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Study on hyperspectral chlorophyll retrieval methods for cold-region rice oriented toward UAV variable fertilization
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Hongbo Li1, 2, Boyuan Tan1, Zhenyang Lü1, 2, Yanhong Zhang1, Tenghui Lin3, Zhongbin Su1, 2
Journal of Intelligent Agricultural Mechanization | 2026, 7(2) : 77 - 90
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Journal of Intelligent Agricultural Mechanization | 2026, 7(2): 77-90
Study on hyperspectral chlorophyll retrieval methods for cold-region rice oriented toward UAV variable fertilization
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Hongbo Li1, 2, Boyuan Tan1, Zhenyang Lü1, 2, Yanhong Zhang1, Tenghui Lin3, Zhongbin Su1, 2
Affiliations
  • 1.College of Intelligent Science and Engineering, Northeast Agricultural University, Harbin 150030, China
  • 2.Key Laboratory of Northeast Smart Agriculture Technology, Ministry of Agriculture and Rural Affairs, Harbin 150030, China
  • 3.College of Agricultural Equipment and Energy Engineering, Harbin 150030, China

    • Li Hongbo, E-mail:

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Li Hongbo, E-mail:

Published: 2026-05-15 doi: 10.12398/j.issn.2096-7217.2026.02.007
Outline
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Unmanned aerial vehicle (UAV)-based fertilization is increasingly applied in rice production; however, fertilization prescriptions are still largely dependent on empirical knowledge and lack decision support based on real-time crop nutrient status. Meanwhile, limitations in hyperspectral data processing and model construction restrict the high-precision estimation of chlorophyll content in cold-region rice, thereby constraining the accurate implementation of variable-rate fertilization. To address these issues, this study combined multiple spectral preprocessing methods with feature variable selection techniques, including uninformative variable elimination (UVE), competitive adaptive reweighted sampling (CARS), iterative retention of informative variables (IRIV), and variable iterative space shrinkage approach (VISSA), to extract key spectral bands. Models including least squares support vector regression (LSSVR), particle swarm optimization-based LSSVR (PSO-LSSVR), partial least squares regression (PLSR), and a deep learning model (1DCNN-WN) were developed for chlorophyll content inversion. Based on the inversion results, nitrogen fertilization prescription maps were generated to guide UAV-based variable-rate fertilization at the tillering stage. The results showed that the SNV-IRIV-1DCNN-WN model achieved the highest inversion accuracy under the optimal preprocessing and feature selection combinations, with an R2 of 0.687, which was 0.047, 0.031, and 0.027 higher than those of SNV-UVE-LSSVR, NL-VISSA-PSO-LSSVR, and MSC-IRIV-PLSR, respectively, while the RMSE was reduced by 0.281, 0.147, and 0.107. The coefficients of variation for no fertilization (N0), low fertilization (N1), standard fertilization (N2), and excessive fertilization (N3) treatments were 6.08%, 4.16%, 3.42%, and 3.07%, respectively, indicating good fertilization uniformity. These findings provide theoretical support and technical guidance for hyperspectral-based chlorophyll inversion and precision fertilization in cold-region rice production.

cold-region rice  /  hyperspectral  /  chlorophyll inversion  /  precision fertilization  /  unmanned aerial vehicle  /  machine learning
李洪波, 谭博源, 吕振阳, 张延鸿, 林腾辉, 苏中滨. 面向无人机变量施肥的寒地水稻叶绿素高光谱反演方法研究. 智能化农业装备学报（中英文）, 2026 , 7 (2) : 77 -90 . DOI: 10.12398/j.issn.2096-7217.2026.02.007
Hongbo Li, Boyuan Tan, Zhenyang Lü, Yanhong Zhang, Tenghui Lin, Zhongbin Su. Study on hyperspectral chlorophyll retrieval methods for cold-region rice oriented toward UAV variable fertilization[J]. Journal of Intelligent Agricultural Mechanization, 2026 , 7 (2) : 77 -90 . DOI: 10.12398/j.issn.2096-7217.2026.02.007
Funding
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  • National Key R&D Program of China(2021YFD200060502)
  • Heilongjiang Provincial Key R&D Program(GZ2024003)
  • Heilongjiang Provincial Collaborative Innovation and Promotion System for Smart Agricultural Industrial Technology(2025)
Appendix
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Year 2026 volume 7 Issue 2
50
14
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Article Info
doi: 10.12398/j.issn.2096-7217.2026.02.007
  • Receive Date：2026-04-10
  • Online Date：2026-11-05
  • Published：2026-05-15
Article Data
Affiliations
History
  • Received：2026-04-10
  • Revised：2026-04-30
Funding
National Key R&D Program of China(2021YFD200060502)
Heilongjiang Provincial Key R&D Program(GZ2024003)
Heilongjiang Provincial Collaborative Innovation and Promotion System for Smart Agricultural Industrial Technology(2025)
Affiliations
    1.College of Intelligent Science and Engineering, Northeast Agricultural University, Harbin 150030, China
    2.Key Laboratory of Northeast Smart Agriculture Technology, Ministry of Agriculture and Rural Affairs, Harbin 150030, China
    3.College of Agricultural Equipment and Energy Engineering, Harbin 150030, China

Corresponding:

* Su Zhongbin, E-mail:
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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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