基于无人机多光谱影像特征估算棉花生物量

doi:10.6048/j.issn.1001-4330.2024.06.004

新疆农业科学 ›› 2024, Vol. 61 ›› Issue (6): 1328-1335.DOI: 10.6048/j.issn.1001-4330.2024.06.004

• 作物遗传育种•种质资源•分子遗传学•耕作栽培•生理生化 • 上一篇下一篇

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

收稿日期:2023-10-24 出版日期:2024-06-20 发布日期:2024-08-08
通信作者: 汤秋香(1980-),女,河南开封人,教授,博士,硕士生/博士生导师,研究方向为农田生态环境,(E-mail)790058828@qq.com;
林涛(1981-),男,新疆玛纳斯人,研究员,博士,研究方向为调优栽培信息化,(E-mail)27427732@qq.com
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)

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 Published:2024-06-20 Online:2024-08-08
Correspondence author: TANG Qiuxiang (1980-), female, from Kaifeng, Henan, professor, Ph.D.,doctoral supervisor, research direction: cotton field ecological environment, (E-mail)790058828@qq.com;
LIN Tao (1981-), male, from Manas, Xinjiang, Ph.D., research fellowship, research direction: tuning cultivation information technology, (E-mail)27427732@qq.com
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)

摘要/Abstract

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

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

中图分类号:

S562

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.

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.

图/表 4

表1 研究采用的植被指数及公式

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	$T C R I = 3 × [(R R E - R r e d) - 0.2 × (R R E - R g r e e n) × R R E R r e d]$	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	$E X G = 2 × R g r e e n - R r e d - R b l u e$	Liu et.al^[22]
EXGR	$E X G R = 3 × R g r e e n - 2.4 × R r e d - R b l u e$	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]

表1 研究采用的植被指数及公式

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	$T C R I = 3 × [(R R E - R r e d) - 0.2 × (R R E - R g r e e n) × R R E R r e d]$	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	$E X G = 2 × R g r e e n - R r e d - R b l u e$	Liu et.al^[22]
EXGR	$E X G R = 3 × R g r e e n - 2.4 × R r e d - R b l u e$	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]

