基于无人机多光谱遥感和机器学习的棉花SPAD值预测

doi:10.6048/j.issn.1001-4330.2025.05.001

新疆农业科学 ›› 2025, Vol. 62 ›› Issue (5): 1041-1050.DOI: 10.6048/j.issn.1001-4330.2025.05.001

• 作物遗传育种·耕作栽培·生理生化 • 上一篇下一篇

基于无人机多光谱遥感和机器学习的棉花SPAD值预测

方万成¹(), 林涛²^,³(), 崔建平², 贾涛¹, 鲍龙龙¹, 王亮², 樊世语¹, 胡正东¹, 邵亚杰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.新疆维吾尔自治区农业科学院棉花研究所,乌鲁木齐 830091
3.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

收稿日期:2024-10-13 出版日期:2025-05-20 发布日期:2025-07-09
通信作者: 林涛(1981-),男,新疆玛纳斯人,研究员,博士,研究方向为棉花智慧生产,(E-mail)27427732@qq.com;
汤秋香(1980-),女,河南开封人,教授,博士,硕士生/博士生导师,研究方向为作物生理生态,(E-mail)790058828@qq.com
作者简介:方万成(1996-),男,甘肃酒泉人,硕士研究生,研究方向为农业遥感与养分诊断,(E-mail)2947283158@qq.com
基金资助:
国家自然科学基金项目(32260542);新疆农业科学院农业科技创新稳定支持专项(xjnkywdzc-2023007);新疆维吾尔自治区重大科技专项(2020A01002-4-4);新疆维吾尔自治区重点研发专项(2022B02033-1);新疆维吾尔自治区重大科技专项(2022A02011-2-1);数字棉花科技创新平台建设项目;新疆农业科学院自主培育专项(xjnkyzzp-2022002);“天山英才”培养计划“棉花轻简高效栽培技术创新团队”(2023TSYCTD004)

Prediction of SPAD value of cotton based on UAV multispectral remote sensing and machine learning

FANG Wancheng¹(), LIN Tao²^,³(), CUI Jianping², JIA Tao¹, BAO Longlong¹, WANG Liang², FAN Shiyu¹, HU Zhengdong¹, SHAO Yajie¹, TANG Qiuxiang¹()

1. Cotton Engineering Research Center of Ministry of Education / College of Agronomy, Xinjiang Agricultural University, Urumqi 830052,China
2. Cotton Research Institute, Xinjiang Uyghur Autonomous Region Academy of Agricultural Sciences, Urumqi 830091, China
3. Key Laboratory of Desert Oasis Crop Physiological Ecology and Tillage, Ministry of Agriculture and Rural Areas, Urumqi 830091, China

Received:2024-10-13 Published:2025-05-20 Online:2025-07-09
Supported by:
Project of National Natural Science Foundation of China(32260542);Agricultural S &T Innovation and Stability Support Special Project of Xinjiang Academy of Agricultural Sciences(xjnkywdzc-2023007);Major Scientific R & D Program Projects of Xinjiang Uygur Autonomous Region(2020A01002-4-4);Major Scientific R & D Program Projects of Xinjiang Uygur Autonomous Region(2022B02033-1);Major Scientific R & D Program Projects of Xinjiang Uygur Autonomous Region(2022A02011-2-1);Digital Cotton Technology Innovation Platform Construction Project;Autonomous Cultivation Project of Xinjiang Academy of Agricultural Sciences(xjnkyzzp-2022002);"Tianshan Talent" Training Program Project "Cotton Light, Simple and Efficient Cultivation Technology Innovation Team"(2023TSYCTD004)

