基于无人机多光谱影像结合机器学习的棉花SPAD值估算

doi:10.6048/j.issn.1001-4330.2025.04.002

新疆农业科学 ›› 2025, Vol. 62 ›› Issue (4): 791-799.DOI: 10.6048/j.issn.1001-4330.2025.04.002

• 作物遗传育种·耕作栽培·生理生化 • 上一篇下一篇

基于无人机多光谱影像结合机器学习的棉花SPAD值估算

李珂¹(), 印彩霞¹, 陈茂光¹, 崔涵予², 王科¹, 刘立杨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.新疆农业大学资源与环境学院,乌鲁木齐 830052

收稿日期:2024-09-15 出版日期:2025-04-20 发布日期:2025-06-20
通信作者: 汤秋香(1981-),女,河南开封人,教授,博士,硕士生/博士生导师,研究方向为农田生态环境与耕作制度,(E-mail)790058828@qq.com
作者简介:李珂(2002-),女,河南新乡人,本科生,研究方向为农学,(E-mail)2041633697@qq.com
基金资助:
新疆维吾尔自治区重大科技专项“农场数字化及智能化关键技术研究”子课题“大田作物生长发育动态预测与智能决策”(2022A02011-2-1);“新疆现代农业产业技术体系-棉花产业技术体系”(XIARS-03);新疆维吾尔自治区重点研发专项“新疆绿色农业生产关键技术研究”课题“农田耕地质量提升与病虫害绿色防控技术研究”(2022B02033-1);新疆农业大学大学生创新项目(S202210758030)

Research on cotton SPAD estimation based on UAV multispectral images combined with machine learning

LI Ke¹(), YIN Caixia¹, CHEN Maoguang¹, CUI Hanyu², WANG Ke¹, LIU Liyang¹, TANG Qiuxiang¹()

1. Cotton Engineering Research Centerof Ministry of Education, College of Agriculture, Xinjiang Agricultural University, Urumqi 830052, China
2. College of Resources and Environment, Xinjiang Agricultural University,Urumqi 830052,China

Received:2024-09-15 Published:2025-04-20 Online:2025-06-20
Supported by:
Sub project "Dynamic Prediction and Intelligent Decision making of Field Crop Growth and Development" of the Major Science and Technology Special Project "Key Technologies Research on Digitalization and Intelligence of Farms" in Xinjiang Uygur Autonomous Region(2022A02011-2-1);"Xinjiang Modern Agricultural Industry Technology System - Cotton Industry Technology System"(XIARS-03);Key Research and Development Project of Xinjiang Uygur Autonomous Region "Key Technologies for Green Agriculture Production in Xinjiang"(2022B02033-1);Innovation Project for College Students of Xinjiang Agricultural University(S202210758030)

摘要/Abstract

摘要：

【目的】基于SPAD值估算进行快速无损监测棉花田间管理,为棉田精准施肥提供技术支撑。【方法】采用大疆创新M350 RTK无人机搭载多光谱传感器获取多时相冠层遥感影像,并计算多光谱植被指数,筛选与SPAD值显著相关的特征,结合极限学习机、随机森林回归、支持向量回归、多元逐步回归四种机器学习算法构建棉花各生育时期SPAD值估算模型。【结果】各植被指数与棉花SPAD值在各生育时期均呈极显著正相关,其中CCCI(冠层叶绿素含量指数)和CIrededge(红边叶绿素指数)与SPAD值间具有较高的相关性,相关系数分别高达0.81、0.78。对比不同生育时期模型精度,开花期模型精度最高,最佳估测模型为ELM,R²为0.741,RMSE为1.448,rRMSE为0.025,现蕾期和吐絮期的最优估算模型为ELM,R²分别为0.656、0.587,盛铃期最优估测模型为RFR,R²为0.577。【结论】棉花叶片SPAD值估算的最佳生育时期处于开花期,最优模型表现为ELM,模型精度R²最高达0.741。

关键词: 无人机; 多光谱遥感; 棉花; 叶绿素相对含量; 机器学习算法

Abstract:

【Objective】 Rapid and non-destructive monitoring of SPAD value has important guiding significance for field management measures. 【Methods】 In this study, the DJI M350 RTK drone equipped with multispectral sensors was used to obtain multi-temporal canopy remote sensing images, calculate the multispectral vegetation index, screen the features significantly related to SPAD value, and four machine learning algorithms including limit learning machine, random forest regression, support vector regression and multiple stepwise regression were combined to construct a SPAD value estimation model for cotton at each growth stage. 【Results】 The results showed that there was a significant positive correlation between vegetation index and cotton SPAD value at each growth stage, and there was a high correlation between CCCI (canopy chlorophyll content index) and CIrededge (red edge chlorophyll index) and SPAD value, with the highest correlation coefficients of 0.81 and 0.78, respectively. Comparing the accuracy of the model at different growth stages, it was found that the model at flowering stage had the highest accuracy, with the best estimation model being ELM, R² being 0.741, RMSE being 1.447, rRMSE being 0.023, ELM being the best estimation model at budding stage and flocculation stage (R² being 0.656 and 0.587, respectively), and RFR (R² being 0.577) at full boll stage. 【Conclusion】 This study shows that the optimal growth period for SPAD value estimation of cotton leaves is atthe flowering stage, the optimal model is ELM, and the model accuracy R² is the highest 0.741.

Key words: UAV; multispectral remote sensing; cotton; SPAD value; machine learning algorithm

中图分类号:

S562

李珂, 印彩霞, 陈茂光, 崔涵予, 王科, 刘立杨, 汤秋香. 基于无人机多光谱影像结合机器学习的棉花SPAD值估算[J]. 新疆农业科学, 2025, 62(4): 791-799.

