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Prediction of Wheat Grain Protein Content Based on Vegetation Indices and Texture Features
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Bingxiao DU, Dasheng ZHENG, Yulan YE, Zhifeng CUI, Lulu YANG, Nannan LIANG, Rui WANG
Journal of Triticeae Crops | 2026, 46(3) : 384 - 392
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Journal of Triticeae Crops | 2026, 46(3): 384-392
Physiology, Ecology and Cultivation
Prediction of Wheat Grain Protein Content Based on Vegetation Indices and Texture Features
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Bingxiao DU, Dasheng ZHENG, Yulan YE, Zhifeng CUI, Lulu YANG, Nannan LIANG, Rui WANG
Affiliations
  • College of Agronomy, Northwest A&F University, Yangling, Shaanxi 712100, China
Published: 2026-03-15 doi: 10.7606/j.issn.1009-1041.2026.03.11
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To achieve rapid pre-harvest prediction of grain protein content (GPC) in winter wheat, this study developed an indirect prediction method integrating multispectral vegetation indices and texture features. Based on nitrogen fertilization experiments and spectral data collected from 2022 to 2024, texture parameters were extracted using Gray-level co-occurrence matrix (GLCM). Leaf nitrogen content (LNC) served as the critical intermediary to link spectral models with GPC, establishing a multispectral-based prediction model for winter wheat GPC. The results demonstrated that during the early grain-filling stage, vegetation indices combined with soil-background-filtered texture features achieved optimal LNC estimation. The exponential model y=0.281exp (0.097x), showed superior performance (r2=0.787, RMSE=0.221 g·kg-1); significant correlations were observed between LNC and GPC across key growth stages, with correlation coefficients of 0.780(anthesis), 0.810 (early grain-filling), 0.704(mid-grain-filling), and 0.714(late grain-filling); the exponential model y=7.160exp (0.018x), developed using early grain-filling stage data, achieved the highest GPC prediction accuracy [r2=0.697, RMSE=0.096 g· (100 g)-1]. The established early grain-filling stage GPC prediction model enhances field management optimization, enables grain quality classification, and provides technical support for applying remote sensing technology in high-quality wheat production and precision agriculture.

Winter wheat  /  Leaf nitrogen content  /  Grain protein content  /  Vegetation indices  /  Texture features  /  Prediction model
Bingxiao DU, Dasheng ZHENG, Yulan YE, Zhifeng CUI, Lulu YANG, Nannan LIANG, Rui WANG. Prediction of Wheat Grain Protein Content Based on Vegetation Indices and Texture Features[J]. Journal of Triticeae Crops, 2026 , 46 (3) : 384 -392 . DOI: 10.7606/j.issn.1009-1041.2026.03.11
Year 2026 volume 46 Issue 3
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doi: 10.7606/j.issn.1009-1041.2026.03.11
  • Receive Date:2025-04-23
  • Online Date:2026-09-11
  • Published:2026-03-15
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  • Received:2025-04-23
  • Revised:2025-06-26
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    College of Agronomy, Northwest A&F University, Yangling, Shaanxi 712100, China
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表12种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

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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