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Monitoring Leaf Chlorophyll Content of Winter Wheat Based on Multispectral Imagery and Canopy Morphology Derived from Unmanned Aerial Vehicles
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Wenhui WANG, Chao TIAN, Shengquan LU, Yueying AN, Xiaoqi WANG, Songyang ZHAO, Shenghua YUAN
Journal of Triticeae Crops | 2026, 46(3) : 404 - 412
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Journal of Triticeae Crops | 2026, 46(3): 404-412
Physiology, Ecology and Cultivation
Monitoring Leaf Chlorophyll Content of Winter Wheat Based on Multispectral Imagery and Canopy Morphology Derived from Unmanned Aerial Vehicles
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Wenhui WANG, Chao TIAN, Shengquan LU, Yueying AN, Xiaoqi WANG, Songyang ZHAO, Shenghua YUAN
Affiliations
  • Langfang Normal University, Langfang, Hebei 0650000, China
Published: 2026-03-15 doi: 10.7606/j.issn.1009-1041.2026.03.13
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In order to clarify the impact of coupling canopy morphology with other modal information on estimating winter wheat leaf chlorophyll content (LCC), two wheat varieties, Shinong 086 and Hemai 2020 were used as materials. Four nitrogen application levels (0, 120, 240, and 360 kg·hm-2) were set up in fields. This study used a low-altitude unmanned aerial vehicle (UAV) remote sensing platform equipped with a multispectral camera to obtain spectral data of winter wheat at key growth stages. The canopy morphology effect was calculated in combination with the digital elevation model (DEM), and Random Forest Regression (RF), Categorical Boosting (Catboost), and eXtreme Gradient Boosting (XGBoost) were applied to analyze the contribution of spectral information, index information, and canopy morphology information, among other multi-modal data, to the monitoring of LCC in winter wheat. The results showed that compared with spectral and vegetation index features, canopy morphology could improve the monitoring accuracy of LCC (r2=0.66), and the importance of DEM was superior to that of slope. Among the three machine learning algorithms, the LCC model constructed by Catboost could achieve high monitoring accuracy both in the case of single feature and multi-source feature coupling, especially when a single canopy morphology feature was used as the input variable. Multi-source feature coupling does not improve the monitoring accuracy of LCC, but the coupling of other features with canopy morphology features can improve the monitoring accuracy to a certain extent.

Unmanned aerial vehicle remote sensing system  /  Winter wheat  /  Leaf chlorophyll content  /  Canopy morphology  /  Multi-modal data
Wenhui WANG, Chao TIAN, Shengquan LU, Yueying AN, Xiaoqi WANG, Songyang ZHAO, Shenghua YUAN. Monitoring Leaf Chlorophyll Content of Winter Wheat Based on Multispectral Imagery and Canopy Morphology Derived from Unmanned Aerial Vehicles[J]. Journal of Triticeae Crops, 2026 , 46 (3) : 404 -412 . DOI: 10.7606/j.issn.1009-1041.2026.03.13
Year 2026 volume 46 Issue 3
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doi: 10.7606/j.issn.1009-1041.2026.03.13
  • Receive Date:2025-04-17
  • Online Date:2026-09-11
  • Published:2026-03-15
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  • Received:2025-04-17
  • Revised:2025-06-10
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    Langfang Normal University, Langfang, Hebei 0650000, 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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