munich, 85354 Freising, Germany
6
UAV Research Center, Department of Plants and Crops, Ghent University, Ghent 9000, Belgium
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Agronomy 2025, 15(10), 2384; https://doi.org/10.3390/agronomy15102384 (registering DOI)
Submission received: 8 September 2025 / Revised: 5 October 2025 / Accepted: 11 October 2025 / Published: 13 October 2025
(This article belongs to the Special Issue Digital Twins in Precision Agriculture)
Versions Notes

Abstract

In the lower and middle sections of the Yangtze River Basin Region (YRBR) in China, challenges posed by climate change and delayed harvesting of preceding crops have hindered the timely sowing of wheat, leading to an increasing prevalence of late-sown wheat fields. This trend has emerged as a significant impediment to achieving high and stable production of wheat in this area. During the growing seasons of 2022–2023 and 2023–2024, an unmanned aerial vehicle (UAV)-based multispectral camera was used to monitor different wheat materials at various growth stages under normal sowing treatment (M1) and late sowing with increased plant density (M2). By assessing yield loss, the wheat tolerance to late sowing was quantified and categorized. The correlation between the differential vegetation indices (D-VIs) and late sowing resistance was examined. The findings revealed that the J2-Logistic model demonstrated optimal classification performance. The precision values of stable type, intermediate type, and sensitive type were 0.92, 0.61, and 1.00, respectively. The recall values were 0.61, 0.92, and 1.00. The mean average precision (mAP) of the model was 0.92. This study proposes a high-throughput and low-cost evaluation method for wheat tolerance to late sowing, which can provide a rapid predictive tool for screening suitable varieties for late sowing and facilitating late-sown wheat breeding.
Keywords: wheat; late sowing; multispectral; yield loss; tolerance evaluation wheat; late sowing; multispectral; yield loss; tolerance evaluation

Share and Cite

MDPI and ACS Style

Zhao, Y.; Wang, H.; Wu, W.; Sun, Y.; Wang, Y.; Zhang, W.; Wang, J.; Wu, F.; H. Maes, W.; Ding, J.; et al. Evaluating the Performance of Winter Wheat Under Late Sowing Using UAV Multispectral Data. Agronomy 2025, 15, 2384. https://doi.org/10.3390/agronomy15102384

AMA Style

Zhao Y, Wang H, Wu W, Sun Y, Wang Y, Zhang W, Wang J, Wu F, H. Maes W, Ding J, et al. Evaluating the Performance of Winter Wheat Under Late Sowing Using UAV Multispectral Data. Agronomy. 2025; 15(10):2384. https://doi.org/10.3390/agronomy15102384

Chicago/Turabian Style

Zhao, Yuanyuan, Hui Wang, Wei Wu, Yi Sun, Ying Wang, Weijun Zhang, Jianliang Wang, Fei Wu, Wouter H. Maes, Jinfeng Ding, and et al. 2025. "Evaluating the Performance of Winter Wheat Under Late Sowing Using UAV Multispectral Data" Agronomy 15, no. 10: 2384. https://doi.org/10.3390/agronomy15102384

APA Style

Zhao, Y., Wang, H., Wu, W., Sun, Y., Wang, Y., Zhang, W., Wang, J., Wu, F., H. Maes, W., Ding, J., Li, C., Sun, C., Liu, T., & Guo, W. (2025). Evaluating the Performance of Winter Wheat Under Late Sowing Using UAV Multispectral Data. Agronomy, 15(10), 2384. https://doi.org/10.3390/agronomy15102384

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