Study on cotton biomass estimation based on multi-spectral imaging features of unmanned aerial vehicle

doi:10.6048/j.issn.1001-4330.2024.06.004

Xinjiang Agricultural Sciences ›› 2024, Vol. 61 ›› Issue (6): 1328-1335.DOI: 10.6048/j.issn.1001-4330.2024.06.004

• Crop Genetics and Breeding • Germplasm Resources?Molecular Genetics • Cultivation Physiology • Physiology and Biochemistry • Previous Articles Next Articles

Study on cotton biomass estimation based on multi-spectral imaging features of unmanned aerial vehicle

SHAO Yajie¹(), LI Ke¹, DING Wenhao¹, LIN Tao²(), CUI Jianping², GUO Rensong², WANG Liang², WU Fengquan¹, WANG Xin¹, TANG Qiuxiang¹()

1. College of Agriculture, Xinjiang Agricultural University/Cotton Engineering Research Center of the Ministry of Education, Urumqi 830052, China
2. Institute of Economic Crops, Xinjiang Academy of Agricultural Sciences/Key Laboratory of Crop Physilolgy,Ecology and Cultivation in Desert Dasis of the Ministry of Agriculture and Rural Affairs /, Urumqi 830091,China

Received:2023-10-24 Online:2024-06-20 Published:2024-08-08
Correspondence author: LIN Tao, TANG Qiuxiang
Supported by:
Stable Support Project of Xinjiang Academy of Agricultural Sciences(xjnkywdzc-2023007-6);Major Scienceand Technology Project of Xinjiang Uygur Autonomous Region(2023A02003-5);Special Financial Project of Xinjiang Uygur Autonomous Region"Digital Cotton Science and Technology Innovation Platform Construction Project";Xinjiang "Tianshan Talents" TrainingProgram "Cotton Light and Efficient Cultivation Technology Innovation Team"(No. unavailable);National Modern Agricultural IndustryTechnology System-Cotton Industry Technology System(CARS-15-13);Xinjiang Modern Agricultural Industry Technology System-CottonIndustry Technology System(XIARS-03);Xinjiang "Tianshan Talents" Training Program "Young Top-notch Talent Project-Young Scientific andTechnological Innovation Talent"(2023TSYCCX0019);Graduate Scientific and Technological Innovation Program Project of XinjiangAgricultural University(XJAUGRI2022036)

基于无人机多光谱影像特征估算棉花生物量

邵亚杰¹(), 李珂¹, 丁文浩¹, 林涛²(), 崔建平², 郭仁松², 王亮², 吴凤全¹, 王心¹, 汤秋香¹()

1.新疆农业大学农学院/棉花教育部工程研究中心,乌鲁木齐 830052
2.新疆农业科学院经济作物研究所/农业农村部荒漠绿洲作物生理生态与耕作重点实验室,乌鲁木齐 830091

通讯作者: 林涛,汤秋香
作者简介:邵亚杰(1996-),男,新疆博乐人,硕士研究生,研究方向为棉花生长信息快速诊断,(E-mail)1519858040@qq.com
基金资助:
新疆农业科学院稳定支持项目(xjnkywdzc-2023007-6);新疆维吾尔自治区重大科技专项(2023A02003-5);新疆维吾尔自治区财政专项“数字棉花科技创新平台建设项目(编号无);新疆“天山英才”培养计划“棉花轻简高效栽培技术创新团队”;国家现代农业产业技术体系-棉花产业技术体系(CARS-15-13);新疆现代农业产业技术体系-棉花产业技术体系(XIARS-03);新疆“天山英才”培养计划“青年拔尖人才项目-青年科技创新人才”(2023TSYCCX0019);新疆农业大学研究生科技创新计划项目(XJAUGRI2022036)

Abstract

Abstract:

【Objective】 To explore the applicability and accuracy of cotton biomass estimation model based on Vegetation Indexes (VIs) and machine learning algorithm. 【Methods】 On the interaction between nitrogen application and density at the experimental and collected AGB data and UAV multispectral remote sensing images of cotton fields at the main fertility periods simultaneously to calculate eight VIs and introduce three VIs with the highest AGB correlation coefficients.Vactor Regression (SVR), Partial Least Squares Regression (PLSR), and Deep Neural Network (DNN), and evaluated the applicability and estimation accuracy of different VIs and models. 【Results】 All eight VIs showed significant correlations with AGB, among which the absolute values of the correlation coefficients |r| of NGBDI, NDREI and EXG reached 0.659-0.788, and there was a significant correlation between them and cotton biomass.(3) Among the three regression models, the SVR model had the best estimation effect, with the model validation accuracy of R²=0.89, RMSE=2.30, and rRMSE=0.20. 【Conclusion】 Compared with the PLSR and DNN estimation models, the SVR model is more suitable for estimating cotton biomass, and the study is important for enriching the remote sensing monitoring technology of cotton biomass and improving the accurate management of production.The study is important to enrich the remote sensing monitoring technology of cotton biomass and improve the accurate management of production.