摘要/Abstract

摘要：

【目的】通过无人机多光谱影像和机器学习算法估测棉花SPAD值,快速准确的获取棉花叶绿素含量(SPAD值),为精准监测棉花生长状态、提高棉花产量和品质预测提供参考。【方法】利用多光谱遥感技术结合机器学习方法,反演新疆阿克苏地区棉花SPAD值。采用裂区试验设计,选择3个施氮水平和3个灌溉定额,分析不同水氮处理下棉花SPAD值的响应规律,研究不同时期棉花多光谱影像的光谱特征并构建植被指数,分析植被指数与SPAD值的相关性,筛选出相关性高的植被指数。通过4种机器学习算法对试验1和试验2全生育期SPAD值数据和多光谱指数进行建模分析,筛选出最优监测模型,分别预测反演不同时期棉花SPAD值,用不同田块数据验证模型。【结果】棉花不同生长期受到灌水和施肥条件影响显著。筛选合适的光谱指数并用随机森林模型建模取得了较好的估测精度,在花铃期模型估测结果最佳,模型的估测进度R²介于0.68~0.73。RF模型在不同田块间进行叶片SPAD值估算具有较优的稳定性。【结论】基于无人机多光谱影像计算光谱指数采用RF算法建模估测棉花叶片SPAD值具有较优的精度和稳定性。

关键词: 无人机多光谱; 棉花; SPAD值; 机器学习; 随机森林

Abstract:

【Objective】 Cotton is an important economic crop in Xinjiang, so obtaining cotton chlorophyll content (SPAD value) quickly and accurately on the field scale is of great significance for accurate monitoring of cotton growth status and improving cotton yield and quality prediction. In this study, multi-spectral remote sensing technology combined with machine learning method was used to retrieve the SPAD value of cotton in Aksu area.A feasible method for large area estimation of SPAD value of cotton in the field, and provides an important reference for non-destructive and real-time monitoring of crop growth index. 【Methods】 The split zone design was used in the experiment, three nitrogen application levels and three irrigation quotas were selected. Firstly, the response law of SPAD value of cotton under different water and nitrogen treatments was analyzed. Then the spectral characteristics of cotton multispectral images in different periods were further analyzed and the vegetation index was constructed. The correlation between vegetation index and SPAD value was analyzed, and the vegetation index with high correlation was selected. Four machine learning algorithms were used to model and analyze the SPAD value and multi-spectral index of the whole growth period of experiment 1 and experiment 2, and the optimal monitoring model was selected. The SPAD value of cotton in different periods were predicted and inversed, and the model was verified by different field data. 【Results】 The SPAD value of cotton was estimated by UAV multispectral images and machine learning algorithm, and it was found that different growth periods were significantly affected by irrigation and fertilization conditions. The better estimation accuracy was obtained by screening the appropriate spectral index and modeling with the random forest model, and the estimation result of the model was the best at the flowering and boll stage, and the estimation progress R²of the model was between 0.68 and 0.73. The RF model had good stability in estimating the SPAD value of leaves among different fields. 【Conclusion】 The estimation of SPAD value of cotton leaves by RF algorithm based on UAV multispectral image calculation has good accuracy and stability.

Key words: UAV multispectral; cotton; SPAD value; machine learning; random forest

中图分类号:

S233.75
S562

方万成, 林涛, 崔建平, 贾涛, 鲍龙龙, 王亮, 樊世语, 胡正东, 邵亚杰, 汤秋香. 基于无人机多光谱遥感和机器学习的棉花SPAD值预测[J]. 新疆农业科学, 2025, 62(5): 1041-1050.

FANG Wancheng, LIN Tao, CUI Jianping, JIA Tao, BAO Longlong, WANG Liang, FAN Shiyu, HU Zhengdong, SHAO Yajie, TANG Qiuxiang. Prediction of SPAD value of cotton based on UAV multispectral remote sensing and machine learning[J]. Xinjiang Agricultural Sciences, 2025, 62(5): 1041-1050.