LI Ke, YIN Caixia, CHEN Maoguang, CUI Hanyu, WANG Ke, LIU Liyang, TANG Qiuxiang. Research on cotton SPAD estimation based on UAV multispectral images combined with machine learning[J]. Xinjiang Agricultural Sciences, 2025, 62(4): 791-799.

图/表 6

图1 研究区概况

Fig.1 Overview of the research area

表1 植被指数计算公式

Tab.1 Calculation formula of Vegetation index

植被指数 Vegetation indexes	公式 Formula	文献 Reference
冠层叶绿素含量指数(CCCI)	$C C C I = N I R - R E N I R + R E N I R - R N I R + R .$	[15]
红边叶绿素指数(CIrededge)	$C I r e d e d g e = N I R R E - 1 .$	[17]
叶片叶绿素指数(LCI)	$L C I = N I R - R E N I R + R .$	[18]
修正型红边变换植被指数(MRETVI)	$M R E T V I = 1.2 × [1.2 × (N I R - G) - 2.5 × (R E - G)] .$	[19]
修正型增强植被指数(MEVI)	$M E V I = 2.5 × N I R - R E N I R + 6 × R E - 7.5 × G + 1 .$	[19]
绿色归一化植被指数(GNDVI)	$G N D V I = N I R - G N I R + G .$	[20]
修正型简单比值指数(MSR)	$M S R = N I R R - 1 N I R R + 1 .$	[21]
双差分指数(DD)	$D D = (N I R - R E) - (R E - R) .$	[19]
陆地叶绿素指数(MTCI)	$M T C I = N I R - R E R E - R .$	[17]

表1 植被指数计算公式

Tab.1 Calculation formula of Vegetation index

植被指数 Vegetation indexes	公式 Formula	文献 Reference
冠层叶绿素含量指数(CCCI)	$C C C I = N I R - R E N I R + R E N I R - R N I R + R .$	[15]
红边叶绿素指数(CIrededge)	$C I r e d e d g e = N I R R E - 1 .$	[17]
叶片叶绿素指数(LCI)	$L C I = N I R - R E N I R + R .$	[18]
修正型红边变换植被指数(MRETVI)	$M R E T V I = 1.2 × [1.2 × (N I R - G) - 2.5 × (R E - G)] .$	[19]
修正型增强植被指数(MEVI)	$M E V I = 2.5 × N I R - R E N I R + 6 × R E - 7.5 × G + 1 .$	[19]
绿色归一化植被指数(GNDVI)	$G N D V I = N I R - G N I R + G .$	[20]
修正型简单比值指数(MSR)	$M S R = N I R R - 1 N I R R + 1 .$	[21]
双差分指数(DD)	$D D = (N I R - R E) - (R E - R) .$	[19]
陆地叶绿素指数(MTCI)	$M T C I = N I R - R E R E - R .$	[17]

表2 各生育时期棉花SPAD值统计特征

Tab.2 Statistical characteristics of SPAD value in cotton at different growth stages

生育时期 Fertility period	样本数 Number of samples	最小值 Minimum value	最大值 Maximum value	平均数 Average	标准差 Standard deviation	变异系数 Cvariation coefficient(%)
现蕾期 Budding stage	96	43.3	62.3	55.1	3.349 2	6.08%
开花期 Flowering period	96	51.5	64.6	57.8	2.744 6	4.75%
盛铃期 Full boll period	96	56.8	72.4	65.2	3.107 4	4.77%
吐絮期 Opening-boll stage	96	53.1	85.7	66.1	7.245 7	10.96%

图2 各生育时期植被指数与棉花SPAD值相关系数的热图注:A:现蕾期;B:开花期;C:盛铃期;D:吐絮期

Fig.2 Thermogram of correlation coefficient between vegetation index and cotton SPAD value at different growth stages Notes:A: Budding stage; B: Flowering period; C: Full boll period; D: Opening-boll stage

表3 各生育时期棉花SPAD值模型估测精度

Tab.3 Estimation accuracy of cotton SPAD value model at different growth stages

生育时期 Fertility period	估算模型 Estimation model	验证集Verification set
生育时期 Fertility period	估算模型 Estimation model	R²	RMSE	rRMSE
现蕾期 Budding stage	随机森林回归RFR	0.534	2.686	0.049
	极限学习机ELM	0.656	2.306	0.042
	多元逐步回归MSR	0.629	2.395	0.043
	支持向量回归SVR	0.512	2.747	0.050
开花期 Flowering period	随机森林回归RFR	0.695	1.571	0.027
	极限学习机ELM	0.741	1.448	0.025
	多元逐步回归MSR	0.719	1.509	0.026
	支持向量回归SVR	0.737	1.458	0.025
盛铃期 Full boll period	随机森林回归RFR	0.577	1.980	0.031
	极限学习机ELM	0.549	2.044	0.032
	多元逐步回归MSR	0.520	2.109	0.033
	支持向量回归SVR	0.569	2.000	0.031
吐絮期 Opening-boll stage	随机森林回归RFR	0.573	5.364	0.080
	极限学习机ELM	0.587	5.274	0.079
	多元逐步回归MSR	0.536	5.594	0.084
	支持向量回归SVR	0.511	5.742	0.086

图3 棉花SPAD值各生育时期ELM模型实测值和预测值的散点图注:A:现蕾期;B:开花期;C:盛铃期;D:吐絮期

Fig.3 Scatter plot of measured and predicted values of ELM model in cotton SPAD value at different growth stages Notes:A: Budding stage; B: Flowering period; C: Full boll period; D: Opening-boll stage

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基于无人机多光谱影像结合机器学习的棉花SPAD值估算

Research on cotton SPAD estimation based on UAV multispectral images combined with machine learning

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