Prediction of compressed sward height of Walloon pastures from sentinel-2 images using machine learning algorithms.
08 января 2021 года
05:45
Prediction of compressed sward height of Walloon pastures from sentinel-2 images using machine learning algorithms.
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Title: Prediction of compressed sward height of Walloon pastures from sentinel-2 images using machine learning algorithms.
Author, co-author: Nickmilder, Charles
Abstract: ROADSTEP is a Walloon research program aiming to develop decision
tools to help farmers in their daily herd monitoring on pastures. One of
the aims is to develop a modeling tool to predict the availability of pasture
feeding based on satellite images, meteorological variables and soil characteristics. 7737 compressed sward heights (CSH) were measured on 2
farms recorded with Jenquip EC20G platemeter in July and August 2019.
They were used to calibrate and validate 73 predictive models of CSH.
The tested algorithms were linear regression, lars, cubist, generalized linear model, neural network, random forest and linear support vector machine. The explaining variables were the 11 sentinel-2 reflectance bands at
the bottom of atmosphere. Those bands and CSH were introduced directly in the model but also through their logarithm, square-root, square and
cube forms to test the possible nonlinear relationships between them. The
reduction of dimensionality of X-matrix through the estimation of principal components as well as partial least squares factors was also tested. To
guarantee independence between calibration and validation, calibration
was made on CSH (ranging from 12 to 158 mm with an average value
of 59.4+-22.3 mm) measured on a farm and validation on CSH (ranging
from 13 to 247.5 mm with an average value of 53.2+-21.6 mm) measured
on another farm. The model that performed the best was a generalized
linear model from the gamma family using an inverse link function. Calibration and validation RMSE were respectively equal to 17.4 and 20.7
mm or 29.3 and 28.9% of their respective mean. These results are only
preliminary. Additional sampling periods and pastures are needed to improve the models’ robustness. Moreover, the second step of this research
will consist in adding information related to meteorological data and soil
characteristics to enhance the prediction power of the developed models.

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Prediction of compressed sward height of Walloon pastures from sentinel-2 images using machine learning algorithms.
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Abstract :
tools to help farmers in their daily herd monitoring on pastures. One of
the aims is to develop a modeling tool to predict the availability of pasture
feeding based on satellite images, meteorological variables and soil characteristics. 7737 compressed sward heights (CSH) were measured on 2
farms recorded with Jenquip EC20G platemeter in July and August 2019.
They were used to calibrate and validate 73 predictive models of CSH.
The tested algorithms were linear regression, lars, cubist, generalized linear model, neural network, random forest and linear support vector machine. The explaining variables were the 11 sentinel-2 reflectance bands at
the bottom of atmosphere. Those bands and CSH were introduced directly in the model but also through their logarithm, square-root, square and
cube forms to test the possible nonlinear relationships between them. The
reduction of dimensionality of X-matrix through the estimation of principal components as well as partial least squares factors was also tested. To
guarantee independence between calibration and validation, calibration
was made on CSH (ranging from 12 to 158 mm with an average value
of 59.4+-22.3 mm) measured on a farm and validation on CSH (ranging
from 13 to 247.5 mm with an average value of 53.2+-21.6 mm) measured
on another farm. The model that performed the best was a generalized
linear model from the gamma family using an inverse link function. Calibration and validation RMSE were respectively equal to 17.4 and 20.7
mm or 29.3 and 28.9% of their respective mean. These results are only
preliminary. Additional sampling periods and pastures are needed to improve the models’ robustness. Moreover, the second step of this research
will consist in adding information related to meteorological data and soil
characteristics to enhance the prediction power of the developed models.
Name of the research project :
ROADSTEP
Автоматическая система мониторинга и отбора информации
Источник
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