图/表 9

参考文献 33

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植被指数 Spectral	全称 Full name	计算公式 Formula	文献来源 Reference
DVI	差值植被指数	DVI=R_NIR-R_Red	[7]
NDVI	归一化植被指数	NDVI=(R_NIR-R_Red)/(R_NIR+R_Red)	[8]
GNDVI	绿色归一化差值植被指数	GNDVI=(R_NIR-R_Green)/(R_NIR+R_Green)	[9]
RDVI	重归一化植被指数	RDVI=(R_NIR-R_Red)/√(R_NIR+R_Red)	[10]
RVI	比值植被指数	RVI=R_NIR/R_Red	[11]
SAVI	土壤调节植被指数	SAVI=(R_NIR-R_Red)/1.5(R_NIR+R_Red+0.5)	[12]
OSAVI	优化土壤调节植被指数	OSAVI=(R_NIR-R_Red)/(R_NIR+R_Red+0.16)	[13]
NLI	非线性植被指数	NLI=(R2NIR-R_Red)/(R2NIR+R_Red)	[14]
MNLI	改进非线性植被指数	MNLI=(1.5(R2NIR-1.5R_Green)/(R2NIR+R_Red+0.5)	[15]
MSR	改进简单比值植被指数	MSR=(R_NIR/R_Red-1)/(√R_NIR/R_Red+1)	[16]
GRVI	红绿植被指数	GRVI=(R_Green-R_Red)/(R_Green+R_Red)	[17]
NDRE	红边植被指数	NDRE=(R_NIR-RRE)/(R_NIR+RRE)	[18]

植被指数 Spectral	全称 Full name	计算公式 Formula	文献来源 Reference
DVI	差值植被指数	DVI=R_NIR-R_Red	[7]
NDVI	归一化植被指数	NDVI=(R_NIR-R_Red)/(R_NIR+R_Red)	[8]
GNDVI	绿色归一化差值植被指数	GNDVI=(R_NIR-R_Green)/(R_NIR+R_Green)	[9]
RDVI	重归一化植被指数	RDVI=(R_NIR-R_Red)/√(R_NIR+R_Red)	[10]
RVI	比值植被指数	RVI=R_NIR/R_Red	[11]
SAVI	土壤调节植被指数	SAVI=(R_NIR-R_Red)/1.5(R_NIR+R_Red+0.5)	[12]
OSAVI	优化土壤调节植被指数	OSAVI=(R_NIR-R_Red)/(R_NIR+R_Red+0.16)	[13]
NLI	非线性植被指数	NLI=(R2NIR-R_Red)/(R2NIR+R_Red)	[14]
MNLI	改进非线性植被指数	MNLI=(1.5(R2NIR-1.5R_Green)/(R2NIR+R_Red+0.5)	[15]
MSR	改进简单比值植被指数	MSR=(R_NIR/R_Red-1)/(√R_NIR/R_Red+1)	[16]
GRVI	红绿植被指数	GRVI=(R_Green-R_Red)/(R_Green+R_Red)	[17]
NDRE	红边植被指数	NDRE=(R_NIR-RRE)/(R_NIR+RRE)	[18]

植被指数 Vegetation Indexes	蕾期 Bud stage	花期 Flower season	花铃期 Flower and boll stage	吐絮期 Boll opening stage
NIR	-0.561	0.165	-0.346	-0.269
RED	0.337	-0.292	0.260	0.274
RE	-0.178	-0.443	-0.702	-0.461
GREEN	0.203	-0.517	-0.306	0.286
BLUE	0.379	-0.308	0.353	0.263
DVI	-0.500	0.190	-0.361	-0.305
NDVI	-0.422	0.259	-0.382	-0.309
GNDVI	-0.372	0.439	-0.201	-0.326
RDVI	-0.464	0.215	-0.384	-0.309
RVI	-0.426	0.251	-0.258	-0.298
SAVI	-0.527	0.171	-0.343	-0.299
OSAVI	-0.443	0.238	-0.395	-0.310
NLI	-0.468	0.243	-0.403	-0.306
MNLI	-0.476	0.257	-0.317	-0.318
MSR	-0.426	0.255	-0.294	-0.302
GRVI	-0.445	-0.098	-0.455	-0.133
NDRE	-0.193	0.462	0.390	0.087