Key words: cotton; unmanned aerial vehicle(UAV); multispectral image; biomass; estimate

摘要：

【目的】基于植被指数(Vegetation Indexes,VIs)与机器学习算法建立的棉花地上部生物量(Aboveground Biomass,AGB),估算模型并评价其适用性和准确性,为丰富棉花生物量的遥感监测技术、提升生产的精准化管理水平提供科学依据。【方法】设计施氮量与密度互作试验,同步采集主要生育时期的棉田实测AGB数据与无人机多光谱遥感影像数据,计算得到8种VIs,并引入其中与AGB相关系数最高的3种VIs,构建基于机器学习算法的支持向量回归(Support Vactor Regression, SVR)、偏最小二乘回归(Partial Least Squares Regression, PLSR)和深度神经网络(Deep Neural Network,DNN)等AGB估算模型,评估不同VIs和模型的适用性和估算精度。【结果】8种VIs与AGB均呈显著相关,其中NGBDI、NDREI和EXG的相关系数绝对值|r|达到0.659~0.788,且与棉花生物量之间显著相关。三种回归模型中,SVR模型的估算效果最好,模型验证精度为R²=0.89,RMSE=2.30,rRMSE=0.20。【结论】相较于PLSR和DNN估算模型,SVR模型更适合估算棉花生物量。

关键词: 棉花, 无人机, 多光谱影像, 生物量, 估算

CLC Number:

S562

SHAO Yajie, LI Ke, DING Wenhao, LIN Tao, CUI Jianping, GUO Rensong, WANG Liang, WU Fengquan, WANG Xin, TANG Qiuxiang. Study on cotton biomass estimation based on multi-spectral imaging features of unmanned aerial vehicle[J]. Xinjiang Agricultural Sciences, 2024, 61(6): 1328-1335.

邵亚杰, 李珂, 丁文浩, 林涛, 崔建平, 郭仁松, 王亮, 吴凤全, 王心, 汤秋香. 基于无人机多光谱影像特征估算棉花生物量[J]. 新疆农业科学, 2024, 61(6): 1328-1335.

Figures/Tables 4

Tab.1 The vegetation indexs in this study

植被指数 Vegetation index	计算公式 Calculation formula	参考文献 References
DVI	$D V I = R_{n i r} = R_{r e d}$	夏天等^[18]
GNDVI	$G N D V I = (R_{n i r} - R_{g r e e n}) / (R_{n i r} + R_{g r e e n})$	Camille et.al^[19]
TCRI	$T C R I = 3 \times [(R_{R E} - R_{r e d}) - 0.2 \times (R_{R E} - R_{g r e e n}) \times (\frac{R_{R E}}{R_{r e d}})]$	Shao et.al^[20]
EVI	$E V I = 2.5 (R_{n i r} - R_{r e d}) / (R_{n i r} + 6 R_{r e d} - 7.5 R_{b l u e} + 1)$	Liu et.al^[21]
NDREI	$N D R E I = (R_{R E} - R_{g r e e n}) / (R_{R E} + R_{g r e e n})$	Shao et.al^[20]
EXG	$E X G = 2 \times R_{g r e e n} - R_{r e d} - R_{b l u e}$	Liu et.al^[22]
EXGR	$E X G R = 3 \times R_{g r e e n} - 2.4 \times R_{r e d} - R_{b l u e}$	Fu et.al^[23]
NGBDI	$N G B D I = (R_{g r e e n} - R_{b l u e}) / (R_{g r e e n} + R_{b l u e})$	Sulik et.al^[24]

Tab.1 The vegetation indexs in this study

植被指数 Vegetation index	计算公式 Calculation formula	参考文献 References
DVI	$D V I = R_{n i r} = R_{r e d}$	夏天等^[18]
GNDVI	$G N D V I = (R_{n i r} - R_{g r e e n}) / (R_{n i r} + R_{g r e e n})$	Camille et.al^[19]
TCRI	$T C R I = 3 \times [(R_{R E} - R_{r e d}) - 0.2 \times (R_{R E} - R_{g r e e n}) \times (\frac{R_{R E}}{R_{r e d}})]$	Shao et.al^[20]
EVI	$E V I = 2.5 (R_{n i r} - R_{r e d}) / (R_{n i r} + 6 R_{r e d} - 7.5 R_{b l u e} + 1)$	Liu et.al^[21]
NDREI	$N D R E I = (R_{R E} - R_{g r e e n}) / (R_{R E} + R_{g r e e n})$	Shao et.al^[20]
EXG	$E X G = 2 \times R_{g r e e n} - R_{r e d} - R_{b l u e}$	Liu et.al^[22]
EXGR	$E X G R = 3 \times R_{g r e e n} - 2.4 \times R_{r e d} - R_{b l u e}$	Fu et.al^[23]
NGBDI	$N G B D I = (R_{g r e e n} - R_{b l u e}) / (R_{g r e e n} + R_{b l u e})$	Sulik et.al^[24]

Fig.1 Changes of cotton AGB under Different Nitrogen Rates and plant Densities Note: BS : bud stage, FS : flowering stage, FBS : full bolling stage, BOS : boll opening stage

Fig.2 Correlation between Vegetation Indices and AGB

Tab.2 Evaluation of AGB estimation results of cotton based on vegetation index

模型 Model	建模集 Training set			验证集 Testing set
模型 Model	R²	RMSE	rRMSE	R²	RMSE	rRMSE
SVR	0.89	2.23	0.20	0.89	2.30	0.20
PLSR	0.86	2.55	0.23	0.81	3.01	0.27
DNN	0.87	2.47	0.22	0.84	2.79	0.25

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Study on cotton biomass estimation based on multi-spectral imaging features of unmanned aerial vehicle

基于无人机多光谱影像特征估算棉花生物量

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