气候变化领域集成服务门户(CCPortal): Machine learning estimators for the quantity and quality of grass swards used for silage production using drone-based imaging spectrometry and photogrammetry

CCPortal

DOI	10.1016/j.rse.2020.111830
	Machine learning estimators for the quantity and quality of grass swards used for silage production using drone-based imaging spectrometry and photogrammetry
	Oliveira R.A.; Näsi R.; Niemeläinen O.; Nyholm L.; Alhonoja K.; Kaivosoja J.; Jauhiainen L.; Viljanen N.; Nezami S.; Markelin L.; Hakala T.; Honkavaara E.
发表日期	2020
ISSN	00344257
卷号	246
英文摘要	Drones offer entirely new prospects for precision agriculture. This study investigates the utilisation of drone remote sensing for managing and monitoring silage grass swards. In northern countries, grass swards are fertilised and harvested three times per season when aiming to maximise the yield. Information about the grass quantity and quality is necessary to optimise these operations. Our objectives were to investigate and develop machine-learning techniques for estimating these parameters using drone photogrammetry and spectral imaging. Trial sites were established in southern Finland for the primary growth and regrowth of grass in the summer of 2017. Remote-sensing datasets were captured four times during the primary growth season and three times during the regrowth period. Reference measurements included fresh and dry biomass and several quality parameters, such as the digestibility of organic matter in dry matter (the D-value), neutral detergent fibre (NDF), indigestible neutral detergent fibre (iNDF), water-soluble carbohydrates (WSC), the nitrogen concentration (Ncont) in dry matter (DM) and nitrogen uptake (NU). Machine-learning estimators based on random forest (RF) and multiple linear regression (MLR) methods were trained using the reference measurements and tested using independent test datasets. The best results for the biomass estimation, nitrogen amount and digestibility were obtained when using hyperspectral and 3D data, followed by the combination of multispectral and 3D data. During the training process, the best normalised root-mean-square errors (RMSE%) were 14.66% for the dry biomass and 12% for fresh biomass; the best RMSE% values for NU, the D-value and NDF were 13.6%, 1.98% and 3% respectively. For the primary growth, the accuracies of all quality parameters were better than 20% with the independent test datasets; for the regrowth, the estimation accuracies of the D-value, iNDF, NDF, Ncont and NU were better than 20%. The results showed that drone remote sensing was an excellent tool for the efficient and accurate management of silage production. © 2020 The Authors
英文关键词	Biomass; Digestibility; Drone; Grass sward; Hyperspectral; Machine learning; Multiple linear regression; Neutral detergent fibre; Nitrogen; Photogrammetry; Precise agriculture; Random forest
语种	英语
scopus关键词	Agricultural robots; Biomass; Decision trees; Drones; Ecology; Learning algorithms; Linear regression; Machine learning; Mean square error; Nitrogen; Photogrammetry; Remote sensing; Spectrometry; Spectroscopy; Imaging spectrometry; Machine learning techniques; Multiple linear regression method; Neutral detergent fibre; Nitrogen concentrations; Reference measurements; Root mean square errors; Water-soluble carbohydrates; Parameter estimation; biomass; crop yield; grass; growing season; growth; machine learning; organic matter; photogrammetry; remote sensing; silage; spectrometry; sward; Finland
来源期刊	Remote Sensing of Environment
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/179302
作者单位	Department of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute in National Land Survey of Finland (FGI), Finland; Natural Resources Institute Finland (Luke), Finland; Valio Ltd, Farm Services, Helsinki, Finland; Yara Kotkaniemi Research Station, Yara Suomi Oy, Finland
推荐引用方式 GB/T 7714	Oliveira R.A.,Näsi R.,Niemeläinen O.,et al. Machine learning estimators for the quantity and quality of grass swards used for silage production using drone-based imaging spectrometry and photogrammetry[J],2020,246.
APA	Oliveira R.A..,Näsi R..,Niemeläinen O..,Nyholm L..,Alhonoja K..,...&Honkavaara E..(2020).Machine learning estimators for the quantity and quality of grass swards used for silage production using drone-based imaging spectrometry and photogrammetry.Remote Sensing of Environment,246.
MLA	Oliveira R.A.,et al."Machine learning estimators for the quantity and quality of grass swards used for silage production using drone-based imaging spectrometry and photogrammetry".Remote Sensing of Environment 246(2020).