植被指数 Vegetation Indexes	蕾期 Bud stage	花期 Flower season	花铃期 Flower and boll stage	吐絮期 Boll opening stage
NIR	-0.561	0.165	-0.346	-0.269
RED	0.337	-0.292	0.260	0.274
RE	-0.178	-0.443	-0.702	-0.461
GREEN	0.203	-0.517	-0.306	0.286
BLUE	0.379	-0.308	0.353	0.263
DVI	-0.500	0.190	-0.361	-0.305
NDVI	-0.422	0.259	-0.382	-0.309
GNDVI	-0.372	0.439	-0.201	-0.326
RDVI	-0.464	0.215	-0.384	-0.309
RVI	-0.426	0.251	-0.258	-0.298
SAVI	-0.527	0.171	-0.343	-0.299
OSAVI	-0.443	0.238	-0.395	-0.310
NLI	-0.468	0.243	-0.403	-0.306
MNLI	-0.476	0.257	-0.317	-0.318
MSR	-0.426	0.255	-0.294	-0.302
GRVI	-0.445	-0.098	-0.455	-0.133
NDRE	-0.193	0.462	0.390	0.087

植被指数 Vegetation Indexes	蕾期 Bud stage	花期 Flower season	花铃期 Flower and boll stage	吐絮期 Boll opening stage
NIR	-0.124	-0.078	-0.349	-0.203
RED	0.063	0.085	-0.044	0.032
RE	0.055	-0.515	-0.804	-0.481
GREEN	-0.086	-0.322	-0.289	-0.020
BLUE	0.016	0.210	0.078	0.043
DVI	-0.105	-0.082	-0.309	-0.150
NDVI	-0.056	-0.097	-0.157	-0.110
GNDVI	0.026	0.095	-0.069	-0.092
RDVI	-0.082	-0.088	-0.271	-0.132
RVI	-0.126	-0.087	-0.133	-0.103
SAVI	-0.126	-0.077	-0.324	-0.162
OSAVI	-0.068	-0.093	-0.239	-0.124
NLI	-0.066	-0.096	-0.237	-0.141
MNLI	-0.081	-0.024	-0.282	-0.120
MSR	-0.105	-0.091	-0.143	-0.106
GRVI	-0.183	-0.323	-0.222	-0.130
NDRE	-0.121	0.283	0.504	0.091

基于无人机多光谱遥感和机器学习的棉花SPAD值预测

Prediction of SPAD value of cotton based on UAV multispectral remote sensing and machine learning

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图/表 9

参考文献 33

相关文章 15

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Metrics

试验 Experiments	模型 Model	建模集Modeling set			验证集Validation set
试验 Experiments	模型 Model	R²	RMSE	RE	R²	RMSE	RE
试验1 Experiments 1	PLSR	0.210	3.222	0.059	0.299	3.087	0.056
	SVR	0.470	2.621	0.048	0.449	2.819	0.052
	RF	0.720	1.947	0.028	0.360	2.737	0.038
	BPN	0.486	2.596	0.039	0.472	2.713	0.039
试验2 Experiments 2	PLSR	0.230	3.234	0.059	0.226	3.031	0.055
	SVR	0.439	2.694	0.049	0.513	2.668	0.049
	RF	0.679	2.023	0.029	0.585	2.519	0.036
	BPN	0.468	2.641	0.039	0.399	2.893	0.043

试验 Experiments	生育期 Growth stage	建模集Modeling set			验证集Validation set
试验 Experiments	生育期 Growth stage	R²	RMSE	RE	R²	RMSE	RE
试验1 Experiments 1	蕾期	0.641	1.520	0.108	0.452	1.842	0.206
	花期	0.521	1.965	0.167	0.335	2.848	0.249
	花铃期	0.743	2.843	0.152	0.617	3.863	0.204
	吐絮期	0.586	3.046	0.138	0.402	3.922	0.225
试验2 Experiments 2	蕾期	0.407	1.689	0.146	0.237	1.995	0.212
	花期	0.560	1.735	0.137	0.227	1.934	0.269
	花铃期	0.819	1.984	0.100	0.631	2.598	0.193
	吐絮期	0.584	2.438	0.150	0.344	2.846	0.199

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