图1 不同施氮量与密度下棉花生物量的变化注:BS:蕾期,FS:花期,FBS:铃期,BOS:吐絮期

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

图2 植被指数与生物量的相关性

Fig.2 Correlation between Vegetation Indices and AGB

表2 基于植被指数的棉花生物量估算结果评价

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

参考文献 40

[1]	刘杨, 孙乾, 黄珏, 等. 无人机多光谱影像的马铃薯地上生物量估算[J]. 光谱学与光谱分析, 2021, 41(8): 2549-2555.
	LIU Yang, SUN Qian, HUANG Jue, et al. Estimation of potato above ground biomass based on UAV multispectral images[J]. Spectroscopy and Spectral Analysis, 2021, 41(8): 2549-2555.
[2]	樊鸿叶, 李姚姚, 卢宪菊, 等. 基于无人机多光谱遥感的春玉米叶面积指数和地上部生物量估算模型比较研究[J]. 中国农业科技导报, 2021, 23(9): 112-120.
	FAN Hongye, LI Yaoyao, LU Xianju, et al. Comparative analysis of LAI and above-ground biomass estimation models based on UAV multispectral remote sensing[J]. Journal of Agricultural Science and Technology, 2021, 23(9): 112-120.
[3]	黄春燕, 王登伟, 曹连莆, 等. 棉花地上鲜生物量的高光谱估算模型研究[J]. 农业工程学报, 2007, 23(3): 131-135.
	HUANG Chunyan, WANG Dengwei, CAO Lianpu, et al. Models for estimating cotton aboveground fresh biomass using hyperspectral data[J]. Transactions of the Chinese Society of Agricultural Engineering, 2007, 23(3): 131-135.
[4]	李岚涛, 郭宇龙, 韩鹏, 等. 基于高光谱的冬小麦不同生育时期地上部生物量监测[J]. 麦类作物学报, 2021, 41(7): 904-913.
	LI Lantao, GUO Yulong, HAN Peng, et al. Estimation of shoot biomass at different growth stages of winter wheat based on hyperspectral reflectance[J]. Journal of Triticeae Crops, 2021, 41(7): 904-913.
[5]	卜灵心, 来全, 刘心怡. 不同机器学习算法在草原草地生物量估算上的适应性研究[J]. 草地学报, 2022, 30(11): 3156-3164. DOI
	BU Lingxin, LAI Quan, LIU Xinyi. Adaptation of different machine learning algorithms for grassland biomass estimation[J]. Acta Agrestia Sinica, 2022, 30(11): 3156-3164. DOI
[6]	Ballesteros R, Ortega J F, Hernandez D, et al. Onion biomass monitoring using UAV-based RGB imaging[J]. Precision Agriculture, 2018, 19(5): 840-857.
[7]	张正健, 李爱农, 边金虎, 等. 基于无人机影像可见光植被指数的若尔盖草地地上生物量估算研究[J]. 遥感技术与应用, 2016, 31(1): 51-62.
	ZHANG Zhengjian, LI Ainong, BIAN Jinhu, et al. Estimating aboveground biomass of grassland in zoige by visible vegetation index derived from unmanned aerial vehicle image[J]. Remote Sensing Technology and Application, 2016, 31(1): 51-62.
[8]	陆国政, 杨贵军, 赵晓庆, 等. 基于多载荷无人机遥感的大豆地上鲜生物量反演[J]. 大豆科学, 2017, 36(1): 41-50.
	LU Guozheng, YANG Guijun, ZHAO Xiaoqing, et al. Inversion of soybean fresh biomass based on multi-payload unmanned aerial vehicles (UAVs)[J]. Soybean Science, 2017, 36(1): 41-50.
[9]	孙世泽, 汪传建, 尹小君, 等. 无人机多光谱影像的天然草地生物量估算[J]. 遥感学报, 2018, 22(5): 848-856.
	SUN Shize, WANG Chuanjian, YIN Xiaojun, et al. Estimating aboveground biomass of natural grassland based on multispectral images of Unmanned Aerial Vehicles[J]. Journal of Remote Sensing, 2018, 22(5): 848-856.
[10]	Wang D L, Xin X P, Shao Q Q, et al. Modeling aboveground biomass in Hulunber grassland ecosystem by using unmanned aerial vehicle discrete lidar[J]. Sensors, 2017, 17(12): 180.
[11]	张雨欣, 黄健熙, 金云翔, 等. 草地地上生物量估算模型研究进展[J]. 草地学报, 2022, 30(4): 850-858. DOI
	ZHANG Yuxin, HUANG Jianxi, JIN Yunxiang, et al. Estimation of grasslands aboveground biomass: a review[J]. Acta Agrestia Sinica, 2022, 30(4): 850-858. DOI
[12]	Deo R C, Şahin M. Forecasting long-term global solar radiation with an ANN algorithm coupled with satellite-derived (MODIS) land surface temperature (LST) for regional locations in Queensland[J]. Renewable and Sustainable Energy Reviews, 2017, 72: 828-848.
[13]	Safari A, Sohrabi H, Powell S, et al. A comparative assessment of multi-temporal Landsat 8 and machine learning algorithms for estimating aboveground carbon stock in coppice oak forests[J]. International Journal of Remote Sensing, 2017, 38(22): 6407-6432.
