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Measurement of Temporal Wheat Grain Filling Rate Based on Spectral Vegetation Indices
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Pingtao JIANG1, 2, Jia LIU1, 2, Yihang LUO1, 2, Meng YUAN1, 2, Yi XIA1, 2, Jingrui OU1, 2, Yuze WANG1, 2, Ziyi YAN1, 2, Tingting WU3, Shouyang LIU4, Rui YU4, Jianhui WU1, 2, Dejun HAN1, 2
Journal of Triticeae Crops | 2025, 45(11) : 1563 - 1574
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Journal of Triticeae Crops | 2025, 45(11): 1563-1574
• Physiology, Ecology and Cultivation •
Measurement of Temporal Wheat Grain Filling Rate Based on Spectral Vegetation Indices
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Pingtao JIANG1, 2, Jia LIU1, 2, Yihang LUO1, 2, Meng YUAN1, 2, Yi XIA1, 2, Jingrui OU1, 2, Yuze WANG1, 2, Ziyi YAN1, 2, Tingting WU3, Shouyang LIU4, Rui YU4, Jianhui WU1, 2, Dejun HAN1, 2
Affiliations
  • 1.College of Agronomy, Northwest A&F University, Yangling, Shaanxi 712100, China
  • 2.State Key Laboratory of Crop Stress Resistance and High-Efficiency Production, Northwest A&F University, Yangling, Shaanxi 712100, China
  • 3.College of Mechanical and Electronic Engineering, Northwest Agriculture and Forestry University, Yangling, Shaanxi 712100, China
  • 4.Nanjing Agricultural University, Academy for Advanced Interdisciplinary Studies, Nanjing, Jiangsu 210095, China
Published: 2025-11-15 doi: 10.7606/j.issn.1009-1041.2025.11.14
Outline
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To understand the effect of high-throughput non-destructive monitoring of wheat grain filling rate (GFR) based on UAV multi-spectral, 565 wheat germplasm accessions were used as experimental materials over two growing seasons (2022 and 2023). UAVs were used to collect temporal spectral vegetation indices (SVIs) during the grain filling period, along with synchronized measurements of grain weight. The logistic function was applied to fit the dynamic changes between SVIs and grain weight, and the red-edge chlorophyll index (CIRE) was identified as the SVI most closely related to GFR. Machine learning models, including Random Forest Regressor (RFR), Support Vector Regression (SVR), and Linear Regression (LR), were constructed to predict GFR, and their performance was evaluated using r2 and RMSE. The results showed that the r2 values of the logistic fitting dynamic curves between SVIs and measured grain weight reached 0.87 and 0.92, respectively. The dynamic change rate of CIRE exhibited a significantly positive correlation with GFR (r=0.4-0.43, P<0.05). Among the models, RFR demonstrated the best predictive performance, with r2 values of 0.88 and 0.89 and RMSE values of 0.27 and 0.32 for the full datasets in 2022 and 2023, respectively, significantly outperforming SVR and LR. Similarly, RFR achieved higher prediction accuracy and generalization capability in specific genotype datasets. Therefore, based on UAV multi-spectral data, the wheat grain filling rate was monitored by combining CIRE and RFR with high-precision and high-throughout. This method provides essential technical support for the evaluation of wheat germplasm resources and breeding for stress resistance.

Wheat  /  Grain filling rate  /  Spectral vegetation indices (SVIs)  /  Unmanned aerial vehicle (UAV)
姜平涛, 刘佳, 罗一航, 袁梦, 夏艺, 欧京瑞, 王钰泽, 闫子怡, 吴婷婷, 刘守阳, 蔚睿, 吴建辉, 韩德俊. 基于光谱植被指数的小麦灌浆速率时序性表型监测. 麦类作物学报, 2025 , 45 (11) : 1563 -1574 . DOI: 10.7606/j.issn.1009-1041.2025.11.14
Pingtao JIANG, Jia LIU, Yihang LUO, Meng YUAN, Yi XIA, Jingrui OU, Yuze WANG, Ziyi YAN, Tingting WU, Shouyang LIU, Rui YU, Jianhui WU, Dejun HAN. Measurement of Temporal Wheat Grain Filling Rate Based on Spectral Vegetation Indices[J]. Journal of Triticeae Crops, 2025 , 45 (11) : 1563 -1574 . DOI: 10.7606/j.issn.1009-1041.2025.11.14
Year 2025 volume 45 Issue 11
174
40
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Article Info
doi: 10.7606/j.issn.1009-1041.2025.11.14
  • Receive Date:2024-11-21
  • Online Date:2026-09-18
  • Published:2025-11-15
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History
  • Received:2024-11-21
  • Revised:2025-02-13
Funding
Affiliations
    1.College of Agronomy, Northwest A&F University, Yangling, Shaanxi 712100, China
    2.State Key Laboratory of Crop Stress Resistance and High-Efficiency Production, Northwest A&F University, Yangling, Shaanxi 712100, China
    3.College of Mechanical and Electronic Engineering, Northwest Agriculture and Forestry University, Yangling, Shaanxi 712100, China
    4.Nanjing Agricultural University, Academy for Advanced Interdisciplinary Studies, Nanjing, Jiangsu 210095, China
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表12种不同金属材料的力学参数
科
Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)
属
Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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