智能化农业装备学报（中英文）

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

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李洪波 ¹^,² , 谭博源 ¹ , 吕振阳 ¹^,² , 张延鸿 ¹ , 林腾辉 ³ , 苏中滨 ¹^,²^,^*^,^*

智能化农业装备学报（中英文） | 2026,7(2): 77-90

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智能化农业装备学报（中英文） | 2026 , 7 (2) : 77 -90

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

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李洪波¹^,², 谭博源¹, 吕振阳¹^,², 张延鸿¹, 林腾辉³, 苏中滨¹^,²^,^*^,^*

作者信息

^1.东北农业大学智能科学与工程学院，黑龙江哈尔滨，150030

^2.农业农村部东北智慧农业技术重点实验室，黑龙江哈尔滨，150030

^3.东北农业大学农业装备与能源工程学院，黑龙江哈尔滨，150030

通讯作者:

^* 苏中滨，男，1965年生，陕西咸阳人，博士，教授；研究方向为农业人工智能装备设计。E-mail：suzb001@163.com

作者简介:

李洪波，男，1979年生，内蒙古赤峰人，硕士，讲师；研究方向为无人（少人）智慧农场建设与专用装备。E-mail：lihongbo19790127@126.com

Study on hyperspectral chlorophyll retrieval methods for cold-region rice oriented toward UAV variable fertilization

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

Corresponding Author:

^* Su Zhongbin, E-mail: suzb001@163.com

Li Hongbo, E-mail: lihongbo19790127@126.com

出版时间: 2026-05-15 doi: 10.12398/j.issn.2096-7217.2026.02.007

文章导航

摘要

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水稻生产中无人机施肥应用日益广泛，但施肥处方多依赖经验，缺乏基于作物生长实况养分状态的决策支持；同时，高光谱反演在数据处理与模型构建方面仍存在局限，难以实现寒地水稻叶绿素水平的高精度估测，从而制约变量施肥的精准实施。针对上述问题，本研究在多种光谱预处理方法的基础上，结合UVE、CARS、IRIV和VISSA等特征变量筛选方法进行关键波段提取，构建LSSVR、PSO-LSSVR、PLSR及深度学习1DCNN-WN模型对水稻叶绿素含量进行反演，并进一步基于反演结果生成氮素施肥处方图，以指导分蘖期无人机变量施肥作业。结果表明：在各模型最优预处理与特征筛选组合下，SNV-IRIV-1DCNN-WN模型反演精度最高，R²为0.687，较SNV-UVE-LSSVR、NL-VISSA-PSO-LSSVR和MSC-IRIV-PLSR分别提高0.047、0.031和0.027，RMSE分别降低0.281、0.147和0.107。基于施肥决策模型生成的处方图用于指导无人机变量施肥作业时，不施肥（N0）、少施肥（N1）、标准施肥（N2）和过量施肥（N3）处理的变异系数分别为6.08%、4.16%、3.42%和3.07%，施肥均匀性良好。研究结果可为寒地水稻高光谱特征解析及其生长信息的快速无损监测与精准施肥提供理论依据和技术支撑。

关键词

寒地水稻 / 高光谱 / 叶绿素反演 / 精准施肥 / 无人机 / 机器学习

Abstract

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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.

Key words

cold-region rice / hyperspectral / chlorophyll inversion / precision fertilization / unmanned aerial vehicle / machine learning

引用本文

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

基金

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国家重点研发计划(2021YFD200060502)
黑龙江省重点研发计划(GZ2024003)
黑龙江省智慧农业产业技术协同创新推广体系(2025)

参考文献

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2026年第7卷第2期

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文章信息

doi: 10.12398/j.issn.2096-7217.2026.02.007

接收时间：2026-04-10
首发时间：2026-11-05
出版时间：2026-05-15

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出版历史

收稿日期：2026-04-10
修回日期：2026-04-30

基金

National Key R&D Program of China(2021YFD200060502)

国家重点研发计划(2021YFD200060502)

Heilongjiang Provincial Key R&D Program(GZ2024003)

黑龙江省重点研发计划(GZ2024003)

Heilongjiang Provincial Collaborative Innovation and Promotion System for Smart Agricultural Industrial Technology(2025)

黑龙江省智慧农业产业技术协同创新推广体系(2025)

作者信息

^1.东北农业大学智能科学与工程学院，黑龙江哈尔滨，150030

^2.农业农村部东北智慧农业技术重点实验室，黑龙江哈尔滨，150030

^3.东北农业大学农业装备与能源工程学院，黑龙江哈尔滨，150030

通讯作者:

^* 苏中滨，男，1965年生，陕西咸阳人，博士，教授；研究方向为农业人工智能装备设计。E-mail：suzb001@163.com

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2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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