[14]	王玉娜, 李粉玲, 王伟东, 等. 基于连续投影算法和光谱变换的冬小麦生物量高光谱遥感估算[J]. 麦类作物学报, 2020, 40(11): 1389-1398.
	WANG Yuna, LI Fenling, WANG Weidong, et al. Hyper-spectral remote sensing stimation of shoot biomass of winter wheat based on SPA and transformation spectra[J]. Journal of Triticeae Crops, 2020, 40(11): 1389-1398.
[15]	纪伟帅. 基于无人机多光谱的棉花冠层叶片叶绿素相对含量、叶面积指数反演[D]. 泰安: 山东农业大学, 2021: 56.
	JI Weishuai. Inversion of SPAD and LAI in Cotton Canopy Leaves Based on UAV Multispectral[D]. Taian: Shandong Agricultural University, 2021: 56.
[16]	王士红. 增密减氮对棉花产量品质的影响及氮高效生理基础研究[D]. 泰安: 山东农业大学, 2019: 124.
	WANG Shihong. Effects of Increasing Plant Density and Decreasing Nitrogen Rate on Yield and Quality of Cotton, and Physiological Mechanisms of Nitrogen Efficient Utilization[D]. Taian: Shandong Agricultural University, 2019: 124.
[17]	李春艳. 密度与氮肥对机采棉产量及氮肥利用影响的研究[D]. 乌鲁木齐: 新疆农业大学, 2018: 61.
	LI Chunyan. Effects of Density and Nitrogen Fertilizer on Cotton Production and Nitrogen Utilization[D]. Urumqi: Xinjiang Agricultural University, 2018: 61.
[18]	夏天, 周清波, 陈仲新, 等. 基于HJ-1卫星的冬小麦叶片SPAD遥感监测研究[J]. 中国农业资源与区划, 2012, 33(6): 38-44.
	XIA Tian, ZHOU Qingbo, CHEN Zhongxin, et al. Monitoring winter wheat spad based on hj-1 ccd[J]. Chinese Journal of Agricultural Resources and Regional Planning, 2012, 33(6): 38-44.
[19]	Lelong C, Burger P, Jubelin G, et al. Assessment of unmanned aerial vehicles imagery for quantitative monitoring of wheat crop in small plots[J]. Sensors, 2008, 8(5): 3557-3585. DOI PMID
[20]	Shao G M, Han W T, Zhang H H, et al. Estimation of transpiration coefficient and aboveground biomass in maize using time-series UAV multispectral imagery[J]. The Crop Journal, 2022, 10(5): 1376-1385.
[21]	Liu S B, Jin X L, Nie C W, et al. Estimating leaf area index using unmanned aerial vehicle data: shallow vs. deep machine learning algorithms[J]. Plant Physiology, 2021, 187(3): 1551-1576. DOI PMID
[22]	Liu Y, Feng H K, Yue J B, et al. Remote-sensing estimation of potato above-ground biomass based on spectral and spatial features extracted from high-definition digital camera images[J]. Computers and Electronics in Agriculture, 2022, 198: 107089.
[23]	Fu Y Y, Yang G J, Li Z H, et al. Winter wheat nitrogen status estimation using UAV-based RGB imagery and Gaussian processes regression[J]. Remote Sensing, 2020, 12(22): 3778.
[24]	Sulik J J, Long D S. Spectral considerations for modeling yield of canola[J]. Remote Sensing of Environment, 2016, 184: 161-174.
[25]	贾学勤, 冯美臣, 杨武德, 等. 基于多植被指数组合的冬小麦地上干生物量高光谱估测[J]. 生态学杂志, 2018, 37(2): 424-429.
	JIA Xueqin, FENG Meichen, YANG Wude, et al. Hyperspectral estimation of aboveground dry biomass of winter wheat based on the combination of vegetation indices[J]. Chinese Journal of Ecology, 2018, 37(2): 424-429.
[26]	尚珂. 基于支持向量机回归的草地地上生物量遥感估测研究[D]. 昆明: 西南林业大学, 2015: 67.
	SHANG Ke. The Study of Grassland above Ground Biomass Inversion Based on Support Vector Machine Regression[D]. Kunming: Southwest Forestry University, 2015: 67.
[27]	兰仕浩, 李映雪, 吴芳, 等. 基于卫星光谱尺度反射率的冬小麦生物量估算[J]. 农业工程学报, 2022, 38(24): 118-128.
	LAN Shihao, LI Yingxue, WU Fang, et al. Winter wheat biomass estimation based on satellite spectral-scale reflectance[J]. Transactions of the Chinese Society of Agricultural Engineering, 2022, 38(24): 118-128.
[28]	张子慧, 吴世新, 赵子飞, 等. 基于机器学习算法的草地地上生物量估测——以祁连山草地为例[J]. 生态学报, 2022, 42(22): 8953-8963.
