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VIS-NIR, SWIR and LWIR Imagery for Estimation of Ground Bearing Capacity.

Fernández R, Montes H, Salinas C - Sensors (Basel) (2015)

Bottom Line: Nevertheless, commonly known techniques for its estimation are cumbersome and time-consuming.The proposed solution offers notable benefits such as its non-invasiveness to the soil, its spatial coverage without the need for exhaustive manual measurements and its real time operation.Therefore, it can be very useful in decision making processes that tend to reduce ground damage during agricultural and forestry operations.

View Article: PubMed Central - PubMed

Affiliation: Centre for Automation and Robotics (CAR) CSIC-UPM, Ctra. Campo Real, Km. 0.2, La Poveda, Arganda del Rey, Madrid 28500, Spain. roemi.fernandez@car.upm-csic.es.

ABSTRACT
Ground bearing capacity has become a relevant concept for site-specific management that aims to protect soil from the compaction and the rutting produced by the indiscriminate use of agricultural and forestry machines. Nevertheless, commonly known techniques for its estimation are cumbersome and time-consuming. In order to alleviate these difficulties, this paper introduces an innovative sensory system based on Visible-Near InfraRed (VIS-NIR), Short-Wave InfraRed (SWIR) and Long-Wave InfraRed (LWIR) imagery and a sequential algorithm that combines a registration procedure, a multi-class SVM classifier, a K-means clustering and a linear regression for estimating the ground bearing capacity. To evaluate the feasibility and capabilities of the presented approach, several experimental tests were carried out in a sandy-loam terrain. The proposed solution offers notable benefits such as its non-invasiveness to the soil, its spatial coverage without the need for exhaustive manual measurements and its real time operation. Therefore, it can be very useful in decision making processes that tend to reduce ground damage during agricultural and forestry operations.

No MeSH data available.


Related in: MedlinePlus

Soil penetration resistances acquired with the penetrometer.
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sensors-15-13994-f008: Soil penetration resistances acquired with the penetrometer.

Mentions: The third phase of the experimental stage was carried out with the aim of finding out a relationship between the data acquired with the SWIR camera and the measurements obtained with the penetrometer. Datasets included LWIR and SWIR images acquired in scenarios with dry and wet soil. Moreover, in each of the conducted experiments, several measures were also carried out with a penetrometer, so that the soil penetration resistance information can be associated with the corresponding mean reflectance value of the SWIR image acquired with the sensory rig, and in this way, be subsequently used for training the algorithms responsible of estimating the ground bearing capacity. An example of the data obtained in this phase is presented on Figure 7 and Figure 8. Figure 7a shows the normalised image acquired with the SWIR camera, while Figure 7b illustrates the corresponding LWIR image. Note that upper parts of these images correspond to wet soil and bottom parts to dry soil. Red boxes displayed on Figure 7a indicate the dry and wet areas of the soil that were selected not only for the calculation of the mean reflectance percentages but also for the measurement of the penetration resistances by using the penetrometer. Resulting mean reflectance percentages for the selected areas are also displayed in yellow. Figure 8 gathers the measurements acquired with the penetrometer for this test. Black and blue lines represent the penetration resistances measured at different depths when the soil is dry and wet, respectively.


VIS-NIR, SWIR and LWIR Imagery for Estimation of Ground Bearing Capacity.

Fernández R, Montes H, Salinas C - Sensors (Basel) (2015)

Soil penetration resistances acquired with the penetrometer.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4507653&req=5

sensors-15-13994-f008: Soil penetration resistances acquired with the penetrometer.
Mentions: The third phase of the experimental stage was carried out with the aim of finding out a relationship between the data acquired with the SWIR camera and the measurements obtained with the penetrometer. Datasets included LWIR and SWIR images acquired in scenarios with dry and wet soil. Moreover, in each of the conducted experiments, several measures were also carried out with a penetrometer, so that the soil penetration resistance information can be associated with the corresponding mean reflectance value of the SWIR image acquired with the sensory rig, and in this way, be subsequently used for training the algorithms responsible of estimating the ground bearing capacity. An example of the data obtained in this phase is presented on Figure 7 and Figure 8. Figure 7a shows the normalised image acquired with the SWIR camera, while Figure 7b illustrates the corresponding LWIR image. Note that upper parts of these images correspond to wet soil and bottom parts to dry soil. Red boxes displayed on Figure 7a indicate the dry and wet areas of the soil that were selected not only for the calculation of the mean reflectance percentages but also for the measurement of the penetration resistances by using the penetrometer. Resulting mean reflectance percentages for the selected areas are also displayed in yellow. Figure 8 gathers the measurements acquired with the penetrometer for this test. Black and blue lines represent the penetration resistances measured at different depths when the soil is dry and wet, respectively.

Bottom Line: Nevertheless, commonly known techniques for its estimation are cumbersome and time-consuming.The proposed solution offers notable benefits such as its non-invasiveness to the soil, its spatial coverage without the need for exhaustive manual measurements and its real time operation.Therefore, it can be very useful in decision making processes that tend to reduce ground damage during agricultural and forestry operations.

View Article: PubMed Central - PubMed

Affiliation: Centre for Automation and Robotics (CAR) CSIC-UPM, Ctra. Campo Real, Km. 0.2, La Poveda, Arganda del Rey, Madrid 28500, Spain. roemi.fernandez@car.upm-csic.es.

ABSTRACT
Ground bearing capacity has become a relevant concept for site-specific management that aims to protect soil from the compaction and the rutting produced by the indiscriminate use of agricultural and forestry machines. Nevertheless, commonly known techniques for its estimation are cumbersome and time-consuming. In order to alleviate these difficulties, this paper introduces an innovative sensory system based on Visible-Near InfraRed (VIS-NIR), Short-Wave InfraRed (SWIR) and Long-Wave InfraRed (LWIR) imagery and a sequential algorithm that combines a registration procedure, a multi-class SVM classifier, a K-means clustering and a linear regression for estimating the ground bearing capacity. To evaluate the feasibility and capabilities of the presented approach, several experimental tests were carried out in a sandy-loam terrain. The proposed solution offers notable benefits such as its non-invasiveness to the soil, its spatial coverage without the need for exhaustive manual measurements and its real time operation. Therefore, it can be very useful in decision making processes that tend to reduce ground damage during agricultural and forestry operations.

No MeSH data available.


Related in: MedlinePlus