	ZHANG Zihui, WU Shixin, ZHAO Zifei, et al. Estimation of grassland biomass using machine learning methods: a case study of grassland in Qilian Mountains[J]. Acta Ecologica Sinica, 2022, 42(22): 8953-8963.
[29]	孙全. 基于无人机可见光-近红外图像的小麦关键生长指标监测研究[D]. 扬州: 扬州大学, 2021: 92.
	SUN Quan. Key Wheat Growth Indicators Monitoring Based on UAV Visible-NIR Imagery[D]. Yangzhou: Yangzhou University, 2021: 92.
[30]	吴芳, 李映雪, 张缘园, 等. 基于机器学习算法的冬小麦不同生育时期生物量高光谱估算[J]. 麦类作物学报, 2019, 39(2): 217-224.
	WU Fang, LI Yingxue, ZHANG Yuanyuan, et al. Hyperspectral estimation of biomass of winter wheat at different growth stages based on machine learning algorithms[J]. Journal of Triticeae Crops, 2019, 39(2): 217-224.
[31]	李鹏程, 董合林, 刘爱忠, 等. 施氮量对棉花功能叶片生理特性、氮素利用效率及产量的影响[J]. 植物营养与肥料学报, 2015, 21(1): 81-91.
	LI Pengcheng, DONG Helin, LIU Aizhong, et al. Effects of nitrogen application rates on physiological characteristics of functional leaves, nitrogen use efficiency and yield of cotton[J]. Journal of Plant Nutrition and Fertilizer, 2015, 21(1): 81-91.
[32]	李飞, 郭莉莉, 赵瑞元, 等. 氮肥减量深施对油后直播棉花干物质与氮素积累、分配及产量的影响[J]. 棉花学报, 2022, 34(3): 198-214. DOI
	LI Fei, GUO Lili, ZHAO Ruiyuan, et al. Effects of increasing application depth and decreasing nitrogen rate on dry matter, nitrogen accumulation and distribution, and yield of direct seeding cotton after rape harvest[J]. Cotton Science, 2022, 34(3): 198-214.
[33]	辛明华, 王占彪, 李小飞, 等. 南疆棉区机采种植模式下棉花种植密度研究[J]. 山东农业科学, 2020, 52(1): 46-52.
	XIN Minghua, WANG Zhanbiao, LI Xiaofei, et al. Study on suitable planting density of cotton under machine-picked planting mode in South Xinjiang[J]. Shandong Agricultural Sciences, 2020, 52(1): 46-52.
[34]	支晓宇, 韩迎春, 王国平, 等. 不同密度下棉花群体光辐射空间分布及生物量和纤维品质的变化[J]. 棉花学报, 2017, 29(4): 365-373. DOI
	ZHI Xiaoyu, HAN Yingchun, WANG Guoping, et al. Changes to the PAR spatial distribution, biomass, and fiber quality in response to plant densities[J]. Cotton Science, 2017, 29(4): 365-373.
[35]	牛玉萍, 陈宗奎, 杨林川, 等. 干旱区滴灌模式和种植密度对棉花生长和产量性能的影响[J]. 作物学报, 2016, 42(10): 1506-1515. DOI
	NIU Yuping, CHEN Zongkui, YANG Linchuan, et al. Effect of drip irrigation pattern and planting density on growth and yield performance of cotton in arid area[J]. Acta Agronomica Sinica, 2016, 42(10): 1506-1515. DOI
[36]	王士红, 杨中旭, 史加亮, 等. 增密减氮对棉花干物质和氮素积累分配及产量的影响[J]. 作物学报, 2020, 46(3): 395-407. DOI
	WANG Shihong, YANG Zhongxu, SHI Jialiang, et al. Effects of increasing planting density and decreasing nitrogen rate on dry matter, nitrogen accumulation and distribution, and yield of cotton[J]. Acta Agronomica Sinica, 2020, 46(3): 395-407. DOI
[37]	Sankaran S, Zhou J F, Khot L R, et al. High-throughput field phenotyping in dry bean using small unmanned aerial vehicle based multispectral imagery[J]. Computers and Electronics in Agriculture, 2018, 151: 84-92.
[38]	Hunt E R Jr, Hively W D, Fujikawa S, et al. Acquisition of NIR-green-blue digital photographs from unmanned aircraft for crop monitoring[J]. Remote Sensing, 2010, 2(1): 290-305.
[39]	岳继博, 杨贵军, 冯海宽. 基于随机森林算法的冬小麦生物量遥感估算模型对比[J]. 农业工程学报, 2016, 32(18): 175-182.
	YUE Jibo, YANG Guijun, FENG Haikuan. Comparative of remote sensing estimation models of winter wheat biomass based on random forest algorithm[J]. Transactions of the Chinese Society of Agricultural Engineering, 2016, 32(18): 175-182.
[40]	肖武, 陈佳乐, 笪宏志, 等. 基于无人机影像的采煤沉陷区玉米生物量反演与分析[J]. 农业机械学报, 2018, 49(8): 169-180.
	XIAO Wu, CHEN Jiale, DA Hongzhi, et al. Inversion and analysis of maize biomass in coal mining subsidence area based on UAV images[J]. Transactions of the Chinese Society for Agricultural Machinery, 2018, 49(8): 169-180.

基于无人机多光谱影像特征估算棉花生物量

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

在线阅读

PDF下载

可视化

摘要/Abstract

引用本文

使用本文

图/表 4

参考文献 40

相关文章 15

编辑推荐

Metrics

[1]	董志多, 徐菲, 付秋萍, 黄建, 祁通, 孟阿静, 付彦博, 开赛尔·库尔班. 不同类型盐碱胁迫对棉花种子萌发的影响[J]. 新疆农业科学, 2024, 61(8): 1831-1844.
[2]	李汝勇, 任久明, 雷霆, 王克林, 刘鹏程, 李江涛. 基于光合法和生物量法分析塔里木沙漠公路防护林带碳汇估算差异性[J]. 新疆农业科学, 2024, 61(8): 2014-2022.
[3]	赖成霞, 杨延龙, 李春平, 玛依拉·玉素音, 王燕, 杨栋, 阳妮, 葛风伟, 汪鹏龙, 马君. 落叶型棉花黄萎病的生物学特征及药剂防治分析[J]. 新疆农业科学, 2024, 61(8): 2034-2042.
[4]	刘慧杰, 王俊豪, 龚照龙, 梁亚军, 王俊铎, 李雪源, 郑巨云, 王冀川. 197份陆地棉品种萌发期耐盐性鉴定[J]. 新疆农业科学, 2024, 61(7): 1574-1581.
[5]	高君, 侯献飞, 苗昊翠, 贾东海, 顾元国, 汪天玲, 黄奕, 陈晓露, 李强. 棉花-花生轮作模式对花生干物质积累量分配及产量的影响[J]. 新疆农业科学, 2024, 61(7): 1648-1656.
[6]	侯献飞, 李强, 苗昊翠, 贾东海, 顾元国, 买买提依明·斯马依, 崔福洋. 棉花-花生轮作模式对土壤养分及其产量的影响[J]. 新疆农业科学, 2024, 61(7): 1657-1665.
[7]	张振飞, 郭靖, 颜安, 侯正清, 袁以琳, 肖淑婷, 孙哲. 多光谱无人机不同飞行高度下苹果树高的提取[J]. 新疆农业科学, 2024, 61(7): 1710-1716.
[8]	马百幻, 赵强, 谢佳, 徐开玥, 任若飞, 宋兴虎. 生物药剂复配对棉花黄萎病防治及生长发育的影响[J]. 新疆农业科学, 2024, 61(7): 1748-1756.
[9]	叶萍毅, 龙遗磊, 谭彦平, 杜霄, 安梦洁, 陶志鑫, 梁发瑞, 艾先涛, 胡守林. 陆地棉果枝夹角与机采农艺性状鉴定评价[J]. 新疆农业科学, 2024, 61(6): 1318-1327.
[10]	张振飞, 郭靖, 颜安, 袁以琳, 肖淑婷, 侯正清, 孙哲. 基于多光谱无人机不同飞行高度下苹果树冠幅信息的提取[J]. 新疆农业科学, 2024, 61(6): 1468-1476.
[11]	李雪瑞, 翟梦华, 徐新龙, 孙明辉, 张巨松. 无人机喷施不同浓度缩节胺对棉花生长发育的影响[J]. 新疆农业科学, 2024, 61(5): 1085-1093.
[12]	董祯林, 万素梅, 熊世武, 马云珍, 毛廷勇, 杨北方, 骆磊, 刘超群, 陈国栋, 李亚兵. 不同种植密度对中棉113农艺性状及产量的影响[J]. 新疆农业科学, 2024, 61(5): 1102-1111.
[13]	刘超群, 董合林, 万素梅, 郑苍松, 骆磊, 马云珍, 董祯林, 陈国栋, 李鹏程. 不同行距配置方式下棉花适宜种植密度的筛选[J]. 新疆农业科学, 2024, 61(5): 1112-1121.
[14]	耿美菊, 王新绘, 刘晓颖, 吕佩. 封育对荒漠草原真菌群落的影响[J]. 新疆农业科学, 2024, 61(5): 1250-1258.
[15]	叶扬, 侯振安, 闵伟, 郭慧娟. 添加脲酶/硝化抑制剂对棉花养分吸收和产量的影响[J]. 新疆农业科学, 2024, 61(4): 814-822.